<?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%">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.
&lt;/p&gt;
</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>5</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, Z</style></author><author><style face="normal" font="default" size="100%">Ma, J. Devalland</style></author><author><style face="normal" font="default" size="100%">Zerhouni, N</style></author><author><style face="normal" font="default" size="100%">Leila Hayet Mouss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data labeling impact on deep learning models indigital pathology: A breast cancer case study. ICCIS</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Vision in Healthcare</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><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%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Yue, Meiling</style></author><author><style face="normal" font="default" size="100%">Jemei, Samir</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 data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</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://www.sciencedirect.com/science/article/abs/pii/S0306261922002756</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">313</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fuel cell technology&amp;nbsp;has been rapidly developed in the last decade owing to its clean characteristic and high efficiency.&amp;nbsp;Proton exchange membrane fuel cells&amp;nbsp;(PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful&amp;nbsp;commercialization&amp;nbsp;and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in&amp;nbsp;fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the&amp;nbsp;degradation performance&amp;nbsp;of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated&amp;nbsp;convolutional neural network&amp;nbsp;is for the first time applied for predicting the time-dependent&amp;nbsp;fuel cell performance&amp;nbsp;and is proved to be more efficient than other&amp;nbsp;recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.</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%">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%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Meiling, Yue</style></author><author><style face="normal" font="default" size="100%">Jemei, Samir</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 Knowledge Transfer Approach for Online PEMFC Degradation prediction with Uncertainty Quantification</style></title><secondary-title><style face="normal" font="default" size="100%">12th International Conference on Power, Energy and Electrical Engineering (CPEEE)</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/9738717</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Shiga, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Proton Exchange Membrane Fuel Cells (PEMFCs) are a key challenger for the world’s future clean and renewable energy solution. Yet, fuel cells are susceptible to operating conditions and hydrogen impurities, leading to performance loss over time in service. Hence, performance degradation prediction is gaining attention recently for fuel cell system reliability. In this work, we present a knowledge transfer approach for online voltage drop prediction. A dual-path convolution neural network is proposed to extract linearity and non-linearity from historical data and performs multi-steps ahead prediction with uncertainty quantification. Online voltage prediction is then evaluated with and without knowledge transfer using two different PEMFC datasets. Results indicate that our proposed approach with transfer knowledge can predict the voltage drop accurately with a small uncertainty range compared to the conventional approach.</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%">Zuluaga-Gomez, J</style></author><author><style face="normal" font="default" size="100%">Al Masry, Z</style></author><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%">Zerhouni, Noureddine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A CNN-based methodology for breast cancer diagnosis using thermal images</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization</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.tandfonline.com/doi/abs/10.1080/21681163.2020.1824685</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">131-145</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs</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%">Gougam, Fawzi</style></author><author><style face="normal" font="default" size="100%">Chemseddine, Rahmoune</style></author><author><style face="normal" font="default" size="100%">Benazzouz, Djamel</style></author><author><style face="normal" font="default" size="100%">Zerhouni, Noureddine</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%">Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</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://journals.sagepub.com/doi/abs/10.1177/0954406220976154</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">235</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.</style></abstract><issue><style face="normal" font="default" size="100%">20</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%">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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><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%">Terrissa, Sadek-Labib</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</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%">Towards Digital Twins Driven Breast Cancer Detection</style></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/10.1007/978-3-030-80129-8_7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lecture Notes in Networks and Systems</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.</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%">Meraghni, Safa</style></author><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%">Terrissa, Labib</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</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%">Towards Digital Twins Driven Breast Cancer Detection</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Networks and 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://link.springer.com/chapter/10.1007/978-3-030-80129-8_7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">285</style></volume><pages><style face="normal" font="default" size="100%">87–99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Digital twins have transformed the industrial world by changing the development phase of a product or the use of equipment. With the digital twin, the object’s evolution data allows us to anticipate and optimize its performance. Healthcare is in the midst of a digital transition towards personalized, predictive, preventive, and participatory medicine. The digital twin is one of the key tools of this change. In this work, DT is proposed for the diagnosis of breast cancer based on breast skin temperature. Research has focused on thermography as a non-invasive scanning solution for breast cancer diagnosis. However, body temperature is influenced by many factors, such as breast anatomy, physiological functions, blood pressure, etc. The proposed DT updates the bio-heat model’s temperature using the data collected by temperature sensors and complementary data from smart devices. Consequently, the proposed DT is personalized using the collected data to reflect the person’s behavior with whom it is connected.</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%">Zuluaga-Gomez, Juan</style></author><author><style face="normal" font="default" size="100%">Al Masry, Zeina</style></author><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%">Zerhouni, Nourredine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A CNN-based methodology for breast cancer diagnosis using thermal images</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization</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><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/abs/10.1080/21681163.2020.1824685</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">131-145</style></pages><isbn><style face="normal" font="default" size="100%">2168-1163</style></isbn><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;
	A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs
&lt;/p&gt;
</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%">Gougam, Fawzi</style></author><author><style face="normal" font="default" size="100%">Chemseddine, Rahmoune</style></author><author><style face="normal" font="default" size="100%">Benazzouz, Djamel</style></author><author><style face="normal" font="default" size="100%">Benaggoune, Khaled</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%">Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering ScienceProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</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><number><style face="normal" font="default" size="100%">20</style></number><volume><style face="normal" font="default" size="100%">235</style></volume><pages><style face="normal" font="default" size="100%">5186-5197</style></pages><isbn><style face="normal" font="default" size="100%">0954-4062</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%">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, 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><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></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><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">291-310</style></pages><isbn><style face="normal" font="default" size="100%">1754-8632</style></isbn><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;
	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).
&lt;/p&gt;
</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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meraghni, Safa</style></author><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%">Terrissa, Labib Sadek</style></author><author><style face="normal" font="default" size="100%">Devalland, Christine</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%">Towards Digital Twins Driven Breast Cancer Detection</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Computing</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><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">87-99</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%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">Leila Hayet Mouss</style></author><author><style face="normal" font="default" size="100%">A Abdessemed</style></author><author><style face="normal" font="default" size="100%">Bensakhria, Mohamed</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Holonic agent-based approach for system-level remaining useful life estimation with stochastic dependence</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Integrated Manufacturing</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/10.1080/0951192X.2020.1795926?journalCode=tcim20</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.</style></abstract><issue><style face="normal" font="default" size="100%">10</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%">Benaggoune, Khaled</style></author><author><style face="normal" font="default" size="100%">LH Mouss</style></author><author><style face="normal" font="default" size="100%">Abdessemed, AA</style></author><author><style face="normal" font="default" size="100%">Bensakhria, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Holonic agent-based approach for system-level remaining useful life estimation with stochastic dependence</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Integrated ManufacturingInternational Journal of Computer Integrated Manufacturing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">10-11</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">1089-1104</style></pages><isbn><style face="normal" font="default" size="100%">0951-192X</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%">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%">LH Mouss</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%">2020 prognostics and health management conference (phm-besancon)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">194-199</style></pages><isbn><style face="normal" font="default" size="100%">1-72815-675-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>