@article {Ajallouda2022283,
	title = {KP-USE: An Unsupervised Approach for Key-Phrases Extraction from Documents},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {4},
	year = {2022},
	note = {cited By 4},
	pages = {283-289},
	abstract = {Automatic key-phrase extraction (AKE) is one of the most popular research topics in the field of natural language processing (NLP). Several techniques were used to extract the key-phrases: statistical, graph-based, classification algorithms, deep learning, and embedding techniques. AKE approaches that use embedding techniques are based on calculating the semantic similarity between a vector representing the document and the vectors representing the candidate phrases. However, most of these methods only give acceptable results in short texts such as abstracts paper, but on the other hand, their performance remains weak in long documents because it is represented by a single vector. Generally, the key phrases of a document are often expressed in certain parts of the document as, the title, the summary, and to a lesser extent in the introduction and the conclusion, and not of the entire document. For this reason, we propose in this paper KP-USE. A method extracts key-phrases from long documents based on the semantic similarity of candidate phrases to parts of the document containing keyphrases. KP-USE makes use of the Universal Sentence Encoder (USE) as an embedding method for text representation. We evaluated the performance of the proposed method on three datasets containing long papers, namely, NUS, Krapivin2009, and SemEval2010, where the results showed its performance outperforms recent AKE methods which are based on embedding techniques. {\textcopyright} 2022. All Rights Reserved.},
	keywords = {Deep learning, Embedding technique, Embeddings, Extraction, Graph-based classifications, Graphic methods, Key-phrase, Key-phrases extractions, Natural language processing systems, Performance, Research topics, Semantic similarity, Semantics, Signal encoding, Statistical graphs, Universal sentence encoder, Unsupervised approaches},
	doi = {10.14569/IJACSA.2022.0130433},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129921696\&doi=10.14569\%2fIJACSA.2022.0130433\&partnerID=40\&md5=880d07ce539281dced3a51a82de6a16c},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Lahmar, E.B.}
}
@conference {Ajallouda2020169,
	title = {K-means hac and fcm which clustering approach for arabic text?},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {169-174},
	abstract = {Today we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas opinions and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which in reality is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint clustering approaches such as K-mean HAC or FCM can be exploited. In this paper we present and compare these approaches. And to show the importance of exploiting clustering algorithms to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco which is the case of teachers contracting with academies. {\textcopyright} 2020 ACM.},
	keywords = {Arabic texts, Classification (of information), Classification approach, Clustering approach, Intelligent systems, Internet users, K-means clustering, Rapid growth, Real case, Textual data, Web resources},
	doi = {10.1145/3419604.3419779},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096415898\&doi=10.1145\%2f3419604.3419779\&partnerID=40\&md5=8aca383d8f485d15b3da191f03c5bf62},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@article {Elkoubaiti2019100,
	title = {Key elements of educational augmented and virtual reality applications},
	journal = {Smart Innovation, Systems and Technologies},
	volume = {111},
	year = {2019},
	note = {cited By 0},
	pages = {100-105},
	doi = {10.1007/978-3-030-03577-8_12},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056485160\&doi=10.1007\%2f978-3-030-03577-8_12\&partnerID=40\&md5=ab9898d522b17e78775577d244fb3190},
	author = {Elkoubaiti, H. and Mrabet, R.}
}
@article { ISI:000390485500002,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS},
	volume = {97},
	year = {2017},
	month = {JAN},
	pages = {12-32},
	abstract = {Context: Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. Objective: The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. Method: We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. Results: A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques. (C) 2016 Elsevier Ireland Ltd. All rights reserved.},
	issn = {1386-5056},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J. L.}
}
@article {Kadi201712,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {International Journal of Medical Informatics},
	volume = {97},
	year = {2017},
	note = {cited By 0},
	pages = {12-32},
	abstract = {Context Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. Objective The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. Method We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. Results A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques. {\textcopyright} 2016 Elsevier Ireland Ltd},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988689033\&doi=10.1016\%2fj.ijmedinf.2016.09.005\&partnerID=40\&md5=9bd4f89bacb9b8dea94dd1f7eefef431},
	author = {Kadi, I.a and Idri, A.b and Fern{\'a}ndez-Alem{\'a}n, J.L.b}
}
@article {Kadi201712,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {International Journal of Medical Informatics},
	volume = {97},
	year = {2017},
	note = {cited By 6},
	pages = {12-32},
