@article {100437,
	title = {Designand Assessment of an IndustrialMaintenanceAssistanceSystemBasedon MixedReality},
	journal = {Revue d{\textquoteright}Intelligence Artificielle},
	volume = {38},
	number = {3},
	year = {2024},
	pages = {867-876},
	abstract = {
	Maintenance, storage and warehousing are complex processes required in many industries such as automotive, aerospace, manufacturing and logistic companies. These processes, often, involve moving objects in crowded environments using robots or human operators. Particularly, replacement and assembly of machine parts in crowded environments when performed by a human being require high technical skills. These tasks may be performed using robots to reduce costs due to human errors and execution time. However, robots under open world assumptions could neither operate in all environments nor perform tasks not modeled by the designer. In this paper, we introduce a mixed reality system to assist human operators in moving objects in crowded environments for maintenance tasks such as: parts assembly or replacement, and storage of objects. The introduced system consists of a mobile application exploited through a hands-free VR box. The proposed Mixed Reality for Industrial Maintenance (MRIM) system enhances the perception of a human operator by overlaying 3D real world visual information and virtual objects, such as: orientation guidelines including rotating angles, moving direction and displacement of carried objects. These guidelines allow for gaining execution time, and reducing human errors that might cause industrial parts damage. The proposed work brings two main contributions. First, it makes use of a new algorithm based on recasting, named R star (R*) that allows for optimizing pathfinding in 3D space. This later outperforms the two commonly used baseline 3D pathfinding algorithms of at least 87.5\% in terms of execution time. Second, MRIM provides an easy-to-use interface that exploits information provided by the R* algorithm. The experiments, conducted in real condition for the task of part replacement in a crowded environment, show that MRIM reduces considerably execution time and human errors.

},
	url = { https://doi.org/10.18280/ria.380313},
	author = {Hamidane, Rabie and Mouss, Leila-Hayet and Mahdaoui, Rafik and Bentrcia, Toufik}
}
@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 Benbouzid, Mohamed and Bentrcia, Toufik and Lim, Wei-Hong and Amirat, Yassine}
}
@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 Benbouzid, Mohamed and Bentrcia, Toufik and Amirat, Yassine and Le{\"{\i}la-Hayet Mouss}
}
@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 Bentrcia, Toufik and Ferrag, Mohamed-Amine and Benbouzid, Mohamed}
}
@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 Mouss, Le{\"\i}la-Hayet and Farid Melgani and Bentrcia, Toufik}
}
@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 Mouss, Leila-Hayet and Bentrcia, Toufik and Benbouzid, Mohamed}
}
@article {62493,
	title = {New fusion frameworks including explicit weighting functions for the remaining useful life prognostics},
	journal = {Expert Systems with Applications},
	volume = {189},
	year = {2022},
	month = {2022},
	pages = {116091},
	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 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.

},
	isbn = {0957-4174},
	url = {https://doi.org/10.1016/j.eswa.2021.116091},
	author = {Bouzenita, Mohammed and Mouss, Le{\"\i}la-Hayet and Farid Melgani and Bentrcia, Toufik}
}
@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 Benbouzid, Mohamed and Muyeen, S-M and Bentrcia, Toufik and Mouss, Le{\"\i}la-Hayet}
}
@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 Mouss, Leila-Hayet and Bentrcia, Toufik and Elbouchikhi, Elhoussin and Benbouzid, Mohamed}
}
@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 Benbouzid, Mohamed and Bentrcia, Toufik and Ma, Xiandong and Djurovi{\'c}, Sini\v sa and Mouss, Leila-Hayet}
}
@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 Mouss, Le{\"\i}la-Hayet and Bentrcia, Toufik and Benbouzid, Mohamed}
}
@article {62709,
	title = {Auto-NAHL: A neural network approach for condition-based maintenance of complex industrial systems},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	month = {2021},
	pages = {152829-152840},
	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.

},
	isbn = {2169-3536},
	url = {https://ieeexplore.ieee.org/abstract/document/9610082},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Muyeen, SM and Bentrcia, Toufik and Mouss, Le{\"\i}la-Hayet}
}
@article {62707,
	title = {A deep supervised learning approach for condition-based maintenance of naval propulsion systems},
	journal = {Ocean EngineeringOcean Engineering},
	volume = {221},
	year = {2021},
	month = {2021},
	pages = {108525},
	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.

