@mastersthesis {7316,
	title = {Pronostic/diagnostic appliqu{\'e} aux syst{\`e}mes complexes dans un contexte d\&$\#$39;optimisation des strat{\'e}gies de maintenance},
	year = {2022},
	type = {Habilitation Universitaire},
	author = {Djelloul, Imen}
}
@mastersthesis {7315,
	title = {Management Industrie},
	year = {2022},
	type = {Habilitation Universitaire},
	author = {Bala, Kamel}
}
@mastersthesis {7304,
	title = {Exploration du Potentiel de la vision artificielle pour lareconnaissance d\&$\#$39;objets en vue d\&$\#$39;une conception d\&$\#$39;un dispositif intelligent dans un context industriel},
	year = {2022},
	type = {Doctorat en sciences  },
	url = {http://eprints.univ-batna2.dz/1994/},
	author = {Bellal, Salah-Eddine}
}
@proceedings {7288,
	title = {Multi-objective production planning of new and remanufactured products in hybrid production system},
	journal = {10th IFAC Conference Onmanufacturing Modelling, Management And Control 22-24 June },
	year = {2022},
	address = { Nantes, France},
	author = {Lahmar, Houria and Dahane, Mohammed and Mouss, Nadia-Kenza and Haoues, Mohammed}
}
@conference {7287,
	title = {Integrated production and maintenance planning in hybrid manufacturing-remanufacturing system with outsourcing opportunities},
	booktitle = {4th International Conference on Industry 4.0 and Smart Manufacturing Procedia Computer Science },
	year = {2022},
	publisher = {ScienceDirect},
	organization = {ScienceDirect},
	author = {Merghem, Mohammed and Haoues, Mohammed and Mouss, Kinza-Nadia and Dahane, Mohammed and Senoussi, Ahmed}
}
@conference {7286,
	title = {Production planning optimisation in a sustainable hybrid manufacturing remanufacturing production system},
	booktitle = {3rd International Conference on Industry 4.0 and Smart Manufacturing Procedia Computer Science 200},
	year = {2022},
	publisher = {ScienceDirect},
	organization = {ScienceDirect},
	author = {Lahmar, Houria and Dahane, Mohammed and Mouss, Nadia-Kinza and Haoues, Mohammed}
}
@proceedings {7285,
	title = {Products exchange in a multi-level multi-period distribution network with limited storage capacity},
	journal = {2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)},
	year = {2022},
	address = {2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)},
	abstract = {
	Cooperation in distribution network has attracted the interest of researchers. In this study we analyse an inventory problem in distribution network, where we propose a cooperative platform that allow the members of the network to share and use local inventory of other members to meet their local demand. We develop a MIP models representing the traditional network and the network with the cooperative platform. Then we solve it using LINGO solver. We found that the proposed approach has reduced the total cost of the network and reduce the overstock and stock-out situation, which lead to improve the quality of service.

},
	url = {https://ieeexplore.ieee.org/document/9738100},
	author = {Benfriha, Abdennour -Ilyas and Triqui-Sari, Lamia and Bougloula, Aimed-Eddine and BENNEKROUF, Mohammed}
}
@proceedings {7280,
	title = {Use of Petri Nets to Model the Maintenance of Multi Site Compagny},
	journal = {International Congress of Energies and Engineering of Industrial ProcessesCEGPI{\textquoteright}22, 23 - 25 May},
	year = {2022},
	address = {Algiers, Algeria },
	author = {Soltani Khaoula and Benzouai Messaoud and Mouss Mohamed Djamel}
}
@proceedings {7279,
	title = {Use of Petri Nets to Model the Maintenance of Multi Site Compagny},
	journal = {International Congress of Energies and Engineering of Industrial ProcessesCEGPI{\textquoteright}22 23 - 25 May },
	year = {2022},
	address = { Algiers, Algeria },
	author = {Soltani, Khaoula and Benzouai, Messaoud and Mouss, Mohamed-Djamel}
}
@proceedings {7272,
	title = {Selection The Appropriate Learning Machine For Fault Diagnosis With Big-Data Environment In Photovoltaic Systems},
	journal = {IGSCONG{\textquoteright}22, Jun },
	year = {2022},
	address = {Turkey},
	author = {Hadjidj, Nadjiha and Meriem Benbrahim and Leila-Hayet Mouss}
}
@proceedings {7271,
	title = {Web Fuzzy Based Autonomous Control System},
	journal = {4th International Conference on Engineering Science and Technology (ICEST2022) 16th-17th of February },
	year = {2022},
	address = {Luxor, Egypt},
	author = {Zermane, Hannane}
}
@proceedings {7270,
	title = {Improving Supervised Machine Learning Models for Face Recognition: a Comparative Study},
	journal = {4th International Conference on Engineering Science and Technology (ICEST2022) 16th-7th of February},
	year = {2022},
	address = {Luxor, Egypt},
	author = {Zermane, Hannane}
}
@proceedings {7258,
	title = {Detecting Cyberthreats in Smart Grids Using Small-Scale Machine Learning},
	journal = {ELECTRIMACS 2022},
	year = {2022},
	address = {Nancy, France},
	abstract = {
	Due to advanced monitoring technologies including the plug-in of the cyber and physical layers on the Internet, cyber-physical systems are becoming more vulnerable than ever to cyberthreats leading to possible damage of the system. Consequently, many researchers have devoted to studying detection and identification of such threats in order to mitigate their drawbacks. Among used tools, Machine Learning (ML) has become dominant in the field due to many usability characteristics including the blackbox models availability. In this context, this paper is dedicated to the detection of cyberattacks in Smart Grid (SG) networks which uses industrial control systems (ICS), through the integration of ML models assembled on a small scale. More precisely, it therefore aims to study an electric traction substation system used for the railway industry. The main novelty of our contribution lies in the study of the behaviour of more realistic data than the traditional studies previously shown in the state of the art literature by investigating even more realistic types of attacks. It also emulates data analysis and a larger feature space under most commonly used connectivity protocols in today{\textquoteright}s industry such as S7Comm and Modbus.

