<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">HADJIDJ, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Ounnas, Djamel</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global maximum power point tracking method for photovoltaic systems using Takagi-Sugeno fuzzy models and ANFIS approach</style></title><secondary-title><style face="normal" font="default" size="100%">Electrical Engineering &amp; Electromechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">10.20998/2074-272X.2025.2.05</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.
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&lt;p style=&quot;text-align: justify;&quot;&gt;
	Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.
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&lt;p style=&quot;text-align: justify;&quot;&gt;
	Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.
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&lt;p style=&quot;text-align: justify;&quot;&gt;
	The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb &amp;amp; Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamidane, Rabie</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Mahdaoui, Rafik</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Designand Assessment of an IndustrialMaintenanceAssistanceSystemBasedon MixedReality</style></title><secondary-title><style face="normal" font="default" size="100%">Revue d'Intelligence Artificielle</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%"> https://doi.org/10.18280/ria.380313</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">867-876</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	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.
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</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdessemed, Ahmed-Adnane</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Production Management and Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://polipapers.upv.es/index.php/IJPME/article/view/18077/16035</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">167-177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive performance compared with some algorithms extracted from the literature.
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</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Al-Masry, Zeina</style></author><author><style face="normal" font="default" size="100%">Ma, Jian</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A deep learning pipeline for breast cancer ki-67 proliferation index scoring</style></title><secondary-title><style face="normal" font="default" size="100%">Image and Video Processing (eess.IV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2203.07452</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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’s performance is validated on a dataset from the Department of Pathology at Hôpital Nord Franche-Comté hospital.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadjidj, Nadjiha </style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Selection The Appropriate Learning Machine For Fault Diagnosis With Big-Data Environment In Photovoltaic Systems.</style></title><secondary-title><style face="normal" font="default" size="100%">IGSCONG’22. Jun 2022</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><pub-location><style face="normal" font="default" size="100%">Turkey</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">HADJIDJ, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Selection The Appropriate Learning Machine For Fault Diagnosis With Big-Data Environment In Photovoltaic Systems</style></title><secondary-title><style face="normal" font="default" size="100%">IGSCONG’22, Jun</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><pub-location><style face="normal" font="default" size="100%">Turkey</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Energy Conversion</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9552475</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">37</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadjidj, Nadjiha </style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Ounnas, Djamel</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES’21)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://revue.cder.dz/index.php/rer/article/view/1081</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Adrar, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">HADJIDJ, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Ounnes, D</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis and Design of Modified IncrementalConductance-BasedMPPT Algorithm for Photovoltaic System</style></title><secondary-title><style face="normal" font="default" size="100%">The First International Conference on Renewable Energy Advanced Technologies and Applications (ICREATA’21 ), October 25-27</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA’21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">October 25-27</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Nowadays, solar energy, which is the direct conversion of light into electricity, occupies a very important place among renewable energy resources due to its daily availability in most regions of the globe. Therefore, the wise exploitation of this clean energy will ultimately drive to cover all needed demands [1, 2]. This paper deals with the design of Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) system using a modified incremental conductance (IncCond) algorithm to extract maximum power from PV module. The considered PV system consists of a PV module, a DC-DC converter and a resistive load. In the literature, it is known that the conventional MPPT algorithms suffer from serious disadvantages such as fluctuations around the MPP and slow tracking during a rapid change in atmospheric conditions. Therefore, in this paper, and attempting to overcome the shortcomings of conventional approach. In this work, a new modified incremental conductance algorithm is proposed to find the Maximum Power Point Tracking (MPPT) of the Photovoltaic System. Simulation tests with different atmospheric conditions are provided to demonstrate the validity and the effectiveness of the proposed algorithm.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanane Zermane</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Sonia Benaicha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automation and fuzzy control of a manufacturing system</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.naun.org/main/NAUN/fuzzy/2021/a082017-004(2021).pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The automation of manufacturing systems is a major obligation to the developments because of exponential industrial equipment, and programming tools, so that growth needs and customer requirements. This automation is achieved in our work through the application programming tools from Siemens, which are PCS 7 (Process Control System) for industrial process control and FuzzyControl++ for fuzzy control. An industrial application is designed, developed and implemented in the cement factory in Ain-Touta (S.CIM.AT) located in the province of Batna, East of Algeria. Especially in the cement mill which gives the final product that is the cement.