@article {7689,
	title = {Quo Vadis Machine Learning-Based Systems Condition Prognosis?\&mdash;A Perspective},
	journal = {Electronics },
	volume = {12},
	number = {3},
	year = {2023},
	pages = {527},
	abstract = {
	Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.

},
	url = {https://www.mdpi.com/2079-9292/12/3/527},
	author = {Mohamed Benbouzid and Berghout, Tarek}
}
@article {7688,
	title = {Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach},
	journal = {IEEE Transactions on Transportation Electrification},
	year = {2023},
	abstract = {
	Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to emulate the degradation process and to achieve an SoH estimate. However, accelerated aging generates limited deterioration patterns suffering from a higher level of complexity due to the non-linearity and non-stationarity imposed by harsh conditions. In this context, this paper aims to provide a predictive model capable of solving incomplete data problems by providing two main solutions for each of the problems of complexity and missing patterns, respectively. First, to overcome the problem of lack of patterns, a robust collaborative feature extractor (RCFE) is designed by collaborating between a set of improved restricted Boltzmann machines (I-RBMs) to be able to share learning knowledge among different locally trained I-RBMs to create a more generalized global extraction model. Second, a set of RCFEs is then evolved through a neural network with an augmented hidden layer (NAHL) to enhance the predictive ability by further exploring representation learning to overcome pattern complexity issues. The designed RCFE-NAHL is trained to predict SoH using constant current (CC) discharge characteristics by implying multiple characteristics recorded through the constant voltage (CV) charging process as indicators of health. The proposed SoH prediction approach performances are evaluated on a set of battery life cycles from the well-known NASA database. In this context, the achieved results clearly highlight the higher accuracy and robustness of the proposed learning model.

},
	url = {https://ieeexplore.ieee.org/document/10018362},
	author = {Berghout, Tarek and Mohamed Benbouzid and Amirat, Yassine and Yao, Gang}
}
@article {7687,
	title = {2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT},
	journal = {Computers \& Security},
	volume = {127},
	year = {2023},
	abstract = {
	Advanced technologies, such as the\&nbsp;Internet of Things\&nbsp;(IoT) and\&nbsp;Artificial Intelligence\&nbsp;(AI), underpin many of the innovations in\&nbsp;Industry 4.0. However, the\&nbsp;interconnectivity\&nbsp;and open nature of such systems in smart industrial facilities can also be targeted and abused by\&nbsp;malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP)\&nbsp;Federated Learning\&nbsp;(FL)-based\&nbsp;IDS\&nbsp;(2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three\&nbsp;building blocks, namely: a\&nbsp;key exchange protocol\&nbsp;(for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of\&nbsp;cyber attacks\&nbsp;in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37\%) with the centralized learning approach (94.37\%) and outperforms the FL-based approach (93.91\%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12\%, 13\%, and 9\% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based\&nbsp;IDS\&nbsp;solutions.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S016740482300007X},
	author = {Othmane, Friha and Ferrag, Mohamed-Amine and Mohamed Benbouzid and Berghout, Tarek and Burak, Kantarci and Kim-Kwang, Raymond-Choo}
}
@article {7686,
	title = {Faulty Detection System Based on SPC and Machine Learning Techniques},
	journal = {Revue de l{\textquoteright}Intelligence Artificielle},
	year = {2023},
	pages = {969-977},
	abstract = {
	Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

},
	url = {https://www.iieta.org/journals/ria/paper/10.18280/ria.360619},
	author = {Benrabah, Mohamed-Elamine and Ouahab Kadri and Mouss, Nadia-Kenza}
}
@article {7231,
	title = {IoT-based food traceability system: Architecture, technologies, applications, and future trends},
	journal = {Food Control},
	volume = {145},
	year = {2023},
	abstract = {
	An effective Food Traceability System (FTS) in a Food Supply Chain (FSC) should adequately provide all necessary information to the consumer(s), meet the requirements of the relevant agencies, and improve food safety as well as consumer confidence. New information and communication technologies are rapidly advancing, especially after the emergence of the Internet of Things (IoT). Consequently, new food traceability systems have become mainly based on IoT. Many studies have been conducted on food traceability. They mainly focused on the practical implementation and theoretical concepts. Accordingly, various definitions, technologies, and principles have been proposed. The {\textquotedblleft}traceability{\textquotedblright} concept has been defined in several ways and each new definition has tried to generalize its previous ones. Nevertheless, no standard definition has been reached. Furthermore, the architecture of IoT-based food traceability systems has not yet been standardized. Similarly, used technologies in this field have not been yet well classified. This article presents an analysis of the existing definitions of food traceability, and thus proposes a new one that aims to be simpler, general, and encompassing than the previous ones. We also propose, through this article, a new architecture for IoT-based food traceability systems as well as a new classification of technologies used in this context. We do not miss discussing the applications of different technologies and future trends in the field of IoT-based food traceability systems. Mainly, an FTS can make use of three types of technologies: Identification and Monitoring Technologies (IMT), Communication Technologies (CT), and Data Management Technologies (DMT). Improving a food traceability system requires the use of the best new technologies. There is a variety of promising technologies today to enhance FTS, such as fifth-generation (5G) mobile communication systems and distributed ledger technology (DLT).

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0956713522006028$\#$!},
	author = {Mehannaoui, Raouf and Mouss, Kinza-Nadia and Aksa,  Karima}
}
@article {7225,
	title = {Development of an advanced application process of Lean Manufacturing approach based on a new integrated MCDM method under Pythagorean fuzzy environment},
	journal = {Journal of Cleaner Production},
	volume = {386},
	year = {2023},
	abstract = {
	The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0959652622053057},
	author = {Mohyiddine Soltani and Aouag, Hichem and Anass, Cherrafi and Mohamed Djamel Mouss}
}
@article {7224,
	title = {Improvement of Lean Manufacturing approach based on MCDM techniques for sustainable manufacturing},
	journal = {International Journal of Manufacturing Research},
	volume = {18},
	number = {1},
	year = {2023},
	abstract = {
	Over the past few decades, Lean Manufacturing (LM) has been the pinnacle of strategies applied for cost and waste reduction. However as the search for competitive advantage and production growth continues, there is a growing consciousness towards environmental preservation. With this consideration in mind this research investigates and applies Value Stream Mapping (VSM) techniques to aid in reducing environmental impacts of manufacturing companies. The research is based on empirical observation within the Chassis weld plant of Company X. The observation focuses on the weld operations and utilizes the cross member line of Auxiliary Cross as a point of study. Using various measuring instruments to capture the emissions emitted by the weld and service equipment, data is collected. The data is thereafter visualised via an Environmental Value Stream Map (EVSM) using a 7-step method. It was found that the total lead-time to build an Auxiliary Cross equates to 16.70 minutes and during this process is emitted. It was additionally found that the UPR x LWR stage of the process indicated both the highest cycle time and carbon emissions emitted and provides a starting point for investigation on emission reduction activity. The EVSM aids in the development of a method that allows quick and comprehensive analysis of energy and material flows. The results of this research are important to practitioners and academics as it provides an extension and further capability of Lean Manufacturing tools. Additionally, the EVSM provides a gateway into realising environmental benefits and sustainable manufacturing through Lean Manufacturing.

},
	url = {https://www.sciencegate.app/document/10.1504/ijmr.2023.10040708},
	author = {Aouag, Hichem and Mohyiddine Soltani}
}
@article {7205,
	title = {Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects},
	journal = {Electronics},
	volume = {12},
	number = {1},
	year = {2023},
	abstract = {
	Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

},
	url = {https://www.mdpi.com/2079-9292/12/1/158?type=check_update\&version=1},
	author = {Berghout, Tarek and Mohamed Benbouzid and Toufik Bentrcia and Lim, Wei-Hong and Amirat, Yassine}
}
@article {7204,
	title = {ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions},
	journal = {Aerospace},
	volume = {10},
	number = {1},
	year = {2023},
	abstract = {
	Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models.

},
	url = {https://www.mdpi.com/2226-4310/10/1/10},
	author = {Berghout, Tarek and Mouss, Mohamed-Djamel and Le{\"\i}la-Hayet Mouss and Mohamed Benbouzid}
}
@article {7234,
	title = {Surveillance Des Zones Critiques Et Des Acc{\`e}s Non Autoris{\'e}s En Utilisant La Technologie Rfid},
	journal = {khazzartech الاقتصاد الصناعي},
	volume = {12},
	number = {1},
	year = {2022},
	pages = {702-717},
	abstract = {
	La surveillance est la fonction d{\textquoteright}observer toutes activit{\'e}s humaine ou environnementales dans le but de superviser, contr{\^o}ler ou m{\^e}me r{\'e}agir sur un cas particulier; ce qu{\textquoteright}on appelle la supervision ou le monitoring. La technologie de la radio-identification, connue sous l{\textquoteright}abr{\'e}viation RFID (de l{\textquoteright}anglais Radio Frequency IDentification), est l{\textquoteright}une des technologies utilis{\'e}es pour r{\'e}cup{\'e}rer des donn{\'e}es {\`a} distance de les m{\'e}moriser et m{\^e}me de les traiter. C{\textquoteright}est une technologie d{\textquoteright}actualit{\'e} et l{\textquoteright}une des technologies de l{\textquoteright}industrie 4.0 qui s{\textquoteright}int{\`e}gre dans de nombreux domaines de la vie quotidienne notamment la surveillance et le contr{\^o}le d{\textquoteright}acc{\`e}s. L{\textquoteright}objectif de cet article est de montrer comment prot{\'e}ger et surveiller en temps r{\'e}el des zones industrielles critiques et de tous types d{\textquoteright}acc{\`e}s non autoris{\'e}s de toute personne (employ{\'e}s, visiteurs{\textellipsis}) en utilisant la technologie RFID et cela {\`a} travers des exemples de simulation {\`a} l{\textquoteright}aide d{\textquoteright}un simulateur d{\'e}di{\'e} aux r{\'e}seaux de capteurs.

},
	url = {https://www.asjp.cerist.dz/en/article/194786},
	author = {Aksa,  Karima and Harrag,Mohieddine}
}
@article {7230,
	title = {Production planning optimisation in a sustainable hybrid manufacturing remanufacturing production system},
	journal = {Procedia Computer Science},
	volume = {200},
	year = {2022},
	pages = {1244-1253},
	abstract = {
	In this study, we investigate a production planning problem in hybrid manufacturing remanufacturing production system. The objective is the determine the best mix between the manufacturing of new products, and the remanufacturing of recovered products, based on economic and environmental considerations. It consists to determine the best manufacturing and remanufacturing plans to minimising the total economic cost (start-up and production costs of new and remanufactured products, storage costs of new and returned products and disposal costs) and the carbon emissions (new products, remanufactured products and disposed products). The hybrid system consists of a set of machines used to produce new products and remanufactured products of different grades (qualities). We assume that remanufacturing is more environmentally efficient, because it allows to reduce the disposal of used products. A multi-objective mathematical model is developed, and a non dominated sorting genetic algorithm (NSGA-II) based approach is proposed. Numerical experience is presented to study the impact of carbon emissions generated by new, remanufactured and disposed products, over a production horizon of several periods.

},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050922003349},
	author = {Lahmar, Houria and Dahane, Mohammed and Mouss, Nadia-Kinza and Haoues, Mohammed}
}
@article {7223,
	title = {Benchmarking framework for sustainable manufacturing based MCDM techniques Benchmarking},
	journal = {Benchmarking: An International Journal},
	volume = {29},
	number = {1},
	year = {2022},
	abstract = {
	Purpose



	
		The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.
	



	Design/methodology/approach



	
		The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman{\textquoteright}s correlation coefficient.
	



	Findings



	
		The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers{\textquoteright} staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.
	



	Research limitations/implications



	
		The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.
	



	Originality/value



	
		This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.
	

},
	url = {https://www.emerald.com/insight/content/doi/10.1108/BIJ-08-2020-0452/full/html},
	author = {Aouag, Hichem and Soltani, Mohyeddine and Soltani, Mohyeddine}
}
@article {7222,
	title = {A multiple criteria decision-making improvement strategy in complex manufacturing processes},
	journal = {International Journal of Operational Research},
	volume = {45},
	number = {2},
	year = {2022},
	abstract = {
	The purpose of this paper is to propose an improvement strategy based on multi-criteria decision making approaches, including fuzzy analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation II (PROMETHEE) and vi{\v s}ekriterijumsko kompromisno rangiranje (VIKOR) for the objective of simplifying and organising the improvement process in complex manufacturing processes. Firstly, the proposed strategy started with the selection of decision makers{\textquoteright}, such as company leaders, to determine performance indicators. Then fuzzy AHP is used to quantify the weight of each defined indicators. Finally, the weights carried out from fuzzy AHP approach are used as input in VIKOR and PROMETHE II to rank the operations according to their improvement priority. The results obtained from each outranking method are compared and the best method is determined.

},
	url = {https://www.inderscience.com/info/inarticle.php?artid=126075},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohammed-Djamel}
}
@article {7218,
	title = {Deep learning in Logistics: systematic review},
	journal = {International Journal of Logistics Systems and Management},
	year = {2022},
	abstract = {
	Logistics is one of the main tactics that countries and businesses are improving in order to increase profits. Another prominent theme in today{\textquoteright}s logistics is emerging technologies. Today{\textquoteright}s developments in logistics and industry are how to profit from collected and accessible data to use it in various processes such as decision making, production plan, logistics delivery programming, and so on, and more specifically deep learning methods. The aim of this paper is to identify the various applications of deep learning in logistics through a systematic literature review. A set of research questions had been identified to be answered by this article.

},
	url = {https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijlsm},
	author = {Sahraoui, Khaoula and Samia Aitouche and Aksa,  Karima}
}
@article {7217,
	title = {Development of an efficient cement production monitoring system based on the improved random forest algorithm},
	journal = {The International Journal of Advanced Manufacturing Technology},
	volume = {120},
	year = {2022},
	pages = {1853},
	abstract = {
	Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies{\textquoteright} value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97\%, compared to the SVM model{\textquoteright}s accuracy which is 94.18\%. However, the K-NN model{\textquoteright}s accuracy is about 93.83\%. An accuracy of 80.25\% is achieved by the logistic regression model. About 83.73\% is obtained by the decision tree{\textquoteright}s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

},
	url = {https://link.springer.com/article/10.1007/s00170-022-08884-z},
	author = {Hanane Zermane and Drardja, Abbes}
}
@article {7203,
	title = {Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects},
	journal = {Electronics},
	volume = {11},
	number = {23},
	year = {2022},
	abstract = {
	Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber{\textendash}physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.

},
	url = {https://www.mdpi.com/2079-9292/11/23/3854},
	author = {Inayat, Usman and Zia, Muhammad-Fahad and Mahmood, Sajid and Berghout, Tarek and Mohamed Benbouzid}
}
@article {7202,
	title = {Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service},
	journal = {International Journal of Cloud Applications and Computing (IJCAC) },
	volume = {12},
	number = {1},
	year = {2022},
	abstract = {
	Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.

},
	url = {https://www.igi-global.com/article/tifinagh-handwriting-character-recognition-using-a-cnn-provided-as-a-web-service/297093},
	author = {Ouahab Kadri and Abderrezak Benyahia and Adel Abdelhadi}
}
@article {7201,
	title = {A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell},
	journal = {Applied Energy},
	volume = {313},
	number = {1},
	year = {2022},
	abstract = {
	Fuel cell technology\&nbsp;has been rapidly developed in the last decade owing to its clean characteristic and high efficiency.\&nbsp;Proton exchange membrane fuel cells\&nbsp;(PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful\&nbsp;commercialization\&nbsp;and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in\&nbsp;fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the\&nbsp;degradation performance\&nbsp;of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated\&nbsp;convolutional neural network\&nbsp;is for the first time applied for predicting the time-dependent\&nbsp;fuel cell performance\&nbsp;and is proved to be more efficient than other\&nbsp;recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0306261922002756},
	author = {Benaggoune, Khaled and Yue, Meiling and Jemei, Samir and Zerhouni, Noureddine}
}
@article {7200,
	title = {An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm},
	journal = { Journal of Intelligent \& Fuzzy Systems},
	volume = {43},
	number = {3},
	year = {2022},
	pages = {2315-2331},
	abstract = {
	Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is\&nbsp;important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle{\textquoteright}s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class {\textquotedblleft}Normal{\textquotedblright} and 12 rules for the class {\textquotedblleft}CAD{\textquotedblright}. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54\%, 80\% and 100\% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

},
	url = {https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs213257},
	author = {Haouassi, Hichem and Rafik Mahdaoui and Ouahiba Chouhal and Bekhouche, Abdelaali}
}
@article {7199,
	title = {A new binary grasshopper optimization algorithm for feature selection problem},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	volume = {34},
	number = {2},
	year = {2022},
	abstract = {
	The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary\&nbsp;optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in\&nbsp;data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the\&nbsp;classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

},
	url = {https://www.sciencedirect.com/science/article/pii/S1319157819308900},
	author = {Haouassi, Hichem and Haouassi, Hichem and Mehdaoui, Rafik and Maarouk,Toufik Mesaaoud and Ouahiba Chouhal}
}
@article {7198,
	title = {A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed},
	journal = {Mathematics},
	volume = {10},
	number = {19},
	year = {2022},
	abstract = {
	Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.

},
	url = {https://www.mdpi.com/2227-7390/10/19/3528},
	author = {Berghout, Tarek and Toufik Bentrcia and Ferrag, Mohamed-Amine and Mohamed Benbouzid}
}
@article {7196,
	title = {Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis},
	journal = {Le{\"\i}la-Hayet },
	volume = {24},
	number = {7},
	year = {2022},
	abstract = {
	The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.

},
	url = {https://www.mdpi.com/1099-4300/24/7/1009},
	author = {Berghout, Tarek and Mohamed Benbouzid and Toufik Bentrcia and Amirat, Yassine and Le{\"\i}la-Hayet Mouss}
}
@article {7195,
	title = {Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects},
	journal = {International Journal of Critical Infrastructure Protection},
	volume = {38},
	year = {2022},
	abstract = {
	In modern Smart Grids (SGs) ruled by advanced computing and networking technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this connection, a portion of transported data, containing confidential information, must be protected as it is vulnerable and subject to several cyber threats. SG cyberspace adversaries attempt to gain access through networking platforms to commit several criminal activities such as disrupting or malicious manipulation of whole electricity delivery process including generation, distribution, and even customer services such as billing, leading to serious damage, including financial losses and loss of reputation. Therefore, human awareness training and software technologies are necessary precautions to ensure the reliability of data traffic and power transmission. By exploring the available literature, it is undeniable that Machine Learning (ML) has become the latest in the timeline and one of the leading artificial intelligence technologies capable of detecting, identifying, and responding by mitigating adversary attacks in SGs. In this context, the main objective of this paper is to review different ML tools used in recent years for cyberattacks analysis in SGs. It also provides important guidelines on ML model selection as a global solution when building an attack predictive model. A detailed classification is therefore developed with respect to data security triad, i.e., Confidentiality, Integrity, and Availability (CIA) within different types of cyber threats, systems, and datasets. Furthermore, this review highlights the various encountered challenges, drawbacks, and possible solutions as future prospects for ML cybersecurity applications in SGs.

},
	url = {https://ideas.repec.org/a/eee/ijocip/v38y2022ics1874548222000348.html},
	author = {Berghout, Tarek and Mohamed Benbouzid and Muyeen, S-M}
}
@article {7194,
	title = {EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids},
	journal = {Reliability Engineering \& System Safety},
	volume = {226},
	year = {2022},
	abstract = {
	Reliability and security\&nbsp;of power distribution\&nbsp;and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today{\textquoteright}s ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale\&nbsp;ML algorithm\&nbsp;that attempts to reduce computational costs is proposed. The algorithm adopts a\&nbsp;neural network\&nbsp;with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction\&nbsp;substation\&nbsp;used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0951832022003131},
	author = {Berghout, Tarek and Mohamed Benbouzid}
}
@article {7193,
	title = {A Systematic Guide for Predicting Remaining Useful Life with Machine Learning},
	journal = {Electronics},
	volume = {11},
	number = {7},
	year = {2022},
	abstract = {
	Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system{\textquoteright}s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

},
	url = {https://www.mdpi.com/2079-9292/11/7/1125},
	author = {Berghout, Tarek and Mohamed Benbouzid}
}
@article {7192,
	title = {A deep learning pipeline for breast cancer ki-67 proliferation index scoring},
	journal = {Image and Video Processing (eess.IV)},
	year = {2022},
	abstract = {
	The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nuclei overlapping and complex variations in their properties. This paper proposes an integrated pipeline for accurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted. First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. The extracted nuclei are then divided into overlapped and non-overlapped regions based on eight geometric and statistical features. A marker-based Watershed algorithm is subsequently proposed and applied only to the overlapped regions to separate nuclei. Finally, deep features are extracted from each nucleus patch using Resnet18 and classified into positive or negative by a random forest classifier. The proposed pipeline{\textquoteright}s performance is validated on a dataset from the Department of Pathology at H{\^o}pital Nord Franche-Comt{\'e} hospital.

},
	url = {https://arxiv.org/abs/2203.07452},
	author = {Benaggoune, Khaled and Al-Masry, Zeina and Ma, Jian and Devalland, Christine and Leila-Hayet Mouss and Zerhouni, Noureddine}
}
@article {7191,
	title = {New Approach for Failure Prognosis Using a Bond Graph, Gaussian Mixture Model and Similarity Techniques},
	journal = {Processes},
	volume = {10},
	number = {3},
	year = {2022},
	abstract = {
	This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

},
	url = {https://www.mdpi.com/2227-9717/10/3/435},
	author = {Mebarki, Nassima and Benmoussa, Samir and Djeziri, Mohand and Le{\"\i}la-Hayet Mouss}
}
@article {7190,
	title = {New fusion frameworks including explicit weighting functions for the remaining useful life prognostics},
	journal = {Expert Systems with Applications},
	volume = {189},
	number = {1},
	year = {2022},
	abstract = {
	In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and\&nbsp;inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0957417421014263},
	author = {Bouzenita, Mohammed and Le{\"\i}la-Hayet Mouss and Melgani, Farid and Toufik Bentrcia}
}
@article {7179,
	title = {A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Energy Conversion},
	volume = {37},
	number = {2},
	year = {2022},
	abstract = {
	Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.

},
	url = {https://ieeexplore.ieee.org/document/9552475},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Toufik Bentrcia and Mohamed Benbouzid}
}
@article {7252,
	title = {Development of an Industrial Application with Neuro-Fuzzy Systems},
	journal = {INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS },
	volume = {8},
	year = {2021},
	abstract = {
	In this paper, our objective is dedicated to the detection of a deterioration in the estimated operating time by giving preventive action before a failure, and the classification of breakdowns after failure by giving the action of the diagnosis and / or maintenance. For this reason, we propose a new Neuro-fuzzy assistance prognosis system based on pattern recognition called "NFPROG" (Neuro Fuzzy Prognosis). NFPROG is an interactive simulation software, developed within the Laboratory of Automation and Production (LAP) -University of Batna, Algeria. It is a four-layer fuzzy preceptor whose architecture is based on Elman neural networks. This system is applied to the cement manufacturing process (cooking process) to the cement manufacturing company of Ain-Touta-Batna, Algeria. And since this company has an installation and configuration S7-400 of Siemens PLC PCS7was chosen as a programming language platform for our system.

},
	url = {https://www.naun.org/main/NAUN/fuzzy/2021/a062017-003(2021).pdf},
	author = {Benaicha Sonia and Zermane, Hannane and Leila-Hayet Mouss and Bencherif, Fateh}
}
@article {7233,
	title = {Principles of Biology in Service of Technology: DNA Computing},
	journal = {Algerian Journal of Environmental Science and Technology (ALJEST)},
	volume = {7},
	number = {20},
	year = {2021},
	abstract = {
	\&nbsp;As commonly known that living beings cannot survive without natural sources available on earth, technology is no exception; it cannot develop without the inspiring help given by the same nature.



	The field of biology has extensively participated in the computing field through the "code of life" DNA (Deoxyribo Nucleic Acid) since it was discovered by Adelman in the past century. This combination gave birth to DNA Computing, which is a very interesting new aspect of biochemistry. It works massively parallel with high energy efficiency, and requiring almost no space.



