@mastersthesis {7314,
	title = {{\'E}tude, mise en {\oe}uvre et adaptabilit{\'e} des outils de l\&rsquo;am{\'e}lioration continue dans une industrie alg{\'e}rienne : Approches Th{\'e}orique et Pratique},
	year = {2021},
	type = {Habilitation Universitaire},
	author = {Aouag, Hichem}
}
@mastersthesis {7311,
	title = {Vers une Approche des Syst{\`e}mes Multi-Agents et M{\'e}thodes d\&rsquo;Emergence pour la Maintenance Syst{\'e}matique},
	year = {2021},
	author = {Adel Abdelhadi}
}
@mastersthesis {7308,
	title = {Optimisation Des Techniques de S{\'e}curisation Du Logiciel Via l\&rsquo;Analyse des Codes Malveillants},
	year = {2021},
	type = {Doctorat en sciences  },
	abstract = {
	De nos jours, la plupart des moteurs d{\textquoteright}analyse de solutions anti-malware sont heuristiques. Ils classent les objets, les flux de donn{\'e}es ainsi que les zones m{\'e}moires comme b{\'e}nins ou malveillants en fonction de leur comportement. La plupart des fabricants d{\textquoteright}antivirus reconnaissent que l{\textquoteright}approche heuristique permet d{\textquoteright}atteindre jusqu{\textquoteright}{\`a} 90\% d{\textquoteright}efficacit{\'e} en termes de taux de d{\'e}tection, mais consomme davantage de ressources syst{\`e}mes : de tels moteurs anti-malware, disponibles en open-source, sont extr{\^e}mement inefficaces en termes d{\textquoteright}utilisation de ressources syst{\`e}me car ils font souvent appel {\`a} des algorithmes d{\textquoteright}apprentissage automatique. Pour r{\'e}duire cette charge syst{\`e}me, il est fortement recommand{\'e} d{\textquoteright}utiliser la d{\'e}tection par signature statique, qui permet de filtrer {\`a} elle seule la majorit{\'e} des {\'e}chantillons de programmes malveillants connus, en conjonction avec les technologies de d{\'e}tection heuristiques et celles bas{\'e}es sur le cloud. Dans ce travail, nous pr{\'e}sentons un scanner de signatures rapide pour la d{\'e}tection de programmes malveillants, bas{\'e} sur une version am{\'e}lior{\'e}e de l{\textquoteright}algorithme Aho-Corasick pour la recherche de cha{\^\i}nes de caract{\`e}res (ou motifs), con{\c c}ue pour pouvoir b{\'e}n{\'e}ficier des techniques de vectorisation qui ajoutent une forme de parall{\'e}lisme de donn{\'e}es au code de l{\textquoteright}algorithme. La solution propos{\'e}e est impl{\'e}ment{\'e}e en utilisant le jeu d{\textquoteright}instructions d{\textquoteright}Intel{\textregistered} Advanced Vector Extensions (AVX2).

},
	url = {http://eprints.univ-batna2.dz/1922/},
	author = {Ourlis, Lazhar}
}
@mastersthesis {7307,
	title = {INT{\'E}GRATION D\&rsquo;UN MODULE DE RECONNAISSANCE DE LA PAROLE AU NIVEAU D\&rsquo;UN SYST{\`E}ME AUDIOVISUEL - APPLICATION T{\'E}L{\'E}VISEUR},
	year = {2021},
	type = {Doctorat en sciences  },
	abstract = {
	Cette th{\`e}se propose de concevoir et r{\'e}aliser un syst{\`e}me de reconnaissance automatique de la parole destin{\'e} {\`a} commander {\`a} distance un syst{\`e}me audiovisuel {\`a} savoir : un T{\'e}l{\'e}viseur. Le syst{\`e}me global "bout en bout" se scinde en deux blocs : le premier cherche {\`a} extraire les meilleures caract{\'e}ristiques {\`a} partir du signal vocal d{\textquoteright}entr{\'e}e. A cet effet, plusieurs techniques d{\textquoteright}extraction de caract{\'e}ristiques vont {\^e}tre examin{\'e}es et test{\'e}es. Concernant le deuxi{\`e}me bloc, nous mettons en {\'e}vidence une multitude de techniques relevant du domaine de l{\textquoteright}apprentissage profond, dont l{\textquoteright}impact est d{\textquoteright}adapter et de d{\textquoteright}affirmer les caract{\'e}ristiques extraites pour donner en final la classe de l{\textquoteright}{\'e}nonc{\'e}. La validation des diff{\'e}rentes m{\'e}thodologies pr{\'e}sent{\'e}es dans cette th{\`e}se a {\'e}t{\'e} effectu{\'e}e sur la base de deux jeux de donn{\'e}es r{\'e}elles, le premier est tenu compte pour une {\'e}valuation initiale, tandis que le second est con{\c c}u exclusivement pour le syst{\`e}me ASR propos{\'e} dans cette th{\`e}se. Les r{\'e}sultats obtenus ont certifi{\'e} l{\textquoteright}efficience des approches propos{\'e}es. Le d{\'e}fi pour les travaux futurs est d{\textquoteright}{\'e}valuer ce type de syst{\`e}me dans des conditions plus r{\'e}alistes avec des signaux vocaux issus des milieux bruit{\'e}s.

},
	url = {http://eprints.univ-batna2.dz/1918/},
	author = {Zerari, Naima}
}
@mastersthesis {7306,
	title = {Am{\'e}lioration du processus de capitalisation et de partage des connaissances pour la maximisation de la valeur d\&$\#$39;un syst{\`e}me de production},
	year = {2021},
	type = {Doctorat en sciences  },
	abstract = {
	Dans cette th{\`e}se, nous nous sommes int{\'e}ress{\'e}s {\`a} un mod{\`e}le de gestion des connaissances des entreprises industrielles. Certaines t{\^a}ches manufacturi{\`e}res impliquent un niveau {\'e}lev{\'e} de connaissance tacite des op{\'e}rateurs qualifi{\'e}s. L{\textquoteright}industrie a besoin des m{\'e}thodes fiables pour la capture et l{\textquoteright}analyse de ces connaissances tacites afin qu{\textquoteright}elles puissent {\^e}tre partag{\'e}es et sans aucune perte. Nous proposons, un mod{\`e}le de gestion contenant deux processus de gestion, le premier processus est la capitalisation des connaissances bas{\'e}e sur une t{\^a}che industrielle. Nous avons utilis{\'e} une combinaison de deux m{\'e}thodologies : une m{\'e}thodologie d{\textquoteright}ing{\'e}nierie de connaissances CommonKADS et une m{\'e}thodologie d{\textquoteright}{\'e}licitation des connaissances MACTAK. Dans la phase de mod{\'e}lisation, nous avons utilis{\'e} deux diff{\'e}rentes techniques de mod{\'e}lisation, une mod{\'e}lisation bas{\'e}e sur les connaissances d{\textquoteright}expert et la deuxi{\`e}me une repr{\'e}sentation ontologique. Ce mod{\`e}le facilite la capture des connaissances d{\textquoteright}experts et transforme les connaissances tacites en explicites avec une maximisation des r{\`e}gles de production. Le deuxi{\`e}me processus concerne le partage des connaissances {\`a} base d{\textquoteright}une ontologie des T{\^a}ches Manufacturi{\`e}res MATO en identifiant un ensemble des concepts de fabrication et leurs relations, cette ontologie propos{\'e}e facilite le partage des connaissances entre les t{\^a}ches de fabrication et aide {\`a} partager et {\`a} r{\'e}utiliser les connaissances durant l{\textquoteright}ex{\'e}cution des t{\^a}ches. Ensuite, une application propos{\'e}e pour le diagnostic de syst{\`e}me d{\textquoteright}alarme dans une centrale thermique a {\'e}t{\'e} pr{\'e}sent{\'e}e pour d{\'e}montrer l{\textquoteright}importance et l{\textquoteright}apport de l{\textquoteright}ontologie.

