Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

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.

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.

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.

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.

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.

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.

This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71 $ and the COE equal to 0.79 $/kWh for an LPSP = 0.01%, where the ten households are 0.99 % satisfied by renewable energy sources.

This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71 $ and the COE equal to 0.79 $/kWh for an LPSP = 0.01%, where the ten households are 0.99 % satisfied by renewable energy sources.

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.

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.

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.

De nos jours, la plupart des moteurs d’analyse de solutions anti-malware sont heuristiques. Ils classent les objets, les flux de données ainsi que les zones mémoires comme bénins ou malveillants en fonction de leur comportement. La plupart des fabricants d’antivirus reconnaissent que l’approche heuristique permet d’atteindre jusqu’à 90% d’efficacité en termes de taux de détection, mais consomme davantage de ressources systèmes : de tels moteurs anti-malware, disponibles en open-source, sont extrêmement inefficaces en termes d’utilisation de ressources système car ils font souvent appel à des algorithmes d’apprentissage automatique. Pour réduire cette charge système, il est fortement recommandé d’utiliser la détection par signature statique, qui permet de filtrer à elle seule la majorité des échantillons de programmes malveillants connus, en conjonction avec les technologies de détection heuristiques et celles basées sur le cloud. Dans ce travail, nous présentons un scanner de signatures rapide pour la détection de programmes malveillants, basé sur une version améliorée de l’algorithme Aho-Corasick pour la recherche de cha{\^ınes de caractères (ou motifs), con\c cue pour pouvoir bénéficier des techniques de vectorisation qui ajoutent une forme de parallélisme de données au code de l’algorithme. La solution proposée est implémentée en utilisant le jeu d’instructions d’Intel® Advanced Vector Extensions (AVX2).

 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.  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.