@article {Asaad2022,
	title = {AsthmaKGxE: An asthma{\textendash}environment interaction knowledge graph leveraging public databases and scientific literature},
	journal = {Computers in Biology and Medicine},
	volume = {148},
	year = {2022},
	note = {cited By 1},
	abstract = {Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma{\textendash}Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog{\textquoteright}s most substantiated results are then integrated into the KG. Results: The resulting environmentally rich knowledge graph {\textquotedblright}AsthmaKGxE{\textquotedblright} aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma{\textendash}environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability: A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE). {\textcopyright} 2022 Elsevier Ltd},
	keywords = {allergen, Article, Artificial intelligence, Association reactions, asthma, Automated, automated pattern recognition, data base, data extraction, Databases, Diseases, environmental factor, Factual, factual database, Gene-Environment Interaction, Genetic factors, genotype environment interaction, Graphic methods, Heterogeneous disease, human, Humans, Knowledge graph, Knowledge graphs, Knowledge management, Language processing, Learning algorithms, Machine learning, Machine-learning, NAtural language processing, Natural language processing systems, Natural languages, nutritional assessment, obesity, pathophysiology, Pattern recognition, pharmacogenetics, physiological stress, psychological aspect, Public database, Scientific literature, socioeconomics, User interfaces},
	doi = {10.1016/j.compbiomed.2022.105933},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135702024\&doi=10.1016\%2fj.compbiomed.2022.105933\&partnerID=40\&md5=2022f55e1de0bbaa947ba6699af6a143},
	author = {Asaad, C. and Ghogho, M.}
}
@article {BenRabia2022,
	title = {Simulation-based analytics: A systematic literature review},
	journal = {Simulation Modelling Practice and Theory},
	volume = {117},
	year = {2022},
	note = {cited By 1},
	abstract = {Over time, Decision Support Systems have helped decision makers solve complex problems through Operational Research and Simulation. Nowadays, data explosion is having a profound effect on the ways in which many sectors operate. This advent of massive data gives rise to new concepts and requires new methods and analysis tools. In this paper, we highlight the role of simulation in Business Analytics. In a framework-based analytics, simulation is a technique that can be incorporated into predictive or prescriptive stage. For that, we have posed research questions to limit results to what give a comprehensive description of models, techniques and architectures used in the hybridization between simulation and business analytics. The presented analyses confirm that simulation remains an indispensable mechanism for adding value to analytics project and the coupling between the two techniques is in its embryonic phase. A conclusion presented prospects and future improvements found during the writing of the research. {\textcopyright} 2022 Elsevier B.V.},
	keywords = {Artificial intelligence, Big data, Business analytics, Competitive intelligence, Data Analytics, Data driven decision, Data-driven decision making, Decision making, Decision support systems, Decisions makings, Model-driven, Model-driven decision making, Predictive analytics, Simulation \& analytic ops, Systematic literature review, What-if Analysis},
	doi = {10.1016/j.simpat.2022.102511},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125647406\&doi=10.1016\%2fj.simpat.2022.102511\&partnerID=40\&md5=b8a2d6f53d7e2a53c6c1e7675770c74b},
	author = {Ben Rabia, M.A. and Bellabdaoui, A.}
}
@article {Hakkoum2021587,
	title = {Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {6},
	year = {2021},
	note = {cited By 11},
	pages = {587-599},
	abstract = {Breast cancer is the most common type of cancer among women. Thankfully, early detection and treatment improvements helped decrease the number of deaths. Data Mining techniques have always assisted BC tasks whether it is screening, diagnosis, prognosis, treatment, monitoring, and/or management. Nowadays, the use of Data Mining is witnessing a new era. In fact, the main objective is no longer to replace humans but to enhance their capabilities, which is why Artificial Intelligence is now referred to as Intelligence Augmentation. In this context, interpretability is used to help domain experts learn new patterns and machine learning experts debug their models. This paper aims to investigate three black-boxes interpretation techniques: Feature Importance, Partial Dependence Plot, and LIME when applied to two types of feed-forward Artificial Neural Networks: Multilayer perceptrons, and Radial Basis Function Network, trained on the Wisconsin Original dataset for breast cancer diagnosis. Results showed that local LIME explanations were instance-level interpretations that came in line with the global interpretations of the other two techniques. Global/local interpretability techniques can thus be combined to define the trustworthiness of a black-box model. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer classifications, cancer diagnosis, Computer aided diagnosis, cross validation, Data mining, Data-mining techniques, Diseases, Domain experts, early diagnosis, entropy, Explainability, Feature importance, Interpretability, Learn+, learning, learning algorithm, Lime, Machine learning, Multilayer neural networks, nerve cell, nonhuman, Partial dependence plot, perceptron, prediction, prognosis, Radial basis function networks, Treatment monitoring},
	doi = {10.1080/21681163.2021.1901784},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103249025\&doi=10.1080\%2f21681163.2021.1901784\&partnerID=40\&md5=78e1e57a62692bab2b39984182af7904},
	author = {Hakkoum, H. and Idri, A. and Abnane, I.}
}
@article {ElOuassif202150,
	title = {Classification techniques in breast cancer diagnosis: A systematic literature review},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {1},
	year = {2021},
	note = {cited By 12},
	pages = {50-77},
	abstract = {Data mining (DM) consists in analysing a~set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women~worldwide.~BC diagnosis is a~challenging medical task and many studies have attempted to apply classification techniques to it. The objective of the present study is to identify studies on classification techniques~in~BC diagnosis and to analyse them from~three perspectives: classification techniques used, accuracy of the classifiers, and comparison of performance. We performed a~systematic literature review (SLR) of 176 selected studies published between January~2000 and November~2018. The results show that, of the nine classification techniques investigated, artificial neural networks, support vector machines and decision trees were the most frequently used. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95\%, 95\% and 96\% respectively. Most of the selected studies (57.4\%) used datasets containing different types of images such as mammographic, ultrasound, and microarray images. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer diagnosis, cancer diagnosis, cause of death, Causes of death, Classification (of information), Classification technique, Comparison of performance, Computer aided diagnosis, data extraction, Data mining, data synthesis, decision tree, Decision trees, Diseases, human, k nearest neighbor, Machine learning, Medical fields, Medical informatics, Network support, Neural networks, publication, qualitative research, Quality control, support vector machine, Support vector machine classifiers, Support vector machines, Support vectors machine, Systematic literature review, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1811159},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098842973\&doi=10.1080\%2f21681163.2020.1811159\&partnerID=40\&md5=9a48998b1c44d263863efcfb25f9920f},
	author = {ElOuassif, B. and Idri, A. and Hosni, M. and Abran, A.}
}
@article {Cherkaoui-Semmouni202142,
	title = {Cryptanalysis of RSA Variants with Primes Sharing Most Significant Bits},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13118 LNCS},
	year = {2021},
	note = {cited By 0},
	pages = {42-53},
	abstract = {We consider four variants of the RSA cryptosystem with an RSA modulus N= pq where the public exponent e and the private exponent d satisfy an equation of the form ed- k(p2- 1 ) (q2- 1 ) = 1. We show that, if the prime numbers p and q share most significant bits, that is, if the prime difference | p- q| is sufficiently small, then one can solve the equation for larger values of d, and factor the RSA modulus, which makes the systems insecure. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Artificial intelligence, Computers, Continued fraction, Coppersmith{\textquoteright}s method, Cryptography, Lattice reduction, Most significant bit, Prime differences, Prime number, RSA cryptosystems, RSA moduli, RSA variant, S-method, Security of data},
	doi = {10.1007/978-3-030-91356-4_3},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121867699\&doi=10.1007\%2f978-3-030-91356-4_3\&partnerID=40\&md5=c9be6e3c3f4c8ed53f31b0bad5cba512},
	author = {Cherkaoui-Semmouni, M. and Nitaj, A. and Susilo, W. and Tonien, J.}
}
@article {Daosabah2021324,
	title = {Dynamic composition of services: an approach driven by the user{\textquoteright}s intention and context},
	journal = {International Journal of Web Engineering and Technology},
	volume = {16},
	number = {4},
	year = {2021},
	note = {cited By 1},
	pages = {324-354},