	The field of molecular computing is still new and as the field progresses from concepts to engineering, researchers will address these important issues.



	\&nbsp;By the use of encoding data into DNA strands, many NP-complete problems have been solved and many new efficient techniques have been proposed in cryptography field.



	The aim of this paper is to give an overview of bio-inspired system and to summarize the great role of DNA molecule in servicing of the technology field.

},
	url = {https://www.aljest.net/index.php/aljest/article/view/292},
	author = {Aksa,  Karima}
}
@article {7229,
	title = {A Hybrid Methodology based on heuristic algorithms for a production distribution system with routing decisions},
	journal = {. BizInfo (Blace) Journal of Economics, Management and Informatics},
	volume = {12},
	number = {2},
	year = {2021},
	pages = {1-22},
	abstract = {
	In this paper, we address the integration of a two-level supply chain with multiple items. This two-level production-distribution system features a capacitated production facility supplying several retailers located in the same region. If production does occur, this process incurs a fixed setup cost and unit production costs. Besides, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles, routing costs incurred. This work aims to implement a minimization solution that reduces the total costs in both the production facility and retailers. The methodology adopted based on a hybrid heuristic, greedy and genetic algorithm uses strong formulation to provide a suitable solution of a guaranteed quality that is as good or better than those provided by the MIP optimizer. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

},
	url = {https://scindeks-clanci.ceon.rs/data/pdf/2217-2769/2021/2217-27692102001B.pdf},
	author = {Bensakhria, Mohamed and Samir Abdelhamid}
}
@article {7228,
	title = {Dynamic planning design of three level distribution network with horizontal and vertical exchange},
	journal = { Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central ware},
	year = {2021},
	abstract = {
	\&nbsp;Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.

},
	author = {Benfriha, Abdennour -Ilyas and Triqui-Sari, Lamia and Bougloula, Aimade-Eddine and BENNEKROUF, Mohammed}
}
@article {7216,
	title = {Vers une Nouvelle R{\'e}volution Industrielle : Industrie 4.0},
	journal = {Revue M{\'e}diterran{\'e}enne des T{\'e}l{\'e}communications},
	volume = {11},
	number = {1},
	year = {2021},
	abstract = {
	La quatri{\`e}me r{\'e}volution industrielle (nomm{\'e}e aussi l{\textquoteright}Internet Industriel des Objets) d{\'e}pend totalement sur la num{\'e}risation {\`a} travers l{\textquoteright}Internet des objets et les r{\'e}seaux virtuels. Cette r{\'e}volution qui {\'e}volue {\`a} un rythme exponentiel, et non plus lin{\'e}aire, va permettre la cr{\'e}ation d{\textquoteright}usines, d{\textquoteright}industries et de processus plus intelligents qui vont ensuite se traduire par une am{\'e}lioration de la flexibilit{\'e}, de la productivit{\'e} et une meilleure utilisation des ressources mat{\'e}rielles et humaines.



	Cet article est consacr{\'e} {\`a} introduire cette nouvelle r{\'e}volution industrielle (industrie4.0), les technologies majeurs participant {\`a} son apparition, leur b{\'e}n{\'e}fices attendus ainsi que leurs enjeux {\`a} prendre en consid{\'e}ration.

},
	url = {https://revues.imist.ma/index.php/RMT/article/view/24027},
	author = {Aksa,  Karima and BOUHAFNA, Khayreddine and BELAYATI, Souleymen and DJEGHAR, Dina}
}
@article {7215,
	title = {Developing a Web Platform for the Management of the Predictive Maintenance in Smart Factories},
	journal = {Wireless Personal Communications},
	volume = {119},
	year = {2021},
	pages = {pages1469{\textendash}1497},
	abstract = {
	Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning{\textellipsis}etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

},
	url = {https://link.springer.com/article/10.1007/s11277-021-08290-w},
	author = {Aksa,  Karima and Samia Aitouche and Bentoumi, Hamza and Sersa,  Ibrahim}
}
@article {7214,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	abstract = {
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

},
	author = {Mohamed Benbouzid and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {7197,
	title = {A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Instrumentation and Measurement (2022)},
	volume = {37},
	number = {2},
	year = {2021},
	abstract = {
	Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.

},
	url = {https://ieeexplore.ieee.org/document/9552475},
	author = {Berghout, Tarek and Le{\"\i}la-Hayet Mouss and Toufik Bentrcia and Mohamed Benbouzid}
}
@article {7188,
	title = {Towards Digital Twins Driven Breast Cancer Detection},
	journal = { Lecture Notes in Networks and Systems },
	volume = {285},
	year = {2021},
	pages = {87{\textendash}99},
	abstract = {
	Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object{\textquoteright}s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model{\textquoteright}s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person{\textquoteright}s behavior with whom it is connected.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7},
	author = {Meraghni, Safa and Benaggoune, Khaled and Al-Masry, Zeina and Terrissa, Labib and Devalland, Christine and Zerhouni, Noureddine}
}
@article {7187,
	title = {Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction},
	journal = {Energies},
	volume = {14},
	number = {8},
	year = {2021},
	abstract = {Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long{\textendash}short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.},
	url = {https://www.mdpi.com/1996-1073/14/8/2163},
	author = {Berghout, Tarek and Mohamed Benbouzid and Le{\"\i}la-Hayet Mouss}
}
@article {7186,
	title = {Detection of Flooding Attack on OBS Network Using Ant Colony Optimization and Machine Learning},
	journal = {Computaci{\'o}n y Sistemas},
	volume = {25},
	number = {2},
	year = {2021},
	abstract = {
	Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

},
	url = {https://www.scielo.org.mx/scielo.php?script=sci_arttext\&pid=S1405-55462021000200423},
	author = {Seddik, Mohamed-Takieddine and Ouahab Kadri and Bouarouguene, Chakir and Brahimi, Houssem}
}
@article {7185,
	title = {Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	abstract = {
	Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

},
	url = {https://ieeexplore.ieee.org/document/9610082},
	author = {Berghout, Tarek and Mohamed Benbouzid and Muyeen, S-M and Toufik Bentrcia and Le{\"\i}la-Hayet Mouss}
}
@article {7184,
	title = {A CNN-based methodology for breast cancer diagnosis using thermal images},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
	volume = {9},
	number = {2},
	year = {2021},
	pages = {131-145},
	abstract = {
	A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92\%) and F1-score (92\%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33\%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/21681163.2020.1824685},
	author = {Zuluaga-Gomez, J and Al Masry, Z and Benaggoune, Khaled and Meraghni, Safa and Zerhouni, Noureddine}
}
@article {7183,
	title = {Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal},
	journal = {Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science},
	volume = {235},
	number = {20},
	year = {2021},
	abstract = {
	Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.

},
	url = {https://journals.sagepub.com/doi/abs/10.1177/0954406220976154},
	author = {Gougam, Fawzi and Chemseddine, Rahmoune and Benazzouz, Djamel and Zerhouni, Noureddine and Benaggoune, Khaled}
}
@article {7182,
	title = {Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition},
	journal = { International Journal on Electrical Engineering and Informatics},
	volume = {13},
	number = {1},
	year = {2021},
	abstract = {
	In this paper, we present a system for handwriting digit recognition using different invariant features extraction and multiple classifiers. In the feature extraction we use four types: cavities, Zernike moments, Hu moments, Histogram of Gradient (HOG). Firstly, the features are used independently by five classifiers: K-nearest neighbor (KNN), Support Vector Machines (SVM) one versus one, SVM one versus all, Decision Tree, MLP. Then to achieve the best possible classification performance in terms of recognition rate, three methods of classifiers Combination rule employed: majority vote, Borda count and maximum rule. Experiments are performed on the well-known MNIST database of handwritten digits. The results demonstrated that the combination of KNN using HOG features with SVMOVA using Zernike moments by Borda count rule have considered to be good based on a geometric transformation invariance.

},
	url = {https://www.proquest.com/openview/72acbcf9bceca23bc38c7d1df8734acb/1?pq-origsite=gscholar\&cbl=316223},
	author = {Derdour, Khedidja and Leila-Hayet Mouss and Rafik Bensaadi}
}
@article {7181,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	abstract = {
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

},
	url = {https://ideas.repec.org/a/gam/jeners/v14y2021i18p5967-d639498.html},
	author = {Mohamed Benbouzid and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {7180,
	title = {Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	abstract = {
	To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

},
	url = {https://www.mdpi.com/1996-1073/14/19/6316},
	author = {Berghout, Tarek and Mohamed Benbouzid and Toufik Bentrcia and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Leila-Hayet Mouss}
}
@article {7178,
	title = {Industrial IoT middleware using a multi-agent system for consistency-based diagnostic in cement factory},
	journal = {International Journal of Autonomous and Adaptive Communications Systems},
	volume = {14},
	number = {3},
	year = {2021},
	abstract = {
	With the evolution of the internet of things (IoT), and due to its significant need in the industry, Industrial IoT (IIoT) becomes the suitable naming for this accompaniment. IIoT changed the view of the industry intelligently and over the internet. This overlapping of IoT and industry requires special treatment when systems deal with heterogeneous devices in a distributed environment and complex tasks. In this paper, we propose a middleware solution based on multi-agents system (MAS) to handle the distributed control of complex systems autonomously in an industrial environment. The proposed middleware enables machine-to-machine (M2M) communications among the system{\textquoteright}s components. In this work, we also addressed the distributed diagnostic for real industrial system using MAS with a new suitable communication strategy to support the heterogeneity and interoperability issued in IIoT and assure real-time monitoring and control. Finally, we present a qualitative evaluation of our solution on real case study (cement factory).

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJAACS.2021.117805},
	author = {Ayoub Benayache and Azeddine Bilami and Benaggoune, Khaled and Leila-Hayet Mouss}
}
@article {7177,
	title = {A deep supervised learning approach for condition-based maintenance of naval propulsion systems},
	journal = {Ocean Engineering},
	volume = {221},
	number = {1},
	year = {2021},
	abstract = {
	In the last years,\&nbsp;predictive maintenance\&nbsp;has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of\&nbsp;extreme learning machines\&nbsp;using data issued from a\&nbsp;propulsion system\&nbsp;simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.




},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Toufik Bentrcia and Elbouchikhi, Elhoussin and Mohamed Benbouzid}
}
@article {7163,
	title = {SOA-based distributed fault prognostic and diagnosis framework: an application for preheater cement cyclones},
	journal = {International Journal of Internet Manufacturing and Services},
	volume = {8},
	number = {1},
	year = {2021},
	abstract = {
	Complex engineering manufacturing systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralised, but these solutions are difficult to implement on distributed systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, controlling process plant from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and service-oriented architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for web service-based distributed fault prognostic and diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJIMS.2021.114539},
	author = {Ouahiba Chouhal and Rafik Mahdaoui and Leila-Hayet Mouss}
}
@article {7143,
	title = {Recherche Documentaire et Conception du M{\'e}moire},
	year = {2021},
	abstract = {
	Le 4{\`e}me semestre d{\textquoteright}un mast{\`e}re de recherche est consacr{\'e} {\`a} la r{\'e}alisation d{\textquoteright}un travail de recherche qui sera traduit par une conception et une r{\'e}daction d{\textquoteright}un m{\'e}moire de fin d{\textquoteright}{\'e}tudes et finalement la pr{\'e}paration d{\textquoteright}un expos{\'e} oral puis une soutenance.Le m{\'e}moire de fin d{\textquoteright}{\'e}tudes est une {\'e}tape tr{\`e}s importante dans la voie des {\'e}tudes universitaires, car sans elle, l{\textquoteright}{\'e}tudiant ne peut pas acqu{\'e}rir la qualit{\'e} de dipl{\^o}m{\'e}.Alors, dans ce petit livre vous pouvez trouver un petit guide sur: - La fa{\c c}on d{\textquoteright}organisation de votre m{\'e}moire. - La pr{\'e}sentation de votre soutenance. - La r{\'e}daction d{\textquoteright}un travail de recherche. - La pr{\'e}paration d{\textquoteright}un poster.Le 4{\`e}me semestre d{\textquoteright}un mast{\`e}re de recherche est consacr{\'e} {\`a} la r{\'e}alisation d{\textquoteright}un travail de recherche qui sera traduit par une conception et une r{\'e}daction d{\textquoteright}un m{\'e}moire de fin d{\textquoteright}{\'e}tudes et finalement la pr{\'e}paration d{\textquoteright}un expos{\'e} oral puis une soutenance.Le m{\'e}moire de fin d{\textquoteright}{\'e}tudes est une {\'e}tape tr{\`e}s importante dans la voie des {\'e}tudes universitaires, car sans elle, l{\textquoteright}{\'e}tudiant ne peut pas acqu{\'e}rir la qualit{\'e} de dipl{\^o}m{\'e}.Alors, dans ce petit livre vous pouvez trouver un petit guide sur:



	
		La fa{\c c}on d{\textquoteright}organisation de votre m{\'e}moire.
	
	
		La pr{\'e}sentation de votre soutenance.
	
	
		La r{\'e}daction d{\textquoteright}un travail de recherche.
	
	
		La pr{\'e}paration d{\textquoteright}un poster.
	

},
	author = {Aksa,  Karima}
}
@article {5154,
	title = {Capacity Planning With Outsourcing Opportunities Under Reliability And Maintenance Constraints. Status},
	journal = {International Journal of Industrial and Systems Engineering},
	volume = {37},
	number = {3},
	year = {2021},
	pages = {382-409},
	abstract = {
	This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer{\textquoteright}s company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.

},
	url = {https://ideas.repec.org/a/ids/ijisen/v37y2021i3p382-409.html},
	author = {Haoues, Mohamed and Dahane, Mohammed and Mouss, Nadia-Kenza}
}
@article {7232,
	title = {An Indoor Tutorial For Maintenance And Production: Case Of Textile Batna},
	journal = {khazzartech الاقتصاد الصناعي},
	volume = {10},
	number = {2},
	year = {2020},
	pages = {216-231},
	abstract = {
	Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

},
	url = {https://www.asjp.cerist.dz/en/article/141068},
	author = {Ag Hameyni, Abdoulmadjid and Samia Aitouche and Taouririt, Kamel and Aksa,  Karima}
}
@article {7221,
	title = {Determination and Classification of Explosive Atmosphere Zones While Considering the Height of Discharges},
	journal = {Journal of Failure Analysis and Prevention },
	volume = {20},
	year = {2020},
	pages = {503{\textendash}512},
	abstract = {
	Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The {\textquotedblleft}classical and point-source{\textquotedblright} approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology{\textquoteright}s principle in determining ATEX zones consists in making a comparison with typical examples {\textquotedblleft}classical approach{\textquotedblright} and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone {\textquotedblleft}source point approach.{\textquotedblright} The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

},
	url = {https://link.springer.com/article/10.1007/s11668-020-00851-8},
	author = {Mihoub, Zakarya and Ouslati, Ahmed and Hacene Smadi and May, Bilal}
}
@article {7213,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new value stream mapping approach},
	journal = {International Journal of Productivity and Quality Management},
	volume = {29},
	number = {4},
	year = {2020},
	abstract = {
	The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJPQM.2020.106399},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohammed-Djamel}
}
@article {7212,
	title = {Enhancement of value stream mapping application process through using fuzzy DEMATEL and fuzzy QFD approaches: a case study considering economic and environmental perspectives},
	volume = {16},
	number = {3},
	year = {2020},
	abstract = {
	Purpose



	
		This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances.
	



	Design/methodology/approach



	
		The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method.
	



	Findings



	
		The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method.
	



	Research limitations/implications



	
		The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators.
	



	Originality/value



	
		The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.
	

},
	url = {Journal of Modelling in Management},
	author = {Aouag, Hichem and Mohyiddine Soltani and Mouss, Mohamed-Djamel}
}
@article {7211,
	title = {HARDWARE IMPLEMENTATION USING XSG OF NEW FAULT DETECTION METHOD APPLIED TO ROBOT MANIPULATOR},
	journal = {Mechatronic Systems and Control },
	year = {2020},
	abstract = {
	This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

},
	url = {https://www.actapress.com/Abstract.aspx?paperId=47131},
	author = {Ghrieb, Abdel-O and Kourd, Yahia and Messaoudi, Kamel and Mouss, Mohamed-Djamel and Bakir, Toufik}
}
@article {7209,
	title = {DIGITAL LEARNING WITH COVID-19 IN ALGERIA},
	journal = {INTERNATIONAL JOURNAL OF 3D PRINTING TECHNOLOGIES AND DIGITAL INDUSTRY},
	volume = {4},
	number = {2},
	year = {2020},
	pages = { 161-170},
	abstract = {
	The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

},
	url = {https://dergipark.org.tr/en/download/article-file/1237519},
	author = {Hanane Zermane and Samia Aitouche}
}
@article {7207,
	title = {An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process},
	journal = {Journal of Engineering, Design and Technology},
	volume = {18},
	number = {1},
	year = {2020},
	abstract = {
	Purpose



	
		The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes.
	



	Design/methodology/approach



	
		The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side.
	



	Findings



	
		The results carried out from this study help the managers{\textquoteright} staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production.
	



	Research limitations/implications



	
		The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated.
	



	Originality/value



	
		The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.
	

},
	url = {https://www.emerald.com/insight/content/doi/10.1108/JEDT-09-2018-0166/full/html},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mohamed Djamel Mouss}
}
@article {7206,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new value stream mapping approach},
	journal = {International Journal of Productivity and Quality Management},
	volume = {29},
	number = {4},
	year = {2020},
	abstract = {
	The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJPQM.2020.106399},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohammed-Djamel}
}
@article {7189,
	title = {HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST},
	journal = {ACADEMIC JOURNAL OF MANUFACTURING ENGINEERING},
	volume = {18},
	number = {3},
	year = {2020},
	abstract = {
	The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

},
	url = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	author = {Adel Abdelhadi and Leila-Hayet Mouss and Ouahab Kadri}
}
@article {7176,
	title = {A new approach agent-based for distributing association rules by business to improve decision process in ERP systems},
	journal = {International Journal of Information and Decision Sciences},
	volume = {12},
	number = {1},
	year = {2020},
	abstract = {
	Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

},
	url = {https://www.inderscience.com/info/inarticle.php?artid=104993},
	author = {Zoubeidi, Merouane and Kazar, Okba and Benharzallah, Saber and Mesbahi, Nadjib and Merizig, Abdelhak and Rezki, Djamil}
}
@article {7175,
	title = {HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST},
	journal = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	volume = {18},
	number = {3},
	year = {2020},
	abstract = {
	The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

},
	url = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	author = {Adel Abdelhadi and Leila-Hayet Mouss and Ouahab Kadri}
}
@article {7174,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {ICT for an Inclusive World},
	year = {2020},
	abstract = {
	This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-34269-2_37},
	author = {Rezki, Djamil and Leila Hayet Mouss and Baaziz,  Abdelkader and Rezki, Nafissa}
}
@article {7173,
	title = {User behaviour-based approach to define mobility devices needs of disabled person in Algeria: a questionnaire study},
	journal = {Disability and Rehabilitation: Assistive Technology},
	volume = {17},
	number = {4},
	year = {2020},
	pages = { 453-461},
	abstract = {
	This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/17483107.2020.1791263},
	author = {Bellal, Salah-Eddine and Leila-Hayet Mouss and Sahnoun, M{\textquoteright}hammed and Messaadia, Mourad}
}
@article {7172,
	title = {Complex network to enhance characterization analysis in modelling product development process},
	journal = {African Journal of Science, Technology, Innovation and Development },
	volume = {21},
	number = {7},
	year = {2020},
	pages = {797-811},
	abstract = {
	Nowadays, successful and innovative product development is highly correlated with the company{\textquoteright}s success and reason for existence. A development process is a major factor influencing cost, timing and quality of product development. It requires additional attention to decisions made about programme, budget, technical and market risks. In this paper a product development process model is proposed in an innovation context and strategy framework of design process and project management. The process modelling is complex network theory based, to improve characterization analysis for product development process modelling. Required concepts for complex process are established to build product development mathematical model, and provide an overview of key definitions and complex networks advanced tools. Finally, a case study for an Algerian electric generator company is carried out to prove the practicality of the proposed model.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/20421338.2020.1762355?journalCode=rajs20},
	author = {Bencherif, Fateh and Leila-Hayet Mouss}
}
@article {7171,
	title = {Holonic agent-based approach for system-level remaining useful life estimation with stochastic dependence },
	journal = {International Journal of Computer Integrated Manufacturing},
	volume = {33},
	number = {10},
	year = {2020},
	abstract = {
	The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/0951192X.2020.1795926?journalCode=tcim20},
	author = {Benaggoune, Khaled and Leila Hayet Mouss and A Abdessemed and Bensakhria, Mohamed}
}
@article {7170,
	title = {New fusion and selection approaches for estimating the remaining useful life using Gaussian process regression and induced ordered weighted averaging operators},
	journal = {Quality and Reliability Engenieering International Journal (QREIJ) },
	volume = {36},
	number = {6},
	year = {2020},
	pages = {2146-2169},
	abstract = {
	In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium-ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances.

},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2688},
	author = {Bouzenita, Mohammed and Leila-Hayet Mouss and Melgani, Farid and Toufik Bentrcia}
}
@article {7169,
	title = {Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine},
	journal = {Appl. Sci},
	volume = {10},
	number = {3},
	year = {2020},
	abstract = {
	The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

},
	url = {https://www.mdpi.com/2076-3417/10/3/1062},
	author = {Berghout, Tarek and Le{\"\i}la-Hayet Mouss and Ouahab Kadri and Sa{\"\i}di, Lotfi and Mohamed Benbouzid}
}
@article {7168,
	title = {Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning},
	journal = { International Journal of Fuzzy System Applications (IJFSA)},
	volume = {9},
	number = {1},
	year = {2020},
	abstract = {
	Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.

},
	url = {https://www.igi-global.com/article/intelligent-industrial-process-control-based-on-fuzzy-logic-and-machine-learning/245272},
	author = {Hanane Zermane and Kasmi, Rached}
}
@article {7167,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {Lecture Notes in Information Systems and Organisation},
	year = {2020},
	author = {Rezki, Djamil and Leila-Hayet Mouss and Baaziz,  Abdelkader and Rezki, Nafissa}
}
@article {7227,
	title = {Optimization of single outsourcer\&ndash;single subcontractor outsourcing relationship under reliability and maintenance constraints},
	journal = { Journal of Industrial Engineering International},
	volume = {15},
	year = {2019},
	pages = {pages395{\textendash}409},
	abstract = {
	In this paper, we focus on outsourcing activities optimization problem in single period setting. In some situations, capacity planning or outsourcing is a one-time event and can be modeled as a single period problem. The aim of this research is to balance the trade-off between two echelons of a supply chain consisting of a single outsourcer and a single subcontractor. Each part is composed of a failure-prone single machine that produces one product type to satisfy market requirements. The outsourcer{\textquoteright}s manufacturing system is not able to satisfy the demand; in this case, outsourcing is allowed to recover the lack of capacity. We consider that the subcontractor can satisfy the demands of strategic clients and rent his machine for the outsourcer under a win{\textendash}win partnership contract. We assume that the hazard failure rate depends on time and the adopted manufacture rate. When unforeseen failures occur, minimal repairs are implemented. Overhaul can be performed to reduce the degradation effects. Hence, we develop a mathematical model to define a profitability interval so that both parties of supply chain can be considered as winners. We seek to determine the contract parameters that suit both parties (duration, start and end dates, the production and outsourcing rates). Then, we develop an exact algorithm to solve the problem of single period optimization, which offers a better execution time through a series of test problems. Finally, we consider a sensitivity analysis based on outsourcing parameters (cost, periodicities, etc) to analyze their effects on partial costs and individual profit of each part, as well as the total profit generated by the system.

},
	url = {https://link.springer.com/article/10.1007/s40092-019-0307-9},
	author = {Haoues, Mohammed and Dahane, Mohammed and Mouss, Nadia-Kinza}
}
@article {7226,
	title = {Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints},
	journal = {Journal of Intelligent Manufacturing},
	volume = {30},
	year = {2019},
	pages = {701{\textendash}725},
	abstract = {
	In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.

},
	url = {https://link.springer.com/article/10.1007/s10845-016-1273-3},
	author = {Haoues, Mohammed and Dahane, Mohammed and Mouss, Nadia-Kenza}
}
@article {7220,
	title = {SIMD Implementation of the Aho-Corasick Algorithm using Intel AVX2},
	journal = {Scalable Computing: Practice and Experience},
	volume = {20},
	number = {3},
	year = {2019},
	abstract = {
	The Aho-Corasick (AC) algorithm is a multiple pattern exact string-matching algorithm proposed by Alfred V. Aho and Margaret J. Corasick. It is used to locate all occurrences of a finite set of patterns within an input text simultaneously. The AC algorithm is in the heart of many applications including digital forensics such as digital signatures demonstrating the authenticity of a digital message or document, full text search (utility programs such as grep, awk and sed of Unix systems), information retrieval (biological sequence analysis and gene identification), intrusion detection systems (IDS) in computer networks like SNORT, web filtering, spam filters, and antimalware solutions (virus scanner). In this paper we present a vectorized version of the AC algorithm designed with the use of packed instructions based on the Intel streaming SIMD (Single Instruction Multiple Data) extensions AVX2 (Advanced Vector Extensions 2.0) technology. This paper shows that the vectorized AC algorithm reduces significantly the time matching process comparing to the implementation of the original AC algorithm.

},
	url = {https://scpe.org/index.php/scpe/article/view/1572},
	author = {Ourlis, Lazhar and Djamel Bellala}
}
@article {7219,
	title = {Reliability and safety analysis using fault tree and Bayesian networks},
	journal = {International Journal of Computer Aided Engineering and Technology},
	volume = {11},
	number = {1},
	year = {2019},
	abstract = {
	Fault tree analysis (FTA) is one of the most prominent techniques used in risk analysis, this method aimed to identify how component failures lead to system failure using logical gates (i.e. AND, OR gates). However, some limitations appear on FTA due to its static structure. Bayesian networks (BNs) have become a popular technique used in reliability analysis; it represents a set of random variables and their conditional dependencies. This paper discusses the advantages of Bayesian networks over fault tree in reliability and safety analysis. Also, it shows the ability of BN to update probabilities, to represent multi-state variables, dependent failures, and common cause failure. An example taken from the literature is used to illustrate the application and compare the results of both fault tree and bayesian networks techniques.

},
	url = {https://www.inderscience.com/info/inarticle.php?artid=96720},
	author = {Zerrouki Hamza and Smadi Hacene}
}
@article {7210,
	title = {An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process},
	journal = {Journal of Engineering, Design and Technology},
	volume = {18},
	number = {1},
	year = {2019},
	abstract = {
	Purpose



	
		The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes.
	



	Design/methodology/approach



	
		The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side.
	



	Findings



	
		The results carried out from this study help the managers{\textquoteright} staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production.
	



	Research limitations/implications



	
		The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated.
	



	Originality/value



	
		The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.
	

},
	url = {https://www.emerald.com/insight/content/doi/10.1108/JEDT-09-2018-0166/full/html},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohamed-Djamel}
}
@article {7208,
	title = {Bidirectional deep architecture for Arabic speech recognition},
	journal = { Open Computer Science},
	volume = {9},
	number = {1},
	year = {2019},
	abstract = {
	Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the hu-man senses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is princi-pally used to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the non-uniform sequence length of the speech utterances issuedfrom both feature extraction techniques, (1) Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architec-ture can recognize the isolated Arabic speech via classifi-cation technique. The proposed system involves, first, ex-tracting pertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep ar-chitecture represented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the se-quences of MFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a com-parison with other related works in the literature is per-formed, whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.

},
	url = {https://www.degruyter.com/document/doi/10.1515/comp-2019-0004/html},
	author = {Zerari, Naima and Samir Abdelhamid and Bouzgou, Hassen and Raymond, Christian}
}
@article {7166,
	title = {Bidirectional deep architecture for Arabic speech recognition},
	journal = { Open Computer Science},
	volume = {9},
	year = {2019},
	pages = {92-102},
	abstract = {
	Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the hu-man senses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is princi-pally used to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the non-uniform sequence length of the speech utterances issuedfrom both feature extraction techniques, (1) Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architec-ture can recognize the isolated Arabic speech via classifi-cation technique. The proposed system involves, first, ex-tracting pertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep ar-chitecture represented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the se-quences of MFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a com-parison with other related works in the literature is per-formed, whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.

},
	url = {https://www.degruyter.com/document/doi/10.1515/comp-2019-0004/html},
	author = {Zerari, Naima and Samir Abdelhamid and Bouzgou, Hassen and Raymond, Christian}
}
@article {7165,
	title = {Fast short-term global solar irradiance forecasting with wrapper mutual information},
	journal = {Renewable Energy},
	volume = {133},
	year = {2019},
	pages = {1055-1065},
	abstract = {
	Accurate solar irradiance forecasts are now key to successfully integrate the (variable) production from large\&nbsp;solar energy systems\&nbsp;into the electricity grid. This paper describes a wrapper forecasting methodology for irradiance time series that combines mutual information and an\&nbsp;Extreme Learning Machine\&nbsp;(ELM), with application to short forecast horizons between 5-min and 3-h ahead. The method is referred to as Wrapper Mutual Information Methodology (WMIM). To evaluate the proposed approach, its performance is compared to that of three dimensionality reduction scenarios: full space (latest 50 variables), partial space (latest 5 variables), and the usual Principal Component Analysis (PCA). Based on measured irradiance data from two arid sites (Madina and Tamanrasset), the present results reveal that the reduction of the historical input space increases the forecasting performance of global solar radiation. In the case of Madina and forecast horizons from 5-min to 30-min ahead, the WMIM forecasts have a better coefficient of determination (R2\&nbsp;between 0.927 and 0.967) than those using the next best performing strategy, PCA (R2\&nbsp;between 0.921 and 0.959). The Mean Absolute Percentage Error (MAP) is also better for WMIM [7.4{\textendash}10.77] than for PCA [8.4{\textendash}11.55]. In the case of Tamanrasset and forecasting horizons from 1-h to 3-h ahead, the WMIM forecasts have an R2\&nbsp;between 0.883 and 0.957, slightly better than the next best performing strategy (PCA) (R2\&nbsp;between 0.873 and 0.910). The Normalized\&nbsp;Mean Squared Error\&nbsp;(NMSE) is similarly better for WMIM [0.048{\textendash}0.128] than for PCA [0.105{\textendash}0.130]. It is also found that the ELM technique is considerably more computationally efficient than the more conventional Multi Layer\&nbsp;Perceptron\&nbsp;(MLP). It is concluded that the proposed mutual information-based variable selection method has the potential to outperform various other proposed techniques in terms of prediction performance.

},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0960148118313028},
	author = {Bouzgou, Hassen and Gueymard,Christian A}
}
@article {7164,
	title = {Multimodel ensemble approach for hourly global solar irradiation forecasting},
	journal = {The European Physical Journal Plus },
	volume = {134},
	year = {2019},
	abstract = {
	This contribution proposes a novel solar time series forecasting approach based on multimodel statistical ensembles to predict global horizontal irradiance (GHI) in short-term horizons (up to 1 hour ahead). The goal of the proposed methodology is to exploit the diversity of a set of dissimilar predictors with the purpose of increasing the accuracy of the forecasting process. The performance of a specific multimodel ensemble forecast showing an improved forecast skill is demonstrated and compared to a variety of individual single models. The proposed system can be applied in two distinct ways. The first one incorporates the forecasts acquired from the different forecasting models constituting the ensemble via a linear combination (combination-based). The other one consists of a novel methodology that delivers as output the forecast provided by the specific model (involved in the ensemble) that delivers the maximum precision in the zone of the variable space connected with the considered GHI time series (selection-based approach). This forecasting model is issued from an appropriate division of the variable space. The efficiency of the proposed methodology has been evaluated using high-quality measurements carried out at 1min intervals at four radiometric sites representing widely different radiative climates (Arid, Temperate, Tropical, and High Albedo). The obtained results emphasize that, at all sites, the proposed multi-model ensemble is able to increase the accuracy of the forecasting process using the different combination approaches, with a significant performance improvement when using the classification strategy.

},
	url = {https://link.springer.com/article/10.1140/epjp/i2019-12966-5},
	author = {Zemouri, Nahed and Bouzgou, Hassen and Gueymard, Christian A.}
}
@article {7162,
	title = {A portable breast cancer detection system based on smartphone with infrared camera},
	journal = {Vibroengineering PROCEDIA},
	volume = {26},
	year = {2019},
	pages = {57-63},
	abstract = {
	The traditional detection methods have the disadvantages of radiation exposure, high cost, and shortage of medical resources, which restrict the popularity of early screening for breast cancer. An inexpensive, accessible, and friendly way to detect is urgently needed. Infrared thermography, an emerging means to breast cancer detection, is extremely sensitive to tissue abnormalities caused by inflammation and vascular proliferation. In this work, combined with the temperature and texture features, we designed a breast cancer detection system based on smart phone with infrared camera, achieving the accuracy of 99.21\&nbsp;\% with the k-Nearest Neighbor classifier. We compared the diagnostic results of the low resolution, originated from the phone camera, with the high resolution of the conventional infrared camera. It was found that the accuracy and sensitivity decreased slightly, but both of them were over than 98\&nbsp;\%. The proposed breast cancer detection system not only has excellent performance but also dramatically saves the detection cost, and its prospect will be fascinating.

},
	url = {https://www.extrica.com/article/20978},
	author = {Ma, Jian and Shang, Pengchao and Lu, Chen and Meraghni, Safa and Benaggoune, Khaled and Zuluaga, Juan and Zerhouni, Noureddine and Devalland, Christine and Al-Masry, Zeina}
}
@article {7161,
	title = {Distributed Control And Monitoring Based On Cooperating Agents: An Application For Manufacturing System},
	journal = {Journal of New Technology and Materials},
	volume = {8},
	number = {3},
	year = {2019},
	pages = {25-28},
	abstract = {
	Control and monitoring of current manufacturing systems has become increasingly a complex problem. To expand their reliability we propose in this work a distributed approach for control and monitoring using the Multi Agents Systems. This approach is based on the decomposition of the complex system into subsystems easier to manage, and the design of several agents each one on these agents is dedicated to a particular task. A software application supporting this approach is developed for the cement clinker system of the Ain Touta cement plant. It is chosen to test the approach on real data. The results show that our distributed approach produces better results than the centralized health monitoring and control.

},
	url = {https://www.asjp.cerist.dz/en/article/85300},
	author = {Ouahiba Chouhal and Rafik Mahdaoui and Leila-Hayet Mouss}
}
@article {7160,
	title = {SOA-based distributed fault prognostic and diagnosis framework: an application for preheater cement cyclones},
	journal = {International Journal of Internet Manufacturing and Services},
	volume = {8},
	number = {1},
	year = {2019},
	abstract = {
	Complex engineering manufacturing systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralised, but these solutions are difficult to implement on distributed systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, controlling process plant from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and service-oriented architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for web service-based distributed fault prognostic and diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJIMS.2021.114539},
	author = {Ouahiba Chouhal and Rafik Mahdaoui and Leila-Hayet Mouss}
}
@article {7159,
	title = {A Temporal Neuro-Fuzzy System for Estimating Remaining Useful Life in Preheater Cement Cyclones},
	journal = {International Journal of Reliability, Quality and Safety Engineering},
	volume = {26},
	number = {3},
	year = {2019},
	abstract = {
	Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components{\textquoteright} degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.

},
	url = {https://www.worldscientific.com/doi/10.1142/S0218539319500128},
	author = {Rafik Mahdaoui and Leila-Hayet Mouss and Haboussi, Amar and Ouahiba Chouhal and Haouassi, Hichem and Maarouk,Toufik-Messoud}
}
@article {5156,
	title = {An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process, ISSN 1726-0531},
	journal = {Journal of Engineering Design and Technology},
	year = {2019},
	abstract = {Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers{\textquoteright} staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.},
	url = {https://www.researchgate.net/publication/335614660_An_integrated_framework_using_VSM_AHP_and_TOPSIS_for_simplifying_the_sustainability_improvement_process_in_a_complex_manufacturing_process},
	author = {Soltani Mohyiddine and Aouag Hichem and Mouss Mohamed Djamel}
}
@article {5153,
	title = {Optimization of single outsourcer\&ndash;single subcontractor outsourcing relationship under reliability and maintenance constraints, ISSN / e-ISSN  1735-5702  / 2251-712X},
	journal = {Journal of Industrial Engineering International volume },
	volume = {Vol 15},
	number = {Issue 3},
	year = {2019},
	pages = { 395{\textendash}409},
	abstract = {In this paper, we focus on outsourcing activities optimization problem in single period setting. In some situations, capacity planning or outsourcing is a one-time event and can be modeled as a single period problem. The aim of this research is to balance the trade-off between two echelons of a supply chain consisting of a single outsourcer and a single subcontractor. Each part is composed of a failure-prone single machine that produces one product type to satisfy market requirements. The outsourcer{\textquoteright}s manufacturing system is not able to satisfy the demand; in this case, outsourcing is allowed to recover the lack of capacity. We consider that the subcontractor can satisfy the demands of strategic clients and rent his machine for the outsourcer under a win{\textendash}win partnership contract. We assume that the hazard failure rate depends on time and the adopted manufacture rate. When unforeseen failures occur, minimal repairs are implemented. Overhaul can be performed to reduce the degradation effects. Hence, we develop a mathematical model to define a profitability interval so that both parties of supply chain can be considered as winners. We seek to determine the contract parameters that suit both parties (duration, start and end dates, the production and outsourcing rates). Then, we develop an exact algorithm to solve the problem of single period optimization, which offers a better execution time through a series of test problems. Finally, we consider a sensitivity analysis based on outsourcing parameters (cost, periodicities, etc) to analyze their effects on partial costs and individual profit of each part, as well as the total profit generated by the system.},
	url = {https://link.springer.com/article/10.1007/s40092-019-0307-9},
	author = {Haoues Mohammed and Dahane Mohammed and Mouss Nadia Kinza}
}
@article {5130,
	title = {Supervision of an Industrial Process using Artificial Intelligence, ISSN / e-ISSN  2347-6982 / 2349-204X},
	journal = {International Journal of Industrial Electronics and Electrical Engineering},
	volume = {Vol 7},
	number = {Issue 6},
	year = {2019},
	author = {Zermane Hanane and Kasmi Rached and Aitoche Samia}
}
@article {5053,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new Value Stream Mapping approach, ISSN / e-ISSN 1746-6474 / 1746-6482},
	journal = {International Journal of Productivity and Quality Management},
	volume = {1},
	number = {1},
	year = {2019},
	pages = {1},
	abstract = {The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.},
	url = {https://www.researchgate.net/publication/331309801_Enhancement_of_the_competitiveness_and_the_financial_capability_of_a_manufacturing_process_through_a_new_Value_Stream_Mapping_approach},
	author = {Soltani Mohyiddine and Aouag Hichem and Mouss Med Djamel}
}
@article {5052,
	title = {An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process, ISSN 1726-0531},
	journal = {Journal of Engineering, Design and Technology},
	volume = {Volume 17},
	number = { Issue 6},
	year = {2019},
	abstract = {Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers{\textquoteright} staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.},
	url = {https://www.researchgate.net/publication/335614660_An_integrated_framework_using_VSM_AHP_and_TOPSIS_for_simplifying_the_sustainability_improvement_process_in_a_complex_manufacturing_process},
	author = {Mohyiddine Soltani and Aouag Hichem and Mouss Med Djamel}
}
@article {5050,
	title = {Bidirectional deep architecture for Arabic speech recognition,  e-ISSN  2299-1093},
	journal = {Open Computer Science},
	volume = {Volume 9},
	number = {Issue 1},
	year = {2019},
	pages = {92-102},
	abstract = {Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the humansenses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is principallyused to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the nonuniformsequence length of the speech utterances issuedfromboth feature extraction techniques, (1)Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architecturecan recognize the isolated Arabic speech via classificationtechnique. The proposed system involves, first, extractingpertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep architecturerepresented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the sequencesofMFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a comparisonwith other related works in the literature is performed,whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.},
	url = {https://www.degruyter.com/view/journals/comp/9/1/article-p92.xml},
	author = {Zerari Naima and Abdelhamid Samir and Bouzgo Hassen and Raymond Christian}
}
@article {4906,
	title = {Bidirectional deep architecture for Arabic speech recognition.e,  e-ISSN  2299-1093},
	journal = { Open Computer Science},
	volume = {9},
	number = {1},
	year = {2019},
	pages = {pp. 92-102},
	abstract = {Nowadays, the real life constraints necessitates controlling modern machines using human intervention by means of sensorial organs. The voice is one of the human senses that can control/monitor modern interfaces. In this context, Automatic Speech Recognition is principally used to convert natural voice into computer text as well as to perform an action based on the instructions given by the human. In this paper, we propose a general framework for Arabic speech recognition that uses Long Short-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the nonuniform sequence length of the speech utterances issued fromboth feature extraction techniques, (1)Mel Frequency Cepstral Coefficients MFCC (static and dynamic features), (2) the Filter Banks (FB) coefficients. The neural architecture can recognize the isolated Arabic speech via classification technique. The proposed system involves, first, extracting pertinent features from the natural speech signal using MFCC (static and dynamic features) and FB. Next, the extracted features are padded in order to deal with the non-uniformity of the sequences length. Then, a deep architecture represented by a recurrent LSTM or GRU (Gated Recurrent Unit) architectures are used to encode the sequences of MFCC/FB features as a fixed size vector that will be introduced to a Multi-Layer Perceptron network (MLP) to perform the classification (recognition). The proposed system is assessed using two different databases, the first one concerns the spoken digit recognition where a comparison with other related works in the literature is performed, whereas the second one contains the spoken TV commands. The obtained results show the superiority of the proposed approach.},
	url = {https://www.degruyter.com/view/j/comp.2019.9.issue-1/comp-2019-0004/comp-2019-0004.xml},
	author = {Zerari Naima and Abdelhamid Samir and Bouzgou Hassen and Raymond Christian}
}
@article {4905,
	title = {Supervision of an Industrial Process using Artificial Intelligence, ISSN / e-ISSN 2347-6982 / 2349-204X},
	journal = {International Journal of Industrial Electronics and Electrical Engineering},
	volume = {Vol 7},
	number = {Issue 6},
	year = {2019},
	abstract = {Process controls (basic as well as advanced) are implemented within the process control system, which may mean a distributed control system (DCS), programmable logic controller (PLC), and/or a supervisory control computer. DCSs and PLCs are typically industrially hardened and fault-tolerant. Supervisory control computers are often not hardened or faulttolerant, but they bring a higher level of computational capability to the control system, to host valuable, but not critical , advanced control applications. Advanced controls may reside in either the DCS or the supervisory computer, depending on the application. Basic controls reside in the DCS and its subsystems, including PLCs. Because we usually deal with real - world systems with real - world constraints (cost, computer resources, size, weight, power, heat dissipation, etc.), it is understood that the simplest method to accomplish a task is the one that should be used. Experts usually rely on common sense when they solve problems. They also use vague and ambiguous terms. Other experts have no difficulties with understanding and interpreting this statement because they have the background to hearing problems described like this. However, a knowledge engineer would have difficulties providing a computer with the same level of understanding. In a complex industrial process, how can we represent expert knowledge that uses vague and fuzzy terms in a computer to control it? In this context, the application is developed to control the pretreatment and pasteurization station of milk localized in Batna (Algeria) by adopting a control approach based on expert knowledge and fuzzy logic. Keywords - Intelligent Control; Data Acquisition; Industrial Process Control; Fuzzy Control},
	url = {http://www.iraj.in/journal/journal_file/journal_pdf/11-577-156602891045-50.pdf},
	author = {Hanane Zermane and Kasmi Rached and Samia Aitouche}
}
@article {4904,
	title = {Distributed control and monitoring based on cooperating agents: an application for manufacturing system, ISSN / e-ISSN 2170-161X  / 2488-2082},
	journal = {Journal of New Technology and Materials },
	volume = {Vol 8},
	number = {issue 3},
	year = {2019},
	pages = {pp. 25-28},
	abstract = {Control and monitoring of current manufacturing systems has become increasingly a complex problem. To expand their reliability we propose in this work a distributed approach for control and monitoring using the Multi Agents Systems. This approach is based on the decomposition of the complex system into subsystems easier to manage, and the design of several agents each one on these agents is dedicated to a particular task. A software application supporting this approach is developed for the cement clinker system of the Ain Touta cement plant. It is chosen to test the approach on real data. The results show that our distributed approach produces better results than the centralized health monitoring and control.},
	url = {https://www.asjp.cerist.dz/en/article/85300},
	author = {Chouhal Ouahiba and Mahdaoui Rafik and Mouss Leila Hayet}
}
@article {4903,
	title = {SOA-based distributed fault prognostic and diagnosis framework: An application for preheater cement cyclones, ISSN / e-ISSN 1751-6048 / 1751-6056},
	journal = {International Journal of Internet Manufacturing and Services},
	year = {2019},
	abstract = {Complex engineering manufacturing systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralized, but these solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, having the capability to control and observe process plant of a manufacturing system from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and Service-Oriented Architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for Web Service-based Distributed Fault Prognostic and Diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.},
	url = {https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijims},
	author = {Chouhal Ouahiba and Mahdaoui Rafik and Mouss Leila Hayet}
}
@article {4901,
	title = {Fast short-term global solar irradiance forecasting with wrapper mutual information. Renewable Energy, ISSN 0960-1481},
	journal = {Renewable Energy},
	volume = {Volume 133},
	year = {2019},
	pages = {pp. 1055-1065},
	abstract = {
	
		Accurate solar irradiance forecasts are now key to successfully integrate the (variable) production from large solar energy systems into the electricity grid. This paper describes a wrapper forecasting methodology for irradiance time series that combines mutual information and an Extreme Learning Machine (ELM), with application to short forecast horizons between 5-min and 3-h ahead. The method is referred to as Wrapper Mutual Information Methodology (WMIM). To evaluate the proposed approach, its performance is compared to that of three dimensionality reduction scenarios: full space (latest 50 variables), partial space (latest 5 variables), and the usual Principal Component Analysis (PCA). Based on measured irradiance data from two arid sites (Madina and Tamanrasset), the present results reveal that the reduction of the historical input space increases the forecasting performance of global solar radiation. In the case of Madina and forecast horizons from 5-min to 30-min ahead, the WMIM forecasts have a better coefficient of determination (R2 between 0.927 and 0.967) than those using the next best performing strategy, PCA (R2 between 0.921 and 0.959). The Mean Absolute Percentage Error (MAP) is also better for WMIM [7.4{\textendash}10.77] than for PCA [8.4{\textendash}11.55]. In the case of Tamanrasset and forecasting horizons from 1-h to 3-h ahead, the WMIM forecasts have an R2 between 0.883 and 0.957, slightly better than the next best performing strategy (PCA) (R2 between 0.873 and 0.910). The Normalized Mean Squared Error (NMSE) is similarly better for WMIM [0.048{\textendash}0.128] than for PCA [0.105{\textendash}0.130]. It is also found that the ELM technique is considerably more computationally efficient than the more conventional Multi Layer Perceptron (MLP). It is concluded that the proposed mutual information-based variable selection method has the potential to outperform various other proposed techniques in terms of prediction performance.
	

