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 višekriterijumsko 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', 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.

Logistics is one of the main tactics that countries and businesses are improving in order to increase profits. Another prominent theme in today’s logistics is emerging technologies. Today’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.

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’ 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’s accuracy which is 94.18%. However, the K-NN model’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’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.

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–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.

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.

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (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 commercialization 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 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 degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other 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.