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">HADJIDJ, Nadjiha</style></author><author><style face="normal" font="default" size="100%">Benbrahim, Meriem</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study Between Data-Based Approaches Under Earlier Failure Detection</style></title><secondary-title><style face="normal" font="default" size="100%">ICCIS2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-981-16-1089-9_20</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lecture Notes in Networks and Systems</style></publisher><pub-location><style face="normal" font="default" size="100%">India</style></pub-location><volume><style face="normal" font="default" size="100%">204</style></volume><pages><style face="normal" font="default" size="100%">235–239</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bellal, Salah-Eddine</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Sahnoun, M’hammed</style></author><author><style face="normal" font="default" size="100%">Messaadia, Mourad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cost Optimisation for Wheelchair Redesign</style></title><secondary-title><style face="normal" font="default" size="100%">1st International Conference On Cyber Management And Engineering (CyMaEn), 26-28 May</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9497281</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Hammamet, Tunisia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don’t meet international standards. However, developing countries are obliged to purchase products from developed countries that don’t necessarily meet all user’s requirements. The modification of these requirements will generate disruption on all the supply chain. This paper proposes a model for optimising the cost of requirement modification on the supply chain and seeks to evaluate the introduction of a new requirement on an existing product/process. This model is adapted to the redesign and development of products, such as wheelchairs, satisfying specific Algerian end-user requirements.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Elbouchikhi, Elhoussin</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A deep supervised learning approach for condition-based maintenance of naval propulsion systems</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">221</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In the last years,&amp;nbsp;predictive maintenance&amp;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&amp;nbsp;extreme learning machines&amp;nbsp;using data issued from a&amp;nbsp;propulsion system&amp;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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sonia, Benaicha</style></author><author><style face="normal" font="default" size="100%">Zermane, Hannane</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bencherif, Fateh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an Industrial Application with Neuro-Fuzzy Systems</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.naun.org/main/NAUN/fuzzy/2021/a062017-003(2021).pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, our objective is dedicated to the detection of a deterioration in the estimated operating time by giving preventive action before a failure, and the classification of breakdowns after failure by giving the action of the diagnosis and / or maintenance. For this reason, we propose a new Neuro-fuzzy assistance prognosis system based on pattern recognition called &quot;NFPROG&quot; (Neuro Fuzzy Prognosis). NFPROG is an interactive simulation software, developed within the Laboratory of Automation and Production (LAP) -University of Batna, Algeria. It is a four-layer fuzzy preceptor whose architecture is based on Elman neural networks. This system is applied to the cement manufacturing process (cooking process) to the cement manufacturing company of Ain-Touta-Batna, Algeria. And since this company has an installation and configuration S7-400 of Siemens PLC PCS7was chosen as a programming language platform for our system.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benayache, Ayoub</style></author><author><style face="normal" font="default" size="100%">Azeddine Bilami</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Industrial IoT middleware using a multi-agent system for consistency-based diagnostic in cement factory</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Autonomous and Adaptive Communications Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.inderscienceonline.com/doi/abs/10.1504/IJAACS.2021.117805</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">With the evolution of the internet of things (IoT), and due to its significant need in the industry, Industrial IoT (IIoT) becomes the suitable naming for this accompaniment. IIoT changed the view of the industry intelligently and over the internet. This overlapping of IoT and industry requires special treatment when systems deal with heterogeneous devices in a distributed environment and complex tasks. In this paper, we propose a middleware solution based on multi-agents system (MAS) to handle the distributed control of complex systems autonomously in an industrial environment. The proposed middleware enables machine-to-machine (M2M) communications among the system’s components. In this work, we also addressed the distributed diagnostic for real industrial system using MAS with a new suitable communication strategy to support the heterogeneity and interoperability issued in IIoT and assure real-time monitoring and control. Finally, we present a qualitative evaluation of our solution on real case study (cement factory).</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author><author><style face="normal" font="default" size="100%">Djurović, Siniša</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning for Photovoltaic Systems Condition Monitoring: A Review</style></title><secondary-title><style face="normal" font="default" size="100%">47th Annual Conference of the IEEE Industrial Electronics Society, IECON</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9589423</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Toronto, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Ma, Xiandong</style></author><author><style face="normal" font="default" size="100%">Djurović, Sini\v sa</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects</style></title><secondary-title><style face="normal" font="default" size="100%">Energies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/1996-1073/14/19/6316</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">19</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Derdour, Khedidja</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bensaadi, Rafik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal on Electrical Engineering and Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.proquest.com/openview/72acbcf9bceca23bc38c7d1df8734acb/1?pq-origsite=gscholar&amp;cbl=316223</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning</style></title><secondary-title><style face="normal" font="default" size="100%">47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9589114</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Toronto, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chouhal, Ouahiba</style></author><author><style face="normal" font="default" size="100%">Mahdaoui, Rafik</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SOA-based distributed fault prognostic and diagnosis framework: an application for preheater cement cyclones</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Internet Manufacturing and Services</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.inderscienceonline.com/doi/abs/10.1504/IJIMS.2021.114539</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Complex engineering manufacturing systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis and prognosis approaches are centralised, but these solutions are difficult to implement on distributed systems; whereas a distributed approach of multiple diagnosis and prognosis agents can offer a solution. Also, controlling process plant from a remote location has several benefits including the ability to track and to assist in solving a problem that might arise. This paper presents a distributed and over prognosis and diagnosis approach for physical systems basing on multi agent system and service-oriented architecture. Specifics prognostic and diagnostic procedures and key modules of the architecture for web service-based distributed fault prognostic and diagnosis framework are detailed and developed for the preheater cement cyclones in the workshop of SCIMAT clinker. The experimental case study, reported in the present paper, shows encouraging results and fosters industrial technology transfer.