},
	url = {https://www.sciencedirect.com/science/article/pii/S0960148118313028},
	author = {Bouzgou Hassen and Gueymard Christian}
}
@article {5155,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new Value Stream Mapping approach, ISSN / e-ISSN 1746-6474 / 1746-6482},
	journal = {International Journal of Productivity and Quality Management},
	year = {2018},
	abstract = {The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.},
	url = {https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijpqm},
	author = {Soltani Mohyiddine and Aouag Hichem and Mouss Mohamed Djamel}
}
@article {5152,
	title = {Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows, ISSN / e-ISSN  1942-3608 / 1942-3594},
	journal = {International Journal of Applied Evolutionary Computation (IJAEC)},
	volume = {Vol 9},
	number = {Issue 1},
	year = {2018},
	pages = {1-16},
	abstract = {In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.},
	url = {https://www.researchgate.net/publication/323466976_Efficient_Golden-Ball_Algorithm_Based_Clustering_to_solve_the_Multi-Depot_VRP_With_Time_Windows},
	author = {Guezouli Lahcene and Bensakhria Mohamed and Abdelhamid Samir}
}
@article {5151,
	title = {Multi-objective optimization using genetic algorithm based clustering for multi- depot heterogeneous fleet vehicle routing problem with time windows, ISSN / e-ISSN  1757-585 / 1757-5869},
	journal = {International Journal of Mathematics in Operational Research},
	volume = {Vol 13},
	number = {Issue 3},
	year = {2018},
	pages = {30-48},
	abstract = {Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. For this purpose, we propose in this study a decision support system which aims to optimise the classical capacitated vehicle routing problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that we call the multi-depot heterogeneous vehicle routing problem with time window (MDHVRPTW) by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and we solve this problem by proposing a new scheme based on the application of the bio-inspired genetic algorithm heuristics and by embedding a clustering algorithm within a VRPTW optimisation frame work, that we will specify later. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with the best previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that our proposed genetic algorithm is effective in solving the MDHVRPTW problem and hence has a great potential.},
	url = {https://www.researchgate.net/publication/327901926_Multi-objective_optimisation_using_genetic_algorithm_based_clustering_for_multi-depot_heterogeneous_fleet_vehicle_routing_problem_with_time_windows},
	author = {Guezouli Lahcene and Abdelhamid Samir}
}
@article {5146,
	title = {Heuristics Based on Genetic Algorithms for the Capacitated Multi Vehicle Production Distribution Problem, ISSN / e-ISSN 0305-0548 / 1873-765X},
	journal = {Computers \& Operations Research},
	volume = {Volume 96},
	year = {2018},
	pages = {108-119},
	abstract = {In this paper, we consider the integration of production, inventory and distribution decisions in a supply chain composed of one production facility supplying several retailers located in the same region. The supplier is far from the retailers compared to the distance between retailers. Thus, the traveling cost of each vehicle from the supplier to the region is assumed to be fixed and there is a fixed delivery (service) cost for each visited retailer. The objective is to minimize the sum of the costs at the production facility and at the retailers. The problem is more general than the One-Warehouse Multi-Retailer problem and is a special case of the Production Routing Problem. Five heuristics based on a Genetic Algorithm are proposed to solve the problem. In particular, three of them include the resolution of a Mixed Integer Program as subproblem to generate new individuals in the population. The results show that the heuristics can find optimal solutions for small and medium size instances. On large instances, the gaps obtained by the heuristics in less than 300\&nbsp;s are better than the ones obtained by a standard solver in two hours.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0305054818301011},
	author = {Senoussi Ahmed and Dauz{\`e}re-P{\'e}r{\`e}s St{\'e}phane and Brahimi Nadjib and Penz Bernard and Moussa Nadia Kinza}
}
@article {5124,
	title = {Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems, ISSN / e-ISSN 0924-669X / 1573-7497},
	journal = {Applied Intelligence volume},
	volume = {Volume 48},
	number = {Issue 5},
	year = {2018},
	pages = {3143{\textendash}3160},
	abstract = {This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function {\textquotedblleft}PDF{\textquotedblright} that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach {\textquotedblleft}Variable Learning Rate Gradient Descent with Bayes{\textquoteright} Maximum Likelihood formula{\textquotedblright} VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem.},
	url = {https://link.springer.com/article/10.1007/s10489-017-1132-8},
	author = {Djelloul Imene and Sari Zaki and Latreche Khaled}
}
@article {5049,
	title = {Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows, ISSN / e-ISSN 1942-3594 / 1942-3608},
	journal = {International Journal of Applied Evolutionary Computation},
	volume = {9},
	number = {1},
	year = {2018},
	pages = {1-16},
	abstract = {In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.},
	url = {https://pdfs.semanticscholar.org/03af/ae92ff6294d650339e9122c883b16a36e35a.pdf },
	author = {Guezouli Lahcene and Bensakhria Mohamed and Abdelhamid Samir}
}
@article {5048,
	title = {Multi-objective optimization using genetic algorithm based clustering for multi- depot heterogeneous fleet vehicle routing problem with time windows, ISSN / e-ISSN 1757-5850 / 1757-5869},
	journal = {International Journal of Mathematics in Operational Research},
	volume = {Vol 13},
	number = { Issue 3},
	year = {2018},
	pages = {30-48},
	abstract = {Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. To this purpose, we propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that we call the Multi-Depot Heterogeneous Vehicle Routing Problem with Time Window (MDHVRPTW) by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and we solve this problem by proposing a new scheme based on the application of the bio-inspired genetic algorithm heuristics and by embedding a clustering algorithm within a VRPTW optimization frame work, that we will specify later. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with the best previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that our proposed genetic algorithm is effective in solving the MDHVRPTW problem and hence has a great potential.},
	url = {https://www.researchgate.net/publication/327901926_Multi-objective_optimisation_using_genetic_algorithm_based_clustering_for_multi-depot_heterogeneous_fleet_vehicle_routing_problem_with_time_windows},
	author = {Guezouli Lahcene and Abdelhamid Samir}
}
@article {5047,
	title = { A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window, ISSN / e-ISSN 2155-4153 /  2155-4161},
	journal = { International Journal of Applied Industrial Engineering IJAIE},
	volume = {4},
	number = {1},
	year = {2018},
	pages = {1-18},
	abstract = {One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.},
	url = {https://www.igi-global.com/gateway/article/173693$\#$pnlRecommendationForm},
	author = {Guezouli Lahcene and Abdelhamid Samir}
}
@article {5038,
	title = {Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems ISSN /e-ISSN  0924-669X / 1573-7497},
	journal = {Applied Intelligence},
	volume = {Volume 48},
	number = {Issue 5},
	year = {2018},
	abstract = {This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function "PDF" that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach "Variable Learning Rate Gradient Descent with Bayes{\textquoteright} Maximum Likelihood formula" VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem.},
	url = {https://dl.acm.org/doi/abs/10.1007/s10489-017-1132-8},
	author = {Djelloul Imene and Sari Zaki and Latreche Khaled}
}
@article {4908,
	title = {Fuzzy control of an industrial process system using internet and web services, ISSN / e-ISSN \&lrm;1748-5037 / 1748-5045},
	journal = {International Journal of Industrial and Systems Engineering},
	volume = {Vol 29 },
	number = {3},
	year = {2018},
	pages = {pp. 389-404},
	abstract = {This paper illustrates an internet-based fuzzy control system of complex industrial manufacturing, which is cement production. It ensures remote and fuzzy control of the process in real time in cement factories in Algeria. The remote control system contains several tasks, such as alarms diagnostic, e-maintenance and synchronising regulation loops, to guarantee the automated performance. To evolve the system, we propose firstly, fuzzy logic to control the cement mill workshop and ensure that the system is operational with minimal downtime. Secondly, we integrate internet technology to remote control via internet to secure human life and render it unnecessary for operators to be at site. When there is a breakdown, it is not necessary to send an expert to diagnose and solve problems. Therefore, the system reduces travel costs by sending reports and transmitting process data. Operators can execute and monitor the system according to authentication access in main control room or via internet.},
	url = {https://www.inderscience.com/info/inarticle.php?artid=93054},
	author = {Zermane Hanane and Mouss Leila Hayet}
}
@article {4907,
	title = {A Temporal Neuro-Fuzzy System for Estimating Remaining Usefull Life in Preheater Cement Cyclones,  ISSN / e-ISSN 0218-5393 / 1793-6446},
	journal = {International Journal of Reliability Quality and Safety Engineering},
	volume = {26},
	number = {3},
	year = {2018},
	abstract = {Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components{\textquoteright} degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.},
	url = {https://doi.org/10.1142/S0218539319500128},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Haboussi  Amar and Chouhal Ouahiba and Haouassi Hichem}
}
@article {5150,
	title = {A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window, ISSN / e-ISSN  2155-4153 / 2155-4161},
	journal = {International Journal of Applied Industrial Engineering (IJAIE)},
	volume = {Vol 4},
	number = {Issue 1},
	year = {2017},
	pages = {1-18},
	abstract = {One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.},
	url = {https://www.igi-global.com/gateway/article/173693},
	author = {Guezouli Lahcene and Abdelhamid Samir}
}
@article {5149,
	title = {Externalising and reusing of tacit knowledge in manufacturing task, ISSN / e-ISSN 1743-8268 / 1743-8276},
	journal = {International Journal of Knowledge Management Studies },
	volume = {Vol 8},
	number = {N{\textdegree} 3/4},
	year = {2017},
	pages = {351-374},
	abstract = {In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.},
	url = {https://www.researchgate.net/publication/320246966_Externalising_and_reusing_of_tacit_knowledge_in_manufacturing_task},
	author = {Titah Mawloud and Aitouche Samia and Mouss Mohamed Djamel and Soussa Abdelkrim}
}
@article {5132,
	title = {Electronic Waste Generation and Management in the Middle East and North Africa (MENA) Region: Algeria as a Case Study, ISSN 1520-6483},
	volume = { Vol 26},
	number = {Issue 4},
	year = {2017},
	pages = {5-16},
	abstract = {Electronic waste (e-waste) is one of the fastest growing waste streams in the world. Notwithstanding increasing concern worldwide, e-waste has not yet been discussed in depth in the Middle East and North Africa (MENA) region. This study first reviews the literature regarding the estimation of e-waste in the MENA region. It then gives an estimate of the past and future trends in the generation of obsolete computers in Algeria. For this purpose, the study combines two models: the Carnegie Mellon model and the market supply (distribution delay) model. The Carnegie Mellon model offers the following options for obsolete computers and monitors: the device could be reused, stored, or discarded. The amounts of devices falling into each category were determined based on these options. The outcomes from the market supply (distribution delay) model show that high amounts of computer and monitor waste were registered for the period from 2014 to 2016.},
	url = {https://www.researchgate.net/publication/320599708_Electronic_Waste_Generation_and_Management_in_the_Middle_East_and_North_Africa_MENA_Region_Algeria_as_a_Case_Study},
	author = {Hamouda Karima and Adjroudi Rachid}
}
@article {5131,
	title = {Methodology for WEEE assessment in Algeria, ISSN / e-ISSN 0020-7233 / 1029-0400},
	journal = {International Journal of Environmental Studies},
	volume = {Vol 74},
	number = {Issue 4},
	year = {2017},
	pages = {568-585},
	abstract = {Waste electrical and electronic equipment (WEEE) generated in emerging countries is increasing. This study presents a methodology to improve assessment and monitoring WEEE in Algeria. The proposed methodology is a two-step approach. The first step is the collection and collation of existing data from different national and international sources. In the second step, different assessment and forecasting methods are applied. Forecasting methods were selected from those models which provided minimum error indices. The paper considers also the availability and reliability of data in order to provide a future reliable assessment of WEEE in Algeria. The study revealed that the forecasting methods do not have a big influence on the results contrary to the inputs of the model.},
	url = {https://www.researchgate.net/publication/316654273_Methodology_for_WEEE_assessment_in_Algeria},
	author = {Hamouda Karima and Adjroudi Rachid and Rotter Vera Susanne}
}
@article {5129,
	title = {Measurement of Intellectual Capital in an Algerian Company, e-ISSN 1307-6892},
	journal = {International Journal of Economics and Management Engineering},
	volume = {Vol 11},
	number = {N{\textdegree}5},
	year = {2017},
	pages = {965-968},
	abstract = {Every modern company should measure the value of its intellectual capital and to report to complement the traditional annual balance sheets. The purpose of this work is to measure the intellectual capital in an Algerian company (or production system) using the Weightless Wealth Tool Kit (WWTK). The results of the measurement of intellectual capital are supplemented by traditional financial ratios. The measurement was applied to the National Company of Wells Services (ENSP) in Hassi Messaoud city, in the south of Algeria. We calculated the intellectual capital (intangible resources) of the ENSP to help the organization to better capitalize on its potential of workers and their know-how. The intangible value of the ENSP is evaluated at 16,936,173,345 DA in 2015.},
	url = {https://publications.waset.org/10006999/measurement-of-intellectual-capital-in-an-algerian-company},
	author = {Brahmi Soumia and Aitouche Samia and Mouss Med Djamel}
}
@article {5128,
	title = {Bayesian Belief Network Used in the Chemical and Process Industry: A Review and Application, ISSN 1547-7029},
	journal = { Journal of Failure Analysis and Prevention},
	volume = {Volume 17},
	number = {Issue 1},
	year = {2017},
	pages = {159{\textendash}165},
	abstract = {With the increasing growth of the chemical and process industries, it is necessary to ensure the safe operation of their complex and often hazardous installations, given their proximity to residential areas. Several techniques, such as fault tree analysis (FTA), bow-tie analysis (BTA), and Bayesian belief networks (BBNs), have been developed for adequate probabilistic risk assessment and management. The current work is aimed at performing a brief statistical review of the use of Bayesian networks in the chemical and process industry within the last decade. The review reveals that Bayesian networks have been used extensively in various forms of safety and risk assessment. This trend is attributable to the complexity of the installations found in this industry and the ability of BBN to intuitively represent these complexities, handle uncertainties, and update event probabilities. The paper is concluded with an illustrative example of the use of BBN to investigate the effectiveness of the safety barriers of a gas facility.},
	url = {https://www.researchgate.net/publication/311879867_Bayesian_Belief_Network_Used_in_the_Chemical_and_Process_Industry_A_Review_and_Application},
	author = {Zerrouki Hamza and Smadi Hacene}
}
@article {5127,
	title = {The MAED and SVM for fault diagnosis of wind turbine system, e-ISSN 1309-0127},
	volume = {Vol 7},
	number = {N{\textdegree}2},
	year = {2017},
	pages = { 758-769},
	abstract = {Fault diagnosis is the best discipline to control the operation and maintenance costs of the wind turbine system. However, the fault diagnosis of wind turbine finds difficulties with the variation of wind speed and electrical energy (generator torque). In this work, the proposed fault diagnosis approach is based on the Feature set algorithm, manifold learning and the Support Vector Machine classifier. First, the construction of the feature set is very important step, with the high dimension after application the MAED (Manifold Adaptive Experimental Design) algorithm on the data set. Moreover, the NPE (Neighborhood Preserving Embedding) manifold learning algorithm is applied for directional reduction of feature set by the eigenvectors; it is easy to use as the input for the last step. Finally, the low dimensions of eigenvectors are exploited by the (SVM) Support Vector Machine classifier for recognition fault and making the maintenance decision. This approach is implanted on the faults of the benchmark wind turbine and gives the best performance.},
	url = {https://www.researchgate.net/publication/317934350_The_MAED_and_SVM_for_fault_diagnosis_of_wind_turbine_system},
	author = {Soussa Abdelkrim and Mouss Med Djamel and Aitoche Samia and Melakhessou Hayet and Titah Mawloud}
}
@article {5126,
	title = {Externalising and reusing of tacit knowledge in manufacturing task, ISSN / e-ISSN 1743-8268 / 1743-8276},
	journal = {International Journal of Knowledge Management Studies},
	volume = {Vol 8},
	number = {N{\textdegree}3/4},
	year = {2017},
	pages = {351-374},
	abstract = {In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.},
	url = {https://www.researchgate.net/publication/320246966_Externalising_and_reusing_of_tacit_knowledge_in_manufacturing_task},
	author = {Titah Mawloud and Aitouche Samia and Mouss Med Djamel and Soussa Abdelkrim}
}
@article {5097,
	title = {Implantation d\&rsquo;une GMAO dans un syst{\`e}me de production pour l\&rsquo;am{\'e}lioration de la performance de l\&rsquo;entreprise},
	journal = {JD{\textquoteright}2017 Journ{\'e}es des doctorants, Batna, Alg{\'e}rie},
	year = {2017},
	author = {Belhoul Haroun}
}
@article {5055,
	title = {An Efficient Hybrid Approach Based on Multi-Agent System and Emergence Method for the Integration of Systematic Preventive Maintenance Policies},
	journal = {IJIME International Journal of Industrial and Manufacturing Engineering },
	volume = {Vol 4},
	number = {N{\textdegree}5},
	year = {2017},
	abstract = {This paper proposes a novel hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling},
	url = {https://www.researchgate.net/publication/303858216_An_Efficient_Hybrid_Approach_Based_on_Multi-Agent_System_and_Emergence_Method_for_the_Integration_of_Systematic_Preventive_Maintenance_Policies},
	author = {Kadri Ouahab and Abdelhadi Adel}
}
@article {5054,
	title = {An Improved Approach Based on MAS Architecture and Heuristic Algorithm for Systematic Maintenance},
	journal = {International Journal of Industrial and Manufacturing Engineering},
	volume = { Vol 11},
	number = {N{\textdegree}5},
	year = {2017},
	abstract = {This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.},
	url = {https://publications.waset.org/abstracts/69375/pdf},
	author = {Abdelhadi Adel and Kadri Ouahab}
}
@article {5051,
	title = {Gestion dynamique des carrefours {\`a} feux, e-ISSN 2421-9606},
	journal = {IJMS - The International Journal of Multi-disciplinary Sciences},
	volume = {Volume 1-17},
	year = {2017},
	abstract = {Les technologies utilis{\'e}es dans les syst{\`e}mes de transport intelligents varient, allant de syst{\`e}mes de gestion basiquescomme les syst{\`e}mes de gestion des carrefours {\`a} feux, les syst{\`e}mes de gestion des conteneurs, les panneaux {\`a} messages variables, radars automatiques ou la vid{\'e}osurveillance aux applications plus avanc{\'e}es qui int{\`e}grent des donn{\'e}es en temps-r{\'e}el avec retours d{\textquoteright}informations de nombreuses sources, comme les informations m{\'e}t{\'e}orologiques, ...etc.Cet article donne un bref aper{\c c}u surunegestion intelligente des carrefours {\`a} feuxutilisant des capteurs sans fils},
	url = {http://www.ijmsonline.net/wp-content/uploads/2017/06/1-17-2-Gestion-Dynamique-des-Carrefours-\%C3\%A0-Feux.pdf},
	author = {Aksa Karima and Titouna Faiza and Benali Bilal and Djettaou Bilel}
}
@article {5046,
	title = {
	Measurement of Intellectual Capital in an Algerian Company, e-ISSN 2010-376X


 

},
	journal = {International Journal of Economics and Management Engineering},
	volume = {11},
	number = {5},
	year = {2017},
	pages = {965-968},
	abstract = {Every modern company should measure the value of its intellectual capital and to report to complement the traditional annual balance sheets. The purpose of this work is to measure the intellectual capital in an Algerian company (or production system) using the Weightless Wealth Tool Kit (WWTK). The results of themeasurement of intellectual capital are supplemented by traditional financial ratios. The measurement was applied to the National Company of Wells Services (ENSP) in Hassi Messaoud city, in thesouth of Algeria. We calculated the intellectual capital (intangible resources) of the ENSP to help the organization to better capitalize on its potential of workers and their know-how. The intangible value of the ENSP is evaluated at 16,936,173,345 DA in 2015},
	url = {https://waset.org/Publication/measurement-of-intellectual-capital-in-an-algerian-company/10006999},
	author = {Brahmi Samia and Aitouche Samia and Mouss Med Djamel}
}
@article {5045,
	title = {The MAED and SVM for fault diagnosis of wind turbine system,  e-ISSN 1309-0127},
	journal = {International Journal of Renewable Energy Research-IJRER },
	volume = {Vol 7},
	number = {N{\textdegree}2},
	year = {2017},
	pages = {758-769},
	abstract = {
	
		Fault diagnosis is the best discipline to control the operation and maintenance costs of the wind turbine system. However, the fault diagnosis of wind turbine finds difficulties with the variation of wind speed and electrical energy (generator torque).
	

	
		In this work, the proposed fault diagnosis approach is based on the Feature set algorithm, manifold learning and the Support Vector Machine classifier. First, the construction of the feature set is very important step, with the high dimension after application the MAED (Manifold Adaptive Experimental Design) algorithm on the data set. Moreover, the NPE(Neighborhood Preserving Embedding)manifold learning algorithm is applied for dimensionally reduction of feature set by the eigenvectors; it is easy to use as the input for the last step. Finally, the low dimension of eigenvectors is exploited by the Support Vector Machine classifier for recognition fault and making the maintenance decision.
	

	
		This approach is implanted on the faults of the benchmark wind turbine and gives the best performance.
	

},
	url = {https://www.researchgate.net/publication/317934350_The_MAED_and_SVM_for_fault_diagnosis_of_wind_turbine_system},
	author = {Soussa Abdelkrim and Mouss Mohamed Djamel and Aitouche Samia and Melakhessou Hayet}
}
@article {5044,
	title = {
	Externalising and reusing of tacit knowledge in manufacturing task, ISSN / e-ISSN 1743-8268 / 1743-8276


 

},
	journal = { International Journal of Knowledge Management Studies },
	volume = {Vol 8},
	number = {N{\textdegree} 3-4},
	year = {2017},
	pages = {351-374},
	abstract = {In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJKMS.2017.087078},
	author = {Titah Mawloud and Aitouche Samia and Mouss Mohamed Djamel and Soussa Abdelkrim}
}
@article {5039,
	title = {
	Billiardo: A Novel Virtual Coordinates Routing Protocol Based on Multiple Sinks for Wireless Sensor Network, ISSN /e-ISSN  0929-6212 /  1572-834X


 

