@article {Zarnoufi2022223, title = {Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media}, journal = {Communications in Computer and Information Science}, volume = {1577 CCIS}, year = {2022}, note = {cited By 0}, pages = {223-235}, abstract = {Cyber-violence is a largely addressed problem in e-health researches, its focus is the detection of harmful behavior from the online user-generated text in order to prevent and protect victims. In this work, we tackle the problem of Social Media (SM) text analysis to detect the harmful content that is the common characteristic of cyber-violence acts. For that, we use classical Machine Learning (ML) based on user psychological features that we compare with Deep Learning (DL) techniques in a small dataset setting. The results were in favor of classical ML. The findings highlight that psychological characteristics extracted from user-generated text are strong predictors of his harmful behavior. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.}, keywords = {Behavioral research, Classical machine learning, Cybe-violence, Deep learning, E health, Ehealth, Feature engineerings, Harmful behavior, Machine-learning, social media, Social networking (online), User-generated}, doi = {10.1007/978-3-031-04447-2_15}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128968964\&doi=10.1007\%2f978-3-031-04447-2_15\&partnerID=40\&md5=70577a9ac5b248762e97757da4349369}, author = {Zarnoufi, R. and Abik, M.} } @article {Zarnoufi2020182, title = {AI to prevent cyber-violence: Harmful behaviour detection in social media}, journal = {International Journal of High Performance Systems Architecture}, volume = {9}, number = {4}, year = {2020}, note = {cited By 1}, pages = {182-191}, abstract = {Social media has allowed people to communicate freely. This total freedom has led to the emergence of cyber-violence with a growing number of victims. Many researches in psychology and e-health have been conducted to detect the act of cyber-violence. In computational field, most of works have focused on multiple aspects of cyber-violence, but none of them, to our knowledge, have studied the perpetrator{\textquoteright}s harmful behaviour from an emotional dimension. Our goal in this work is to discover the relationship between the emotional state of social media users and their harmful behaviour while engaged in the act of cyber-violence. Our approach is based on Ensemble Machine Learning and engineered features related to Plutchik wheel of basic emotions extracted with semantic similarity and word embedding. The results show a significant association between the individual{\textquoteright}s emotional state and the harmful intent, which may be a good indicator for cyber-violence detection. Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.}, keywords = {Basic emotions, Behaviour detections, Computational field, Emotional dimensions, Emotional state, Semantic similarity, Semantics, social media, Social networking (online), Violence detections}, doi = {10.1504/IJHPSA.2020.113679}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102891888\&doi=10.1504\%2fIJHPSA.2020.113679\&partnerID=40\&md5=3e11e2e6a4032f608b4b71f2d426dbca}, author = {Zarnoufi, R. and Boutbi, M. and Abik, M.} } @article {Mrhar202072, title = {A dropout predictor system in moocs based on neural networks}, journal = {Journal of Automation, Mobile Robotics and Intelligent Systems}, volume = {14}, number = {4}, year = {2020}, note = {cited By 2}, pages = {72-80}, abstract = {Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator{\textquoteright}s community as one of the most important problems. It{\textquoteright}s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90\% and the model with the feature of the learner{\textquoteright}s sentiments analysis attained average increase in AUC of 0.5\%. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.}, doi = {10.14313/JAMRIS/4-2020/48}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102757989\&doi=10.14313\%2fJAMRIS\%2f4-2020\%2f48\&partnerID=40\&md5=4cbaa0f7f458ce320541450af5ff8a46}, author = {Mrhar, K. and Douimi, O. and Abik, M.