BRAHIM Bouderah
بودراح براهيم
brahim.bouderah@univ-msila.dz
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- Mathematics Department
- Faculty of Mathematics and Informatics
- Grade Prof
About Me
Location
Msila, Msila
Msila, ALGERIA
Code RFIDE- 1962-02-27 00:00:00
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BRAHIM Bouderah birthday
- 2025-06-16
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2025-06-16
Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination
Federated learning (FL) allows distributed devices to jointly train a global model while safeguarding the privacy of their local data. However, selecting and securing clients, especially in environments with potentially malicious participants, remains a critical challenge. This study proposes an innovative participant selection method to enhance both security and efficiency in centralized and decentralized FL frameworks. In the centralized framework, this method effectively excludes clients with weak privacy protections and optimization capabilities, thus increasing overall system security. For decentralized FL, a blockchain-supported approach is introduced, which further strengthens the robustness of the system. Using a dynamic role assignment algorithm, roles such as worker, validator, and miner are allocated based on security and performance metrics for each training round. The findings show that this method performs on a par with the scenarios free of malicious clients, demonstrating the value of blockchain technology in improving FL protocols. By addressing security vulnerabilities and improving training efficiency, this research contributes to the development of more secure and efficient FL systems, underscoring the importance of advanced participant selection and role assignment strategies.
Citation
WAFA Bouras , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@ensia.edu.dz, , (2025-06-16), Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination, Computing and Informatics, Vol:44, Issue:3, pages:35, OJS/PKP
- 2025-03-18
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2025-03-18
Homotopy Perturbation ρ-Laplace Transform Approach for Numerical Simulation of Fractional Navier-Stokes Equations
In this study, we tackle the time fractional discrete Navier-Stokes equation by employing the homotopyperturbation ρ-Laplace transform method (HPLTM), utilizing the Caputo-Katugampola fractional derivative of time.Additionally, we present graphical representations of the solution generated using Matlab software, comparing it withthe exact solution for α = 1. We perform two test problems to verify and demonstrate the effectiveness of our approach.Our numerical findings and graphical analyses indicate that the proposed approach exhibits remarkable efficiency andsimplicity, rendering it suitable for addressing a diverse array of challenges encountered in engineering and the sciences
Citation
YACINE Arioua , Hamza Mihoubi , BRAHIM Bouderah , Awatif Alghahtani, , (2025-03-18), Homotopy Perturbation ρ-Laplace Transform Approach for Numerical Simulation of Fractional Navier-Stokes Equations, Contemporary Mathematics, Vol:6, Issue:3, pages:1-33, Universal Wiser Publisher
- 2024-12-10
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2024-12-10
Parallel Association Rules Mining Using GPUs and Reptile Search Algorithm
This paper proposes a novel approach to accelerate association rule mining using the Reptile Search Algorithm (RSA) in conjunction with GPU-based parallel processing. Traditional association rule mining techniques can be computationally expensive, especially with large datasets. By utilizing the inherent parallelism of Graphics Processing Units (GPUs), we significantly speed up the fitness evaluation process, a core component of the Reptile Search Algorithm. Our results show a marked improvement in the performance of RSA on large datasets, making it feasible for real-time or large scale applications such as market basket analysis, healthcare for drug interaction analysis, and web usage mining. We also analyze the impact of various GPU optimizations and present a comparison with CPU-based execution.
Citation
Abderrahim Boukhalat , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@univ-msila.dz, ,(2024-12-10), Parallel Association Rules Mining Using GPUs and Reptile Search Algorithm,The Sixth International Symposium on Informatics and Its Applications (ISIA),Université Mohamed Boudiaf M'Sila
- 2024-06-01
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2024-06-01
Reptile Search Algorithm for Association Rule Mining
Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.
Citation
Abderrahim Boukhalat , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@univ-msila.dz, , (2024-06-01), Reptile Search Algorithm for Association Rule Mining, International Journal of Computing and Digital Systems, Vol:14, Issue:1, pages:16, University of Bahrain