},
	journal = {Wireless Personal Communications },
	volume = {94},
	number = {3},
	year = {2017},
	pages = {1147{\textendash}1164},
	abstract = {Geographic routing protocols based on virtual coordinate system are used in wireless sensor networks without GPS assistance or any localization technique. They rely completely on virtual coordinates derived from relative distances or hop counting to a set of anchor nodes in the sensor network. Despite the fact that the recently proposed virtual coordinate protocols have gained advantages as they are GPS free, they suffer from crucial inevitable problems. The reason for such a case lies, in fact, on these protocols which depend widely on the characteristic of {\textquotedblleft}fixed reference points{\textquotedblright} (called anchors). The worst of these engendered problems is that of the unique reference framework where it is quite difficult to assign the existing nodes a unique identity. This lack of uniqueness cannot guarantee delivery and fails most of the time to forward the packet successfully. Moreover, a question rises here on how to select the anchors in order to use them in the field of work. Therefore; this paper comes to find out another way to solve the above-mentioned problems. The proposed routing protocol {\textquotedblleft}Billiardo{\textquotedblright} is of greedy type based on virtual coordinates system. Its key idea is to use more than one sink, and all these sinks are used as anchors to allow each sensor to get its virtual coordinates. This protocol depends on hops{\textquoteright} count to find the shortest path towards just one selected sink among the other sinks without any complicated formula. Through tested simulation Billiardo proves to be far better and more efficient than the others to avoid all the thwarting problems in forwarding the packet.},
	url = {https://link.springer.com/article/10.1007/s11277-016-3675-0},
	author = {Aksa Karima}
}
@article {4924,
	title = {An Improved Approach Based on MAS Architecture and Heuristic Algorithm for Systematic Maintenance,},
	journal = {International Journal of Industrial and Manufacturing Engineering},
	volume = {Vol 11},
	number = {N{\textdegree}5},
	year = {2017},
	abstract = {This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.},
	url = {https://publications.waset.org/abstracts/69375/an-improved-approach-based-on-mas-architecture-and-heuristic-algorithm-for-systematic-maintenance},
	author = {Abdelhadi Adel and Kadri Ouahab}
}
@article {4918,
	title = {Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, ISSN / e-ISSN 1582-4594 / 1335-3632},
	journal = {Journal of Electrical Engineering},
	volume = {14},
	number = {1},
	year = {2017},
	abstract = {the work presented in this paper is dedicated to improving the methods of detection and diagnosis of faults affecting production systems, particularly photovoltaic systems. We proposed a new intelligent algorithm for the detection and diagnosis of PV installations, capable of detecting and resonate to define the type of defects that can affect this type of system. This new algorithm is based on the notion of pattern recognition, for that it is able to prepare the representation space and the decision space on the one hand, and on the other hand, the classification of all new observations collected during the functioning of the system. This algorithm mainly based on the method of knearest neighbor and two tools of artificial intelligence to improve this method and increasing the rate of its classification, which are fuzzy logic to optimize the location of the centers of gravity of classes and also the new observations, and the neural network that can classify the case of discharges ambiguity and releases distance which presents the limitations of the method of the k-nearest neighbor. We tested the performance of our algorithm on a database of a photovoltaic system at the research unit of GHARDAIA Algeria},
	url = {https://pdfs.semanticscholar.org/8d54/cc8ebdfc720b5d95cbb6d4238667874f8799.pdf},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss Med Djamel and Kadri Ouahab}
}
@article {4917,
	title = {Minimum redundancy\&ndash;Maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series, ISSN 0038-032X},
	journal = {Solar Energy},
	volume = {Volume 158,},
	year = {2017},
	pages = {pp. 595-609},
	abstract = {Solar energy is expected to provide a major contribution to the future global energy supply, while helping the transition toward a carbon-free economy. Because of its variable character, its efficient use will necessitate trustworthy forecast information of its availability in a variety of spatial and time scales, depending on application. This study proposes a new forecasting approach for irradiance time series that combines mutual information measures and an Extreme Learning Machine (ELM). The method is referred to as Minimum Redundancy {\textendash} Maximum Relevance (MRMR). To assess the proposed approach, its performance is evaluated against four scenarios: long window (latest 50 variables), short window (latest 5 variables), standard Principal Components Analysis (PCA) and clear-sky model. All these scenarios are applied to three typical forecasting horizons (15-min ahead, 1-h ahead and 24-h ahead). Based on measured irradiance data from 20 sites representing a variety of climates, the test results reveal that the selection of a good set of relevant variables positively impacts the forecasting performance of global solar radiation. The present findings show that the proposed approach is able to improve the accuracy of solar irradiance forecasting compared to other proposed scenarios.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0038092X17309052},
	author = {Bouzgou Hassen and Gueymard Christian}
}
@article {4916,
	title = {A Hybrid Approach for Complex Industrial Process Monitoring, ISSN / e-ISSN \&lrm;1748-5045 / 1748-5045},
	journal = {Journal of scientific and industrial research },
	volume = {Vol 76},
	number = {issue 10},
	year = {2017},
	pages = { pp. 608-613},
	abstract = {This study proposes a multi-agent system with several intelligences for complex industrial process monitoring. The suggested multi-agent system combines a set of techniques which are: multivariate control charts, neural networks, and Bayesian networks. The proposed approach has been evaluated on the TEP (Tennessee Eastman Process). The obtained results have been compared with set of methods that were applied to the Tennessee Eastman Process in the literature; our system performs better on the faults diagnosis.},
	url = {https://www.researchgate.net/publication/322694692_A_Hybrid_Approach_for_Complex_Industrial_Process_Monitoring},
	author = {Rezki Nafissa and Kazar Okba and Mouss Leila Hayet and Kahloul Laid}
}
@article {4915,
	title = {A novel approach for multivariate process monitoring using several intelligences, ISSN / e-ISSN \&lrm;1748-5045 / 1748-5045},
	journal = {International Journal of Industrial and Systems Engineering },
	volume = {Vol.26},
	number = {N{\textdegree}3},
	year = {2017},
	pages = { pp. 344-363},
	abstract = {This paper presents a multi-agent system for multivariate process monitoring. The proposed multi-agent system combines several intelligences which are: multivariate control charts, neural networks, Bayesian networks, and expert systems. This system aims to realise a complete control of complex industrial process. In order to demonstrate the efficiency of the proposed multi-agent system, it has been applied and evaluated in the monitoring of the complex process Tennessee Eastman process (TEP).},
	url = {https://www.researchgate.net/publication/317419686_A_novel_approach_for_multivariate_process_monitoring_using_several_intelligences},
	author = {Rezki Nafissa and Kazar Okba and Mouss Leila Hayet and Kahloul Laid}
}
@article {4914,
	title = { O.M-maintenance approach based on mobile agent technology, ISSN 1082-1910},
	journal = {International Journal of Operations and Quantitative Management},
	volume = {Vol.23},
	number = { N{\textdegree}1},
	year = {2017},
	pages = {pp. 1{\textendash}21},
	abstract = {This paper proposes an approach of mobile maintenance (m-maintenance) that aims is to reduce the maintenance cost and to overcome the unavailability of experts. The Condition-Based Maintenance strategy is chosen and a three-layered framework based on mobile agent technology and web services is proposed. To ensure the reliability, Petri Nets are used in order to formally model and verify the proposed approach. Agents are modeled and their behavior with Reconfigurable Object Nets formalism. A case study on a production line is conducted to evaluate the proposed approach using LabVIEW environment. Results provide evidence of the applicability of the model.},
	url = {https://www.researchgate.net/publication/311861545_M-Maintenance_Approach_Based_on_Mobile_Agent_Technology},
	author = {Abid Khadija and Mansour Ikbal and Cherifi Chantal and Mouss Leila Hayet}
}
@article {4913,
	title = {Formal approach based on petri nets using agent paradigm for m-maintenance, ISSN / e-ISSN 1757-8787 / 1757-8779},
	journal = { International Journal of Critical Computer-Based Systems },
	volume = {Vol 7},
	number = {N{\textdegree}1},
	year = {2017},
	pages = {pp. 91 - 117},
	abstract = {The long use of a system in a manufacturing environment causes its degradation, thus the maintenance activity is required in this environment to keep and to improve the efficiency of the system. The new development in networking technologies enhances maintenance strategies and gives birth to remote maintenance (tele-maintenance, e-maintenance, m-maintenance). This maintenance makes information available anywhere/anytime and provides maintenance-personnel with the necessary information at the suitable time. This new type of maintenance reduces the maintenance costs and solves the problem of the unavailability of experts. Mobile agent as a rich design concept brings many facilities in the development of m-maintenance, however few works are elaborated in this stage. The objective of this work is both: 1) the proposition of a based mobile multi-agent architecture dedicated for m-maintenance in manufacturing systems; 2) the exploitation of high level petri nets in the specification, simulation and verification phases of the architecture development.},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJCCBS.2017.084066},
	author = {Abid Khadija and Mouss Leila Hayet and Kazar Okba and Kahloul Laid}
}
@article {4912,
	title = {Fault diagnosis for a milk pasteurisation plant with missing data, ISSN / e-ISSN 1757-2185 / 1757-2177},
	journal = {International Journal of Quality Engineering and Technology},
	volume = {Vol 6},
	number = {3},
	year = {2017},
	pages = {pp. 123{\textendash}136},
	abstract = {This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.},
	url = {https://www.inderscience.com/info/inarticle.php?artid=88858},
	author = {Kadri Ouahab and Mouss Leila Hayet and Abdelhadi Adel}
}
@article {4911,
	title = {Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization, ISSN 1583-7904},
	journal = {Academic Journal of Manufacturing Engineering },
	volume = {Vol 15},
	number = {Issue 2},
	year = {2017},
	abstract = {The aim of this paper is to propose a new fault diagnosis method for complex manufacturing system. We have used an artificial neural network (ANN) and an Ant Colony Optimization (ACO) algorithm to diagnosis the condition monitoring of a rotary cement kiln. The Ant Colony algorithm can found a small features subset from the original real time signals and the Extreme Learning Machine (ELM) enables a good accuracy with a limiting learning time. Many benchmark datasets have used to evaluate the performances of our algorithm and the result indicates its higher efficiency and effectiveness comparing to other methods.},
	url = {https://www.researchgate.net/publication/318838061_Identification_and_detection_of_the_process_fault_in_a_cement_rotary_kiln_by_extreme_learning_machine_and_ant_colony_optimization},
	author = {Kadri Ouahab and Mouss Leila Hayet}
}
@article {4910,
	title = {Internet and fuzzy based control system for rotary kiln in cement manufacturing plant, ISSN / e-ISSN  1875-6883 / 1875-6891},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {Vol 10},
	number = {issue 1},
	year = {2017},
	pages = {pp. 835{\textendash}850},
	abstract = {This paper develops an Internet-based fuzzy control system for an industrial process plant to ensure the remote and fuzzy control in cement factories in Algeria. The remote process consists of control, diagnosing alarms occurs, maintaining and synchronizing different regulation loops. Fuzzy control of the kiln ensures that the system be operational at all times, with minimal downtime. Internet technology ensures remote control. The system reduces downtimes and can guided by operators in the main control room or via Internet.},
	url = {https://www.atlantis-press.com/journals/ijcis/25874663},
	author = {Zermane Hanane and Mouss Leila Hayet}
}
@article {4909,
	title = { Development of an internet and fuzzy based control system of manufacturing process, ISSN / e-ISSN  1476-8186 / 1751-8520},
	journal = {International Journal of Automation and Computing volume },
	volume = {Vol 14},
	number = {Issue 6},
	year = {2017},
	pages = {pp. 706{\textendash}718},
	abstract = {The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK (Setif), SCIMAT (Batna), and SCT (Tebessa), are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station (OP), alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize (costs, visas, risks). So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control++ Siemens tools.},
	url = {https://link.springer.com/article/10.1007/s11633-016-1027-x},
	author = {Zermane Hanane and Mouss Leila Hayet}
}
@article {5145,
	title = {Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints, ISSN /e-ISSN 0268-3768 / 1433-3015},
	journal = {Journal of Intelligent Manufacturing },
	volume = {Vol 30},
	number = {Issue 2},
	year = {2016},
	pages = {701{\textendash}725},
	abstract = {In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.},
	url = {https://link.springer.com/article/10.1007/s10845-016-1273-3},
	author = {Haoues Mohammed and Dahane Mohammed and Mouss Nadia Kenza}
}
@article {5037,
	title = {Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO, ISSN / e-ISSN  1738-494X / 1976-3824},
	journal = {J MECH SCI TECHNOL},
	year = {2016},
	abstract = {
	In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer \&amp; Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

},
	url = {https://www.semanticscholar.org/paper/Fault-Diagnosis-of-Manufacturing-Systems-Using-with-Kadri-Mouss/01cea502034f7b8f0ed9a1e79c4ac927afa245b6$\#$paper-header},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {4923,
	title = { An Efficient Hybrid Approach Based on Multi-Agent System and Emergence Method for the Integration of Systematic Preventive Maintenance Policies, ISSN 2279-0764},
	journal = {IJCIE International Journal of Computer and Information Engineering},
	volume = { Vol 3},
	number = {N{\textdegree}4},
	year = {2016},
	abstract = {This paper proposes a novel hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.},
	url = {https://www.researchgate.net/publication/303858216_An_Efficient_Hybrid_Approach_Based_on_Multi-Agent_System_and_Emergence_Method_for_the_Integration_of_Systematic_Preventive_Maintenance_Policies},
	author = {Abdelhadi Adel and Kadri Ouahab}
}
@article {4922,
	title = {Toolbox Supports Group Awareness In Groupware, ISSN 1583-7165},
	journal = { Annals. Computer Science Series.},
	year = {2016},
	pages = {pp. 117-122},
	abstract = {Group awareness tools are developed to minimize the time of cooperative application realization and spare designers a lot of effort devoted to integrating the group awareness aspect into groupware. But these tools have several disadvantages, such as dependence on a single type of application or overloading the minds of users with unnecessary information. From here comes the need to develop a tool that allows to offer information of group awareness configurable and to be both generic and easy to use. Our article presents some tools that have inspired several ideas. It proposes a design of a new toolbox that allows a better interpretation of group awareness information. Finally, it presents a variant of the client/server architecture based on work area.},
	url = {https://anale-informatica.tibiscus.ro/download/lucrari/14-2-16-Kadri.pdf},
	author = {Kadri Ouahab and Abdelhadi Adel and Mouss Leila Hayet}
}
@article {4921,
	title = {A Multi-Agent Solution to Distributed Fault Diagnosis of Preheater Cement Cyclone,  ISSN / e-ISSN 0219-6867 / 1793-6896},
	journal = {Journal of Advanced Manufacturing Systems},
	volume = {Vol 15},
	number = {Issue 04},
	year = {2016},
	pages = {pp. 209-221},
	abstract = {Systems health monitoring is essential to guaranteeing the safe, efficient, and reliable operation of engineering systems. Integrated systems health management methodologies include fault diagnosis mechanism. Diagnosis involves detecting when a fault has occurred, isolating the true fault, and identifying the true damage to the system. This important issue is even harder when the systems to be diagnosed are dynamic and spatially distributed systems with their successively increasing complexity. For such systems, a single diagnostic entity having a model of the whole system approach is inappropriate. Whereas a distributed approach of multiple diagnostic agents can offer a solution. An overall systematic solution for these issues could be obtained by an artificial intelligent mechanism called the multi-agent system (MAS). This paper presents a MAS model for fault diagnosis based on logical theory of diagnosis. In this approach, each local diagnostic agent has knowledge above its subsystem and an abstract view of the neighboring subsystems and it is able to determine the local minimal diagnoses that are consistent with global diagnoses. The multi-agent models are simulated in Java Agent Development Framework and are applied to the preheated cement cyclone in the workshop of SCIMAT clinker.},
	url = {https://www.worldscientific.com/doi/10.1142/S021968671650015},
	author = {Chouhal Ouahiba and Mouss Leila Hayet and Benaggoune Said and Mahdaoui Rafik}
}
@article {4920,
	title = {On the use of multi-agent systems for the monitoring of industrial systems, ISSN /e-ISSN 1735-5702 / 1735-5702},
	journal = {Journal of Industrial Engineering International},
	volume = {vol 12},
	year = {2016},
	pages = {pp. 111{\textendash}118},
	abstract = {The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.},
	url = {https://link.springer.com/article/10.1007/s40092-015-0133-7},
	author = {Rezki Nafissa and Kazar Okba and Mouss Leila Hayet and Kahloul Laid}
}
@article {4919,
	title = {A Novel Approach for Mobile Maintenance Using Mobile Agents Technology and Mobile Devices, ISSN / e-ISSN 0219-6867 / 1793-6896},
	journal = {Journal of Advanced Manufacturing Systems},
	volume = {14},
	number = {2},
	year = {2016},
	pages = {pp. 55-74},
	abstract = {The long use of a system causes its degradation. Hence, the maintenance activity is required in order to keep and improve the efficiency in the system. With the rapid development in networking technology, a need appears to change the manufacturing strategies. These new technologies improve the maintenance process, and establish remote maintenance (tele-maintenance, e-maintenance and m-maintenance). These kinds of maintenance try to provide personnel maintenance with the right information at the suitable time, which makes information available, anywhere and anytime. Our proposition is the use of mobile agent technology to reduce the maintenance costs and solve the problem of the unavailability of an expert in all phases of condition-based maintenance (CBM) strategy. The mobile agent technology overcomes a lot of problems and there is not much work that has used this technology. We have also used the web services (WS) to insure interoperability between machines and to support interaction over the network. Our approach gives great support to the maintenance engineer as it facilitates the access to decision-making support, work order, etc. which are available in the device like smartphone. This paper presents the development of a mobile maintenance support system based on mobile agent technology. The proposed system, the web and agent technology as well as remote communication were tested successfully.},
	url = {https://www.worldscientific.com/doi/10.1142/S0219686715500055},
	author = {Abid Khadija and Mouss Leila Hayet and Kazar Okba and Kahloul Laid}
}
@article {3229,
	title = {
	Data monitoring and performance analysis of a 1.6kWp grid connected PV system in Algeria, ISBN 1309-0127


 

},
	journal = {IJRER International Journal of Renewable Energy Research},
	volume = {Vol 6},
	number = {N{\textdegree}1},
	year = {2016},
	pages = {34-42},
	abstract = {
	The present study deals with the performance of a 1.6kWp grid connected PV system installed at Batna University, in Algeria. The average solar energy received was 5.21 kWh/m{\texttwosuperior}.d, the grid connected PV system seems to be a good candidate for generating electricity in this region. The system was monitored during one year of continuous operation and data analysis to evaluate the performance of the grid connected PV system. The performance ratio of the system ranged between 51 and 61\%. Furthermore, the total produced energy by the PV array was 1931.7kWh and the supplied energy to the grid was 1705kWh. The annual final yield was 1065.6kWh/kWp. Moreover, an analysis of the energy losses in the system was performed, this makes it possible to determine the effect of the capture and system losses on the total energy balance of the system. All the electricity generated by the system was fed into the internal electrical grid of the university.

},
	url = {https://www.ijrer.org/ijrer/index.php/ijrer/article/view/3020},
	author = {Chouari Adel and Hamouda Chaabane and Ghagui Azziz}
}
@article {3228,
	title = {Theoretical and experimental investigation of solar heat potential at low temperatures: Towards large scale integration in the agro food sector, ISBN 1309-0127},
	journal = {IJRER International Journal of Renewable Energy Research},
	volume = {Vol.6},
	number = { No.1},
	year = {2016},
	abstract = {
	Several applications in the agro food sub-sector have been classified as suitable to be supplied by solar heating systems. The present work deals with the potential evaluation of solar thermal energy under climatic conditions of Algeria. In a first part, an experimental investigation conducted on small scale solar water heating system is presented followed by simulation. The experimental tests revealed the thermal behavior of the system as well as the outlet temperature levels which can be reached by locally manufactured flat plat collectors. The second part presents the potential of solar heat for industrial applications, particularly those consisting of heating make-up water and water feedback from heat recovery wich exists in almost all the agro food industries. Thus, water inlet temperature through the secondary circuit is considered to be ranging from 20 to 90{\textdegree}C. The results showed that the annual specific energy yield delivered by the solar system decreases by increasing water feedback temperature.\&nbsp;