},
	isbn = {0029-8018},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323},
	author = {Berghout, Tarek and Mouss, Le{\"\i}la-Hayet and Bentrcia, Toufik and Elbouchikhi, Elhoussin and Benbouzid, Mohamed}
}
@article {62713,
	title = {Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects},
	journal = {Energies},
	volume = {14},
	year = {2021},
	month = {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 Benbouzid, Mohamed and Bentrcia, Toufik and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Mouss, Le{\"\i}la-Hayet}
}
@article {62708,
	title = {A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Energy ConversionIEEE Transactions On Energy Conversion},
	year = {2021},
	month = {2021},
	isbn = {0885-8969},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Bentrcia, Toufik and Benbouzid, 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 Mouss, Leila-Hayet and Farid Melgani and Bentrcia, Toufik}
}
@proceedings {63112,
	title = {An ANFIS-based Computation to Study the Degradation-related Ageing effects in Nanoscale GAA-TFETs},
	journal = {Proceedings of the 10th International Conference on Information Systems and Technologies},
	year = {2020},
	month = {2020},
	pages = {1-5},
	author = {Bentrcia, Toufik and Djeffal, Faycal and Ferhati, Hichem}
}
@article {63111,
	title = {A comparative study on scaling capabilities of Si and SiGe nanoscale double gate tunneling FETs},
	journal = {SiliconSilicon},
	volume = {12},
	year = {2020},
	month = {2020},
	pages = {945-953},
	isbn = {1876-9918},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Ferhati, Hichem and Zohir Dibi}
}
@article {63077,
	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 Engineering InternationalQuality and Reliability Engineering International},
	volume = {36},
	year = {2020},
	month = {2020},
	pages = {2146-2169},
	isbn = {0748-8017},
	author = {Bouzenita, Mohammed and Mouss, Leila-Hayet and Farid Melgani and Bentrcia, Toufik}
}
@proceedings {63049,
	title = {Novel Infrared phototransistor based on Junctionless TFET design: Numerical Analysis and Performance Assessment},
	journal = {Proceedings of the 10th International Conference on Information Systems and Technologies},
	year = {2020},
	month = {2020},
	pages = {1-5},
	author = {Ferhati, Hichem and Djeffal, Faycal and Bentrcia, Toufik}
}
@article {63113,
	title = {Performance evaluation of nanoscale halo dual-material double gate SiGe MOSFET using 2-D numerical simulation},
	journal = {Materials Today: ProceedingsMaterials Today: Proceedings},
	volume = {20},
	year = {2020},
	month = {2020},
	pages = {348-355},
	isbn = {2214-7853},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Chahdi, Mohamed}
}
@proceedings {63048,
	title = {Role of Material Gate Engineering in Improving Gate All Around Junctionless (GAAJL) MOSFET Reliability Against Hot-Carrier Effects},
	journal = {2020 32nd International Conference on Microelectronics (ICM)},
	year = {2020},
	month = {2020},
	pages = {1-4},
	publisher = {IEEE},
	isbn = {1-72819-664-7},
	author = {Ferhati, Hichem and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {63606,
	title = {A nonlocal approach for semianalytical modeling of a heterojunction vertical surrounding-gate tunnel FET},
	journal = {Journal of Computational ElectronicsJournal of Computational Electronics},
	volume = {18},
	year = {2019},
	month = {2019},
	pages = {104-119},
	isbn = {1572-8137},
	author = {Abdelmalek, Nidhal and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {65017,
	title = {ANFIS-based approach to predict the degradation-related ageing of Junctionless GAA MOSFET},
	journal = {Materials Today: ProceedingsMaterials Today: Proceedings},
	volume = {5},
	year = {2018},
	month = {2018},
	pages = {15949-15958},
	isbn = {2214-7853},
	author = {Chebaki, Elasaad and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {65207,
	title = {Continuous semianalytical modeling of vertical surrounding-gate tunnel FET: analog/RF performance evaluation},
	journal = {Journal of Computational ElectronicsJournal of Computational Electronics},
	volume = {17},
	year = {2018},
	month = {2018},
	pages = {724-735},
	isbn = {1572-8137},
	author = {Abdelmalek, Nidhal and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {64957,
	title = {The role of the Ge mole fraction in improving the performance of a nanoscale junctionless tunneling FET: concept and scaling capability},