},
	url = {http://eprints.univ-batna2.dz/1944/},
	author = {Titah, Mawloud}
}
@mastersthesis {7303,
	title = {Mod{\'e}lisation de projet de conception de produit pour att{\'e}nuer les risques dans les processus de d{\'e}veloppement: {\'E}tude et analyse de la performance industrielle },
	year = {2021},
	type = {Doctorat en sciences  },
	author = {Bencherif, Fateh}
}
@mastersthesis {7302,
	title = {Un Syst{\`e}me de Reconnaissance de Formes Bas{\'e} sur une Approche Multi-Classifieurs},
	year = {2021},
	type = {Doctorat en sciences  },
	abstract = {
	Am{\'e}liorer la performance d{\textquoteright}un syst{\`e}me de reconnaissance de formes fait l{\textquoteright}objet de recherche dans de nombreuses disciplines. On obtient cette am{\'e}lioration par l{\textquoteright}optimisation dans les diff{\'e}rents partis du syst{\`e}me de reconnaissance de formes : les pr{\'e}traitements, l{\textquoteright}extraction des param{\`e}tres caract{\'e}ristique (primitives), la classification. Les travaux de recherche pr{\'e}sent{\'e}s dans cette th{\`e}se abordent le probl{\`e}me de la reconnaissance des chiffres arabe imprim{\'e}s et manuscrits. L{\textquoteright}objectif principal de ce travail est l{\textquoteright}am{\'e}lioration de la performance en terme de taux de reconnaissance par l{\textquoteright}application d{\textquoteright}un syst{\`e}me multi classificateur (MCS). Diff{\'e}rents classifieurs sont utilis{\'e}s (KPPV, PMC, SVM, LDA, Arbre de d{\'e}cision, Na{\"\i}ve Bayesien, Pseudo inverse), {\`a} l{\textquoteright}aide de diff{\'e}rents types de vecteurs de caract{\'e}ristiques extraite de l{\textquoteright}image. Enfin, comme les performances de MCS d{\'e}pendent des performances des classifieurs appliqu{\'e}e (les performances individuelles des classifieurs), et comme la performance d{\textquoteright}un classifieur d{\'e}pend ainsi des caract{\'e}ristiques utilis{\'e} l{\textquoteright}optimisation de l{\textquoteright}extractions des primitives pertinentes est {\'e}galement abord{\'e}e dans la th{\`e}se. Nous avons r{\'e}alis{\'e} plusieurs simulations pour {\'e}prouver les classifications, en introduisant des am{\'e}liorations dans les param{\`e}tres caract{\'e}ristiques et en faisant des combinaisons de classifieurs. Dans une premi{\`e}re partie, nous montrons l{\textquoteright}int{\'e}r{\^e}t de l{\textquoteright}utilisation des param{\`e}tres caract{\'e}ristique pertinente, {\`a} l{\textquoteright}aide des classifieur individuelle (s{\'e}par{\'e}, ind{\'e}pendants) comme source d{\textquoteright}inspiration pour la conception de nouveaux param{\`e}tres. Nous proposons en particulier une am{\'e}lioration de primitives pour la caract{\'e}risation des chiffres. On montre qu{\textquoteright}il est possible de d{\'e}velopper une solution efficace, {\`a} moindre co{\^u}t en terme de r{\'e}duction de vecteur caract{\'e}ristique et transformation g{\'e}om{\'e}trique. Donc, Le syst{\`e}me d{\'e}velopp{\'e} s{\textquoteright}articule autour de quatre modules distincts. Un module de pr{\'e}traitements, un d{\textquoteright}extraction des param{\`e}tres caract{\'e}ristique, un module de reconnaissance (classification) et un module de combinaison de classifieurs. Ce dernier est charg{\'e} de fusionner les sorties (d{\'e}cisions) de chaque classifieur basant sur des m{\'e}thodes (r{\`e}gles) de fusion. Les r{\'e}sultats obtenus sur les bases des chiffres imprim{\'e}s et la base MNIST des chiffres manuscrits sont prometteurs. Cette th{\`e}se apporte quelques contributions pour faire avancer notre compr{\'e}hension dans ce domaine de recherche en pleine expansion

},
	url = {http://eprints.univ-batna2.dz/1905/},
	author = {Derdour, Khedidja}
}
@mastersthesis {7300,
	title = {Supervision de Robot Manipulateur virtuel par Les r{\'e}seaux de neurones et les r{\'e}seaux de Petri},
	year = {2021},
	type = {Doctorat LMD},
	abstract = {
	Dans ce travail de th{\`e}se, nous avons propos{\'e} un syst{\`e}me de supervision appliqu{\'e} sur un robot manipulateur {\`a} deux degr{\'e}s de libert{\'e}. La supervision est utilis{\'e}e pour assurer la reconfiguration en temps r{\'e}el du robot. Dans ce syst{\`e}me nous avons utilis{\'e} une nouvelle m{\'e}thode de d{\'e}tection de d{\'e}faut (FD) de frottement visqueux du robot supervis{\'e} combin{\'e}e avec un module de commande tol{\'e}rante aux d{\'e}fauts (FTC).Le premier module, bas{\'e} sur une m{\'e}thode de traitement appliqu{\'e}e sur des r{\'e}sidus, va permettre la d{\'e}tection de d{\'e}faut pour bien estimer les corrections n{\'e}cessaires du deuxi{\`e}me module. Une {\'e}valuation de l{\textquoteright}effet de d{\'e}faut durant la supervision a {\'e}t{\'e} faite. Par ailleurs, le protocole TCP pour le transfert des donn{\'e}es entre le robot superviseur et le robot supervis{\'e} a {\'e}t{\'e} utilis{\'e}. Les r{\'e}sultats de simulation montrent que la m{\'e}thode propos{\'e}e corrige l{\textquoteright}effet de d{\'e}faut en utilisant les donn{\'e}es qui arrivent d{\textquoteright}un robot superviseur {\`a} distance. Ensuite, nous avons propos{\'e} une impl{\'e}mentation mat{\'e}rielle sur cible FPGA de l{\textquoteright}algorithme de supervision dont le but est de valider notre contribution et d{\textquoteright}assurer un traitement en temps r{\'e}el dans le cas o{\`u} il y a des robots r{\'e}els. Par ailleurs, une {\'e}tude comparative entre les performances des deux impl{\'e}mentations a {\'e}t{\'e} effectu{\'e}e

},
	url = {https://www.theses-algerie.com/2211862124311993/these-de-doctorat/universite-mustapha-ben-boulaid---batna-2/supervision-d-un-robot-manipulateur-virtuel-par-les-r\%C3\%A9seaux-de-neurones},
	author = {Ghrieb, Abdel-Ouahab}
}
@mastersthesis {7299,
	title = {Impl{\'e}mentation et d{\'e}ploiement d\&rsquo;une nouvelle approche {\`a} base de Lean Six Sigma pour le d{\'e}veloppement et l\&rsquo;am{\'e}lioration de la durabilit{\'e} de la production des petites et moyennes entreprises},
	year = {2021},
	type = {Doctorat LMD},
	abstract = {
	L{\textquoteright}objectif de ce travail de th{\`e}se et de d{\'e}velopper des nouvelles approches permettant aux petites et moyennes entreprises d{\textquoteright}am{\'e}liorer les performances de leur processus de fabrication. Nous avons d{\'e}velopp{\'e} trois approches aspir{\'e}es du Lean Six Sigma (LSS) pour l{\textquoteright}am{\'e}lioration de la production dans un contexte conventionnel et classique d{\textquoteright}une part et d{\textquoteright}autre part dans un contexte de production durable. Dans la premi{\`e}re approche nous avons propos{\'e} une approche Lean Six Sigma conventionnelle pour {\'e}valuer et suivre la comp{\'e}titivit{\'e} d{\textquoteright}une PME en fonction des r{\'e}sultats obtenus par la m{\'e}thode VSM. Dans la deuxi{\`e}me approche, nous avons propos{\'e} une nouvelle extension de l{\textquoteright}approche LSS vers le contexte de la production durable en incorporant des algorithmes multicrit{\`e}res quantitatives. Cette approche nous a permis de surmonter quelques barri{\`e}res au niveau du processus de l{\textquoteright}application du LSS. Dans La troisi{\`e}me approche nous avons pr{\'e}sent{\'e} une am{\'e}lioration de l{\textquoteright}approche LSS qui vise {\`a} montrer l{\textquoteright}effet positif des algorithmes multicrit{\`e}res qualitatives flous pour surmonter certaines barri{\`e}res du Lean Six Sigma li{\'e}es aux phases d{\textquoteright}analyse et d{\textquoteright}am{\'e}lioration de l{\textquoteright}{\'e}tat actuel des processus de fabrication. Les approches propos{\'e}es sont appliqu{\'e}es dans deux entreprise alg{\'e}riennes pour am{\'e}liorer et contr{\^o}ler la durabilit{\'e} de leurs processus de fabrication.

},
	url = {http://eprints.univ-batna2.dz/1941/},
	author = {Soltani, Mohyeddine}
}
@mastersthesis {7298,
	title = {Pronostic industriel distribu{\'e} des syst{\`e}mes complexes {\`a} base d\&$\#$39;agents},
	year = {2021},
	type = {Doctorat LMD},
	abstract = {
	Les syst{\`e}mes industriels deviennent plus complexes en raison, notamment, de leur taille croissante et de l{\textquoteright}int{\'e}gration des nouvelles technologies. En vieillissant, ces syst{\`e}mes deviennent plus vuln{\'e}rables aux d{\'e}faillances et leurs activit{\'e}s de maintenance sont difficiles et co{\^u}teuses. Cette situation, combin{\'e}e aux exigences de productivit{\'e}, de croissance des b{\'e}n{\'e}fices, de disponibilit{\'e} op{\'e}rationnelle et de s{\'e}curit{\'e}, pousse les praticiens et les chercheurs {\`a} d{\'e}velopper des outils et des m{\'e}thodes innovants. Les activit{\'e}s de maintenance constituent l{\textquoteright}un des leviers possibles. En entretenant le syst{\`e}me, nous pouvons r{\'e}duire les co{\^u}ts globaux de son cycle de vie, augmenter sa disponibilit{\'e}, am{\'e}liorer la s{\'e}curit{\'e} des op{\'e}rateurs et r{\'e}duire les incidents environnementaux. Les t{\^a}ches de maintenance peuvent {\^e}tre soit curatives, soit pr{\'e}ventives. Cependant, cela n{\textquoteright}est pas encore suffisant parce que les pi{\`e}ces de rechange ne sont pas disponibles ou pas suffisantes au moment de la panne ou simplement parce que les ressources n{\'e}cessaires (les responsables de la maintenance) sont occup{\'e}es. Une "meilleure" solution pourrait alors {\^e}tre une maintenance pr{\'e}dictive, qui peut {\^e}tre effectu{\'e}e dans le framework du pronostic. Dans ce cadre nous essayons de pr{\'e}voir l{\textquoteright}{\'e}tat de sant{\'e} du syst{\`e}me, puis on planifie les actions appropri{\'e}es en fonction des r{\'e}sultats des pr{\'e}visions. Ce travail s{\textquoteright}inscrit dans au domaine du pronostic de la PHM et de la gestion de la sant{\'e}. Les techniques de PHM visent {\`a} pr{\'e}dire la dur{\'e}e de vie restante de l{\textquoteright}{\'e}quipement. Cependant, elles ont eu tendance {\`a} {\^e}tre utilis{\'e}es dans un contexte local avec une int{\'e}gration limit{\'e}e des solutions distribu{\'e}es. Dans les syst{\`e}mes complexes, le comportement {\'e}mergent est plus compliqu{\'e} que la somme des comportements de leurs parties constitutives. Ce comportement implique la propagation de d{\'e}fauts entre les parties et n{\'e}cessite des informations sur la fa{\c c}on dont les parties sont li{\'e}es. Dans cette th{\`e}se, nous proposons une approche multi-agent pour la pr{\'e}diction de la RUL au niveau du syst{\`e}me. Ensuite, l{\textquoteright}approche propos{\'e}e est {\'e}tendue {\`a} la PHM m{\'e}dicale avec une {\'e}tude de cas