},
	url = {https://www.ijrer.org/ijrer/index.php/ijrer/article/view/3085},
	author = {Boutagriout Badreddine and Chaabane  Hamouda and Smadi Hacene}
}
@article {5148,
	title = {SKACICM a method for development of knowledge management and innovation system e-KnowSphere,  ISSN /e-ISSN 1755-8255 / 1755-8263},
	journal = {International, Journal. Knowledge and Web Intelligence},
	volume = {Vol 5},
	number = {issue 2},
	year = {2015},
	pages = {105-126},
	abstract = {The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26\% and could be generalised to other manufacturing or service systems.},
	url = {https://dl.acm.org/doi/abs/10.1504/IJKWI.2015.075153},
	author = {Aitouche Samia and Mouss Mohamed Djamel and Kaanit Abdelghafour and Bouhafna Khayreddine}
}
@article {5147,
	title = {Smart algorithm based on the optimization of SVR technique by k-NNR method for the prognosis of the open-circuit and the reversed polarity faults in a PV generator,  ISSN / e-ISSN  1974-9821 / 2533-1701},
	journal = {International Review on Modelling and Simulations},
	volume = {Vol 8},
	number = {issue 1},
	year = {2015},
	pages = {863-870},
	abstract = {This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.},
	url = {https://www.praiseworthyprize.org/jsm/index.php?journal=iremos\&page=article\&op=view\&path[]=16927},
	author = {Wail Rezgui and Mouss Nadia Kenza and Mouss Leila Hayet and Mouss Mohamed Djamel}
}
@article {5125,
	title = {Analysis of competitiveness level in an industrial company using a continuous improvement-based approach, ISSN / e-ISSN 1479-2494 / 1479-2753},
	journal = {International Journal of Six Sigma and Competitive Advantage },
	volume = {Vol 9},
	number = {2/3/4},
	year = {2015},
	pages = {87-108},
	abstract = {In recent years, companies have emerged in an advanced competitive environment. To meet the requirements of cost reduction, customer demand, minimising delays, quality and variety improvement, companies must improve their performance to remain competitive, survive and expand. To achieve this goal, several models are used such as total quality management, Kaizen, just in time, enterprise resource planning, business process reengineering and Six Sigma, etc. In this work, we look for an effective model (drawn from Six Sigma approach) used mainly to warrant the competitiveness of a company denoted as the weighted defects per million opportunities model. The aim of this paper is to apply this model to measure process levels (weights) and assess the company competitiveness. The results of this model are applied in a real manufacturing system which produces gas bottles.},
	url = {https://www.researchgate.net/publication/297943401_Analysis_of_competitiveness_level_in_an_industrial_company_using_a_continuous_improvement-based_approach},
	author = {Aouag Hichem and Kobi Abdessamad and Mechenene Athmane}
}
@article {5043,
	title = {SCADA system for the modeling and optimization of oil collecting pipeline network: A case study of hassi messaoud oilfield ISSN / e-ISSN 2040-7459 /  2040-7467},
	journal = {Research Journal of Applied Sciences, Engineering and Technology },
	volume = {Vol 10},
	number = {Issue 7 },
	year = {2015},
	pages = {789-804},
	abstract = {This study aims are data acquisition, control and online modeling of an oil collection pipeline network using a SCADA {\guillemotleft}Supervisory Control and Data Acquisition{\guillemotright} system, allowing the optimization of this network in real time by creating more exact models of onsite facilities. Indeed, fast development of computing systems makes obsolete usage of old systems for which maintenance became more and more expensive and their performances don{\textquoteright}t comply any more with modern company operations. SCADA system is a telemetry and control system adapted for particular requirements of an oilfield management. Thanks to its different functions, we take advantage of this system to solve production problems especially those related to oil collecting pipeline network. In fact this network is confronted to some problems, in particular pressure losses which has significant effect on the production. This problem can be taken under control by the awareness of pipeline network operation and all its process data (especially junctions) in real time. This will allow online creation of representative and accurate computerized models for the oil collecting pipeline network including producing wells, collecting pipelines, manifolds and others facilities.},
	url = {https://www.researchgate.net/publication/285429170_SCADA_System_for_the_Modeling_and_Optimization_of_Oil_Collecting_Pipeline_Network_A_Case_Study_of_Hassi_Messaoud_Oilfield},
	author = {Aouadj Mounir and Naceri Farid and Touileb Mustapha and Sellami Dorra}
}
@article {5042,
	title = {Comparison and prioritisation of measurement methods of intellectual capital; IC-dVal, VAIC and NICI, ISSN / e-ISSN  1479-4853 / 1479-4861},
	journal = {International Journal of Learning and Intellectual Capital },
	volume = {12},
	number = {2},
	year = {2015},
	abstract = {The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.},
	url = {https://www.researchgate.net/publication/276178196_Comparison_and_prioritisation_of_measurement_methods_of_intellectual_capital_IC-dVal_VAIC_and_NICI},
	author = {Aitouche Samia and Mouss Nadia Kinza and Mouss Mohamed Djamel and Kaanit Abdelghafour and Marref Tarek}
}
@article {5041,
	title = {Smart algorithm based on the optimization of SVR technique by k-NNR method for the prognosis of the open-circuit and the reversed polarity faults in a PV generator},
	journal = {IREMOS  International Review on Modelling and Simulations..  5197 DOI: https://doi.org/10.15866/iremos.},
	volume = {Vol 8 },
	number = { issue 1},
	year = {2015},
	pages = {18-25},
	abstract = {This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.},
	url = {https://hal.archives-ouvertes.fr/hal-01154195/document},
	author = {Rezgui Wail and Mouss Nadia Kinza and Mouss Leila Hayet and Mouss Med Djamel and Amirat Yassine and Benbouzid Mohamed}
}
@article {5040,
	title = {SKACICM a method for development of knowledge management and innovation system e-KnowSphere, ISSN / e-ISSN 1755-8255 / 1479-4861},
	journal = {International Journal of Knowledge and Web Intelligence},
	volume = {Vol 5},
	number = {No 2},
	year = {2015},
	abstract = {The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26\% and could be generalised to other manufacturing or service systems.},
	url = {https://dl.acm.org/doi/abs/10.1504/IJKWI.2015.075153},
	author = {Aitouche Samia and Mouss Med Djamel and Mammeri Walid and Chabbi Abdeldjalil}
}
@article {4902,
	title = {Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO, ISSN / e-ISSN  1738-494X / 1976-3824},
	journal = {J MECH SCI TECHNOL},
	year = {2015},
	abstract = {In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer \&amp; Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.},
	url = {https://www.researchgate.net/publication/285578275_Fault_Diagnosis_of_Manufacturing_Systems_Using_AntTreeStoch_with_Parameter_Optimization_by_ACO},
	author = {Kadri Ouahab and Mouss Leila Hayet}
}
@article {3352,
	title = {Analysis of competitiveness level in an industrial company using a continuous improvement based approach, ISSN/ISBN 1479-27531479-2494},
	journal = {IJSSCA International journal of Six sigma and Competetive Advantage},
	volume = {Vol 9},
	number = { n{\textdegree}3},
	year = {2015},
	pages = {pp 87-108 },
	abstract = {In recent years, companies have emerged in an advanced competitive environment. To meet the requirements of cost reduction, customer demand, minimising delays, quality and variety improvement, companies must improve their performance to remain competitive, survive and expand. To achieve this goal, several models are used such as total quality management, Kaizen, just in time, enterprise resource planning, business process reengineering and Six Sigma, etc. In this work, we look for an effective model (drawn from Six Sigma approach) used mainly to warrant the competitiveness of a company denoted as the weighted defects per million opportunities model. The aim of this paper is to apply this model to measure process levels (weights) and assess the company competitiveness. The results of this model are applied in a real manufacturing system which produces gas bottles.},
	url = {https://hal.archives-ouvertes.fr/hal-01392070},
	author = {Aoueg Hicham and Mechennene Athmane}
}
@article {3351,
	title = {Comparison and prioritisation of measurement methods of intellectual capital; IC-dVal, VAIC and NICI,  ISSN 1755-8263},
	journal = {IJLIC International journal of learning and intellectual capital},
	volume = {Vol. 12},
	number = { N{\textdegree}2},
	year = {2015},
	pages = {122 - 145},
	abstract = {The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.},
	url = {https://www.inderscience.com/info/inarticle.php?artid=68984},
	author = {Aitoche Samia and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3341,
	title = {Modeling and solving a one supplier multi-vehicle production-inventory-distribution ptoblem xith clustered retailers,  ISSN/ISBN 0268-3768 / 1433-3015},
	journal = {INT J ADV MANUF TECH},
	volume = {Volume 85},
	number = { Issue 5{\textendash}8},
	year = {2015},
	pages = {pp 971{\textendash}989},
	abstract = {This paper considers a supply chain management problem which integrates production, inventory, and distribution decisions. The supply chain is composed of one supplier production facility and several retailers located in a given geographic region. The supplier is responsible for the production and the replenishment of the inventory of retailers, in a vendor managed inventory (VMI) context. The distance between retailers is negligible compared to the distance between the supplier and the retailers{\textquoteright} region. Thus, for each vehicle, there is a major fixed cost for traveling to the cluster of retailers and a minor fixed cost for visiting each individual retailer. The problem consists of determining quantities to be produced, quantities to be delivered to retailers, vehicles to be used, and retailers to be serviced by each vehicle. This problem is an extension of the one warehouse multi-retailer problem with the consideration of production planning and storage and vehicle capacity limitations in addition to fixed vehicle utilization costs and retailer servicing costs. The objective is to minimize a total cost composed of production, transportation, and inventory holding costs at the supplier and at the retailers. Two mixed integer linear programming formulations are proposed and six families of valid inequalities are added to strengthen these formulations. Two of these families are new and the others are adapted from the literature. The numerical results show that the valid inequalities considerably improve the quality of the formulations. Moreover, the parameters that influence the most computational times are analyzed},
	url = {https://link.springer.com/article/10.1007/s00170-015-7966-5},
	author = {Senoussi Ahmed and Mouss kinza Nadia and Dauzere-Peres}
}
@article {3340,
	title = {A New Genetic Algorithm or the Flexible Job Shop scheduling problems, ISSN/ISBN 1738-494X / 1976-3824},
	journal = {J MECH SCI TECHNOL},
	volume = {Volume 29},
	number = {Issue 3},
	year = {2015},
	pages = { pp 1273{\textendash}1281},
	abstract = {Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.},
	url = {https://link.springer.com/article/10.1007/s12206-015-0242-7},
	author = {Driss Imen and Mouss kinza Nadia and Laggoun Assia}
}
@article {3339,
	title = {Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs,  ISSN/ISBN 0268-3768/1433-3015},
	journal = {INT J ADV MANUF TECH},
	volume = {Volume 80},
	number = {Issue 5},
	year = {2015},
	pages = {pp 1369{\textendash}1385 },
	abstract = {The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.},
	url = {https://link.springer.com/article/10.1007/s00170-015-7100-8},
	author = {Bentercia Toufik and Mouss Leila Hayet and Mouss kinza Nadia}
}
@article {3227,
	title = {Auditing of Energy Use and Costs Analysis in the Algerian Agro Food Sector: A Case Study in Three Dairies, ISSN/ISBN 0199-8595/1546-0118},
	journal = {JAEE Journal of the Association of Energy Engineering Auditing of Energy Use and Costs Analysis in the Algerian },
	year = {2015},
	abstract = {The industrial sector accounts for almost one fourth of the final energy consumption in Algeria. An energy survey was carried out in three Algerian dairies, and the energy use and cost analysis is presented in this article. Electricity and natural gas are the main resources that are used in the dairy industry. The specific energy consumption related to electricity and natural gas was found to be in the ranges of 0.03{\textendash}0.08 kWh/kg and 0.10-0.22 kWh/kg respectively. Also, a relationship of energy consumption, energy costs and production is presented and the findings could be used to establish an energy management system aimed at using energy more efficiently. Finally, this paper calls for more investigations of other industries to obtain reliable indicators.},
	url = {https://www.tandfonline.com/doi/full/10.1080/01998595.2016.11668652},
	author = {Boutagriout Badreddine and Hamouda Cha{\^a}bane and Smadi Hacene}
}
@article {3226,
	title = {Comparison and prioritization of measurement methods of intellectual capital, IC-dVal, VAIC and NICI, Janvier  ISSN/ISBN 1755-8263/1479-4853},
	journal = {IJLIC International journal of learning and intellectual capital},
	volume = {Vol. 12},
	number = {N{\textdegree}2 },
	year = {2015},
	pages = {pp 122-145},
	abstract = {The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis},
	url = {https://www.inderscience.com/info/inarticle.php?artid=68984},
	author = {Aitoche Samia and Mouss Med Djamel and Kaanit Abdelghafor}
}
@article {3225,
	title = {SKACICM a method for development of knowledge management and innovation system e-KnowSphere, Janvier, ISSN/ISBN 1755-8263/1479-4861},
	journal = { IJKWI. International journal of knowledge and web intelligence},
	volume = {Vol. 6 },
	number = {N{\textdegree} 1},
	year = {2015},
	pages = {pp.105{\textendash}126},
	abstract = {
	The purpose of this paper is to propose a hybrid method\&nbsp;SKACICM of development of knowledge management systems. Based on\&nbsp;weaknesses of the method of performance dashboards SKANDIA, we proposed\&nbsp;a pragmatisation and adaptation of Skandia to give (ASKANDIA), by\&nbsp;enrichment of its performance book. We ameliorated CICM model against the\&nbsp;requirements of GERAM to give ACICM model by mappings between\&nbsp;their proposed metamodels. We tried to hybridise ACICM, ASKANDIA\&nbsp;and business intelligence to propose a new method SKACICM of development\&nbsp;of knowledge management systems. We applied SKACICM on a cement\&nbsp;company to develop software containing three main modules, module\&nbsp;knowledge management, module business intelligence and performance\&nbsp;dashboard system. The developed system ameliorated the performance of the\&nbsp;enterprise by 26\% and could be generalised to other manufacturing or service\&nbsp;systems.

},
	url = {10.1504/IJKWI.2015.075153},
	author = {Aitoche Samia and Mouss Med Djamel and Kaanit Abdelghafor}
}
@article {3224,
	title = {An effective Meta-Heuristic Algoritm for solve multicriteria job Jhop Scheluding problem with maintenance activitie, ISSN/ISBN 2040-7459/1479-4861},
	journal = {RJSAET.Research Journal of Applied Sciences Engineering and technology},
	volume = {Vol. 11},
	number = {issue 9},
	year = {2015},
	pages = {pp. 01-12},
	abstract = {In this study, a metaheuristic based on the Non-dominated Sorting Genetic Algorithm type II (NSGA-II) is proposed to solve the Multi-Criterions Job Shop Scheduling Problem (MCJSSP) under resources availability constraints. Availability periods and starting time of maintenance activities are supposed to be flexible. The MCJSSP requires, simultaneous minimization several antagonistic criteria, such as the maximum completion time of all jobs (Makespan), production cost and maintenance cost. To validate the proposed approach we tested it on forty-four instances references. The results show that our approach is experimentally promising to solve practical problems.},
	url = {http://dx.doi.org/10.19026/rjaset.11.2134},
	author = {Bahmani Younes and Smadi Hacene and Benzouai Mzssaoud}
}
@article {3072,
	title = {Comparison and prioritization of measurement methods of intellectual capital; IC-dVal, VAIC and NICI, Janvier, ISSN/ISBN 1755-8263/1479-4853},
	journal = { IJLIC International journal of learning and intellectual capital },
	volume = {Vol. 12},
	number = {N{\textdegree}2},
	year = {2015},
	pages = {pp 122-145 },
	abstract = {The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.},
	url = {https://ideas.repec.org/a/ids/ijlica/v12y2015i2p122-145.html},
	author = {Aitoche Samia and Mouss Leila Hayet and Kaanit Abdelghafour}
}
@article {3067,
	title = {Smart Algorithm Based on the Optimization of SVR Technique by k-NNR Method for the Prognosis of the Open-Circuit and the Reversed Polarity Faults in a PV Generator, Decembre, ISSN/ISBN 1974-98211974-983X},
	journal = {IREMOS International Review on Modelling and Simulations.},
	volume = { Vol 8},
	number = {N{\textdegree}5},
	year = {2015},
	pages = {pp .18-25},
	abstract = {This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent},
	url = {https://hal.archives-ouvertes.fr/hal-01154195},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3064,
	title = {SKACICM a method for development of knowledge management and innovation system e-KnowSphere, Janvier, ISSN/ISBN 1755-8255/1479-4861},
	journal = {JKWI. International journal of knowledge and web intelligence},
	volume = {Volume-6},
	number = { N{\textdegree} 1},
	year = {2015},
	author = {Aitoche Samia and Mouss Med Djamel and Mamri Walid}
}
@article {3003,
	title = {A Novel Approach for Mobile Maintenance Using Mobile Agents Technology and Mobile Devices, ISSN / ISBN 0219-6867 / 1793-6896},
	journal = {JAMS Journal of Advanced Manufacturing Systems},
	volume = {14},
	number = {2},
	year = {2015},
	pages = { pp. 55-74 },
	abstract = {The long use of a system causes its degradation. Hence, the maintenance activity is required in order to keep and improve the efficiency in the system. With the rapid development in networking technology, a need appears to change the manufacturing strategies. These new technologies improve the maintenance process, and establish remote maintenance (tele-maintenance, e-maintenance and m-maintenance). These kinds of maintenance try to provide personnel maintenance with the right information at the suitable time, which makes information available, anywhere and anytime. Our proposition is the use of mobile agent technology to reduce the maintenance costs and solve the problem of the unavailability of an expert in all phases of condition-based maintenance (CBM) strategy. The mobile agent technology overcomes a lot of problems and there is not much work that has used this technology. We have also used the web services (WS) to insure interoperability between machines and to support interaction over the network. Our approach gives great support to the maintenance engineer as it facilitates the access to decision-making support, work order, etc. which are available in the device like smartphone. This paper presents the development of a mobile maintenance support system based on mobile agent technology. The proposed system, the web and agent technology as well as remote communication were tested successfully.},
	url = {https://doi.org/10.1142/S0219686715500055},
	author = {Abid Khadidja and Mouss Leila Hayet and Kazar Okba and Kahloul Laid}
}
@article {1730,
	title = {Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs, September 2015, ISSN/ISBN: 0268-3768/ 1433-3015},
	journal = {The International Journal of Advanced Manufacturing Technology},
	volume = {80},
	number = {05},
	year = {2015},
	pages = {1369{\textendash}1385},
	abstract = {The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.},
	url = {https://doi.org/10.1007/s00170-015-7100-8},
	author = {Bentercia Toufik and Mouss Leila Hayet and Mouss kinza Nadia}
}
@article {3354,
	title = {l\&rsquo;am{\'e}lioration du processus de fabrication d\&rsquo;une entreprise alg{\'e}rienne par le niveau sigma,  ISSN/ISBN 1112-3613 / 2437-0843},
	journal = {El-Bahith Review},
	volume = {Volume 14},
	number = {Num{\'e}ro 14},
	year = {2014},
	pages = { Pages 61-73 },
	abstract = {Ce modeste travail a pour objectif d{\textquoteright}{\'e}valuer la performance du processus de fabrication de l{\textquoteright}entreprise par un nouvel outil de mesure, en l{\textquoteright}occurrence le niveau sigma dont la finalit{\'e} consiste {\`a} chiffrer les couts inh{\'e}rents {\`a} chaque sous processus de production, mesurer les niveaux six sigma des sous processus adjacents, les pond{\'e}rer pour parvenir {\`a} calculer le DPMO (Defects Per Million Opportunity) et {\'e}valuer ainsi la comp{\'e}titivit{\'e} globale de l{\textquoteright}entreprise. Ce nouvel outil de mesure de la performance du processus de fabrication (niveau sigma) sera appliqu{\'e} {\`a} une entreprise de fabrication de Bouteilles A Gaz (BAG {\textendash} Batna).},
	url = {https://www.asjp.cerist.dz/en/article/260},
	author = {Aoueg Hicham and Kaanit Abdelghafour}
}
@article {3350,
	title = {Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, March 2014, ISSN/ISBN 1582-4594. 1335-3632},
	journal = {JEE Journal of Electrical Engineering,},
	volume = {Vol. 14},
	number = {Issue.1},
	year = {2014},
	pages = {pp. 77-84},
	abstract = {In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.},
	url = {https://hal.archives-ouvertes.fr/hal-01154200/document},
	author = {Rezgui Wail    Mouss Kinza Nadia and Mouss Leila Hayet and Mouss kinza Nadia}
}
@article {3348,
	title = {Faults modeling of the impedance and reversed polarity types within the PV generator operation, ISSN/ISBN 1974-98211974-983X},
	journal = {IREMOS International Review on Modelling and Simulations.},
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.},
	url = {https://ieeexplore.ieee.org/document/7059979},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3347,
	title = {Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults,  ISSN/ISBN 1974-9821 / 1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations},
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of a faulty photovoltaic generator operation. It presents its behavior, when it{\textquoteright}s subjected to the open-circuit and the short-circuit faults at its basic components as: cells, bypass diodes and blocking diodes. Such kind of modeling will allow developing fault detection and diagnosis methods. Indeed, the proposed model will be used to set normal and fault operation conditions database, which will facilitate learning and classifications phases.},
	url = {https://ieeexplore.ieee.org/document/7059978/metrics$\#$metrics},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Leila Hayet}
}
@article {3346,
	title = {Electrical faults modeling of the photovoltaic generator,  ISSN/ISBN 1974-98211974-983X},
	journal = {IREMOS International Review on Modelling and Simulations },
	volume = {Vol.7},
	number = {Issue 2},
	year = {2014},
	pages = {p.245-257},
	abstract = {n this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator{\textquoteright}s characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator{\textquoteright}s defaults.},
	url = {https://hal.archives-ouvertes.fr/hal-01017387/document},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3345,
	title = {Smart Algorithm Based on the Optimization of SVR Technique by k-NNR Method for the Prognosis of the Open-Circuit and the Reversed Polarity Faults in a PV Generator, Dec. 2015, ISSN/ISBN  1974-9821 / 1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	volume = {Vol 8},
	number = {N{\textdegree} 5},
	year = {2014},
	pages = {pp 18-25},
	abstract = {This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent},
	url = {https://hal.archives-ouvertes.fr/hal-01154195},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss kinza Nadia}
}
@article {3343,
	title = {Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator, Dec 2014,  ISSN/ISBN 1974-9821 / 1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations },
	volume = {Vol.7},
	number = {N{\textdegree}5},
	year = {2014},
	pages = {pp 77-84},
	abstract = {This paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area.},
	url = {https://www.praiseworthyprize.org/jsm/index.php?journal=iremos\&page=article\&op=view\&path\%5B\%5D=16281},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3342,
	title = {{\'E}tude, mise en {\oe}uvre et adaptabilit{\'e} des outils de l\&rsquo;am{\'e}lioration continue dans une industrie alg{\'e}rienne : Approches Th{\'e}orique et Pratique,  ISSN 1479-2494},
	journal = {IJSSCA International Journal of SIX SIGMA and Competitive Advantage},
	volume = {Vol. 9},
	year = {2014},
	pages = { pp. 01-19},
	author = {Aoueg Hicham and Mechennene Athmane}
}
@article {3073,
	title = {Faults modeling of the impedance and reversed polarity types within the PV generator operation, ISSN/ISBN 1974-98211974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.},
	url = {https://ieeexplore.ieee.org/document/7059979},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Leila Hayet}
}
@article {3071,
	title = {Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, March, ISSN/ISBN 1582-4594/1335-3632.},
	journal = {JEE Journal of Electrical Engineering,},
	volume = {Vol. 14},
	number = {Issue. 1},
	year = {2014},
	pages = {pp. 77-84},
	abstract = {the work presented inthis paper isdedicated to improvingthe methods ofdetection and diagnosisoffaults affectingproduction systems,particularlyphotovoltaic systems.We proposeda newintelligent algorithmfor the detectionand diagnosis ofPVinstallations, capable of detecting and resonateto define thetype of defectsthat canaffectthis typeofsystem. This new algorithmis based onthe notion ofpattern recognition,for that it isable to preparethe representation spaceandthe decision spaceon the one hand,and on the otherhand, theclassificationof all newobservationscollected duringthe functioning of the system. This algorithmmainly based onthe method ofk-nearest neighbor and two toolsof artificial intelligenceto improve thismethod andincreasing the rate ofits classification, which arefuzzy logic tooptimizethe location of thecenters of gravity ofclassesandalsothe new observations,and the neural network thatcan classify thecase of dischargesambiguityandreleasesdistancewhich presentsthe limitations of the methodof thek-nearest neighbor. Wetested the performanceof our algorithm ona databaseofa photovoltaic system at theresearch unit ofGHARDAIAAlgeria.},
	url = {https://pdfs.semanticscholar.org/8d54/cc8ebdfc720b5d95cbb6d4238667874f8799.pdf},
	author = {Rezgui Wail and Mouss Leila Hayet and Kadri Ouahab}
}
@article {3069,
	title = {Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults, ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of a faulty photovoltaic generator operation. It presents its behavior, when it{\textquoteright}s subjected to the open-circuit and the short-circuit faults at its basic components as: cells, bypass diodes and blocking diodes. Such kind of modeling will allow developing fault detection and diagnosis methods. Indeed, the proposed model will be used to set normal and fault operation conditions database, which will facilitate learning and classifications phases.},
	url = {https://ieeexplore.ieee.org/document/7059978},
	author = {Rezgui Wail  Mouss Kinza Nadia  Mouss Leila Hayet}
}
@article {3068,
	title = {Electrical faults modeling of the photovoltaic generator, ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	volume = {Vol 7},
	number = {N{\textdegree}2},
	year = {2014},
	pages = {pp.245-257},
	abstract = {n this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator{\textquoteright}s characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator{\textquoteright}s defaults. },
	url = {https://hal.archives-ouvertes.fr/hal-01017387/document},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3065,
	title = {Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator, Decembre, ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations},
	volume = {Vol.7},
	number = {N{\textdegree}5},
	year = {2014},
	pages = {pp 77-84},
	abstract = {This paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area.},
	url = { https://doi.org/10.15866/iremos.v7i5.3442},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3001,
	title = {Faults modeling of the impedance and reversed polarity types within the PV generator operation, ISSN/ISBN 1974-9821 / 1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations.},
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.},
	url = {https://hal.archives-ouvertes.fr/hal-01154200/document},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Leila Hayet}
}
@article {3000,
	title = {A New Method Based on Multi Agent System and Artificial Immune System for Systematic Maintenance, ISSN/ISBN 2040-7459 /2040-7467 \&copy; Maxwell Scientific Organization, 2014},
	journal = {RJASET Research Journal of Applied Sciences, Engineering and Technology},
	volume = {7},
	number = {19},
	year = {2014},
	pages = {4008-4017},
	abstract = {This study propose a novel method for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling. The proposed approach is inspired by the behavior of the human body. We have implemented a problem-solving approach for optimizing the processing time, methods based on M{\'e}taheuristiques. This hybridization is between a Multi agent system and inspirations of the human body, especially artificial immune system. The effectiveness of our approach has been demonstrated repeatedly in this study. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.},
	url = {https://pdfs.semanticscholar.org/452d/4b6197de0ff91f28e20c4558cfd750365772.pdf},
	author = {Abdelhadi Adel and Mouss Leila Hayet}
}
@article {1867,
	title = {Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, March 2014, ISSN/ISBN: 1582-4594 / 1335-3632.},
	journal = { Journal of Electrical Engineering (JEE)},
	volume = {14},
	number = {01},
	year = {2014},
	pages = {77-84},
	abstract = {
	In this paper, we proposed a new



	mathematical model of the I-V characteristic of a faulty



	photovoltaic generator. It presents its behavior in normal



	and faulty operations. In particular, when its basic



	components such as cells, bypass and blocking diodes are



	subjected to the impedance or reversed polarity faults.



	The developed model of the faulty PV generator will



	allow studying of the I-V characteristic, measures the



	tolerances of the technical functions, avoids numerous



	experiments, and ensure better assessment of fault



	consequences.