} } @article {Sanak2020203, title = {MARCO Gene Variations and Their Association with Cardiovascular Diseases Development: An In-Silico Analysis}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12108 LNBI}, year = {2020}, note = {cited By 0}, pages = {203-212}, abstract = {Cardiovascular diseases (CVDs) represent the leading cause of morbidity and mortality in both developed and developing countries. They have complex etiology, influenced by several risk factors including the genetic component. The genetic variations were shown to be highly associated with different CVD forms, in this objective we proceeded to analyze the Macrophage Receptor with Collagen structure gene (MARCO), we performed an in-silico study with a genomic functional analysis, to evaluate the mutations{\textquoteright} effects on the proteins{\textquoteright} structures and functionalities. Indeed, we used dbSNP to retrieve single nucleotide polymorphisms (SNPs) of MARCO gene. We proceeded then to a filtration and a stability analysis using several bioinformatics tools to evaluate the most deleterious variations. Moreover we predicted the 3D structures of the encoded proteins by MARCO gene, which was validated using PROCHECK. Then we analyzed and visualize the proteins{\textquoteright} 3D structures. The extraction of the human MARCO gene SNPs revealed that dbSNP contains more than 14000 SNPs. The filtration process revealed the variations G241V and G262W to be the most deleterious SNPs, indeed, I-Mutant and DUET showed decreased protein stability. The validation using PROCHECK revealed a total of 89.9\% MARCO protein residues to be in the favored region. As conclusion, our results let suggesting that G241V and G262W variations can cause alteration in the proteins{\textquoteright} structures and functions. Hence, to improve the health management, screening precariously these variants, can be useful as model for CVD diagnosis and helpful in pharmacogenomics. {\textcopyright} Springer Nature Switzerland AG 2020.}, keywords = {Bioinformatics, Bioinformatics tools, Biomedical engineering, Cardio-vascular disease, Cardiology, Collagen structure, Developing countries, Diagnosis, Diseases, Filtration process, Genes, Genetic components, Genetic variation, Health management, Proteins, Single nucleotide polymorphisms}, doi = {10.1007/978-3-030-45385-5_19}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085196738\&doi=10.1007\%2f978-3-030-45385-5_19\&partnerID=40\&md5=1df531cdc3510e8747834a13a996b1b1}, author = {Sanak, K. and Azzouzi, M. and Abik, M. and Radouani, F.} } @article {Bourekkache2020255, title = {Multi-agent approach for collaborative authoring and indexing of pedagogical materials}, journal = {International Journal of Continuing Engineering Education and Life-Long Learning}, volume = {30}, number = {3}, year = {2020}, note = {cited By 1}, pages = {255-275}, abstract = {In e-learning environment, the learner may feel that he is isolated and disoriented because of the absence of the teacher and the huge number of materials. Moreover, the pedagogical documents have several characteristics so that we must offer the appropriate documents for each learner according to his level, characteristics, and preferences{\textellipsis}, etc. Consequently, the adaption of the learning content is an important technique. Creating materials, without additional information, makes the delivering of relevant material an impossible task. Consequently, one has to pay attention to the stage of creating of learning content using new technics. In addition, it may not be convenient if we haven{\textquoteright}t additional information about the learner and the learning material (learning objective, prerequisites, and learner background{\textellipsis}, etc.). Therefore, we develop a multi-agent system that supports a set of authors who create and index educational materials. The indexes are used to manipulate the learning content efficiently by the machine when choosing the appropriate content to satisfy the needs of heterogeneous learners. Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.}, keywords = {Collaborative authoring, Computer aided instruction, E-learning environment, Educational materials, Learning contents, Learning materials, Learning objectives, Multi agent systems, Multi-agent approach}, doi = {10.1504/IJCEELL.2020.108527}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093856724\&doi=10.1504\%2fIJCEELL.2020.108527\&partnerID=40\&md5=5cd4fb75d55b6654a672036b17252936}, author = {Bourekkache, S. and Kazar, O. and Abik, M.} } @article {Mrhar2020535, title = {Towards a semantic integration of data from learning platforms}, journal = {IAES International Journal of Artificial Intelligence}, volume = {9}, number = {3}, year = {2020}, note = {cited By 2}, pages = {535-544}, abstract = {Nowadays, there is a huge production of Massive Open Online Courses MOOCs from universities around the world. The enrolled learners in MOOCs skyrocketed along with the number of the offered online courses. Of late, several universities scrambled to integrate MOOCs in their learning strategy. However, the majority of the universities are facing two major issues: firstly, because of the heterogeneity of the platforms used (e-learning and MOOC platforms), they are unable to establish a communication between the formal and