	abstract = {Context Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. Objective The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. Method We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. Results A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques. {\textcopyright} 2016 Elsevier Ireland Ltd},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988689033\&doi=10.1016\%2fj.ijmedinf.2016.09.005\&partnerID=40\&md5=9bd4f89bacb9b8dea94dd1f7eefef431},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article {Kerzazi2017247,
	title = {Knowledge flows within open source software projects: A social network perspective},
	journal = {Lecture Notes in Electrical Engineering},
	volume = {397},
	year = {2017},
	note = {cited By 0},
	pages = {247-258},
	abstract = {Developing software is knowledge-intensive activity, requiring extensive technical knowledge and awareness. The abstract part of development is the social interactions that drive knowledge flows between contributors, especially for Open Source Software (OSS). This study investigated knowledge sharing and propagation from social perspective using social network analysis (SNA). We mined and analyzed the issue and review histories of three OSS from GitHub. Particular attention has been paid to the socio-interactions through comments from contributors on reviews. We aim at explaining the propagation and density of knowledge flows within contributor networks. The results show that review requests flow from the core contributors toward peripheral contributors and comments on reviews are in a continuous loop from the core teams to the peripherals and back; and the core contributors leverage on their awareness and technical knowledge to increase their notoriety by playing the role of communication brokers supported by comments on work items. {\textcopyright} Springer Science+Business Media Singapore 2017.},
	doi = {10.1007/978-981-10-1627-1_19},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996606731\&doi=10.1007\%2f978-981-10-1627-1_19\&partnerID=40\&md5=a2da5970c55b3e81dc2e09bda67e1649},
	author = {Kerzazi, N. and El Asri, I.}
}
@conference { ISI:000389806000017,
	title = {Knowledge Management Performance Measurement: A Generic Framework},
	booktitle = {DATABASES AND INFORMATION SYSTEMS, DB\&IS 2016},
	series = {Communications in Computer and Information Science},
	volume = {615},
	year = {2016},
	note = {12th International Baltic Conference on Databases and Information Systems (DB and IS), Riga, LATVIA, JUL 04-06, 2016},
	pages = {242-254},
	publisher = {Univ Latvia, Fac Comp; Exigen Serv Latvia; IEEE; Springer},
	organization = {Univ Latvia, Fac Comp; Exigen Serv Latvia; IEEE; Springer},
	abstract = {This theoretical article aims to propose a generic framework for measuring performance of Knowledge Management (KM) projects based on critical literature review. The proposed framework fills the existing gap on KM performance measurement in two points: (i) it provides a generic tool that is able to assess all kinds of KM project as well as the overall organization KM, (ii) it assesses KM projects according to KM objectives in a generic manner. Our framework (GKMPM) relies on a process reference model that provides a KM common understanding in a process based view. It is based on a goal-oriented measurement approach and considers that KM performance dimensions are stakeholder{\textquoteright}s objectives. The framework application follows a procedural approach that begins with the KM project modelling, followed by the objectives prioritization. The next step consists of collecting and analysing data for pre-designed measures, and produces a set of key performance indicators (KPIs) related to the KM project processes and in accordance with its objectives.},
	isbn = {978-3-319-40180-5; 978-3-319-40179-9},
	issn = {1865-0929},
	doi = {10.1007/978-3-319-40180-5\_17},
	author = {Oufkir, Latifa and Fredj, Mounia and Kassou, Ismail},
	editor = {Arnicans, G and Arnicane, V and Borzovs, J and Niedrite, L}
}
@conference {DBLP:conf/balt/OufkirFK16,
	title = {Knowledge Management Performance Measurement: {A} Generic Framework},
	booktitle = {Databases and Information Systems - 12th International Baltic Conference, DB{\&}IS 2016, Riga, Latvia, July 4-6, 2016, Proceedings},
	year = {2016},
	pages = {242{\textendash}254},
	doi = {10.1007/978-3-319-40180-5_17},
	url = {http://dx.doi.org/10.1007/978-3-319-40180-5_17},
	author = {Latifa Oufkir and Mounia Fredj and Ismail Kassou}
}
@article {Oufkir2016242,
	title = {Knowledge management performance measurement: A generic framework},
	journal = {Communications in Computer and Information Science},
	volume = {615},
	year = {2016},
	note = {cited By 0},
	pages = {242-254},
	abstract = {This theoretical article aims to propose a generic framework for measuring performance of Knowledge Management (KM) projects based on critical literature review. The proposed framework fills the existing gap on KM performance measurement in two points: (i) it provides a generic tool that is able to assess all kinds of KM project as well as the overall organization KM, (ii) it assesses KM projects according to KM objectives in a generic manner. Our framework (GKMPM) relies on a process reference model that provides a KM common understanding in a process based view. It is based on a goal-oriented measurement approach and considers that KM performance dimensions are stakeholder{\textquoteright}s objectives. The framework application follows a procedural approach that begins with the KM project modelling, followed by the objectives prioritization. The next step consists of collecting and analysing data for pre-designed measures, and produces a set of key performance indicators (KPIs) related to the KM project processes and in accordance with its objectives. {\textcopyright} Springer International Publishing Switzerland 2016.},