},
	url = {https://www.researchgate.net/publication/360756643_Detecting_Cyberthreats_in_Smart_Grids_Using_Small-Scale_Machine_Learning},
	author = {Berghout, Tarek and Mohamed Benbouzid}
}
@proceedings {7257,
	title = {Improving Small-scale Machine Learning with Recurrent Expansion for Fuel Cells Time Series Prognosis},
	journal = {48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022)},
	year = {2022},
	address = {Brussels, Belgium},
	abstract = {
	The clean energy conversion characteristics of proton exchange membrane fuel cells (PEMFCs) have given rise to many applications, particularly in transportation. Unfortunately, the commercial application of PEMFCs is hampered by the early deterioration and low durability of the cells. In this case, accurate real-time condition monitoring plays an important role in extending the lifespan of PEMFCs through accurate planning of maintenance tasks. Accordingly, among the widely used modeling tools such as model-driven and data-driven, machine learning has received much attention and has been extensively studied in the literature. Small-scale machine learning (SML) and Deep Learning (DL) are subcategories of machine learning that have been exploited so far. In this context and since SML usually contains non-expansive approximators, this study was dedicated to improving its feature representations for better predictions. Therefore, a recurrent expansion experiment was conducted for several rounds to investigate a linear regression model under time series prognosis of PEMFCs. The results revealed that the prediction performance of SML tools under stationary conditions could be further improved.

},
	url = {https://ieeexplore.ieee.org/document/9968566},
	author = {Berghout Tarek and Mohamed Benbouzid and Amirat, Yassine}
}
@proceedings {7256,
	title = {Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids},
	journal = {48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 },
	year = {2022},
	address = {Brussels, Belgium},
	abstract = {
	The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses, but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enables data-driven methods to emerge in such non-technical loss detections as an alternative to traditional experience-based human-centric approaches. In this context, such fraud prediction problems are generally a thematic of missing patterns, class imbalance, and higher level of cardinality where there are many possibilities that a single feature can assume. Therefore, this article is introduced specifically to solve data representation problem and increase the sparseness between different data classes. As a result, deeper representations than deep learning networks are introduced to repeatedly merge the learning models themselves into a more complex architecture in a sort of recurrent expansion. To verify the effectiveness of the proposed recurrent expansion of deep learning (REDL) approach, a realistic dataset of electricity theft is involved. Consequently, REDL has achieved excellent data mapping results proven by both visualization and numerical metrics and shows the ability of separating different classes with higher performance. Another important REDL feature of outliers correction has been also discovered in this study. Finally, comparison to some recent works also proved superiority of REDL model.

},
	url = {https://ieeexplore.ieee.org/document/9968378},
	author = {Berghout, Tarek and Mohamed Benbouzid and Ferrag, Mohamed-Amine}
}
@proceedings {7255,
	title = {Selection The Appropriate Learning Machine For Fault Diagnosis With Big-Data Environment In Photovoltaic Systems.},
	journal = {IGSCONG{\textquoteright}22. Jun 2022 },
	year = {2022},
	address = {Turkey},
	author = {Hadjidj, Nadjiha~ and Meriem Benbrahim and Leila-Hayet Mouss}
}
@proceedings {7254,
	title = {A Knowledge Transfer Approach for Online PEMFC Degradation prediction with Uncertainty Quantification},
	journal = {12th International Conference on Power, Energy and Electrical Engineering (CPEEE)},
	year = {2022},
	address = {Shiga, Japan},
	abstract = {
	Proton Exchange Membrane Fuel Cells (PEMFCs) are a key challenger for the world{\textquoteright}s future clean and renewable energy solution. Yet, fuel cells are susceptible to operating conditions and hydrogen impurities, leading to performance loss over time in service. Hence, performance degradation prediction is gaining attention recently for fuel cell system reliability. In this work, we present a knowledge transfer approach for online voltage drop prediction. A dual-path convolution neural network is proposed to extract linearity and non-linearity from historical data and performs multi-steps ahead prediction with uncertainty quantification. Online voltage prediction is then evaluated with and without knowledge transfer using two different PEMFC datasets. Results indicate that our proposed approach with transfer knowledge can predict the voltage drop accurately with a small uncertainty range compared to the conventional approach.

},
	url = {https://ieeexplore.ieee.org/document/9738717},
	author = {Benaggoune, Khaled and Meiling, Yue and Jemei, Samir and Zerhouni, Noureddine}
}
@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}
}
@book {7146,
	title = {Graph theory},
	year = {2022},
	pages = {76},
	edition = {Editions universitaires europ{\'e}ennes },
	abstract = {Graph theory is a vast field that constitutes a very important body of knowledge. Indeed, this book is just an introduction aiming at clarifying some essential points in this vital field: basic notions, some basic algorithms that are used to solve some classical and famous problems like path finding, tree finding, flow finding, ...etc. Finally, graph theory can be summarized by what Napoleon said: "A little drawing is better than a big speech".},
	author = {Aksa,  Karima}
}
@inbook {7145,
	title = {Data labeling impact on deep learning models indigital pathology: A breast cancer case study. ICCIS},
	booktitle = {Intelligent Vision in Healthcare},
	year = {2022},
	publisher = {Springer },
	organization = {Springer },
	author = {Benaggoune, Khaled and Al-masry, Z and Ma, J. Devalland and Zerhouni, N and Leila Hayet Mouss}
}