},
	url = {http://eprints.univ-batna2.dz/1964/},
	author = {Benaggoune Khaled}
}
@mastersthesis {7297,
	title = {Proposition d\&rsquo;un syst{\`e}me distribu{\'e} de diagnostic et pronostic bas{\'e} sur les services Web et Extr{\^e}me Learning Machine},
	year = {2021},
	type = {Doctorat LMD},
	abstract = {
	Cette th{\`e}se traite de l{\textquoteright}utilisation d{\textquoteright}un outil de Machine Learning appel{\'e} "Extreme Learning Machine" dans le domaine du pronostic et de l{\textquoteright}{\'e}valuation de la sant{\'e} (e.g. estimation de la dur{\'e}e de vie utile restante (RUL)). Cet algorithme implique des m{\'e}thodes de programmation lin{\'e}aires qui reposent principalement sur les moindres carr{\'e}s dans les paradigmes non lin{\'e}aires des r{\'e}seaux de neurones artificiels pour produire des estimateurs de sant{\'e} tr{\`e}s rapides et pr{\'e}cis. D{\textquoteright}autres parts, cette {\'e}tude vise {\`a} distribuer le syst{\`e}me de pr{\'e}diction sur le web {\`a} l{\textquoteright}aide de services Web pour r{\'e}soudre les probl{\`e}mes de r{\'e}partition g{\'e}ographique de surveillance d{\'e}centralis{\'e}e. Sur la base de ce nouvel outil, de nombreux algorithmes d{\textquoteright}apprentissage ont {\'e}t{\'e} d{\'e}velopp{\'e}s dans le cadre de ce travail et compar{\'e}s {\`a} d{\textquoteright}autres algorithmes pr{\'e}sents dans la litt{\'e}rature en termes de temps et de pr{\'e}cision. La plupart des algorithmes d{\'e}velopp{\'e}s sont inspir{\'e}s des th{\'e}ories r{\'e}centes de Deep Learning et ce en raison de leur bonne r{\'e}putation. Les donn{\'e}es {\'e}tudi{\'e}es dans ce travail de recherche sont tir{\'e}es du logiciel C-MAPSS simulateur de syst{\`e}me de propulsion a{\'e}rodynamique d{\'e}velopp{\'e} par la NASA. Les r{\'e}sultats obtenus ont prouv{\'e} l{\textquoteright}efficacit{\'e} des nouveaux algorithmes et les recommandent pour une utilisation future dans le domaine de l{\textquoteright}{\'e}valuation de la sant{\'e}.

},
	url = {http://eprints.univ-batna2.dz/1915/},
	author = {Berghout, Tarek}
}
@proceedings {7284,
	title = {Monitoring The Product Quality Using The Iiot Data},
	journal = {First International Conference On Energy, Thermofluids And Materials Engineering, ICETME 2021 Held Online From 18 To 20 December, 2021.},
	year = {2021},
	address = { Biskra Algerie},
	author = {Baguigui, S and Aksa,  Karima and Habchi, A-S}
}
@proceedings {7278,
	title = {knowledge sharing via the blockchain technology},
	journal = {EKNOW 2021,},
	year = {2021},
	address = { Nice, France},
	author = {Samia Aitouche}
}
@conference {7269,
	title = {Hybrid Heuristic Optimization of an Integrated Production Distribution System with Stock and Transportation Costs},
	booktitle = {International Conference on Computing Systems and Applications},
	year = {2021},
	publisher = { Lecture Notes in Networks and Systems book series },
	organization = { Lecture Notes in Networks and Systems book series },
	abstract = {
	In this paper we address the integration of two-level supply chain with multiple items, production facility and retailers{\textquoteright} demand over a considered discrete time horizon. This two-level production distribution system features capacitated production facility supplying several retailers located in the same region. If production does take place, this process incurs a fixed setup cost as well as unit production costs. In addition, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles and routing costs are incurred. This work aims at implementing a solution to minimize the sum of the costs at the production facility and the retailers. The methodology adopted to tackle this issue is based on a hybrid heuristic, greedy and genetic algorithms that uses strong formulation to provide a good solution of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-69418-0_11},
	author = {Bensakhria, Mohamed and Samir Abdelhamid}
}
@proceedings {7268,
	title = {Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES{\textquoteright}21) },
	year = {2021},
	address = {Adrar, Algeria},
	author = {Hadjidj, Nadjiha~ and Meriem Benbrahim and Ounnas, Djamel and Leila-Hayet Mouss}
}
@proceedings {7267,
	title = {Monitoring The Product Quality Using The Iiot Data First International Conference On Energy, Thermofluids And Materials Engineering},
	journal = {First International Conference On Energy, Thermofluids And Materials Engineering (ICETME 2021), Held Online From 18 To 20 December, },
	year = {2021},
	address = {Biskra, Algerie},
	author = {Baguigui, S and Aksa,  Karima and Habchi, A-S}
}
@conference {7253,
	title = {Towards Digital Twins Driven Breast Cancer Detection},
	year = {2021},
	publisher = {Lecture Notes in Networks and Systems },
	organization = {Lecture Notes in Networks and Systems },
	abstract = {
	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{\textquoteright}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{\textquoteright}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{\textquoteright}s behavior with whom it is connected.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7},
	author = {Meraghni, Safa and Benaggoune, Khaled and Al Masry, Zeina and Terrissa, Sadek-Labib and Devalland, Christine and Zerhouni, Noureddine}
}
@article {7252,
	title = {Development of an Industrial Application with Neuro-Fuzzy Systems},
	journal = {INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS },
	volume = {8},
	year = {2021},
	abstract = {
	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 "NFPROG" (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.

},
	url = {https://www.naun.org/main/NAUN/fuzzy/2021/a062017-003(2021).pdf},
	author = {Benaicha Sonia and Zermane, Hannane and Leila-Hayet Mouss and Bencherif, Fateh}
}
@proceedings {7251,
	title = {Automation and fuzzy control of a manufacturing system},
	journal = {INTERNATIONAL JOURNAL OF FUZZY SYSTEMS and ADVANCED APPLICATIONS},
	year = {2021},
	abstract = {
	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.

