Rezeg Khaled
University of Biskra
5 Papers
23 Citations
Rezeg Khaled is an academic researcher from University of Biskra. The author has contributed to research in topics: Semantic grid & Data Web. The author has an hindex of 2, co-authored 4 publications. Previous affiliations of Rezeg Khaled include Institut national des sciences Appliquées de Lyon.
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Papers
Geospatial Web Services Semantic Discovery Approach Using Quality
TL;DR: The metadata used in this case is designed according to the ISO 19119 standard reinforced by the quality criteria and enhances the accuracy of search results compared to traditional techniques of Web service discovery, the additional matching accuracy in terms of computing power.
Multi-Agent Systems and Ontology for Supporting Management System in Smart School
Zouaoui Samia,Rezeg Khaled,Zouaoui Warda +2 more
- 01 Oct 2018
TL;DR: The interaction of intelligent multi-agent systems running on network, sensor networks and ontologies into the architecture aiming at increasing the management capabilities in smart school and generate automatically detailed reports of the environment with minimal time and effort is described.
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A Smart City System using Backend as a Service Approach: Biskra City Case Study
Hoadjli Abir,Rezeg Khaled +1 more
- 01 Oct 2018
TL;DR: This work proposes a service providing framework based on the backend as a service approach on cloud to improve the quality of the smart city digital services according to the citizen’s needs while enhancing the scalability of the system in large workloads.
3
Geospatial Web Services Semantic Discovery Approach Using Metadata and Multi-Agents System
Rezeg Khaled,Laskri Mohamed Tayeb,Kazar Okba,Sylvie Servigne +3 more
- 31 Jul 2014
TL;DR: In this article, the authors propose an architecture of a semantic discovery of the geospatial Web services based on the metadata and agent paradigm for modeling of the system, which can be obtained using the geographical data through the semantic Web services.
1
Intelligent Farm Based on Deep Reinforcement Learning for optimal control
Hanafi Mohamed Yassine,Viacheslav Shkodyrev,Merizig Abdelhak,Lotfi Zarour,Rezeg Khaled +4 more
- 07 Dec 2022
TL;DR: In this article , the authors proposed a smart farming system that uses deep reinforcement learning to optimize the operation of a farm, which uses deep learning to recognize patterns in a large dataset of real-world agricultural data, such as crop yields and weather.