},
	url = {https://hal.archives-ouvertes.fr/hal-01154200},
	author = {Rezgui Wail and Rezgui Wail and Kadri Ouahab}
}
@article {1835,
	title = {Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults, Novembre 2014, ISSN/ISBN: 1974-9821/1974-983X},
	journal = {International Review on Modelling and Simulations(IREMOS)},
	year = {2014},
	abstract = {In this paper, we proposed a new mathematical model of a faulty photovoltaic generator operation. It presents its behavior, when it{\textquoteright}s subjected to the open-circuit and the short-circuit faults at its basic components as: cells, bypass diodes and blocking diodes. Such kind of modeling will allow developing fault detection and diagnosis methods. Indeed, the proposed model will be used to set normal and fault operation conditions database, which will facilitate learning and classifications phases.},
	url = {https://ieeexplore.ieee.org/document/7059978/metrics},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Leila Hayet}
}
@article {1830,
	title = {Electrical faults modeling of the photovoltaic generator, juillet 2014, ISSN/ISBN: 1974-9821/1974-983X},
	journal = {International Review on Modelling and Simulations(IREMOS)},
	volume = {07},
	number = {02},
	year = {2014},
	pages = {245-257},
	abstract = {In this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator{\textquoteright}s characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator{\textquoteright}s defaults.},
	url = {https://hal.archives-ouvertes.fr/hal-01017387},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {1736,
	title = {Smart Algorithm Based on the Optimization of SVR Technique by k-NNR Method for the Prognosis of the Open-Circuit and the Reversed Polarity Faults in a PV Generator, Decembre 2015, ISSN/ISBN: 1974-9821/1974-983X},
	journal = {International Review on Modelling and Simulations(IREMOS)},
	volume = {08},
	number = {05},
	year = {2014},
	pages = {18-25},
	abstract = {This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent},
	url = {https://hal.archives-ouvertes.fr/hal-01154195},
	author = {Rezgui Wail and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {1733,
	title = {Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator, Decembre 2014, ISSN/ISBN: 1974-9821/1974-983X.},
	journal = {International Review on Modelling and Simulations(IREMOS)},
	volume = {07},
	number = {05},
	year = {2014},
	pages = {77-84. },
	abstract = {his paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area.Copyright {\textcopyright} 2014 Praise Worthy Prize - All rights reserved.},
	url = { https://doi.org/10.15866/iremos.v7i5.3442	},
	author = {Rezgui Wail and Mouss kinza Nadia and Mouss Med Djamel}
}
@article {3393,
	title = {Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults},
	journal = {Springer Berlin Heidelberg, International Journal of Automation and Computing},
	volume = {Vol. 10},
	number = { issue 4},
	year = {2013},
	pages = {pp335-342},
	abstract = {In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example},
	url = {https://link.springer.com/article/10.1007/s11633-013-0729-6},
	author = {Meriem Benbrahim and Najib Essounbouli and Abdelaziz Hamzaoui and Ammar Betta}
}
@article {3363,
	title = {Continuous Improvement of Production system in Algerian industry ISSN/ISBN 1694-08141694-0784},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = { Vol. 10},
	number = {Issue 4},
	year = {2013},
	pages = {pp.337-344},
	abstract = {This paper presents the methodology for process improvement of Algerian Company, which implements a new process by the following of business process reengineering approach. For this, we conducted a comprehensive study of the company activity through a study of the current manufacturing process followed by an evaluation of various performance indicators, this has allowed us to propose a new model production process and identify weaknesses of the activities and services that interact with the company machining process Keywords: Improvement continouus, Kaizen, performance, R{\'e}ingeneering, Six sigma},
	url = {https://search.proquest.com/openview/d77b4751e82ef487aa423e256131c235/1?pq-origsite=gscholar\&cbl=55228},
	author = {Aoueg Hicham and Mechenene Athmane}
}
@article {3353,
	title = {Implementation and adaptability of a continuous improvement tool in Algerian industry, ISSN/ISBN 2162-6359},
	journal = { International Journal of Economics  and  Management Sciences    },
	volume = {Vol. 3},
	number = {No. 11},
	year = {2013},
	pages = {pp. 41-49},
	author = {Aoueg Hicham and Mechenene Athmane}
}
@article {3344,
	title = {Process Optimization by DMAIC Approach in Algerian Industry, Nov 2013},
	journal = { IJMIT International Journal of Management, Information and Technology},
	volume = {Vol. 7},
	number = {No. 2},
	year = {2013},
	pages = { pp 1074-1083},
	abstract = {The aim of this paper is to contribute to the improvement of the performance of the functioning of the SCIMAT company. For that purpose, we made a diagnosis of all workshops to identify the possible axes of improvement and to choose the solutions to be implemented.This diagnosis is made by implementation of DMAIC methodologyof Six sigma approach at SCIMAT company in Algeria, DMAIC is the five-step approach that makes up the Six Sigma tool kit, and its sole objective is to drive costly variation from manufacturing and business processes. The five steps in DMAIC are Define,Measure,Analyze,Improve, andControl.As the backbone of theSix Sigmamethodology, DMAIC delivers sustained defect-free performance and highly competitive quality costs over the long run.},
	url = {https://pdfs.semanticscholar.org/d809/d2943a574f8b29194356adccc25b972f15e7.pdf},
	author = {Aoueg Hicham and Mechennene Athmane}
}
@article {3111,
	title = {Reconnaissance des Formes par SVM pour le Diagnostic du  Syst{\`e}me de Pasteurisation d\&rsquo;une Usine de Lait, janvier, ISSN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Volume 4 },
	number = {N{\textdegree}1},
	year = {2013},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvel algorithme pour la classification de diff{\'e}rents {\'e}tats de fonctionnement et de dysfonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithme offre une bonne qualit{\'e} de classification de donn{\'e}es. Cet algorithme est bas{\'e} sur Les S{\'e}parateurs {\`a} Vastes Marges (SVM). Il permet de classer des donn{\'e}es non lin{\'e}airement s{\'e}parables. L{\textquoteright}algorithme offre aussi la possibilit{\'e} d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Les exp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es issues d{\textquoteright}un syst{\`e}me de pasteurisation montrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {https://www.researchgate.net/publication/278329125_Reconnaissance_des_Formes_par_SVM_pour_le_Diagnostic_du_Systeme_de_Pasteurisation_d{\textquoteright}une_Usine_de_Lait},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3083,
	title = {Reconnaissance des Formes par SVM pour le Diagnostic du Syst{\`e}me de Pasteurisation d\&rsquo;une Usine de Lait, janvier, ISSN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Volume 4},
	number = { N{\textdegree}1},
	year = {2013},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvel algorithme pour la classification de diff{\'e}rents {\'e}tats de fonctionnement et de dysfonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithme offre une bonne qualit{\'e} de classification de donn{\'e}es. Cet algorithme est bas{\'e} sur Les S{\'e}parateurs {\`a} Vastes Marges (SVM). Il permet de classer des donn{\'e}es non lin{\'e}airement s{\'e}parables. L{\textquoteright}algorithme offre aussi la possibilit{\'e} d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Les exp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es issues d{\textquoteright}un syst{\`e}me de pasteurisation montrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {https://www.researchgate.net/publication/278329125_Reconnaissance_des_Formes_par_SVM_pour_le_Diagnostic_du_Systeme_de_Pasteurisation_d\%27une_Usine_de_Lait},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3078,
	title = {Adaptive Type-2 Fuzzy Sliding Mode Controller for SISO Nonlinear Systems Subject to Actuator Faults, ISSN/ISBN 1476-8186/1751-8520},
	journal = {IJAC International Journal of Automation and Computing},
	volume = {Vol. 10},
	number = { Issue 4},
	year = {2013},
	pages = {pp.335-342},
	abstract = {In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.},
	url = {https://link.springer.com/article/10.1007/s11633-013-0729-6},
	author = {Benbrahim Meriem and Enounbouli Nadji}
}
@article {3013,
	title = {A Web service Based Multi Agent System for the Diagnosis of industrial plants, May, ISSN/ISBN 1970 \&ndash; 8734/1970-8742},
	journal = {IREME International Review of Mechanical Engineering.},
	volume = {Vol 5},
	number = {N{\textdegree} 6},
	year = {2013},
	pages = {pp: 1156-1159},
	abstract = {Fault diagnosis and maintenance are considered as vital aspects in industrial process, therefore, fault diagnosis systems should support decision-making tools, new diagnosis approaches and technique. In this paper, we discuss the cooperation of agents and Web Services in order to create highly flexible and dynamic framework for manufacturing systems fault diagnosis based on three-tier architectures that can support a distributed application. We depict also how Web Services can be located and the provided services can be invoked.},
	url = {https://www.researchgate.net/publication/291105216_A_Web_Services_Based_Multi_Agent_System_for_the_Diagnosis_of_Industrial_Plants},
	author = {Chouhal Ouahiba and Mouss Leila Hayet and Mahdaoui Rafik}
}
@article {3009,
	title = {Reconnaissance des Formes par SVM pour le Diagnostic du Syst{\`e}me de Pasteurisation d\&rsquo;une Usine de Lait, janvier, ISSN/ISBN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Volume 4},
	number = {N{\textdegree}1},
	year = {2013},
	pages = {p: 283},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvel algorithme pour la classification de diff{\'e}rents {\'e}tats de fonctionnement et de dysfonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithme offre une bonne qualit{\'e} de classification de donn{\'e}es. Cet algorithme est bas{\'e} sur Les S{\'e}parateurs {\`a} Vastes Marges (SVM). Il permet de classer des donn{\'e}es non lin{\'e}airement s{\'e}parables. L{\textquoteright}algorithme offre aussi la possibilit{\'e} d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Les exp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es issues d{\textquoteright}un syst{\`e}me de pasteurisation montrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {https://www.researchgate.net/publication/278329125_Reconnaissance_des_Formes_par_SVM_pour_le_Diagnostic_du_Systeme_de_Pasteurisation_d{\textquoteright}une_Usine_de_Lait},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3008,
	title = {Recognition of objects in an image for triage, December, ISSN/ISBN 2250-1355/ 2249-9555},
	journal = {IJCSITS International Journal of Computer Science, Information Technology, \& Security},
	volume = {Vol 2},
	number = {Issue 6},
	year = {2013},
	pages = {pp 1170-1176},
	abstract = {In this paper, our aim is to present existing methods and techniques related to the artificial vision which is an active subject of research. In this work, we elaborate a recognition system of objects in an image for a sorting application. The corner stone of our work is based on the recognition and sorting of objects (geometrical shapes) in an image in artificial vision. The application of these images is achieved using a fixed camera having a regular and permanent field of vision with a consequent angle on a conveyer which holds the objects to be observed. The Objects{\textquoteright} recognition process requires knowledge of our camera characteristics. In order to calibrate this camera, we propose to use a simple Web camera configured to get a photo of the conveyer{\textquoteright}s centre. The objective is to seek for objects in the images, which are considered as unitary and not sequences of images. In this case, the development of automatic methods is required to ensure the sorting rapidity but with additional complex processing to make it efficient. The analysis determines simultaneously both the recensement and arithmetic counting, then the detection of faults based mainly on the comparison of the object shape with the shape defined previously and using this operation the sorting is made. A complementary system equipped with sensors and programmable automata is used to eject each object into corresponding panel.},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6689571},
	author = {Bellal Salah Eddine and Mouss Leila Hayet}
}
@article {1732,
	title = {An efficient hybrid approach based on multi agent system and emergence method for the integration of systematic preventive maintenance policies in hybrid flow-shop scheduling to minimize makespan, September 2013, ISSN/ISBN: 2141-2383.},
	journal = {Journal of Mechanical Engineering Research},
	volume = {05},
	number = {06},
	year = {2013},
	pages = {112-122 },
	abstract = {This paper proposes a novel hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time and methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.},
	url = {https://doi.org/10.5897/JMER2013.0275},
	author = {Abdelhadi Adel and Mouss Leila Hayet}
}
@article {3392,
	title = {A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis JSEA, ISSN Print: 1945-3116},
	journal = {Journal of Software Engineering and Applications},
	volume = {Vol 5},
	number = {N{\textdegree}7},
	year = {2012},
	abstract = {As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural net-work architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln},
	url = {https://www.researchgate.net/publication/265800845_A_TSK-Type_Recurrent_Neuro-Fuzzy_Systems_for_Fault_Prognosis},
	author = {Rafik Mahdaoui and Leila Hayet Mouss}
}
@article {3391,
	title = {Installation of Distributed Maintenance Case: Cement Factories of East (SMCE)},
	journal = {Advanced Materials Research},
	volume = {Vol 429},
	year = {2012},
	pages = {PP. 50-55},
	abstract = {In this work, we intend to determine a scheduling rule of maintenance work, which permits the optimization of the random tasks operation (corrective maintenance) while maintaining the works (preventive maintenance) yet scheduled within their temporal intervals. The principle of our approach consists of processing the problem in two stages: Resolution of the static problem for the projected scheduling of the tasks known a priori Resolution of the dynamic problem for random tasks scheduling This work has found a field of application within the maintenance company of the east cements factories, (SMCE) which is in charge of the five (05) east cements companies{\textquoteright} maintenance.},
	url = {https://www.researchgate.net/publication/282498381_Installation_of_Distributed_Maintenance_Case_Cement_Factories_of_East_SMCE},
	author = {Hacene Smadi and Djamel Bellala and Younes Maache}
}
@article {3390,
	title = {Fault diagnosis of rotary kiln using SVM and Binary ACO ISSN 1976-3824},
	journal = {International Journal of Mechanical Science and Technology Springer Berlin Heidelberg},
	volume = {Vol. 26},
	number = {N{\textdegree}2},
	year = {2012},
	pages = {pp. 601-608},
	abstract = {This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms},
	url = {https://link.springer.com/article/10.1007/s12206-011-1216-z},
	author = {Ouahab Kadri and Leila Hayet Mouss and Mohamed Djamel Mouss}
}
@article {3389,
	title = {Development of a fuzzy expert system based on PCS 7 and FuzzyControl++,  JES, ISSN: 2278\&ndash;6538},
	journal = {Journal of Electronic Systems},
	volume = {Vol. 1},
	number = { N{\textdegree}1},
	year = {2012},
	pages = {pp: 18-32},
	abstract = {The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems to control the difference shown in the control system using fuzzy regulators for the operation of the grinding without unnecessary stops, it also helps the operator to know the maintenance task to perform. In addition, regulators decentralization allows the availability of fuzzy control, even if one of the regulators is absent, it does not prevent the other to complete its task to control fineness, mill�s temperature and feed.},
	url = {https://www.researchgate.net/publication/230773223_DEVELOPMENT_OF_A_FUZZY_EXPERT_SYSTEM_BASED_ON_PCS7_AND_FUZZYCONTROL_CEMENT_MILL_CONTROL},
	author = {Hanane Zermane and Leila Hayet Mouss and Sonia Benaicha},
	editor = {Iran University of Science and Technology, Tehran, Iran}
}
@article {3388,
	title = {Recognition of objects in an image for triage ISSN 2249-9555 (Online), 22501355 (Print)},
	journal = {International Journal of Computer Science, Information Technology \& Security (IJCSITS)},
	volume = {Vol.2},
	number = {N{\textdegree}6},
	year = {2012},
	pages = {pp. 1170-1176},
	abstract = {In this paper, our aim is to present existing methods and techniques related to the artificial vision which is an active subject of research. In this work, we elaborate a recognition system of objects in an image for a sorting application. The corner stone of our work is based on the recognition and sorting of objects (geometrical shapes) in an image in artificial vision. The application of these images is achieved using a fixed camera having a regular and permanent field of vision with a consequent angle on a conveyer which holds the objects to be observed. The Objects{\textquoteright} recognition process requires knowledge of our camera characteristics. In order to calibrate this camera, we propose to use a simple Web camera configured to get a photo of the conveyer{\textquoteright}s centre. The objective is to seek for objects in the images, which are considered as unitary and not sequences of images. In this case, the development of automatic methods is required to ensure the sorting rapidity but with additional complex processing to make it efficient. The analysis determines simultaneously both the recensement and arithmetic counting, then the detection of faults based mainly on the comparison of the object shape with the shape defined previously and using this operation the sorting is made. A complementary system equipped with sensors and programmable automata is used to eject each object into corresponding panel},
	url = {https://ieeexplore.ieee.org/document/6689571},
	author = {Bellal Salah Eddine and Mouss Leila Hayet and Bensaadi Rafik}
}
@article {3376,
	title = {Comparative study based on metamodels of methods for controlling performance  ISSN 1694-0814},
	journal = {IJCSI International Journal of computer Science Issues},
	volume = { Vol 9},
	number = { Issue 3 N{\textdegree}2},
	year = {2012},
	pages = {pages  1-9},
	abstract = {The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works. [1][23]},
	url = {https://www.researchgate.net/publication/264274742_Comparative_study_based_on_metamodels_of_methods_for_controlling_performance},
	author = {Samia Aitouche and Mohamed Djamel Mouss and Ratte Sylvie and Abdelghafour Kaanit}
}
@article {3356,
	title = {Comparative study based on metamodels of methods for controlling performance, ISSN/ISBN 1694-08141694-0784I},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol. 9 N 3},
	number = {Issue 3, N 2},
	year = {2012},
	pages = {pp. 1-9 },
	abstract = {The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works. [1][23]},
	url = {http://www.ijcsi.org/articles/Comparative-study-based-on-metamodels-of-methods-for-controlling-performance.php},
	author = {Aitouche Samia and Mouss Med Djamel and Mouss kinza Nadia}
}
@article {3355,
	title = {Une Approche Modulaire Bas{\'e}e sur les R{\'e}seaux d\&rsquo;Automates Stochastiques pour l\&rsquo;Evaluation des Performances des Syst{\`e}mes de Production Manufacturiers, janvier 2013, ISSN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Volume 4},
	number = {N{\textdegree}1},
	year = {2012},
	pages = {pp. 63-81 },
	abstract = {Dans ce papier, nous nous int{\'e}ressons au probl{\`e}me de l{\textquoteright}{\'e}valuation des performances des syst{\`e}mes de production complexes par une approche analytique. Nous proposons une approche modulaire combinant deux outils compl{\'e}mentaires. Le premier est le formalisme des R{\'e}seaux d{\textquoteright}Automates Stochastiques (RAS) pour la mod{\'e}lisation modulaire du syst{\`e}me de production. Le deuxi{\`e}me est bas{\'e} sur les Processus de Markov (PdM) pour l{\textquoteright}analyse des mod{\`e}les repr{\'e}sentant ce syst{\`e}me. L{\textquoteright}avantage de cette approche r{\'e}side dans le fait que la mod{\'e}lisation se fait sur des syst{\`e}mes d{\'e}j{\`a} d{\'e}compos{\'e}s et non sur des syst{\`e}mes d{\'e}composables comme dans la plus part des approches modulaires propos{\'e}es dans la litt{\'e}rature. Nous adoptons une d{\'e}composition {\`a} la fois fonctionnelle et structurelle sur le syst{\`e}me de production. Nous illustrons notre approche par un exemple tr{\`e}s r{\'e}pondu dans la litt{\'e}rature pour {\'e}valuer leurs performances},
	url = {http://193.194.69.34/rst2/images/vol4n1/320.pdf},
	author = {Benfarhi Amor and Mechenene Athmane and Mouss kinza Nadia}
}
@article {3231,
	title = {Comparative study based on metamodels of methods for controlling performance, Mai },
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol. 9},
	number = {N 3 Issue 3  N 2},
	year = {2012},
	pages = {pp 1-9},
	abstract = {The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works. [1][23]},
	url = {https://www.researchgate.net/publication/264274742_Comparative_study_based_on_metamodels_of_methods_for_controlling_performance},
	author = {Aitoche Samia and Mouss Med Djamel and Kaanit Abdelghafor}
}
@article {3155,
	title = {Comparative study based on metamodels of methods for controlling performance, Mai ISSN 1694-0814 },
	journal = {IJCSI International Journal of Computer Science Issues,},
	volume = {Vol. 9},
	number = {Issue 3 N{\textdegree} 2},
	year = {2012},
	pages = {pp 1-9},
	abstract = {The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works. [1][23]},
	url = {https://www.researchgate.net/publication/264274742_Comparative_study_based_on_metamodels_of_methods_for_controlling_performance},
	author = {Aitoche Samia and Mouss Med Djamel and Kaani Abdelghafor}
}
@article {3146,
	title = {he Temporal Neuro-Fuzzy Systems Learning Using Artificial Immune Algorithm,  ISSN/ISBN 1970 \&ndash; 8734/1970-8742},
	journal = {IREME International Review of Mechanical Engineering},
	volume = {Vol.4},
	number = { N.1},
	year = {2012},
	pages = {pp 918-922},
	abstract = {In this work we propose an immune approach for learning neurofuzzy systems, namely NEFDIAG (NEuro Fuzzy DIAGnosis). NEFDIAG is a software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. But in case of great number of input variables NEFDIAG structure grows essentially and the dimensionality of learning task becomes a problem. Existing methods of NEFDIAG learning allow only identifying parameters of NEFDIAG without modifying its structure. We propose an immune Artificial learning approach for NEFDIAG learning based on clonal selection and immune network theories. It allows not only to identify NEFDIAG parameters but also to reduce number of neurons in hidden layers (rules layer) of NEFDIAG.},
	url = {https://www.researchgate.net/publication/290061642_The_temporal_Neuro-Fuzzy_systems_learning_using_artificial_immune_algorithm},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3143,
	title = {SKACICM a method for development of knowledge management and innovation system e-KnowSphere, ISSN  1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Vol. 5},
	number = {No. 2},
	year = {2012},
	abstract = {
	The purpose of this paper is to propose a hybrid method 