},
	url = {https://www.naun.org/main/NAUN/fuzzy/2021/a082017-004(2021).pdf},
	author = {Hanane Zermane and Leila-Hayet Mouss and Sonia Benaicha}
}
@proceedings {7250,
	title = {Sample Entropy with One-Stage Variational Mode Decomposition for Hourly Solar Irradiance Forecasting},
	journal = {The First International Conference on Renewable  Energy Advanced Technologies and Applications},
	year = {2021},
	address = {Adrar, Algeria},
	abstract = {
	Solar radiation forecasting is an important technology that is necessary to increase the performance, management, and control of modern electrical grids. It allows energy regulators to estimate the near-future output power of solar power plants, and can help to reduce the effects of power fluctuations on the electricity grid, thus increasing the overall efficiency and power quality of those plants [1]. However, the variable nature of solar irradiance poses a challenge in the exploitation of solar energy. In this context, forecasting techniques are now essential to ensure sustainable, reliable, and cost-effective solar energy production [2]. This paper proposes a hybrid machine learning model to forecast Global Horizontal Irradiance (GHI) in the short term (1-hour ahead). The experimental assessment of the model is done on the basis of an experimental dataset of 11 years of hourly GHI measurements from the BSRN Tamanrasset station in Algeria. The general framework of the proposed model is explained in Figure 1, and its main steps are summarized as follows:

},
	url = {https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA{\textquoteright}21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf},
	author = {Zemouri, Nahed and Bouzgou, Hassen and Gueymard,Christian A}
}
@proceedings {7249,
	title = {Deep Long Short-Term Memory with Separation Models for Direct Normal Irradiance Forecasting: Application to Tamanrasset, Algeria},
	journal = {The First International Conference on Renewable  Energy Advanced Technologies and Applications},
	year = {2021},
	address = {Adrar, Algeria},
	abstract = {
	Solar energy is a vast and clean resource that can be harnessed with great benefit for humankind. It is still currently difficult, however, to convert it into electricity in an efficient and cost-effective way. One of the ways to produce energy is the use of various focusing technologies that concentrate the direct normal irradiance (DNI) to produce power through highly-efficient modules or conventional turbines. Concentrating technologies have great potential over arid areas, such as Northern Africa. A serious issue is that DNI can vary rapidly under broken-cloud conditions, which complicate its forecasts [1]. In comparison, the global horizontal irradiance (GHI) is much less sensitive to cloudiness. As an alternative to the direct DNI forecasting avenue, a possibility exists to derive the future DNI indirectly by forecasting GHI first, and then use a conventional separation model to derive DNI. In this context, the present study compares four of the most well-known separation models of the literature and evaluates their performance at Tamanrasset, Algeria, when used in combination with a new deep learning machine methodology introduced here to forecast GHI time series for short-term horizons (15-min). The proposed forecast system is composed of two separate blocs. The first bloc seeks to forecast the future value of GHI based on historical time series using the Long Short-Term Memory (LSTM) technique with two different search algorithms. In the second bloc, an appropriate separation (also referred to as {\textquotedblleft}diffuse fraction{\textquotedblright} or {\textquotedblleft}splitting{\textquotedblright}) model is implemented to extract the direct component of GHI. LSTMs constitute a category of recurrent neural network (RNN) structure that exhibits an excellent learning and predicting ability for data with time-series sequences [2]. The present study uses and evaluates the performance of two novel and competitive strategies, which both aim at providing accurate short-term GHI forecasts: Unidirectional LSTM (UniLSTM) and Bidirectional LSTM (BiLSTM). In the former case, the signal propagates backward or forward in time, whereas in the latter case the learning algorithm is fed with the GHI data once from beginning to the end and once from end to beginning. One goal of this study is to evaluate the overall advantages and performance of each strategy. Hence, this study aims to validate this new approach of obtaining 15- min DNI forecasts indirectly, using the most appropriate separation model. An important step here is to determine which model is suitable for the arid climate of Tamanrasset, a high-elevation site in southern Algeria where dust storms are frequent. Accordingly, four representative models have been selected here, based on their validation results [3] and popularity: 1) Erbs model [4]; 2) Maxwell{\textquoteright}s DISC model [5]; 3) Perez{\textquoteright}s DIRINT model [6]; and 4) Engerer2 model [7]. In this contribution, 1-min direct, diffuse and global solar irradiance measurements from the BSRN station of Tamanrasset are first quality-controlled with usual procedures [3, 8] and combined into 15-min sequences over the period 2013{\textendash}2017. The four separation models are operated with the 15-min GHI forecasts obtained with each LSTM model, then compared to the 15-min measured DNI sequences. Table 1 shows the results obtained by the two forecasting strategies, for the experimental dataset.

},
	url = {https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA{\textquoteright}21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf},
	author = {Atmani, Hanane and Bouzgou, Hassen and Gueymard,Christian A}
}
@proceedings {7248,
	title = {Analysis and Design of Modified IncrementalConductance-BasedMPPT Algorithm for Photovoltaic System},
	journal = {The First International Conference on Renewable  Energy Advanced Technologies and Applications (ICREATA{\textquoteright}21 ), October 25-27},
	year = {2021},
	address = {October 25-27},
	abstract = {
	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.

},
	url = {https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA{\textquoteright}21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf},
	author = {Hadjidj, Nadjiha and Meriem Benbrahim and Ounnes, D and Leila-Hayet Mouss}
}
@proceedings {7247,
	title = {Techno-Economic Analysis of a Stand-Alone Hybrid Renewable Energy System for Residentiel Electrification in Tamanrasset, Algeria},
	journal = { International Conference on Renewable Energy Advanced Technologie and Applications (ICREATA{\textquoteright}21)},
	year = {2021},
	address = {Adrar, Algeria},
	author = {Zereg, Hadda and Bouzgou, Hassen}
}
@proceedings {7246,
	title = {Residual Networks with Long Short Term Memory for Hourly Solar Radiation Forecasting},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES{\textquoteright}21) },
	year = {2021},
	address = {Tipasa, Algeria},
	abstract = {
	This paper describes a new approach for hourly global solar radiation forecasting based on a hybrid artificial neural network technique combining a residual neural network (RESNET) for powerful feature extraction of the most relevant moments of the past, and a long short-term memory (LSTM) technique for efficient projection into the future. Based on 11 years of solar irradiance measurements at Tamanrasset, Algeria, four evaluation metrics are used to demonstrate the efficiency of the proposed method: coefficient of determination (R{\texttwosuperior}), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These metrics are also used to evaluate the performance of the model in comparison with two existing forecasting models used as benchmark: a particular technique of convolutional neural network (CNN) called 1-dimensional convolutional neural network (1D-CNN) and a conventional LSTM. The present results indicate that the proposed RESNET-LSTM model outperforms the other models in terms of all statistical indicators.

