MOHAMED Chatra

ATTALAOUI noureddine

تصميم و تطوير موقع للتجارة الإلكترونية

MECHIKI Abdelouahab , BOUBAAYA Aziz

Développement d'un système (ERP) de gestion des stocks dans le cadre d'une application Web

DARAF Saadia

Plateforme d’inscription à l’enseignement à distance pour le secondaire

CHELLALI Mohamed

Design and Implementation of a Decision Support System Civil Protection

ATTABI Imane , MOKRANE Khawla

Conception et réalisation d’une application web d’inventaire et de gestion de pharmacie

بن يحيى أميرة

Simulation de piéton en utilisant l'algorithme A* et la logique floue

صديقي إيمان

Modélisation du comportement de piétons dans un milieu urbain

زاد الخير مريم

Simulation d'évacuation des personnes en utilisant la logique floue

مهدي رياض

Simulation d'une foule virtuelle par la logique floue

يورمش سليم

Navigation réactive d'un piéton en utilisant la logique floue

بوقرفة أحمد

Animation des entités virtuelles par la logique floue

Early detection of Diabetic Retinopathy (DR) is essential to reduce the risk of vision loss. This study introduces a novel framework for DR detection using a Particle Swarm Optimized Autonomous Learning Multiple Model (PSO-ALMMo) system. The proposed approach integrates the adaptive learning capability of the ALMMo* system with particle swarm optimization (PSO) to enhance classification accuracy and model interpretability. The proposed method uses PSO to optimize both antecedent and consequent parameters of the ALMMo* model, enabling high performance while maintaining the ability to learn incrementally from new data without retraining. Hybrid feature extraction techniques are applied to retinal fundus images before classification. Experiments were conducted on the newly introduced LISIA dataset as well as three benchmark datasets: Messidor-2, APTOS 2019, and IDRID. The PSO-ALMMo* system achieved 98.2% accuracy on Messidor-2, 99.7% on APTOS 2019, and 99% on IDRID. On the LISIA dataset, it maintained consistent performance across all DR severity levels. The model facilitates real-time learning and generates interpretable outcomes, owing to its prototype-based structure. These results indicate that the proposed system is well-suited for clinical environments to support early and accurate DR screening.

Citation

Mohamed Chatra , , (2025-10-30), Particle Swarm Optimized ALMMo* For Interpretable And Accurate Diabetic Retinopathy Detection, Journal of Zhengzhou University-Natural Science Edition, Vol:56, Issue:10, pages:314-336, Editorial Department of Journal of Zhengzhou University

Early detection of Diabetic Retinopathy (DR) is essential to reduce the risk of vision loss. This study introduces a novel framework for DR detection using a Particle Swarm Optimized Autonomous Learning Multiple Model (PSO-ALMMo) system. The proposed approach integrates the adaptive learning capability of the ALMMo* system with particle swarm optimization (PSO) to enhance classification accuracy and model interpretability. The proposed method uses PSO to optimize both antecedent and consequent parameters of the ALMMo* model, enabling high performance while maintaining the ability to learn incrementally from new data without retraining. Hybrid feature extraction techniques are applied to retinal fundus images before classification. Experiments were conducted on the newly introduced LISIA dataset as well as three benchmark datasets: Messidor-2, APTOS 2019, and IDRID. The PSO-ALMMo* system achieved 98.2% accuracy on Messidor-2, 99.7% on APTOS 2019, and 99% on IDRID. On the LISIA dataset, it maintained consistent performance across all DR severity levels. The model facilitates real-time learning and generates interpretable outcomes, owing to its prototype-based structure. These results indicate that the proposed system is well-suited for clinical environments to support early and accurate DR screening.

Citation

Mohamed Chatra , Samir Akhrouf , abdelouahab.attia@univ-bba.dz, PARTICLE SWARM OPTIMIZED ALMMO* FOR INTERPRETABLE AND ACCURATE DIABETIC RETINOPATHY DETECTION, zineb.maaref@univ-bba.dz, , (2025-10-30), PARTICLE SWARM OPTIMIZED ALMMO* FOR INTERPRETABLE AND ACCURATE DIABETIC RETINOPATHY DETECTION, JZU NATURAL SCIENCE, Vol:56, Issue:10, pages:23, Journal of Zhengzhou University-Natural Science

Traffic management, urban planning, and emergency management cannot be efficiently done without crowd simulation. This paper proposes a Behavioral Clustering Method (BCM), which tackles the problem of forming crowds in clusters or subgroups based on fundamental behaviors so that congestion is minimized during effective evacuation processes. We designed BCM based on synthetic data obtained from the simulation of the evacuation of a crowd in high-risk situations. Our method regards pedestrians as intelligent agents and predicts key behavioral aspects of future crowd evacuations before they occur. We use cluster analysis on those movement and behavioral data for building as well as evacuation-friendly control strategies by clustering people into subgroups of behavioral similarity. The credibility of the model is validated through Python-based animations to detect and rectify errors. Results from simulation performance evaluations indicate that BCM is successful in modeling the evolution of crowd behavior at the time of evacuation.

Citation

Mohamed Chatra , ghenabzia ahmed , Samir Akhrouf , Mustapha Bourahla , , (2025-08-31), A Simulation-Based Behavioral Clustering Method for Crowd Dynamics Evacuation Analysis, HAUT, Vol:23, Issue:8, pages:20, Open Access

Traffic management, urban planning, and emergency management cannot be efficiently done without crowd simulation. This paper proposes a Behavioral Clustering Method (BCM), which tackles the problem of forming crowds in clusters or subgroups based on fundamental behaviors so that congestion is minimized during effective evacuation processes. We designed BCM based on synthetic data obtained from the simulation of the evacuation of a crowd in high-risk situations. Our method regards pedestrians as intelligent agents and predicts key behavioral aspects of future crowd evacuations before they occur. We use cluster analysis on those movement and behavioral data for building as well as evacuation-friendly control strategies by clustering people into subgroups of behavioral similarity. The credibility of the model is validated through Python-based animations to detect and rectify errors. Results from simulation performance evaluations indicate that BCM is successful in modeling the evolution of crowd behavior at the time of evacuation.

Citation

Mohamed Chatra , , (2025-08-30), A Simulation-Based Behavioral Clustering Method for Crowd Dynamics Evacuation Analysis, Haut, Vol:23, Issue:8, pages:57-77, WPV Wirtschafts- und Praxisverlag GmbH

In this paper, we have developed a description of an agent-based model for simulating the evacuation of crowds from complex physical spaces to escape dangerous situations. This model describes a physical space that contains a set of differently shaped fences and obstacles, and an exit door. The pedestrians composing the crowd and moving in this space in order to be evacuated are described as intelligent agents with a supervised machine learning using perception-based data to perceive particular environment differently. The description of this model is developed with the Python language where its execution represents its simulation. Before the simulation, the model can be validated using an animation written with the Python language and this to fix possible problems of model description. A model performance evaluation is presented using an analysis of simulation results and this evaluation shows that these results are very encouraging.

Citation

Mustapha Bourahla , Bureau de la stratégie de numérisation , Mohamed Chatra , , (2024-02-01), Agent-Based Simulation of Crowd Evacuation Through Complex Spaces, Ingénierie des Systèmes d’Information, Vol:29, Issue:1, pages:1-11, International Information and Engineering Technology Association (IIETA)