non-formal system; secondly, they are incapable to exploit the feedbacks of the learners in a non-formal learning to personalize the learning according to the learner{\textquoteright}s profile. Indeed, the educational platforms contain an extremely large number of data that are stored in different formats and in different places. In order to have an overview of all data related to their students from various educational heterogeneous platforms, the collection and integration of these heterogeneous data in a formal consolidated system is needed. The principal core of this system is the integration layer which is the purpose of this paper. In this paper, a semantic integration system is proposed. It allows us to extract, map and integrate data from heterogeneous learning platforms {\textquotedblleft}MOOCs platforms, e-learning platforms{\textquotedblright} by solving all semantic conflicts existing between these sources. Besides, we use different learning algorithms (Long short-term memory LSTM, Conditional Random Field CRF) to learn and recognize the mapping between data source and domain ontology. {\textcopyright} 2020, Institute of Advanced Engineering and Science. All rights reserved.}, doi = {10.11591/ijai.v9.i3.pp535-544}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090554399\&doi=10.11591\%2fijai.v9.i3.pp535-544\&partnerID=40\&md5=786ffdd85ffa5b0112c0728e4bb50266}, author = {Mrhar, K. and Douimi, O. and Abik, M. and Benabdellah, N.C.} } @article {Mrhar20202934, title = {Towards optimize-ESA for text semantic similarity: A case study of biomedical text}, journal = {International Journal of Electrical and Computer Engineering}, volume = {10}, number = {3}, year = {2020}, note = {cited By 1}, pages = {2934-2943}, abstract = {Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach. Copyright {\textcopyright} 2020 Institute of Advanced Engineering and Science. All rights reserved.}, doi = {10.11591/ijece.v10i3.pp2934-2943}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079383531\&doi=10.11591\%2fijece.v10i3.pp2934-2943\&partnerID=40\&md5=2965a06637b8b80abe9288b66e1082e7}, author = {Mrhar, K. and Abik, M.} } @article {Zarnoufi2019672, title = {Language identification for user generated content in social media}, journal = {Smart Innovation, Systems and Technologies}, volume = {111}, year = {2019}, note = {cited By 0}, pages = {672-678}, doi = {10.1007/978-3-030-03577-8_73}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056449806\&doi=10.1007\%2f978-3-030-03577-8_73\&partnerID=40\&md5=0788a6d5259f75585d3c1d415a6c7300}, author = {Zarnoufi, R. and Jaafar, H. and Abik, M.} } @conference {Mrhar2017557, title = {Making MOOCs matter in formal education through a federating environment}, booktitle = {Proceedings of the European Conference on e-Learning, ECEL}, volume = {2010-October}, year = {2017}, note = {cited By 0}, pages = {557-565}, abstract = {With the emergence of open education and MOOCs, the opportunities and contribution of non-formal learning to the acquisition of knowledge and skills have increased. This type of learning, which has the advantage of being voluntary relying mainly on the learners{\textquoteright} motivation, remains technically invisible to formal learning environments. Finding suggest that the formal learning becomes increasingly less adapted to the learners needs because it does not take into consideration the real learner{\textquoteright}s profile (knowledge, skills, etc.) updated by non-formal learning. In order, to bridging formal and non-formal learning, we are aiming to personalize formal learning by recovering the learner{\textquoteright}s knowledge, abilities and skills which are acquired by non-formal learning (MOOCs). We propose in this paper, a federating environment for MOOCs hosted in different platforms such as (Coursera, open EdX..). The main objective of this environment is to provide to the formal learning environment a recommender system of MOOCs. The technical aspects of a federating environment of MOOCs (FEM) are presented. FEM is composed of an integration system and a recommender system of MOOCs. The integration system is responsible for integrating data emanating from different heterogeneous MOOCs platforms. The recommender system is based on the learners{\textquoteright} profiles and on the pedagogical objectives set by the concerned establishment that integrates the federating environment FEM. FEM also enables establishments to adapt the formal learning through the enriched learners{\textquoteright} profiles. {\textcopyright} The Authors, 2017.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037541912\&partnerID=40\&md5=345a1683d5b625a972e3de33841d44c8}, author = {Mrhar, K. and Zary, N. and Abik, M.