},
	url = {https://www.researchgate.net/publication/355916572_Residual_Networks_with_Long_Short_Term_Memory_for_Hourly_Solar_Radiation_Forecasting},
	author = {Louchene, Houssem-Eddine and Bouzgou, Hassen and Gueymard, Chris}
}
@conference {7245,
	title = {Multi-Objective Optimization of Stand-Alone Hybrid Renewable Energy System for Rural Electrification in Algeria},
	booktitle = {International Conference on Artificial Intelligence in Renewable Energetic Systems(IC-AIRES{\textquoteright}21 )},
	volume = {361},
	year = {2021},
	pages = {21{\textendash}33},
	publisher = { Lecture Notes in Networks and Systems},
	organization = { Lecture Notes in Networks and Systems},
	address = {Tipasa, Algeria  },
	abstract = {
	This paper proposes an optimum design of a diesel/PV/wind/battery hybrid renewable energy system (HRES) for rural electrification in a remote district in Tamanrasset, Algeria. In this study, a particle swarm optimization algorithm (PSO) has been proposed to solve a multi-objective optimization problem, which was created by carrying out simultaneously, the cost of energy (COE) minimization while maximizing the reliability of power supply described as the loss of power supply probability (LPSP) and a renewable fraction (RF). The simulation results show that the PV/WT/DG/BT is the best economic configuration with a reasonable annual cost of the optimal system (ACS) which is about 7798.71\&nbsp;$ and the COE equal to 0.79\&nbsp;$/kWh for an LPSP\&nbsp;=\&nbsp;0.01\%, where the ten households are 0.99 \% satisfied by renewable energy sources.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-92038-8_3},
	author = {Zereg, Hadda and Bouzgou, Hassen}
}
@proceedings {7244,
	title = {Cost Optimisation for Wheelchair Redesign},
	journal = {1st International Conference On Cyber Management And Engineering (CyMaEn), 26-28 May},
	year = {2021},
	address = {Hammamet, Tunisia},
	abstract = {
	Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don{\textquoteright}t meet international standards. However, developing countries are obliged to purchase products from developed countries that don{\textquoteright}t necessarily meet all user{\textquoteright}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.

},
	url = {https://ieeexplore.ieee.org/document/9497281},
	author = {Bellal, Salah-Eddine and Leila-Hayet Mouss and Sahnoun, M{\textquoteright}hammed and Messaadia, Mourad}
}
@conference {7243,
	title = {A Comparative Study Between Data-Based Approaches Under Earlier Failure Detection},
	booktitle = {ICCIS2020 },
	volume = {204},
	year = {2021},
	pages = {235{\textendash}239},
	publisher = {Lecture Notes in Networks and Systems},
	organization = {Lecture Notes in Networks and Systems},
	address = {India},
	abstract = {
	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 {\textquotedblleft}time-varying{\textquotedblright} 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.

},
	url = {https://link.springer.com/chapter/10.1007/978-981-16-1089-9_20},
	author = {Hadjidj, Nadjiha and Meriem Benbrahim and Berghout, Tarek and Leila-Hayet Mouss}
}
@proceedings {7242,
	title = {Machine Learning for Photovoltaic Systems Condition Monitoring: A Review},
	journal = {47th Annual Conference of the IEEE Industrial Electronics Society, IECON },
	year = {2021},
	address = {Toronto, Canada},
	abstract = {
	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.

},
	url = {https://ieeexplore.ieee.org/document/9589423},
	author = {Berghout, Tarek and Mohamed Benbouzid and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Leila-Hayet Mouss}
}
@proceedings {7241,
	title = {Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning},
	journal = {47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021},
	year = {2021},
	address = {Toronto, Canada},
	abstract = {
	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.

},
	url = {https://ieeexplore.ieee.org/document/9589114},
	author = {Berghout, Tarek and Mohamed Benbouzid and Le{\"\i}la-Hayet Mouss}
}
@article {7233,
	title = {Principles of Biology in Service of Technology: DNA Computing},
	journal = {Algerian Journal of Environmental Science and Technology (ALJEST)},
	volume = {7},
	number = {20},
	year = {2021},
	abstract = {
	\&nbsp;As commonly known that living beings cannot survive without natural sources available on earth, technology is no exception; it cannot develop without the inspiring help given by the same nature.



	The field of biology has extensively participated in the computing field through the "code of life" DNA (Deoxyribo Nucleic Acid) since it was discovered by Adelman in the past century. This combination gave birth to DNA Computing, which is a very interesting new aspect of biochemistry. It works massively parallel with high energy efficiency, and requiring almost no space.



	The field of molecular computing is still new and as the field progresses from concepts to engineering, researchers will address these important issues.



	\&nbsp;By the use of encoding data into DNA strands, many NP-complete problems have been solved and many new efficient techniques have been proposed in cryptography field.



	The aim of this paper is to give an overview of bio-inspired system and to summarize the great role of DNA molecule in servicing of the technology field.

},
	url = {https://www.aljest.net/index.php/aljest/article/view/292},
	author = {Aksa,  Karima}
}
@article {7229,
	title = {A Hybrid Methodology based on heuristic algorithms for a production distribution system with routing decisions},
	journal = {. BizInfo (Blace) Journal of Economics, Management and Informatics},
	volume = {12},
	number = {2},
	year = {2021},
	pages = {1-22},
	abstract = {
	In this paper, we address the integration of a two-level supply chain with multiple items. This two-level production-distribution system features a capacitated production facility supplying several retailers located in the same region. If production does occur, this process incurs a fixed setup cost and unit production costs. Besides, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles, routing costs incurred. This work aims to implement a minimization solution that reduces the total costs in both the production facility and retailers. The methodology adopted based on a hybrid heuristic, greedy and genetic algorithm uses strong formulation to provide a suitable solution of a guaranteed quality that is as good or better than those provided by the MIP optimizer. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

},
	url = {https://scindeks-clanci.ceon.rs/data/pdf/2217-2769/2021/2217-27692102001B.pdf},
	author = {Bensakhria, Mohamed and Samir Abdelhamid}
}
@article {7228,
	title = {Dynamic planning design of three level distribution network with horizontal and vertical exchange},
	journal = { Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central ware},
	year = {2021},
	abstract = {
	\&nbsp;Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.

},
	author = {Benfriha, Abdennour -Ilyas and Triqui-Sari, Lamia and Bougloula, Aimade-Eddine and BENNEKROUF, Mohammed}
}
@article {7216,
	title = {Vers une Nouvelle R{\'e}volution Industrielle : Industrie 4.0},
	journal = {Revue M{\'e}diterran{\'e}enne des T{\'e}l{\'e}communications},
	volume = {11},
	number = {1},
	year = {2021},
	abstract = {
	La quatri{\`e}me r{\'e}volution industrielle (nomm{\'e}e aussi l{\textquoteright}Internet Industriel des Objets) d{\'e}pend totalement sur la num{\'e}risation {\`a} travers l{\textquoteright}Internet des objets et les r{\'e}seaux virtuels. Cette r{\'e}volution qui {\'e}volue {\`a} un rythme exponentiel, et non plus lin{\'e}aire, va permettre la cr{\'e}ation d{\textquoteright}usines, d{\textquoteright}industries et de processus plus intelligents qui vont ensuite se traduire par une am{\'e}lioration de la flexibilit{\'e}, de la productivit{\'e} et une meilleure utilisation des ressources mat{\'e}rielles et humaines.