	SKACICM of development of knowledge management systems. Based on 



	weaknesses of the method of performance dashboards SKANDIA, we proposed 



	a pragmatisation and adaptation of Skandia to give (ASKANDIA), by 



	enrichment of its performance book. We ameliorated CICM model against the 



	requirements of GERAM to give ACICM model by mappings between 



	their proposed metamodels. We tried to hybridise ACICM, ASKANDIA



	and business intelligence to propose a new method SKACICM of development



	of knowledge management systems. We applied SKACICM on a cement 



	company to develop software containing three main modules, module 



	knowledge management, module business intelligence and performance 



	dashboard system. The developed system ameliorated the performance of the



	enterprise by 26\% and could be generalised to other manufacturing or service 



	systems

},
	url = {https://www.researchgate.net/publication/297647140_SKACICM_a_method_for_development_of_knowledge_management_and_innovation_system_e-KnowSphere},
	author = {Aitoche Samia and Mouss Med Djamel}
}
@article {3139,
	title = {An Efficient optimization method for the detection of new failures modes in industrial plants, Janvier,  ISSN  1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Vol 03},
	number = {N{\textdegree}1},
	year = {2012},
	pages = {pp 43-52},
	abstract = {This paper addresses the problem of the detection of new failures modes in industrial plants. Since its associated optimization problem is NP-hard, an efficient method based on taboo search and Bezdeck criterion is proposed, intensification and diversification strategies are also included because of the non connectivity of the solutions space. The proposed approach exercised on a simulated industrial system was shown to exhibit good performances in dealing with the occurrence of new faults in industrial processes, despite that such search algorithm is perfectly general, it can be easily extended to more complicated schemes. },
	url = {http://193.194.69.34/rst2/images/vol3n1/5BENTERCIUA.pdf},
	author = {Bentercia Toufik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3112,
	title = {Vers une Optimisation de l\&rsquo;Algorithme AntTreeStoch. Revue, ISSN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Vol. 3},
	number = {N{\textdegree}1},
	year = {2012},
	pages = {p p. 125-134},
	abstract = {Dans cet article, nous pr{\'e}sentons AntTreeStoch un nouvel algorithme de classification hi{\'e}rarchique et non supervis{\'e}e. Cet algorithme est bas{\'e} sur l{\textquoteright}auto-organisation observ{\'e}e chez les fourmis r{\'e}elles. L{\textquoteright}{\'e}mergence des d{\'e}placements et les assemblages des fourmis, bas{\'e}s sur la similarit{\'e} entre les individus permet d{\textquoteright}identifier les {\'e}tats de fonctionnements d{\textquoteright}un syst{\`e}me industriel dynamique et complexe. L{\textquoteright}algorithme offre aussi la possibilit{\'e} de cr{\'e}er de nouvelles classes pour les donn{\'e}es non identifi{\'e}es. En effet le choix d{\textquoteright}utiliser AntTreeStoch dans notre syst{\`e}me de diagnostic a {\'e}t{\'e} motiv{\'e} par la possibilit{\'e} d{\textquoteright}utiliser {\`a} la fois des donn{\'e}es num{\'e}riques et symboliques. Les exp{\'e}riences effectu{\'e}es sur la base de donn{\'e}es Iris, montrent que AntTreeStoch fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {https://www.researchgate.net/publication/329886361_Vers_une_Optimisation_de_l{\textquoteright}Algorithme_AntTreeStoch},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3085,
	title = {An Efficient optimization method for the detection of new failures modes in industrial plants, Janvier, ISSN 1112-9697},
	journal = { RST, Revue des Sciences et Technologie},
	volume = {Vol 03},
	number = { N{\textdegree}1},
	year = {2012},
	pages = {pp 43-52},
	author = {Bentercia Toufik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3084,
	title = {Vers une Optimisation de l\&rsquo;Algorithme AntTreeStoch. Revue, ISSN 1112-9697},
	journal = { RST, Revue des Sciences et Technologie},
	volume = { Vol. 3 },
	number = {N{\textdegree}1},
	year = {2012},
	pages = {p. 125-134 },
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3070,
	title = {A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis, ISSN/ISBN 1945-3116/1945-3124},
	journal = {JESA  Journal of Software Engineering and Applications.},
	volume = {Vol. 58},
	number = { Issue 7},
	year = {2012},
	pages = { pp.449-458},
	abstract = {As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural net-work architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln.},
	url = {https://www.researchgate.net/publication/265800845_A_TSK-Type_Recurrent_Neuro-Fuzzy_Systems_for_Fault_Prognosis},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3066,
	title = {Fault diagnosis of rotary kiln using SVM and Binary ACO, ISSN 1976-3824},
	journal = {JMST Journal of Mechanical Science and Technology. Editeur / Publisher. Springer, Heidelberg, ALLEMAGNE },
	volume = {Vol. 26},
	number = {N{\textdegree}2},
	year = {2012},
	pages = {pp.601-608},
	abstract = {This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms.},
	url = {https://link.springer.com/article/10.1007/s12206-011-1216-z},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3017,
	title = {Comparative study based on metamodels of methods for controlling performance, ISSN 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol~9 N{\textdegree} 3},
	number = { Issue 3 N{\textdegree} 2},
	year = {2012},
	pages = {pp: 1-9},
	abstract = {The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works.},
	url = {https://www.researchgate.net/publication/264274742_Comparative_study_based_on_metamodels_of_methods_for_controlling_performance},
	author = {Aitoche Samia and Mouss Med Djamel and Mouss Leila Hayet}
}
@article {3016,
	title = {Efficient Tool for the Recognition of the Leaves of Plants, ISSN 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issues},
	year = {2012},
	abstract = {This work appears in pattern recognition in the agronomic domain, especially for the identification of the leaves of plants, while using the adaptive technique of neuronal networks. In this article, we will expose our tool; which is intended for two categories of specialists, the first consisting of researchers in the field of botany, as the second, so all scientists, who may use this work in their own applications. We will expose also, the capacities of generalization of the neuronal networks and their implementation to our problem},
	url = {https://www.researchgate.net/publication/266278880_Efficient_Tool_for_the_Recognition_of_the_Leaves_of_Plants},
	author = {Abdelhadi Adel and Mouss Leila Hayet and Mahdaoui Rafik}
}
@article {3015,
	title = {A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems, May, ISSN 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol 8},
	number = { Issue 3},
	year = {2012},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.},
	url = {https://www.researchgate.net/publication/51917361_A_Temporal_Neuro-Fuzzy_Monitoring_System_to_Manufacturing_Systems},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3012,
	title = {The Temporal Neuro-Fuzzy Systems Learning Using Artificial Immune Algorithm, ISSN/ISBN 1970 \&ndash; 8734/1970-8742},
	journal = {IREME International Review of Mechanical Engineering},
	volume = {Vol 4},
	number = {N{\textdegree} 1},
	year = {2012},
	pages = {pp: 918-922},
	abstract = {In this work we propose an immune approach for learning neurofuzzy systems, namely NEFDIAG (NEuro Fuzzy DIAGnosis). NEFDIAG is a software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. But in case of great number of input variables NEFDIAG structure grows essentially and the dimensionality of learning task becomes a problem. Existing methods of NEFDIAG learning allow only identifying parameters of NEFDIAG without modifying its structure. We propose an immune Artificial learning approach for NEFDIAG learning based on clonal selection and immune network theories. It allows not only to identify NEFDIAG parameters but also to reduce number of neurons in hidden layers (rules layer) of NEFDIAG.},
	url = {https://www.researchgate.net/publication/290061642_The_temporal_Neuro-Fuzzy_systems_learning_using_artificial_immune_algorithm},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3011,
	title = {An Efficient optimization method for the detection of new failures modes in industrial plants, Janvier, ISSN/ISBN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Vol 03},
	number = { N{\textdegree}1},
	year = {2012},
	pages = {pp: 43-52},
	author = {Bentercia Toufik and Mouss Leila Hayet and Besaadi Rafik}
}
@article {3010,
	title = {Vers une Optimisation de l\&rsquo;Algorithme AntTreeStoch, ISSN/ISBN 1112-9697},
	journal = {RST, Revue des Sciences et Technologie},
	volume = {Vol 3},
	number = {N{\textdegree}1},
	year = {2012},
	pages = {p: 125-134},
	abstract = {Dans cet article, nous pr{\'e}sentons AntTreeStoch un nouvel algorithme de classification hi{\'e}rarchique et non supervis{\'e}e. Cet algorithme est bas{\'e} sur l{\textquoteright}auto-organisation observ{\'e}e chez les fourmis r{\'e}elles. L{\textquoteright}{\'e}mergence des d{\'e}placements et les assemblages des fourmis, bas{\'e}s sur la similarit{\'e} entre les individus permet d{\textquoteright}identifier les {\'e}tats de fonctionnements d{\textquoteright}un syst{\`e}me industriel dynamique et complexe. L{\textquoteright}algorithme offre aussi la possibilit{\'e} de cr{\'e}er de nouvelles classes pour les donn{\'e}es non identifi{\'e}es. En effet le choix d{\textquoteright}utiliser AntTreeStoch dans notre syst{\`e}me de diagnostic a {\'e}t{\'e} motiv{\'e} par la possibilit{\'e} d{\textquoteright}utiliser {\`a} la fois des donn{\'e}es num{\'e}riques et symboliques. Les exp{\'e}riences effectu{\'e}es sur la base de donn{\'e}es Iris, montrent que AntTreeStoch fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {https://www.researchgate.net/publication/329886361_Vers_une_Optimisation_de_l{\textquoteright}Algorithme_AntTreeStoch},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3007,
	title = {Development of a fuzzy expert system based on PCS 7 and FuzzyControl++, ISSN 2278\&ndash;6538},
	journal = {JES, Journal of Electronic Systems},
	volume = {Vol 1},
	number = {N{\textdegree} 1},
	year = {2012},
	pages = {pp: 18-32},
	abstract = {The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems to control the difference shown in the control system using fuzzy regulators for the operation of the grinding without unnecessary stops, it also helps the operator to know the maintenance task to perform. In addition, regulators decentralization allows the availability of fuzzy control, even if one of the regulators is absent, it does not prevent the other to complete its task to control fineness, mill{\textquoteright}s temperature and feed.},
	url = {https://www.researchgate.net/publication/230773223_DEVELOPMENT_OF_A_FUZZY_EXPERT_SYSTEM_BASED_ON_PCS7_AND_FUZZYCONTROL_CEMENT_MILL_CONTROL},
	author = {Zermane Hanan and Mouss Leila Hayet}
}
@article {1838,
	title = {A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis, ISSN/ISBN: 1945-3116/1945-3124.},
	journal = { Journal of Software Engineering and Applications (JESA)},
	volume = {58},
	number = {07},
	year = {2012},
	pages = {449-458},
	abstract = {As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural network architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln.},
	url = {https://file.scirp.org/Html/5-9301363_19730.htm},
	author = {Mahdaoui Rafik and Mouss Leila Hayet}
}
@article {1734,
	title = {Fault diagnosis of rotary kiln using SVM and binary ACO, 2012, ISSN/ISBN: 1976-3824},
	journal = {Journal of Mechanical Science and Technology},
	volume = {26},
	number = {02},
	year = {2012},
	pages = {601-608},
	abstract = {This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms.},
	url = {https://doi.org/10.1007/s12206-011-1216-z},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3375,
	title = {Drain current model for undoped Gate Stack Double Gate (GSDG) MOSFETs including the hot-carrier degradation effects},
	journal = {Microelectronics Reliability},
	volume = {Volume 51},
	number = {Issue 3},
	year = {2011},
	pages = {pp 550-555},
	abstract = {
	
		
			
				In this paper, analytical models of drain current and small signal parameters for undoped symmetric Gate Stack Double Gate (GSDG) MOSFETs including the interfacial hot-carrier degradation effects are presented. The models are used to study the device behavior with the interfacial traps densities. The proposed model has been implemented in the SPICE circuit simulator and the capabilities of the model have been explored by circuit simulation example. The developed approaches are verified and validated by the good agreement found with the 2D numerical simulations for wide range of device parameters and bias conditions. GSDG MOSFET design and the accurate proposed model can alleviate the critical problem and further improve the immunity of hot-carrier effects of DG MOSFET-based circuits after hot-carrier damage.
			
		
	

},
	url = {https://www.sciencedirect.com/science/article/pii/S0026271410005287},
	author = {Fay{\c c}al Djeffal and Toufik Bentrcia and M.A Abdi and T. Bendib}
}
@article {3374,
	title = {An analytical drain current model for undoped GSDG MOSFETs including interfacial hot-carrier effects},
	journal = {Physica Status Solidi (C) Solid State Physics},
	volume = {Volume 8},
	number = {Issue 3},
	year = {2011},
	pages = {pp. 907-910},
	abstract = {In the present work, a comprehensive drain current model including the interfacial hot-carrier degradation effects for undoped symmetric gate stack double gate (GSDG) MOSFET and the expressions of transconductance and drain conductance have been obtained. Exploiting this new device model, we have found that the incorporation of a high-k layer between oxide region and gate metal leads to drain current enhancement, improved output conductance, increased transconductance parameter and enhanced interfacial hot-carrier immunity. The proposed model has been validated by comparing the analytical I-V characteristics with 2-D numerical results. The obtained results may provide a theoretical basis and physical insights for multigate MOSFET-based circuits design including the hot-carrier degradation effects. ({\textcopyright} 2011 WILEY-VCH Verlag GmbH \&amp; Co. KGaA, Weinheim)},
	url = {https://www.researchgate.net/publication/229913057_An_analytical_drain_current_model_for_undoped_GSDG_MOSFETs_including_interfacial_hot-carrier_effects},
	author = {Fay{\c c}al Djeffal and Toufik Bentrcia and T. Bendib}
}
@article {3373,
	title = {Temporal Neuro-Fuzzy Systems in Fault Diagnosis and Prognosis ISSN 1974-9821},
	journal = {International Review on Modelling and Simulations (IREMOS) },
	volume = {Vol.4 },
	number = {N{\textdegree}1},
	year = {2011},
	pages = {pp 436-440},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an manufacturing systems. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker , cement factory of Ain Touta " Batna, Algeria  ". },
	url = {http://www.msc-les.org/proceedings/emss/2011/EMSS2011_11.pdf},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel and Chouhal Ouahiba}
}
@article {3372,
	title = {Efficient Algorithms for the integration of Arabic Language in Mobile Phone ISSN 1793-8163},
	journal = {International Journal of Computer and Electrical Engineering (IJCEE)},
	volume = {Vol.3},
	number = {N{\textdegree}3},
	year = {2011},
	pages = {pp. 379-383},
	abstract = {This work appears in man-machine interface. Our goal is to study the integration of Arabic language in Mobile Phone, in order to achieve a man-machine interface Arabic. The correct display of Arabic character is essential in a MMI. Since Arabic characters change their forms according to their position in a word, then it is necessary to make a contextual analysis on every word, to find the correct form of each character. The transformation of two or more characters in one form, demand special treatment, as in the case of Arabic ligature LAM-ALEF. The Arabic language has a different direction of writing in relation to other languages embedded in mobile phone, which requires finding an algorithm that provides a bidirectional display of SMS messages. These messages may contain characters from different direction, from right to left, left to right or characters that have no direction. It allows you to make the message understandable.},
	url = {https://www.researchgate.net/publication/271303986_Efficient_Algorithms_for_the_integration_of_Arabic_Language_in_Mobile_Phone},
	author = {Adel Abdelhadi and Mouss Leila Hayet and Ouahab Kadri}
}
@article {3371,
	title = {An Overview of Artificial Immune System Algorithms for Industrial Monitoring ISSN 1974-6059 Cd-Rom ISSN: 1974-6067},
	journal = {IRECOS International Review on Computers and Software},
	volume = {Volume 6},
	number = {Number 2},
	year = {2011},
	pages = {pp 269-274},
	abstract = {We describe in this paper an overview of artificial immune system algorithms to solve the classification problem in industrial monitoring. We present artificial immune system algorithms, starting with the negative selection that happens to be a rich source of inspiration. We also, detail the clonal selection algorithm, which is based on the clonal selection theory. Finally, we detail other algorithms based of agent including the immune system and dendritic cell algorithm. In the end, we summarize the differences and similarities of the works discussed and we conclude on the prospects related to the approach of the algorithms of artificial immune systems for industrial monitoring to solve the classification problem. Copyright {\textcopyright} 2011 Praise Worthy Prize S.r.l. - All rights reserved},
	url = {https://www.researchgate.net/profile/Majid_Haghparast2/publication/216151550_A_New_Nanometric_Reversible_Full_Subtractor_Gate/links/59f67265458515547c231160/A-New-Nanometric-Reversible-Full-Subtractor-Gate.pdf$\#$page=122},
	author = {A. Abdelhadi and Mouss Leila Hayet}
}
@article {3370,
	title = {Efficient Tool for the Recognition of the Leaves of Plants ISSN 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol. 8},
	number = {Issue 2},
	year = {2011},
	pages = {pp 309-314},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.9531\&rep=rep1\&type=pdf$\#$page=331},
	author = {Abdelhadi Adel and Mouss Leila-Hayet}
}
@article {3369,
	title = {La s{\'e}lection de param{\`e}tres d\&rsquo;un syst{\`e}me industriel par les colonies de fourmis ISSN: 1583-7165 (printed journal) ISSN: 2065-7471 (e-journal)},
	journal = {Annals Computer Science Series},
	volume = {Tome 9 },
	number = {Fasc. 1},
	year = {2011},
	pages = {pp 155- 168},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvelalgorithme pour r{\'e}duire la dimension de vecteur d{\textquoteright}{\'e}tat defonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithmepermet de s{\'e}lectionner un sous-ensemble de param{\`e}tres quioffre une d{\'e}tection plus rapide de dysfonctionnement et unebonne qualit{\'e} de classification. Cet algorithme est bas{\'e} sur lecomportement observ{\'e} chez les fourmis r{\'e}elles. Nous montronsici que l{\textquoteright}{\'e}mergence des d{\'e}placements et les interactions desfourmis permet de trouver un ensemble r{\'e}duit de param{\`e}tresqui caract{\'e}risent le fonctionnement d{\textquoteright}un syst{\`e}me industrieldynamique et complexe. L{\textquoteright}algorithme offre aussi la possibilit{\'e}d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Lesexp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es Iris et Vehiclemontrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats.MOTS-CLES: Colonie de fourmis, Classification, Diagnosticindustriel, S{\'e}lection de param{\`e}tres, Syst{\`e}me complexe et dynamique.},
	url = {https://www.researchgate.net/publication/278329066_La_selection_de_parametres_d{\textquoteright}un_systeme_industriel_par_les_colonies_de_fourmis_Ouahab_Kadri_L_Hayet_Mouss_M_Djamel_M},
	author = {Ouahab Kadri and Leila Hayet Mouss and Mohamed Djamel Mouss}
}
@article {3368,
	title = {A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems ISSN (Online): 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issues},
	volume = {Vol. 8},
	number = {Issue 3  No1},
	year = {2011},
	pages = {pp 237-246},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria},
	url = {https://www.researchgate.net/publication/51917361_A_Temporal_Neuro-Fuzzy_Monitoring_System_to_Manufacturing_Systems},
	author = {Rafik Mahdaoui and Leila Hayet Mouss and Mohamed Djamel Mouss and Ouahiba Chouhal}
}
@article {3367,
	title = {Multi agent system using web services for diagnosing Manufacturing Systems ISSN 1970 \&ndash; 8734},
	journal = {International Review of Mechanical engineering (I.RE.M.E)},
	volume = {Vol. 05},
	number = {N. 3},
	year = {2011},
	pages = {pp 1156-1160},
	author = {O. Chouhal and H.L. Mouss and R. Mahdaoui}
}
@article {3365,
	title = {Fuzzy Pattern Regognotion Based Fault Diagonis, ISSN 1974-9821},
	journal = { International Review on Modelling and Simulations (IREMOS)},
	volume = {Volume 4},
	number = {N{\textdegree}6},
	year = {2011},
	pages = {pp 3361-3370},
	abstract = {In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright {\textcopyright} 2011 Praise Worthy Prize S.r.l. -All rights reserved},
	url = {https://pdfs.semanticscholar.org/614c/703df0df4d13c340811424cb51d5b7b2e941.pdf},
	author = {Rafik Bensaadi and Le{\"\i}la-Hayet Mouss and Mohamed Djamel Mouss and Mohamed Benbouzid}
}
@article {3364,
	title = {Prise en consid{\'e}ration des perturbations pour l\&$\#$39;analyse et la conception d\&$\#$39;un syst{\`e}me de production},
	journal = {Journal of Engineering Science and Technologie},
	year = {2011},
	author = {Aitouche Samia and Mouss kinza Nadia and Kaanit Andelghafor}
}
@article {3149,
	title = { A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems, May  ISSN 1694-0814},
	journal = {IJCSI International Journal of Computer Science Issue},
	volume = {Vol. 8},
	number = {Issue 3 N{\textdegree}1},
	year = {2011},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed.  This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria},
	url = {https://arxiv.org/ftp/arxiv/papers/1107/1107.3302.pdf},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3080,
	title = {Fuzzy Pattern Recognotion Based Fault Diagonis. , December, ISSN: 1974-9821, ISSN/ISBN 1945-3116/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	volume = {Vol.4 },
	number = {N{\textdegree}6},
	year = {2011},
	pages = { pp 3361-3370 },
	abstract = {In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright {\textcopyright} 2011 Praise Worthy Prize S.r.l. -All rights reserved.},
	url = {https://hal.archives-ouvertes.fr/hal-00706004/document},
	author = {Bensaadi Rafik and Mouss Med Djamel and Mouss Leila Hayet}
}
@article {3079,
	title = {La s{\'e}lection de param{\`e}tres d\&rsquo;un syst{\`e}me industriel par les colonies de fourmis., Tome 9 Fasc. 1. May ISSN/ISBN 1583-7165/ 2065-7471},
	journal = {Annals Computer Science Series},
	volume = {Vol. IX },
	year = {2011},
	pages = { pp 155- 168},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvel algorithme pour r{\'e}duire la dimension de vecteur d{\textquoteright}{\'e}tat de fonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithme permet de s{\'e}lectionner un sous-ensemble de param{\`e}tres qui offre une d{\'e}tection plus rapide de dysfonctionnement et une bonne qualit{\'e} de classification. Cet algorithme est bas{\'e} sur le comportement observ{\'e} chez les fourmis r{\'e}elles. Nous montrons ici que l{\textquoteright}{\'e}mergence des d{\'e}placements et les interactions des fourmis permet de trouver un ensemble r{\'e}duit de param{\`e}tres qui caract{\'e}risent le fonctionnement d{\textquoteright}un syst{\`e}me industriel dynamique et complexe. L{\textquoteright}algorithme offre aussi la possibilit{\'e} d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Les exp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es Iris et Vehicle montrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats.},
	url = {http://www.anale-informatica.tibiscus.ro/download/lucrari/9-1-14-Kadri.pdf },
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3077,
	title = {Temporal Neuro-Fuzzy systems in fault diagnosis and prognosis, February ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	volume = {vol.4},
	number = { N{\textdegree}1},
	year = {2011},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an manufacturing systems. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker , cement factory of Ain Touta " Batna, Algeria ".   },
	url = {http://www.msc-les.org/proceedings/emss/2011/EMSS2011_11.pdf},
	author = {Mahdaoui Rafik and Mouss Med Djamel and Chouhal Ouahiba}
}
@article {3014,
	title = {An Overview of Artificial Immune System Algorithms for Industrial Monitoring, March, ISSN/ISBN 1828-6003/1828-6011},
	journal = {IRECOS International Review on Computers and Software},
	volume = {Volume 6 },
	number = {N{\textdegree} 2 },
	year = {2011},
	pages = {pp: 268-273},
	abstract = {We describe in this paper an overview of artificial immune system algorithms to solve the classification problem in industrial monitoring. We present artificial immune system algorithms, starting with the negative selection that happens to be a rich source of inspiration. We also, detail the clonal selection algorithm, which is based on the clonal selection theory. Finally, we detail other algorithms based of agent including the immune system and dendritic cell algorithm. In the end, we summarize the differences and similarities of the works discussed and we conclude on the prospects related to the approach of the algorithms of artificial immune systems for industrial monitoring to solve the classification problem},
	url = {https://ieeexplore.ieee.org/document/6481888},
	author = {Abdelhadi Adel and Mouss Leila Hayet}
}
@article {3006,
	title = {Fuzzy Pattern Recognition Based Fault Diagnosis, ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations},
	volume = {4},
	number = {6},
	year = {2011},
	pages = { pp.3361-3370},
	abstract = {In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright {\textcopyright} 2011 Praise Worthy Prize S.r.l. - All rights reserved.},
	url = {https://hal.archives-ouvertes.fr/hal-00706004/document},
	author = {Bensaadi Rafik and Mouss Leila Hayet and Benbouzid Med EL}
}
@article {3005,
	title = {Efficient Algorithms for the integration of Arabic Language in Mobile Phone, ISSN 1793-8163},
	journal = {IJCEE International Journal of Computer and Electrical Engineering},
	volume = {3},
	number = {3},
	year = {2011},
	pages = {pp 379-383},
	abstract = {This work appears in man-machine interface. Our goal is to study the integration of Arabic language in Mobile Phone, in order to achieve a man-machine interface Arabic. The correct display of Arabic character is essential in a MMI. Since Arabic characters change their forms according to their position in a word, then it is necessary to make a contextual analysis on every word, to find the correct form of each character. The transformation of two or more characters in one form, demand special treatment, as in the case of Arabic ligature LAM-ALEF. The Arabic language has a different direction of writing in relation to other languages embedded in mobile phone, which requires finding an algorithm that provides a bidirectional display of SMS messages. These messages may contain characters from different direction, from right to left, left to right or characters that have no direction. It allows you to make the message understandable.},
	url = {http://www.ijcee.org/papers/345-E934.pdf},
	author = {Abdelhadi Adel and Mouss Leila Hayet and Mahdaoui Rafik}
}
@article {3004,
	title = {La s{\'e}lection de param{\`e}tres d\&rsquo;un syst{\`e}me industriel par les colonies de fourmis, Fasc 1, May, ISSN/ISBN 1583-7165/2065-7471},
	journal = {Annals Computer Science Series},
	volume = {9},
	year = {2011},
	pages = {pp 155- 168},
	abstract = {Dans cet article, nous pr{\'e}sentons un nouvel algorithme pour r{\'e}duire la dimension de vecteur d{\textquoteright}{\'e}tat de fonctionnement d{\textquoteright}un syst{\`e}me industriel. Notre algorithme permet de s{\'e}lectionner un sous-ensemble de param{\`e}tres qui offre une d{\'e}tection plus rapide de dysfonctionnement et une bonne qualit{\'e} de classification. Cet algorithme est bas{\'e} sur le comportement observ{\'e} chez les fourmis r{\'e}elles. Nous montrons ici que l{\textquoteright}{\'e}mergence des d{\'e}placements et les interactions des fourmis permet de trouver un ensemble r{\'e}duit de param{\`e}tres qui caract{\'e}risent le fonctionnement d{\textquoteright}un syst{\`e}me industriel dynamique et complexe. L{\textquoteright}algorithme offre aussi la possibilit{\'e} d{\textquoteright}utiliser des bases de donn{\'e}es de grandes tailles. Les exp{\'e}riences effectu{\'e}es sur les bases de donn{\'e}es Iris et Vehicle montrent que notre algorithme fournit de tr{\`e}s bons r{\'e}sultats. MOTS-CLES: Colonie de fourmis, Classification, Diagnostic industriel, S{\'e}lection de param{\`e}tres, Syst{\`e}me complexe et dynamique.},
	url = {http://anale-informatica.tibiscus.ro/download/lucrari/9-1-14-Kadri.pdf},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3002,
	title = {Temporal Neuro-Fuzzy systems in fault diagnosis and prognosis, February, ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations.},
	volume = {4},
	number = {1},
	year = {2011},
	abstract = {Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.},
	url = {http://www.msc-les.org/proceedings/emss/2011/EMSS2011_11.pdf},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Chouhal Ouahiba}
}
@article {1731,
	title = {Fault diagnosis of rotary kiln using SVM and binary ACO, February 2012, ISSN/ISBN: 1738-494X/1976-3824.},
	journal = {Journal of Mechanical Science and Technology},
	volume = {26},
	number = {2},
	year = {2011},
	pages = {601{\textendash}608},
	abstract = {This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms.},
	url = {https://doi.org/10.1007/s12206-011-1216-z},
	author = {Kadri Ouahab and Mouss Leila Hayet and Mouss Med Djamel}
}
@article {3366,
	title = {A Hybrid Feature Subset Selection Approach based on SVM and Binary ACO},
	journal = {Application to industrial diagnosis International Journal of Aerospace and Mechanical Engineering },
	volume = {Volume 4 },
	number = {Number 1},
	year = {2010},
	pages = {pp. 241-247},
	abstract = {This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.},
	url = {https://www.researchgate.net/publication/278328909_A_Hybrid_Feature_Subset_Selection_Approach_based_on_SVM_and_Binary_ACO_Application_to_Industrial_Diagnosis},
	author = {Ouhab Kadri}
}