	journal = {Beilstein Journal of NanotechnologyBeilstein Journal of Nanotechnology},
	volume = {9},
	year = {2018},
	month = {2018},
	pages = {1856-1862},
	isbn = {2190-4286},
	author = {Ferhati, Hichem and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {64491,
	title = {Improved reliability performance of junctionless nanoscale DG MOSFET with graded channel doping engineering},
	journal = {physica status solidi cphysica status solidi c},
	volume = {14},
	year = {2017},
	month = {2017},
	pages = {1700147},
	isbn = {1862-6351},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Arar, Djemai and Chebaki, Elasaad}
}
@article {64490,
	title = {A Kriging framework for the efficient exploitation of the nanoscale junctioless DG MOSFETs including source/drain extensions and hot carrier effect},
	journal = {Materials Today: ProceedingsMaterials Today: Proceedings},
	volume = {4},
	year = {2017},
	month = {2017},
	pages = {6804-6813},
	isbn = {2214-7853},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Chebaki, Elasaad and Arar, Djemai}
}
@inbook {65566,
	title = {Fuzzy Modeling of Single Machine Scheduling Problems Including the Learning Effect},
	booktitle = {Metaheuristics for Production Systems},
	year = {2016},
	month = {2016},
	pages = {315-348},
	publisher = {Springer},
	organization = {Springer},
	author = {Bentrcia, Toufik and Mouss, Leila-Hayet}
}
@inbook {65567,
	title = {Multi-objective Design of Nanoscale Double Gate MOSFET Devices Using Surrogate Modeling and Global Optimization},
	booktitle = {Intelligent Nanomaterials, II, Second Edition},
	year = {2016},
	month = {2016},
	pages = {395-427},
	publisher = {Willey},
	organization = {Willey},
	abstract = {In recent years, the design and fabrication ofmulti-gate Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) have attracted more efforts due to their high appropriateness for advanced integration circuits{\textquoteright} applications. In fact, the boost of MOSFET structures is a battle against parasitic phenomena appearing at the nanoscale level. Short channel and quantum confinement effects are among the critical drawbacks that need to be remedied carefully. On the other hand, the hot carrier degradation effect is mainly a reliability concern affecting the device per- formance after long duration of work. In response to the high computational costs related to the development of physi- cal based models for Double Gate (DG) MOSFETs including all these effects, more flexible alternatives have been proposed for the prediction of device performances. Our aim in this chapter is to investigate the efficiency of a new proposed frame- work, built upon Kriging metamodeling and Non-dominated Sorting Genetic Algorithm version II (NSGA II), for the optimal design in terms of OFF-current, threshold voltage and swing factor. The input variables of interest are limited to the geometrical parameters namely the channel length and thickness. Data generated according to computer experiments, based on ATLAS 2-D simulator, are used to identify and adjust Kriging surrogate models. It is emphasized that the obtained models can be used accurately in a multi-objective context to offer several Pareto optimal configurations. Therefore, a wide range of selection possibilities is avail- able to the designer depending on situations under consideration.},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Chebaki, Elasaad}
}
@article {65568,
	title = {Numerical investigation of nanoscale double-gate junctionless MOSFET with drain and source extensions including interfacial defects},
	journal = {physica status solidi (c)physica status solidi (c)},
	volume = {13},
	year = {2016},
	month = {2016},
	pages = {151-155},
	isbn = {1862-6351},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Arar, Djemai and Meguellati, M}
}
@article {65988,
	title = {Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs},
	journal = {The International Journal of Advanced Manufacturing TechnologyThe International Journal of Advanced Manufacturing Technology},
	volume = {80},
	year = {2015},
	month = {2015},
	pages = {1369-1385},
	isbn = {1433-3015},
	author = {Bentrcia, Toufik and Mouss, Leila-Hayet and Mouss, Nadia-Kinza and Yalaoui, Farouk and Benyoucef, Lyes}
}
@article {65989,
	title = {Numerical investigation of nanoscale SiGe DG MOSFET performance against the interfacial defects},
	journal = {physica status solidi (c)physica status solidi (c)},