	abstract = {With the continuous development of services in ubiquitous systems, service orientation is becoming increasingly important in its structuring. As a result, the design and the development of applications are gradually migrating from a traditional model to a service-oriented model. In this regard, we propose through this work an approach for web service (WS) composition guided by the context and the intention, by which we suggest an architecture for service composition that reduces the complexity of the generated artificial intelligence (AI) planning problem and to ensure the interoperability of any system independently of the domain planners. The general idea of this approach is to conceive an intentional-contextual metamodel that will be transformed into an OWL model using OMG standards, which will be used to map the WS composition problem into AI planning problems. This article describes the architectural, conceptual, and strategic aspects to deal with the WS composition problem. {\textcopyright} 2021 Inderscience Enterprises Ltd.},
	keywords = {Artificial intelligence, Birds, Context- awareness, Intention, interoperability, Object management, Object management group, OMG, Ontology{\textquoteright}s, OWL-S, Quality of service, Services composition, Spring cloud framework, Web service composition, Web services},
	doi = {10.1504/IJWET.2021.122768},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124011575\&doi=10.1504\%2fIJWET.2021.122768\&partnerID=40\&md5=f9387d7a26ae641892da27f98947542d},
	author = {Daosabah, A. and Guermah, H. and Nassar, M.}
}
@conference {Marhraoui2021133,
	title = {An integrated human-AI Framework towards organizational agility and sustainable performance},
	booktitle = {Proceedings - 2021 International Conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2021},
	year = {2021},
	note = {cited By 0},
	pages = {133-139},
	abstract = {Companies are facing important challenges related to markets{\textquoteright} internationalization, regulatory restrictions and fierce competition especially during the COVID19 crisis. They should embrace change and be agile in order to prevent risks and seize opportunities quickly and efficiently. In this article, we examine how artificial intelligence (AI) can help companies to enhance their organizational agility. Based on two systematic literature reviews on the subject, we identified the weaknesses on relying only on a digital enabler or human resource (HR) practices. Thus, we propose a Framework integrating artificial intelligence and human practices in order to help companies in their efforts towards agility. This latter allows companies to adapt to new regulations in the society, to customers{\textquoteright} expectations and to environmental changes. Ultimately, agile companies can ensure a sustainable performance. {\textcopyright} 2021 IEEE.},
	keywords = {Artificial intelligence, Customer expectation, Decision making, Decisions makings, Environmental change, Environmental regulations, Human practices, Human resource practices, internationalisation, Organizational agility, Seizing, Sustainable development, Sustainable performance, Systematic literature review},
	doi = {10.1109/ICDATA52997.2021.00035},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119507801\&doi=10.1109\%2fICDATA52997.2021.00035\&partnerID=40\&md5=af6ef5c548c44726a28be19a747ef11f},
	author = {Marhraoui, M.A. and Idrissi, M.A.J. and El Manouar, A.}
}
@conference {Daosabah2021,
	title = {Integrating Context and Intention for Optimal Semantic Web Service Composition Using AI Planning},
	booktitle = {Proceedings - 4th International Conference on Advanced Communication Technologies and Networking, CommNet 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {This paper deals with an approach for the dynamic composition of web service (WS) based on the user{\textquoteright}s intention and context. Our idea to implement this approach is based on the combination of artificial intelligence (AI) planning techniques and the semantic aspect describing the web service that will be composed. To this end, we will transform the web service composition problem into an AI planning problem. First, conversion of web service semantically described in OWL and OWL-S to an alternative problem and domain description specified in the planning domain description language (PDDL) is made. Then, a planner (AI Planner) using the Genetic Algorithm (GA) features will be invoked, by adapting the PDDL planning language actions to the GA rules, in order to generate the most optimal composition plan (sequence of PDDL actions), which solves the conceived service composition problem, having a goal to achieve (the intention) according to the problem domain description (context). {\textcopyright} 2021 IEEE.},
	keywords = {And OWL, Artificial intelligence, Artificial intelligence planning, Birds, Composition problem, Domain description, Genetic algorithms, Ontology{\textquoteright}s, OWL-S, Planning domain description languages, Quality of service, Semantic Web, Semantic web service compositions, Services composition, Web services, Webs services, Websites},