	Cet article est consacr{\'e} {\`a} introduire cette nouvelle r{\'e}volution industrielle (industrie4.0), les technologies majeurs participant {\`a} son apparition, leur b{\'e}n{\'e}fices attendus ainsi que leurs enjeux {\`a} prendre en consid{\'e}ration.

},
	url = {https://revues.imist.ma/index.php/RMT/article/view/24027},
	author = {Aksa,  Karima and BOUHAFNA, Khayreddine and BELAYATI, Souleymen and DJEGHAR, Dina}
}
@article {7215,
	title = {Developing a Web Platform for the Management of the Predictive Maintenance in Smart Factories},
	journal = {Wireless Personal Communications},
	volume = {119},
	year = {2021},
	pages = {pages1469{\textendash}1497},
	abstract = {
	Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning{\textellipsis}etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

},
	url = {https://link.springer.com/article/10.1007/s11277-021-08290-w},
	author = {Aksa,  Karima and Samia Aitouche and Bentoumi, Hamza and Sersa,  Ibrahim}
}
@article {7214,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	abstract = {
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

},
	author = {Mohamed Benbouzid and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {7197,
	title = {A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Instrumentation and Measurement (2022)},
	volume = {37},
	number = {2},
	year = {2021},
	abstract = {
	Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.

},
	url = {https://ieeexplore.ieee.org/document/9552475},
	author = {Berghout, Tarek and Le{\"\i}la-Hayet Mouss and Toufik Bentrcia and Mohamed Benbouzid}
}
@article {7188,
	title = {Towards Digital Twins Driven Breast Cancer Detection},
	journal = { Lecture Notes in Networks and Systems },
	volume = {285},
	year = {2021},
	pages = {87{\textendash}99},
	abstract = {
	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{\textquoteright}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{\textquoteright}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{\textquoteright}s behavior with whom it is connected.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-80129-8_7},
	author = {Meraghni, Safa and Benaggoune, Khaled and Al-Masry, Zeina and Terrissa, Labib and Devalland, Christine and Zerhouni, Noureddine}
}
@article {7187,
	title = {Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction},
	journal = {Energies},
	volume = {14},
	number = {8},
	year = {2021},
	abstract = {Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long{\textendash}short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.},
	url = {https://www.mdpi.com/1996-1073/14/8/2163},
	author = {Berghout, Tarek and Mohamed Benbouzid and Le{\"\i}la-Hayet Mouss}
}
@article {7186,
	title = {Detection of Flooding Attack on OBS Network Using Ant Colony Optimization and Machine Learning},
	journal = {Computaci{\'o}n y Sistemas},
	volume = {25},
	number = {2},
	year = {2021},
	abstract = {
	Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

},
	url = {https://www.scielo.org.mx/scielo.php?script=sci_arttext\&pid=S1405-55462021000200423},
	author = {Seddik, Mohamed-Takieddine and Ouahab Kadri and Bouarouguene, Chakir and Brahimi, Houssem}
}
@article {7185,
	title = {Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	abstract = {
	Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

},
	url = {https://ieeexplore.ieee.org/document/9610082},
	author = {Berghout, Tarek and Mohamed Benbouzid and Muyeen, S-M and Toufik Bentrcia and Le{\"\i}la-Hayet Mouss}
}
@article {7184,
	title = {A CNN-based methodology for breast cancer diagnosis using thermal images},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
	volume = {9},
	number = {2},
	year = {2021},
	pages = {131-145},
	abstract = {
	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

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/21681163.2020.1824685},
	author = {Zuluaga-Gomez, J and Al Masry, Z and Benaggoune, Khaled and Meraghni, Safa and Zerhouni, Noureddine}
}
@article {7183,
	title = {Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal},
	journal = {Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science},
	volume = {235},
	number = {20},
	year = {2021},
	abstract = {
	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.

},
	url = {https://journals.sagepub.com/doi/abs/10.1177/0954406220976154},
	author = {Gougam, Fawzi and Chemseddine, Rahmoune and Benazzouz, Djamel and Zerhouni, Noureddine and Benaggoune, Khaled}
}
@article {7182,
	title = {Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition},
	journal = { International Journal on Electrical Engineering and Informatics},
	volume = {13},
	number = {1},
	year = {2021},
	abstract = {
	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.

},
	url = {https://www.proquest.com/openview/72acbcf9bceca23bc38c7d1df8734acb/1?pq-origsite=gscholar\&cbl=316223},
	author = {Derdour, Khedidja and Leila-Hayet Mouss and Rafik Bensaadi}
}
@article {7181,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	abstract = {
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

},
	url = {https://ideas.repec.org/a/gam/jeners/v14y2021i18p5967-d639498.html},
	author = {Mohamed Benbouzid and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {7180,
	title = {Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	abstract = {
	To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

},
	url = {https://www.mdpi.com/1996-1073/14/19/6316},
	author = {Berghout, Tarek and Mohamed Benbouzid and Toufik Bentrcia and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Leila-Hayet Mouss}
}
@article {7178,
	title = {Industrial IoT middleware using a multi-agent system for consistency-based diagnostic in cement factory},
	journal = {International Journal of Autonomous and Adaptive Communications Systems},
	volume = {14},
	number = {3},
	year = {2021},
	abstract = {
	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{\textquoteright}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).

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJAACS.2021.117805},
	author = {Ayoub Benayache and Azeddine Bilami and Benaggoune, Khaled and Leila-Hayet Mouss}
}
@article {7177,
	title = {A deep supervised learning approach for condition-based maintenance of naval propulsion systems},
	journal = {Ocean Engineering},
	volume = {221},
	number = {1},
	year = {2021},
	abstract = {
	In the last years,\&nbsp;predictive maintenance\&nbsp;has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of\&nbsp;extreme learning machines\&nbsp;using data issued from a\&nbsp;propulsion system\&nbsp;simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.