	volume = {12},
	year = {2015},
	month = {2015},
	pages = {131-135},
	isbn = {1862-6351},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Dibi, Zouhir and Arar, Djemai}
}
@inbook {66387,
	title = {An ANFIS Based Approach for Prediction of Threshold Voltage Degradation in Nanoscale DG MOSFET Devices},
	booktitle = {Transactions on Engineering Technologies},
	year = {2014},
	month = {2014},
	pages = {339-353},
	publisher = {Springer},
	organization = {Springer},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL}
}
@article {66386,
	title = {Gate-engineering-based approach to improve the nanoscale DG MOSFET behavior against interfacial trap effects},
	journal = {physica status solidi (c)physica status solidi (c)},
	volume = {11},
	year = {2014},
	month = {2014},
	pages = {77-80},
	isbn = {1862-6351},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Arar, Djemai and Dibi, Zouhir}
}
@article {66434,
	title = {New junctionless RADFET dosimeter design for low-cost radiation monitoring applications},
	journal = {physica status solidi (c)physica status solidi (c)},
	volume = {11},
	year = {2014},
	month = {2014},
	pages = {65-68},
	isbn = {1862-6351},
	author = {Arar, Djemai and Fay{\c c}al DJEFFAL and Bentrcia, Toufik and Chahdi, Mohamed}
}
@article {66748,
	title = {An analytical two dimensional subthreshold behavior model to study the nanoscale GCGS DG Si MOSFET including interfacial trap effects},
	journal = {Microelectronics ReliabilityMicroelectronics Reliability},
	volume = {53},
	year = {2013},
	month = {2013},
	pages = {520-527},
	isbn = {0026-2714},
	author = {Bentrcia, Toufik and Fay{\c c}al DJEFFAL and Chahdi, M}
}
@article {67079,
	title = {Continuous analytic I{\textemdash}V model for GS DG MOSFETs including hot-carrier degradation effects},
	journal = {Journal of SemiconductorsJournal of Semiconductors},
	volume = {33},
	year = {2012},
	month = {2012},
	pages = {014001},
	isbn = {1674-4926},
	author = {Bentrcia, Toufik and Djeffal, Faycal and Benhaya, Abdelhamid}
}
@article {67078,
	title = {Continuous analyticI-Vmodel for GS DG MOSFETs including hot-carrier degradation effects},
	journal = {半导体学报半导体学报},
	volume = {1},
	year = {2012},
	month = {2012},
	author = {Bentrcia, Toufik and Djeffal, Faycal and Benhaya, Abdelhamid}
}
@article {67036,
	title = {Two-dimensional numerical analysis of nanoscale junctionless and conventional Double Gate MOSFETs including the effect of interfacial traps},
	journal = {physica status solidi (c)physica status solidi (c)},
	volume = {9},
	year = {2012},
	month = {2012},
	pages = {2041-2044},
	isbn = {1862-6351},
	author = {Chebaki, Elasaad and Fay{\c c}al DJEFFAL and Bentrcia, Toufik}
}
@article {67292,
	title = {An analytical drain current model for undoped GSDG MOSFETs including interfacial hot-carrier effects},
	journal = {physica status solidi cphysica status solidi c},
	volume = {8},
	year = {2011},
	month = {2011},
	pages = {907-910},
	isbn = {1862-6351},
	author = {Fay{\c c}al DJEFFAL and Bentrcia, Toufik and Toufik BENDIB}
}
@article {67293,
	title = {Drain current model for undoped Gate Stack Double Gate (GSDG) MOSFETs including the hot-carrier degradation effects},
	journal = {Microelectronics ReliabilityMicroelectronics Reliability},
	volume = {51},
	year = {2011},
	month = {2011},
	pages = {550-555},
	isbn = {0026-2714},
	author = {Fay{\c c}al DJEFFAL and Bentrcia, Toufik and Abdi, Mohamed Amir and Bendib, T}
}
@proceedings {67333,
	title = {Modeling and evaluation of single machine flexibility using fuzzy entropy and genetic algorithm based approach},
	journal = {ETFA2011},
	year = {2011},
	month = {2011},
	pages = {1-8},
	publisher = {IEEE},
	isbn = {1-4577-0018-2},
	author = {Bentrcia, Toufik and Mouss, Mohamed Djamel and Leila Hayet Mouss and Benbouzid, Mohamed El Hachemi}
}
@article {67334,
	title = {Proposition d{\textquoteright}une approche probabiliste floue pour la mesure de la flexibilit{\'e} des syst{\`e}mes de production},
	journal = {9e Congr{\`e}s International de G{\'e}nie Industriel9e Congr{\`e}s International de G{\'e}nie Industriel},
	year = {2011},
	month = {2011},
	pages = {1-8},
	author = {Bentrcia, Toufik and LH Mouss and Mouss, NK}
}
@article {61945,
	title = {A New Approach Reliability Based for the Design of Hybrid Expert Systems in Industrial Diagnosis Field},
	year = {2005},
	month = {2005},
	author = {Bentrcia, Toufik and Leila Hayet Mouss and Mouss, Mohamed Djamel}
}
