Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item An Enhanced Aquila Optimizer Algorithm for Resource Allocation in Indoor Multi-user IoT VLC System(2023) Yahia, Selma; Meraihi, Yassine; Mekhmoukh Taleb, Sylia; Mirjalili, Seyedali; Ramdane-Cherif, Amar; B. Eldeeb, Hossien; Muhaidat, SamiVisible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and co- channel interferences, which can greatly improve network perfor- mance and user satisfaction. This paper focuses on an indoor IoT- based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.Item Machine learning-based research for COVID-19 detection, diagnosis, and prediction : a survey(Springer, 2022) Meraihi, Yassine; Gabis, Asma Benmessaoud; Mirjalili, Seyedali; Ramdane-Cherif, Amar; Alsaadi, Fawaz EThe year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,..) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employedItem Nodes placement in wireless mesh networks using optimization approaches : a survey(Springer, 2022) Mekhmoukh Taleb, Sylia; Meraihi, Yassine; Benmessaoud Gabis, Asma; Mirjalili, Seyedali; Ramdane-Cherif, AmarWireless mesh networks (WMNs) have grown substantially and instigated numerous deployments during the previous decade thanks to their simple implementation, easy network maintenance, and reliable service coverage. Despite these proprieties, the nodes placement of such networks presents many challenges for network operators. In this paper, we present a survey of optimization approaches implemented to address the WMNs nodes placement problem. These approaches are classified into four main categories: exact approaches, heuristic approaches, meta-heuristic approaches, and hybrid approaches. For each category, a critical analysis is drawn according to targeted objectives, considered constraints, type of positioned nodes (Mesh Router and Mesh Gateway), location (discrete or continuous), and environment (static or dynamic). In the end, several new key search areas for WMNs nodes placement are suggestedItem A comprehensive survey of sine cosine algorithm : variants and applications(Springer, 2021) Benmessaoud Gabis, Asma; Meraihi, Yassine; Mirjalili, Seyedali; Ramdane‑Cherif, AmarSine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA