Prof. Dr. Hakim Jebari
Innovative Systems Engineering Laboratory, ENSATe, Abdelmalek Essaâdi University, Tétouan, Tanger-Tétouan-Al Hoceïma, Morocco
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Academic Career Length
12Years (2015-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications20 June 2023 in International Journal of Interactive Mobile Technologies (iJIM)
Journal Article
Hakim JebariMMMeriem HAYANI MECHKOURI
Meriem HAYANI MECHKOURISRSiham Rekiek
Siham RekiekRKREKLAOUI KAMAL
REKLAOUI KAMALPoultry farms have played a significant role throughout human history, in feeding the growing population. A good environment is a perfect condition for the growth of poultry, preventing disease, and effective production. The temperature higher and the humidity favor the growth of bacteria and hence the production of ammonia (NH3) by the decomposition of organic matter. Ammonia (NH3), carbon monoxide (CO), and carbon dioxide (CO2), Methane (CH4), hydrogen sulfide (H2S) are poisonous gases that can cause poultry diseases and mortality. The combination of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing offer efficient and intelligent stand-alone systems of monitoring in real-time, predicting, and advanced automation. The paper aims to monitor in real-time and predict poultry barns' environmental conditions using an artificial intelligence algorithm. An intelligent system called Poultry-Edge-AI-IoT has been developed to gather, hash, store, pretreat, filter, knowledge extract, and transmit information from a heterogeneous wireless sensor network. The Poultry-Edge-AI-IoT system is based on IoT, AI, and Edge Computing for the detection of potential stress, the harmful gas concentration, and the prediction of poultry barns' environmental conditions. The system is modular and upgradeable.
1 January 2024 in International Journal of Advanced Computer Science and Applications
Journal Article
Accurate demand forecasting is critical for optimizing operations in the tourism and hospitality sectors. This paper proposes a robust multi-algorithmic framework leveraging four advanced models of Artificial Intelligence (LSTM, Random Forest, XGBoost, and Prophet) to predict booking trends and customer demand. In contrast to traditional approaches, this study incorporates external factors such as competitors' pricing strategies, local events, and weather patterns, offering a more holistic view of demand drivers. Using a comprehensive dataset from a leading hotel chain, we systematically compare the performance of these models, providing detailed evaluations. The findings offer actionable insights for hotel managers, demonstrating how predictive analytics can inform revenue management, improve operational efficiency, and enhance marketing initiatives. These results contribute to the evolving field of demand forecasting, offering practical recommendations for data-driven decision-making in the tourism and the hospitality sector.
25 March 2022 in International Journal of Engineering Trends and Technology
Journal Article
Improvement of Nature-Based Optimization Methods for Solving Job shop Scheduling Problems
21 February 2024
Book Chapter
1 January 2022 in International Journal of Advanced Computer Science and Applications
Journal Article
The industry is subject to strong competition, and customer requirements which are increasingly strong in terms of quality, cost, and deadlines. Consequently, the companies must improve their competitiveness. Scheduling is an essential tool for improving business performance. The production scheduling problem is usually an NP-hard problem, its resolution requires optimization methods dedicated to its degree of difficulty. This paper aims to develop multi-hybridization of swarm intelligence techniques to solve job shop scheduling problems. The performance of recommended techniques is evaluated by applying them to all well-known benchmark instances and comparing their results with the results of other techniques obtainable in the literature. The experiment results are concordant with other studies that have shown that the multi hybridization of swarm intelligence techniques improve the effectiveness of the method and they show how these recommended techniques affect the resolution of the job shop scheduling problem.
10 June 2025
Book Chapter
SRSiham Rekiek
Siham RekiekHakim JebariMEMostafa Mostafa Ezziyyani
Mostafa Mostafa EzziyyaniLCLoubna Cherrat
Loubna CherratAbout Me
I am a professor doctor in Computer Science and Artificial Intelligence of Higher Education at ENSATe and ENAT and a researcher at Abdelmalek Essaâdi University.
With more than 15 years of executive experience in multinational automotive companies, having served as Chief Executive Officer, Operations Director, and Production Director within major automotive groups operating in Europe and Morocco, I have developed extensive expertise in industrial strategy, operational excellence, Lean Six Sigma, supply chain management, and QHSE systems. This industrial background informs my applied research, enabling me to translate scientific advances into practical solutions for complex industrial challenges.
My research focuses on the intersection of Artificial Intelligence, IoT, and Industry 4.0, particularly predictive maintenance, smart manufacturing, intelligent agriculture, hybrid AI models, metaheuristic optimization, edge computing, multimodal data fusion, and intelligent decision-support systems. I contribute to the Al-Khawarizmi Program supporting research in AI, Big Data, and their applications.
I have authored and co-authored peer-reviewed publications in international journals and Springer proceedings and have completed numerous peer reviews for international journals and research publications. My mission is to bridge academic research and industrial practice by developing intelligent, efficient, and sustainable solutions.
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