},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Toufik Bentrcia and Elbouchikhi, Elhoussin and Mohamed Benbouzid}
}
@article {7163,
	title = {SOA-based distributed fault prognostic and diagnosis framework: an application for preheater cement cyclones},
	journal = {International Journal of Internet Manufacturing and Services},
	volume = {8},
	number = {1},
	year = {2021},
	abstract = {
	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.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJIMS.2021.114539},
	author = {Ouahiba Chouhal and Rafik Mahdaoui and Leila-Hayet Mouss}
}
@book {7144,
	title = {Proposition d\&rsquo;un tutoriel pour une usine apprenante (Textile de Batna)},
	year = {2021},
	edition = {universitaires europeennes},
	abstract = {
	Le contexte actuel de globalisation et de concurrence accrue a entrain{\'e} les firmes {\`a} reconsid{\'e}rer leurs strat{\'e}gies d{\textquoteright}internationalisation et {\`a} r{\'e}examiner les opportunit{\'e}s offertes et les risques associ{\'e}s. Les alliances strat{\'e}giques apparaissent ainsi comme des vecteurs privil{\'e}gi{\'e}s notamment par les firmes multinationales pour leurs nouvelles implantations.La communication et le travail d{\textquoteright}{\'e}quipe sont parmi les comp{\'e}tences les plus r{\'e}currents associ{\'e}s {\`a} une connaissance des sciences de l{\textquoteright}ing{\'e}nieur. Cependant, leur application n{\textquoteright}est pas simple, en raison de l{\textquoteright}absence d{\textquoteright}approche p{\'e}dagogique contribuant {\`a} d{\'e}velopper des connaissances fond{\'e}es sur l{\textquoteright}exp{\'e}rience.Dans ce travail nous avons d{\'e}fini qu{\textquoteright}est-ce qu{\textquoteright}une organisation apprenante, qu{\textquoteright}est-ce qu{\textquoteright}un tutoriel et pourquoi un tutoriel personnalis{\'e} dans un m{\'e}tier, ses diff{\'e}rentes formes et les d{\'e}marches pour l{\textquoteright}{\'e}laboration d{\textquoteright}un tutoriel.Apr{\`e}s nous avons donn{\'e} une pr{\'e}sentation de l{\textquoteright}entreprise qu{\textquoteright}est Textile Batna. Nous avons con{\c c}u un tutoriel pour l{\textquoteright}entreprise sous forme d{\textquoteright}un site Web. Pour cela, le langage UML a {\'e}t{\'e} utilis{\'e}. Les fonctionnalit{\'e}s du tutoriel ont {\'e}t{\'e} pr{\'e}sent{\'e}es.

},
	author = {Ag Hameyni, Abdoulmadjid and Samia Aitouche and Aksa,  Karima}
}
@article {7143,
	title = {Recherche Documentaire et Conception du M{\'e}moire},
	year = {2021},
	abstract = {
	Le 4{\`e}me semestre d{\textquoteright}un mast{\`e}re de recherche est consacr{\'e} {\`a} la r{\'e}alisation d{\textquoteright}un travail de recherche qui sera traduit par une conception et une r{\'e}daction d{\textquoteright}un m{\'e}moire de fin d{\textquoteright}{\'e}tudes et finalement la pr{\'e}paration d{\textquoteright}un expos{\'e} oral puis une soutenance.Le m{\'e}moire de fin d{\textquoteright}{\'e}tudes est une {\'e}tape tr{\`e}s importante dans la voie des {\'e}tudes universitaires, car sans elle, l{\textquoteright}{\'e}tudiant ne peut pas acqu{\'e}rir la qualit{\'e} de dipl{\^o}m{\'e}.Alors, dans ce petit livre vous pouvez trouver un petit guide sur: - La fa{\c c}on d{\textquoteright}organisation de votre m{\'e}moire. - La pr{\'e}sentation de votre soutenance. - La r{\'e}daction d{\textquoteright}un travail de recherche. - La pr{\'e}paration d{\textquoteright}un poster.Le 4{\`e}me semestre d{\textquoteright}un mast{\`e}re de recherche est consacr{\'e} {\`a} la r{\'e}alisation d{\textquoteright}un travail de recherche qui sera traduit par une conception et une r{\'e}daction d{\textquoteright}un m{\'e}moire de fin d{\textquoteright}{\'e}tudes et finalement la pr{\'e}paration d{\textquoteright}un expos{\'e} oral puis une soutenance.Le m{\'e}moire de fin d{\textquoteright}{\'e}tudes est une {\'e}tape tr{\`e}s importante dans la voie des {\'e}tudes universitaires, car sans elle, l{\textquoteright}{\'e}tudiant ne peut pas acqu{\'e}rir la qualit{\'e} de dipl{\^o}m{\'e}.Alors, dans ce petit livre vous pouvez trouver un petit guide sur:



	
		La fa{\c c}on d{\textquoteright}organisation de votre m{\'e}moire.
	
	
		La pr{\'e}sentation de votre soutenance.
	
	
		La r{\'e}daction d{\textquoteright}un travail de recherche.
	
	
		La pr{\'e}paration d{\textquoteright}un poster.
	

},
	author = {Aksa,  Karima}
}
@book {7142,
	title = {CAPTEURS INTELLIGENTS},
	year = {2021},
	edition = {Bookelis },
	abstract = {
	L{\textquoteright}{\'e}volution r{\'e}cente des moyens de la communication sans fil a permet la manipulation de l{\textquoteright}information {\`a} travers des unit{\'e}s de calculs portables, appel{\'e}s capteurs. Ces derniers, qui ont des caract{\'e}ristiques particuli{\`e}res, sont capables de r{\'e}colter, de traiter et de transmettre des donn{\'e}es environnementales d{\textquoteright}une mani{\`e}re autonome.Dans ce livre sont introduites les connaissances de base n{\'e}cessaires {\`a} la bonne compr{\'e}hension des capteurs intelligents, des r{\'e}seaux de capteurs et les diff{\'e}rents types protocoles de routage sp{\'e}cifiques aux r{\'e}seaux de capteurs. Nous fournirons ainsi les d{\'e}finitions g{\'e}n{\'e}ralement accept{\'e}es par ce type de r{\'e}seau. Nous aborderons {\'e}galement par une description succincte les principales caract{\'e}ristiques, contraintes et facteurs conceptuels qui surviennent dans ces r{\'e}seaux. Nous pr{\'e}senterons ensuite les diff{\'e}rentes orientations prises aux applications des r{\'e}seaux de capteurs.

},
	author = {Aksa,  Karima}
}
@article {5154,
	title = {Capacity Planning With Outsourcing Opportunities Under Reliability And Maintenance Constraints. Status},
	journal = {International Journal of Industrial and Systems Engineering},
	volume = {37},
	number = {3},
	year = {2021},
	pages = {382-409},
	abstract = {
	This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer{\textquoteright}s company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.

},
	url = {https://ideas.repec.org/a/ids/ijisen/v37y2021i3p382-409.html},
	author = {Haoues, Mohamed and Dahane, Mohammed and Mouss, Nadia-Kenza}
}