	doi = {10.1109/CommNet52204.2021.9641912},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123984789\&doi=10.1109\%2fCommNet52204.2021.9641912\&partnerID=40\&md5=582b92b4fd74d9de0d27f804898dee05},
	author = {Daosabah, A. and Guermah, H. and Choukri, I. and Nassar, M.}
}
@conference {Ouarhim2020,
	title = {Business Process Modeling Notation Extension for Real Time Handling - Application to Novel Coronavirus (2019-nCoV) management process},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {The need of real-time enterprises increases according to our life style and customers{\textquoteright} satisfaction goal. Real-time business process is one of the important components of a real-time enterprise. For that, a formal definition of real-time concept and real-time process was indispensable. This work presents a formal definition of real-time concept, real-time ontology and a proposition of a real-time component with real-time attributes. This new dimension of time contains three components: latency time, acceptance interval and theoretical time. This definition gives a new vision of time, not just an ordinary vision as periods and calendar but as a response to our needs in real-time enterprises: real-time. Before the proposition of a real-time component for BPMN language, which we applicate to Novel Coronavirus (2019-nCoV) management process, as case study. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Business process modeling notations, Cloud computing, Customer satisfaction, Customers{\textquoteright} satisfaction, Formal definition, Management process, Real-time business, Real-time handling, Real-time process, Three component},
	doi = {10.1109/CloudTech49835.2020.9365900},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102630158\&doi=10.1109\%2fCloudTech49835.2020.9365900\&partnerID=40\&md5=efa760e4fe29ae0509729e730f662b56},
	author = {Ouarhim, A. and Lakhrouit, J. and Baina, K.}
}
@conference {Hafdi2020,
	title = {Formal Modeling and Validation of Micro Smart Grids Based on ReDy Architecture},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Several cities in the world are moving from traditional power grid to Smart Grids. In order to set up Smart Grids, we should be able to face many challenges related to reliability, scalability, dynamism, technological solutions, security, etc. In this paper, we propose a case study where we model a micro Smart Grid according to the ReDy architecture, which is intended for IoT applications. The ReDy architecture provides a base to implement a scalable, reliable, and dynamic IoT network ready to meet Smart Grid needs. In order to prove those requirements, we opted for formal modeling and validation approach using model checking techniques. This formal analysis is carried out using the CADP toolbox. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Binary alloys, Cloud computing, Dysprosium alloys, Electric power transmission networks, Formal analysis, Formal model, Internet of things, IOT applications, IOT networks, Microgrids, Model checking, Model-checking techniques, Network architecture, Power grids, Rhenium alloys, Smart grid, Smart power grids, Technological solution},
	doi = {10.1109/CloudTech49835.2020.9365923},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102629868\&doi=10.1109\%2fCloudTech49835.2020.9365923\&partnerID=40\&md5=7c48017aaa6f8aa00a31f5ae27bd816c},
	author = {Hafdi, K. and Kriouile, A.}
}
@article {Hosni20202177,
	title = {A mapping study of ensemble classification methods in lung cancer decision support systems},
	journal = {Medical and Biological Engineering and Computing},
	volume = {58},
	number = {10},
	year = {2020},
	note = {cited By 8},
	pages = {2177-2193},
	abstract = {Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. [Figure not available: see fulltext.] {\textcopyright} 2020, International Federation for Medical and Biological Engineering.},
	keywords = {Artificial intelligence, Automatic searches, Biological organs, cancer classification, Classification (of information), Classification accuracy, Classification methods, classifier, decision support system, Decision support systems, decision tree, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Heterogeneous ensembles, human, lung cancer, Lung cancer detections, Majority voting rules, Mapping, priority journal, Review, Systematic mapping studies},
	doi = {10.1007/s11517-020-02223-8},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087501825\&doi=10.1007\%2fs11517-020-02223-8\&partnerID=40\&md5=bdb2bd3f923da33ea3b564a0bd6e739e},
	author = {Hosni, M. and Garc{\'\i}a-Mateos, G. and Carrillo-De-Gea, J.M. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article {ElAissaoui2020655,
