Communications Internationales

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Author: search.filters.author.Acheli, DalilaHas files: Yes

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    Hybrid whale optimization algorithm with simulated annealing for the UAV placement problem
    (Springer Nature, 2024) Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Yahia, Selma; Ramdane-Cherif, Amar; Gabis, Asma Benmessaoud; Acheli, Dalila
    This chapter suggests a hybrid algorithm based on the combination of whale optimization algorithm (WOA) with simulated annealing (SA), called WOA-SA, for solving the unmanned aerial vehicle (UAV) placement problem. WOA-SA combines WOA’s global search functionality with SA’s local search functionality. The main objective of our work is to determine the optimal position of the UAV in order to maximize the total throughput, depending on a given set of user locations and traffic demands. The WOA-SA algorithm is validated in terms of the total throughput using 18 distinct instances with various numbers of users, taking into account the effect of the distribution of user positions. The results of simulation using Matlab demonstrated that the WOA-SA algorithm obtains better results than WOA, SA, Particle Swam Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm (BA).
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    Strategy of detecting abnormal behaviors by fuzzy logic
    (IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
    This work falls within the framework of the video surveillance research axis. This work falls within the scope of video surveillance. It involves a link between automatic processing and problems related to video surveillance. The job is to analyze video streams coming from a network of surveillance cameras, deployed in an area of interest in order to detect abnormal behavior. Our approach in this article relies on the new application and the use of fuzzy logic in the case of division and fusion of the crowd. The detection of these behaviors will increase the speed of response of the security services in order to perform accurate analysis and detection of events in real time
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    Heuristic and learning method for obstacle avoidance with mobile robot
    (IEEE, 2020) Lachekhab, Fadhila; Acheli, Dalila; Tadjine, Mohamed; Meraihi, Yassine
    In this paper, a fuzzy controller obstacle avoidance of the mobile robot Pioneer II is proposed. The fuzzy inference system FIS of this controller is performed by two methods: heuristic and reinforcement learning. the manual tuning of the fuzzy control system can be long and difficult. In contrast, reinforcement learning has proven theoretically and practically its ability to automatically optimize some parameters of the FIS. For that, the Fuzzy Actor-Critic Learning algorithm allows the determination of the parameters of the conclusions among of an available set fixed by the operator. The proposed algorithm allows the automatic determination of the parameters of the conclusions of the fuzzy rules. The simulations show that the two controllers (heuristic, RL controller) are able to avoid the different shapes of obstacles contained in known environments, and they show exceptionally good robustness when changing the environment (shape of obstacles, location of obstacles in the environment
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    Sun trajectory and PV module I–V characteristics estimation using neural networks
    (IEEE, 2017) Miloudi, Lalia; Acheli, Dalila; Kesraoui, Mohamed
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    Strategy of detections abnormal behavior by fuzzy logic
    (IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
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    Detection method without crowd behavior modeling by fuzzy logic
    (2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
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    Analysis of the human behavior from video with covariance matrix
    (2016) Chebi, Hocine; Acheli, Dalila
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    Dynamic detection of anomalies in crowd's behavior analysis
    (IEEE, 2015) Chebi, Hocine; Acheli, Dalila
    The analysis of the human behavior from video is a wide field of the vision by computer. In this work we are interested in the analysis of the crowd behavior and its entities in a dense scene. These scenes are characterized by the presence of a great number of people in the camera's field of vision. A major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by operators. We present in this paper a new approach for the anomalies detection very dense scenes relaying on the speed of both the individuals and the whole group. The various anomalies are detected by switching in a dynamic way between two approaches: the artificial neurons networks "ANN" for the management of group anomalies of people, and the Density Based Spatial Clustering of Application with Noise "DBSCAN" in the case of entities. For more robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions.