Work place: SIA Lab, Faculty of Science and Technology, Fez, Morocco
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Research Interests: Computer systems and computational processes, Artificial Intelligence, Computational Learning Theory, Swarm Intelligence, Data Structures and Algorithms
Biography
Ahlame begdouri is a professor in the department of computer science, head of the VIA research group at the SIA lab "système Intelligents et Applications " of the Faculty of Science and Technology of Fez-University of Sidi Mohamed Ben Abdellah. She received her PhD on the topic of "Quality of services of multimedia traffic over best effort networks" from the Faculty of Science of Rabat-Morocco on 1999. Her current research intersets concern context aware adaptation, ambient intelligence and mobile learning. Prof. Begdouri is the coordinator of an FP7 project called MoICT(2010-2014) of IEEE Moroccan section, Member of the local office of IEEE Moroccan section , Member of IEEE Moroccan Education Chapter, Co-founder and member of the E-NGN research group since 2008,Associate member of the Moroccan FP7-ICT-NCP unit , Program committee chair international conference (WNGN’08, NGNS’09, CIST’11, CIST’12). Her contact address is : BP2202,Route d’Imouzzar, Faculty of Science and Technology of Fez. E-mail:abegdouri@gmail.com
By Gouttaya Nesrine Belghini Naouar Begdouri Ahlame Zarghili Arslane
DOI: https://doi.org/10.5815/ijisa.2015.07.04, Pub. Date: 8 Jun. 2015
Providing spontaneously personalized services to users, at anytime, anywhere and through any devices represent the main objective of pervasive computing. Smart home is an intelligent environment that can provide dozens or even hundreds of smart services. In this paper, we propose an approach to present spontaneously and continuously the most relevant services to the user in response to any significant change of his context. Our approach allows, firstly to assist proactively the user in the tasks of his/her daily life and secondly to help him/her to save energy in the smart home environment. The proposed approach is based on the use of context history information together with user profiling and machine learning techniques. Experimental results show that our approach can efficiently provide the most useful services to the user in a smart home environment.
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