	title = {Mining Learners{\textquoteright} Behaviors: An Approach Based on Educational Data Mining Techniques},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 2},
	pages = {655-670},
	abstract = {Educational data mining is a research field that aims to apply data mining techniques in educational environments. Many data mining techniques such as clustering, classification, and prediction can be performed on educational data in order to analyze the learner behaviors. In this work, we have used the clustering and classification techniques to predict the learners{\textquoteright} learning styles. The students{\textquoteright} behaviors while using the e-learning system have been captured from the log file and given as an input of a clustering algorithm to group them into 16 clusters. The resulted clusters were labeled with learning styles combinations based on the Felder and Silverman learning style model. Then the labeled behaviors were given as input to four classifiers: naive Bayes, Cart, Id3, and C4.5 to compare their performance in predicting students{\textquoteright} learning styles. The four classifiers were performed using Weka data mining tool, and the obtained results showed that Id3 yielded better results than the other classifiers. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Artificial intelligence, Classification technique, Clustering algorithms, Data mining, Data-mining tools, Educational data mining, Educational environment, Embedded systems, Forecasting, Learning Style, Learning systems, Log file, Naive bayes, Research fields, Students},
	doi = {10.1007/978-981-15-0947-6_62},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085173246\&doi=10.1007\%2f978-981-15-0947-6_62\&partnerID=40\&md5=0cb9665b2720bd9b06e011902e8708b5},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
@conference {ElAkrouchi2020,
	title = {Monitoring Early Warning Signs Evolution through Time},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	abstract = {In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts{\textquoteright} opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals{\textquoteright} evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis. {\textcopyright} 2020 ACM.},
	keywords = {Artificial intelligence, Business competition, Competition, Early warning signs, Financial crisis, Possible futures, Semantics, Signal detection, Warning signs, Weak signals},
	doi = {10.1145/3446132.3446173},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102970850\&doi=10.1145\%2f3446132.3446173\&partnerID=40\&md5=4f35fa04a194332457fca88ff3bec0d1},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@article {ElFazziki2020815,
	title = {A Novel Collaborative Filtering Approach Based on the Opposite Neighbors{\textquoteright} Preferences},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 0},
	pages = {815-824},
	abstract = {Collaborative filtering (CF) has become an effective way to predict useful items. It is the most widespread recommendation technique. It relies on users who share similar tastes and preferences to suggest the items that they might be interested in. Despite its simplicity and justifiability, the collaborative filtering approach experiences many problems, including sparsity, gray sheep and scalability. These problems lead to deteriorating the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user +X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Artificial intelligence, Collaborative filtering, Embedded systems, Movielens, Recommendation techniques, Similar neighbors},
	doi = {10.1007/978-981-15-0947-6_77},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085185804\&doi=10.1007\%2f978-981-15-0947-6_77\&partnerID=40\&md5=68fdeb907c6bb3635b6c19f73a225586},
	author = {El Fazziki, A. and El Aissaoui, O. and El Madani El Alami, Y. and Benbrahim, M. and El Allioui, Y.}
}
@conference {ElMrabtt2020,
	title = {Novel Convex Polyhedron Classifier for Sentiment Analysis},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi-squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC-based classifiers. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Chi-Squared, Classification (of information), Cloud computing, Convex hull, Convex polyhedrons, F values, Feature selection methods, Geometric concepts, Geometry, Linearly separable, Piece-wise, Sentiment analysis},
	doi = {10.1109/CloudTech49835.2020.9365906},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102620927\&doi=10.1109\%2fCloudTech49835.2020.9365906\&partnerID=40\&md5=143fd1c523fe8ae24563a9b0482f632c},
	author = {El Mrabtt, S. and Lazaar, M. and Al Achhab, M. and Omara, H.}
}
@article {Yousfi20203,
	title = {Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12496 LNAI},
	year = {2020},
	note = {cited By 0},
	pages = {3-15},