} } @conference {Belarcen2013, title = {ESB based communication in the connectivist learning environment - CLE}, booktitle = {2013 3rd International Symposium ISKO-Maghreb}, year = {2013}, note = {cited By 0}, abstract = {CLE [1] {\guillemotleft}Connectivist Learning Environment{\guillemotright} is a clouded and ubiquitous learning environment conceived by our research team LeRMA {\guillemotleft} Learning and Research in Mobile Age{\guillemotright}. This environment adopts the connectivism as a pedagogical approach and aims to construct knowledge through collaboration and communication between heterogeneous communities composed of humans and smart objects. The objective of our work is to provide for our ubiquitous environment CLE a service-based communication (where each actor in the environment is represented by a set of services). As an implementation, we opted to use an ESB {\guillemotleft}Enterprise Service Bus{\guillemotright} in the SOA layer of CLE. This ESB includes the actors{\textquoteright} services, business services, SLA services for a better quality of service and the bus will be used for connectivity, routing, processing and conversion. Through this proposition, we ensure a better communication between nodes (humans, smart objects and controller unit) and an efficient knowledge construction. {\textcopyright} 2013 IEEE.}, doi = {10.1109/ISKO-Maghreb.2013.6728110}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894197248\&doi=10.1109\%2fISKO-Maghreb.2013.6728110\&partnerID=40\&md5=a6cf6372bc7f3917f7874bbb0e36fbe0}, author = {Belarcen, A. and Abik, M. and Ajroun, R.} } @conference {Belahcen2013, title = {Knowledge construction in the Connectivist Learning Environment - CLE}, booktitle = {2013 12th International Conference on Information Technology Based Higher Education and Training, ITHET 2013}, year = {2013}, note = {cited By 0}, abstract = {Technological advances have brought great changes in all areas, including education. The distance learning evolution (D-learning, E-learning and M-Learning) has prompted the adoption of the most interesting pedagogical approaches such as constructivism and social-constructivism. The adoption of these pedagogical trends improved the quality of learning by providing the personalization of learning and collaborative learning. Through this technological evolution and with the development of the Web 2.0, a new pedagogical approach called Connectivism has emerged [1]. It{\textquoteright}s a promising pedagogical approach that covers learning in heterogeneous communities (humans or not) and is based on the contribution of new technologies. However, Web 2.0 is not efficient to reach distributed knowledge in networks in a smart way and where the ability to learn becomes more and more important [2]. The objective of our works is to conceive and implement CLE {\guillemotleft}Connectivist Learning Environment{\guillemotright} which is a clouded and ubiquitous learning environment. The intended purpose is to adopt connectivism as a pedagogical approach in order to construct knowledge through collaboration between heterogeneous communities composed of humans and intelligent objects. {\textcopyright} 2013 IEEE.}, doi = {10.1109/ITHET.2013.6671027}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893451279\&doi=10.1109\%2fITHET.2013.6671027\&partnerID=40\&md5=04504dab6e5719c4d0224f11f51122e9}, author = {Belahcen, A. and Abik, M. and Ajhoun, R.} } @article {Abik2012224, title = {Impact of technological advancement on pedagogy}, journal = {Turkish Online Journal of Distance Education}, volume = {13}, number = {1}, year = {2012}, note = {cited By 3}, pages = {224-237}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861667801\&partnerID=40\&md5=9e34db346a013df6dc45bddf6665ffec}, author = {Abik, M. and Ajhoun, R.} } @conference {Bensiali2012, title = {Novel approach for accessible visual resources in a Web based learning environment}, booktitle = {IEEE Global Engineering Education Conference, EDUCON}, year = {2012}, note = {cited By 0}, doi = {10.1109/EDUCON.2012.6201150}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864129251\&doi=10.1109\%2fEDUCON.2012.6201150\&partnerID=40\&md5=4f3934faee28a47ea5bff8c231d3b82d}, author = {Bensiali, S. and Ajhoun, R. and Abik, M.} } @article {Abik20094, title = {Normalization and personalization of learning situations: NPLS}, journal = {International Journal of Emerging Technologies in Learning}, volume = {4}, number = {2}, year = {2009}, note = {cited By 6}, pages = {4-10}, doi = {10.3991/ijet.v4i2.818}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960336337\&doi=10.3991\%2fijet.v4i2.818\&partnerID=40\&md5=07fc92375360ec9d618ca959c62d07bb}, author = {Abik, M. and Ajhoun, R.} }