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Zarghili Arslane

Work place: SIA Lab, Faculty of Science and Technology, Fez, Morocco

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Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Information Retrieval, Data Structures and Algorithms

Biography

Arsalane Zarghili is a Doctor of Science from Sidi Mohamed Ben Abdellah University (Fez-Morocco). He received his Ph.D. in 2001 and joined the same University in 2002 as Professor at the computer science department of the Faculty of Science and Technology (FST). In 2007 he was head of the computer sciences department and chair of the Software Quality Master in the FST-Fez. He lectures Programming, Distributed, compilation and Information processing, for both undergraduate and master levels. In 2008 he obtained his HDR in information processing. In 2011, he is the co-founder and the head of the Laboratory of Intelligent Systems and Applications in the FST of Fez. He is a member of the steering committee of the department of computer sciences and was a member of the faculty board. In 2011 he is the chair of Master Intelligent Systems and Networks. He is also IEEE member since 2011. His main research is about pattern recognition, image indexing and retrieval systems in cultural heritage, biometric, etc.

Author Articles
Improving the Proactive Recommendation in Smart Home Environments: An Approach Based on Case Based Reasoning and BP-Neural Network

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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