	abstract = {Holistic schema matching is a fundamental challenge in the big data integration domain. Ideally, clusters of semantically corresponding elements are created and are updated as more schemas are matched. Developing a high-quality holistic schema matching approach is critical for two main reasons. First, identifying as many accurate and holistic semantic correspondences as possible right from the beginning. Second, reducing considerably the search space. Nevertheless, this problem is challenging since overlapping schema elements are not available. Identifying schema overlaps is further complicated for two main reasons: (1) there is a large number of schemas; and (2) overlaps vary for different schemas. In this paper we present HMO, a Holistic schema Matching approach based on schema Overlaps and designed for large-scale schemas. HMO can balance the search space and the quality of the holistic semantic correspondences. To narrow down the search space, HMO matches schemas based on their overlaps. To obtain high-accuracy, HMO uses an existing high-quality semantic similarity measure. Experimental results on four real-world domains show effectiveness and scalability of our matching approach. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Artificial intelligence, Data integration, High quality, High-accuracy, Holistic schema matching, Real world domain, Search spaces, Semantic correspondence, Semantic similarity measures, Semantics},
	doi = {10.1007/978-3-030-63007-2_1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097530171\&doi=10.1007\%2f978-3-030-63007-2_1\&partnerID=40\&md5=aed75e2b584acebebdce7bf0dc2cc076},
	author = {Yousfi, A. and Yazidi, M.H.E. and Zellou, A.}
}
@article {11128948620160201,
	title = {Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques.},
	journal = {International Journal of Intelligent Systems},
	volume = {31},
	number = {2},
	year = {2016},
	pages = {128 - 152},
	abstract = {Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are},
	keywords = {Algorithms, Artificial intelligence, Computer software development, Fuzzy control systems, Numerical analysis, Prototypes},
	issn = {08848173},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=111289486\&site=ehost-live},
	author = {Idri, Ali and Amazal, Fatima Azzahra and Abran, Alain}
}
@article {4562825420091002,
	title = {Resource Optimization of Mobile Intelligent System With Heart MPLS Network.},
	journal = {International Journal of Interactive Mobile Technologies},
	volume = {3},
	number = {S2},
	year = {2009},
	pages = {58 - 64},
	abstract = {In this paper, we introduce the original Mobile Intelligent System (MIS) in embedded FPGA architecture. This node will allow the construction of autonomous mobile network units which can move in unknowns, inaccessible or hostile environment for human beings, in order to collect data by various sensors and transmits them by routing to a unit of distant process. For the sake of improving the performance of transmission, we propose a global schema of QoS management using DiffServ/MPLS backbones. We provide an evaluation of several scenarios for combining QoS IP networks with MIS access network. We conclude with a study on interoperability between QoS patterns in access and backbone networks. [ABSTRACT FROM AUTHOR]},
	keywords = {Artificial intelligence, Computer architecture, DiffServ and MPLS, Information networks, intelligent sensors, Mobile communication systems, Quality of service, Routing (Computer network management) {\textendash} Equipment \& supplies, wireless sensor network},
	issn = {18657923},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=45628254\&site=ehost-live},
	author = {Oubaha, Jawad and Habbani, Ahmed and Elkoutbi, Mohammed}
}
@article {1344754220010801,
	title = {Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization.},
	journal = {International Journal of Intelligent Systems},
	volume = {16},
	number = {8},
	year = {2001},
	pages = {929 - 947},
	abstract = {The text categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which proved effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which (i) uses a finite design set of labeled data to (ii) help agglomerative hierarchical clustering algorithms (AHC) partition a finite set of unlabeled data and then (iii) terminates without the capacity to classify other objects. This algorithm is the {\textquotedblleft}semi-supervised agglomerative hierarchical clustering algorithm{\textquotedblright} (ssAHC). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 89 TOPICS classes of the Reuters collection. Using the vector space model (VSM), each document is repre},
	keywords = {Algorithms, Artificial intelligence, Hierarchy (Linguistics), John Wiley \& Sons Inc., Linguistic analysis (Linguistics), Machine learning},
	issn = {08848173},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=13447542\&site=ehost-live},
	author = {Benkhalifa, Mohammed and Mouradi, Abdelhak and Bouyakhf, Houssaine}
}