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Energy ConversionIEEE Transactions On Energy Conversion</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><isbn><style face="normal" font="default" size="100%">0885-8969</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Sparse On-line Sequential Autoencoder for Sensors Measurements Compression Applied to Military Aircraft Engines</style></title><secondary-title><style face="normal" font="default" size="100%">8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT’2020) April14-16</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/340732102_Adaptive_Sparse_On-line_Sequential_Autoencoder_for_Sensors_Measurements_Compression_Applied_to_Military_Aircraft_Engines</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Algiers, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work a new data-driven compression approach is presented. The compression algorithm is an autoencoder trained with an improved On-line sequential Extreme Learning Machine (OS-ELM). First, a dynamic adaptation of the training algorithm towards the newly coming data is achieved by integrating an updated selection strategy (USS) and dynamic forgetting function (DDF). Second, Singular Value Decomposition (SVD) is involved to enhance hidden layer representation via sparse mapping. This new developed autoencoder (ASOS- AE) is compared with the ordinary OS-ELM autoencoder (OS-AE) and proved its accuracy in CMAPSS dataset (Commercial Modular Aero-Propulsion System Simulation). The C-MAPSS software has revisions in civil and military applications. In the present work we used the military version of its applications.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bencherif, Fateh</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex network to enhance characterization analysis in modelling product development process</style></title><secondary-title><style face="normal" font="default" size="100%">African Journal of Science, Technology, Innovation and Development</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/abs/10.1080/20421338.2020.1762355?journalCode=rajs20</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">797-811</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Nowadays, successful and innovative product development is highly correlated with the company’s success and reason for existence. A development process is a major factor influencing cost, timing and quality of product development. It requires additional attention to decisions made about programme, budget, technical and market risks. In this paper a product development process model is proposed in an innovation context and strategy framework of design process and project management. The process modelling is complex network theory based, to improve characterization analysis for product development process modelling. Required concepts for complex process are established to build product development mathematical model, and provide an overview of key definitions and complex networks advanced tools. Finally, a case study for an Algerian electric generator company is carried out to prove the practicality of the proposed model.</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine</style></title><secondary-title><style face="normal" font="default" size="100%">International conferance of intelligent</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/337945405_Dynamic_Adaptation_for_Length_Changeable_Weighted_Extreme_Learning_Machine</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Amsterdam, The Netherland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelhadi, Adel</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST</style></title><secondary-title><style face="normal" font="default" size="100%">https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelhadi, Adel</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST</style></title><secondary-title><style face="normal" font="default" size="100%">ACADEMIC JOURNAL OF MANUFACTURING ENGINEERING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanane Zermane</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Touahar, Djamel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Industrial supervision system based on machine learning SVM technique</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Robotics, Machine Learning and Artificial Intelligence (ICRMLAI),06 february</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pub-location><style face="normal" font="default" size="100%">Istanbul, Turkey</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bouzenita, Mohammed</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Farid Melgani</style></author><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">New fusion and selection approaches for estimating the remaining useful life using Gaussian process regression and induced ordered weighted averaging operators</style></title><secondary-title><style face="normal" font="default" size="100%">Quality and Reliability Engenieering International Journal (QREIJ)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2688</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">2146-2169</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><author><style face="normal" font="default" size="100%">Ma, Jian</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Post Prognostic Decision for Predictive Maintenance Planning with Remaining Useful Life Uncertainty</style></title><secondary-title><style face="normal" font="default" size="100%">Prognostics and Health Management Conference (PHM-Besan\c con)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9115558</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Besancon, France</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper investigates the use of the Particle Swarm Optimization (PSO) algorithm to quantify the effect of RUL uncertainty on predictive maintenance planning. The prediction of RUL is influenced by many sources of uncertainty, and it is required to quantify their combined impact by incorporating the RUL uncertainty in the optimization process to minimize the total maintenance cost. In this work, predictive maintenance of a multi-functional single machine problem is adopted to study the impact of RUL uncertainty on maintenance planning. Therefore, the PSO algorithm is integrated with a random sampling-based strategy to select a sequence that performs better for different values of RUL associated with different jobs. Through a numerical example, results show the importance of optimizing maintenance actions under the consideration of RUL randomness.