	doi = {10.1007/978-3-319-40180-5_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979022041\&doi=10.1007\%2f978-3-319-40180-5_17\&partnerID=40\&md5=7353ebf3c020ba8a9bc9458832afda06},
	author = {Oufkir, L. and Fredj, M. and Kassou, I.}
}
@conference { ISI:000327787500001,
	title = {Key Management for Secure Multicast Communication: A Survey},
	booktitle = {2013 NATIONAL SECURITY DAYS (JNS3)},
	year = {2013},
	note = {3rd National Security Days (JNS), Mohammed V Souissi Unvi, Rabat, MOROCCO, APR 26-27, 2013},
	publisher = {Assoc Marocaine ConfiAnace Numerique; Ecole Nationale Superieure Informatique \& Analyse Syst; Informat Secur Res Team; IEEE Morocco Sect; Bank Al Maghrib; Natl Ctr Sci \& Technol Res},
	organization = {Assoc Marocaine ConfiAnace Numerique; Ecole Nationale Superieure Informatique \& Analyse Syst; Informat Secur Res Team; IEEE Morocco Sect; Bank Al Maghrib; Natl Ctr Sci \& Technol Res},
	abstract = {Multicast IP communication is increasingly used as an efficient large-scale communication mechanism for one-to-many and many-to-many communication applications on the Internet. Success of such communication mode has required the development and integration of security mechanisms. The main problem in securing a multicast session is the key management group. Membership in a multicast group is dynamic, allowing hosts to enter and leave the multicast session without the permission or knowledge of other hosts making it susceptible to attack unless they are secured. This paper addresses issues relevant to implementing security for IP multicast networks including issues of group membership and key distribution, and principally, several keying architectures are evaluated and compared to determine their strengths and weaknesses.},
	isbn = {978-1-4799-0324-5},
	author = {Baddi, Youssef and Ech-Cherif El Kettani, Mohamed Dafir}
}
@conference {Baddi2013,
	title = {Key management for secure multicast communication: A survey},
	booktitle = {2013 National Security Days - 3eme Edition Des Journees Nationales de Securite, JNS3},
	year = {2013},
	note = {cited By 0},
	abstract = {Multicast IP communication is increasingly used as an efficient large-scale communication mechanism for one-to-many and many-to-many communication applications on the Internet. Success of such communication mode has required the development and integration of security mechanisms. The main problem in securing a multicast session is the key management group. Membership in a multicast group is dynamic, allowing hosts to enter and leave the multicast session without the permission or knowledge of other hosts making it susceptible to attack unless they are secured. This paper addresses issues relevant to implementing security for IP multicast networks including issues of group membership and key distribution, and principally, several keying architectures are evaluated and compared to determine their strengths and weaknesses. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/JNS3.2013.6595453},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884767620\&doi=10.1109\%2fJNS3.2013.6595453\&partnerID=40\&md5=f1f8418f0f0372885a1ac69ef47bb7f2},
	author = {Baddi, Y. and Ech-Cherif El Kettani, M.D.}
}
@conference { ISI:000334934700030,
	title = {Knowledge Construction In the Connectivist Learning Environment -CLE-},
	booktitle = {2013 12TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY BASED HIGHER EDUCATION AND TRAINING (ITHET 2013)},
	series = {International Conference on Information Technology Based Higher Education and Training},
	year = {2013},
	note = {12th International Conference on Information Technology Based Higher Education and Training (ITHET), Antalya, TURKEY, OCT 10-12, 2013},
	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 {\textquoteleft}{\textquoteleft}Connectivist Learning Environment{{\textquoteright}{\textquoteright}} 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.},
	isbn = {978-1-4799-0086-2},
	issn = {2380-1603},
	author = {Belahcen, Anas and Abik, Mounia and Ajhoun, Rachida}
}
@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 {Anter2013158,
	title = {K-Schema: A new approach, based on the distribution of user queries, to create views to materialize in a hybrid integration system},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {47},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {158-170},
	abstract = {The explosion of information technologies and telecommunications has made easy the access and production of information. That is how a very large mass of the latter has generated. This situation has made the integration systems a major need. Among these systems, there is the hybrid mediator. The latter interrogates one part of data on demand as in the virtual approach while charging, filtering and storing the second part, as views, in a local database. The choice of this second part is a critical task. This paper presents a selective approach, which based, essentially, to create these views, on the queries previously posed on the system. Based on the distribution of previous user queries, our approach extract all data most queried by users. The obtained data are classified as candidate views for materialization. Then selecting which one to materialize among all those created in the first step. {\textcopyright} 2005 - 2013 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872374571\&partnerID=40\&md5=c84d7e37d38a4f230cdf4c3a43c22954},
	author = {Anter, S. and Zellou, A. and Idri, A.}
}