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rezki, Djamil</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Baaziz, Abdelkader</style></author><author><style face="normal" font="default" size="100%">Rezki, Nafissa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Information Systems and Organisation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rezki, Djamil</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Baaziz, Abdelkader</style></author><author><style face="normal" font="default" size="100%">Rezki, Nafissa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning</style></title><secondary-title><style face="normal" font="default" size="100%">ICT for an Inclusive World</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://econpapers.repec.org/bookchap/sprlnichp/978-3-030-34269-2_5f37.htm</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">537-549</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regularization Based Particle Swarm Optimization for Length Changeable Extreme Learning Machine under Health State Estimation of Military Aircraft Engines</style></title><secondary-title><style face="normal" font="default" size="100%">8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT’2020) April14-16,</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/340732320_Regularization_Based_Particle_Swarm_Optimization_for_Length_Changeable_Extreme_Learning_Machine_under_Health_State_Estimation_of_Military_Aircraft_Engines</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Algiers, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work a new data-driven approach for Remaining Useful Life estimation of aircraft engines is developed. The proposed approach is a regularized Single Hidden Layer Feedforward Neural network (SLFN) with incremental constructive enhancements. The training rules of this algorithm are inspired form different Extreme Learning Machine (ELM) variants. Particle Swarm Optimization (PSO) algorithm is integrated to enhance tracking ability of the best regularization parameter to reduce the norm of the tuned weights. The proposed approach is evaluated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset and compared to its other derivatives and proved its accuracy. C-MAPSS software has revisions in military and civil applications. In this paper, the military version of its application is the used one.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regularized Length Changeable Extreme Learning Machine with Incremental Learning Enhancements for Remaining Useful Life Prediction of Aircraft Engines</style></title><secondary-title><style face="normal" font="default" size="100%">1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), 16-17 May</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9151607</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">El Oued, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Remaining Useful Life Prediction for aircraft engines with a new Denoising On-Line Sequential Extreme Learning Machine with Double Dynamic Forgetting Factors and Update Selection Strategy</style></title><secondary-title><style face="normal" font="default" size="100%">12th Conference on Mechanical Engineering March 17-18, 2020 Ecole Militaire Polytechnique Bordj El Bahri</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/341509226_Remaining_Useful_Life_Prediction_for_aircraft_engines_with_a_new_Denoising_On-Line_Sequential_Extreme_Learning_Machine_with_Double_Dynamic_Forgetting_Factors_and_Update_Selection_Strategy</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Algiers, Algeria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bellal, Salah-Eddine</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Sahnoun, M’hammed</style></author><author><style face="normal" font="default" size="100%">Messaadia, Mourad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">User behaviour-based approach to define mobility devices needs of disabled person in Algeria: a questionnaire study</style></title><secondary-title><style face="normal" font="default" size="100%">Disability and Rehabilitation: Assistive Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/abs/10.1080/17483107.2020.1791263</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">453-461</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">T Berghout</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Moving From unknown to known feature spaces based on TS-ELM with random kernels and connections</style></title><secondary-title><style face="normal" font="default" size="100%">IntelliSys2018. International Conference on Intelligent Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%"> London, United Kingdom</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.
&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy Modeling of Single Machine Scheduling Problems Including the Learning Effect</style></title><secondary-title><style face="normal" font="default" size="100%">Metaheuristics for Production Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">315-348</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author><author><style face="normal" font="default" size="100%">Abdelhadi, Adel</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TOOLBOX SUPPORTS GROUP AWARENESS IN GROUPWARE</style></title><secondary-title><style face="normal" font="default" size="100%">Annals. 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Computer Science Series</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><isbn><style face="normal" font="default" size="100%">1583-7165</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bentrcia, Toufik</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Mouss, Nadia-Kinza</style></author><author><style face="normal" font="default" size="100%">Yalaoui, Farouk</style></author><author><style face="normal" font="default" size="100%">Benyoucef, Lyes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs</style></title><secondary-title><style face="normal" font="default" size="100%">The International Journal of Advanced Manufacturing TechnologyThe International Journal of Advanced Manufacturing Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">80</style></volume><pages><style face="normal" font="default" size="100%">1369-1385</style></pages><isbn><style face="normal" font="default" size="100%">1433-3015</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benzouai, Messaoud</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">La maitrise de la qualité et de la sécurité sanitaire des aliments: Industrie Agroalimentaire</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Éditions universitaires européennes</style></publisher><isbn><style face="normal" font="default" size="100%">3-8417-4106-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nezzar, Malika</style></author><author><style face="normal" font="default" size="100%">Abdessemed, Rachid</style></author><author><style face="normal" font="default" size="100%">Benbouzid, Mohamed</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic default diagnosis for an asynchronous machine fed by a PWM converter</style></title><secondary-title><style face="normal" font="default" size="100%">JEE, Romania.JEE, Romania.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">23-27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>