Publications Internationales
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Item A Comprehensive Survey of Manta Ray Foraging Optimization: Theory, Variants, Hybridization, and Applications(Springer Science and Business Media, 2025) Yahia, Selma; Taleb, Sylia Makhmoukh; Ait Saadi, Amylia; Meraihi, Yassine; Bhuyan, Bikram Pratim; Mirjalili, Seyedali; Ramdane-Cherif, AmarThe Manta Ray Foraging Optimization (MRFO) algorithm is a recent Swarm-based meta-heuristic optimization algorithm inspired by the foraging behavior of manta rays in catching and hunting their prey, utilizing three main techniques (i.e.: chain foraging, somersault foraging, and cyclone foraging). Since its development by Zhao et al. (Neural Comput Appl 32:9777–9808, 2020; Eng Appl Artif Intell 87:103300, 2020), the MRFO algorithm has garnered significant attention among researchers and has been applied across various fields to solve real-world optimization problems. This is due to its simple structure, flexibility, ease of implementation, and reasonable convergence rate. This paper provides an extensive and in-depth survey of the MRFO algorithm including modification, multi-objective, and hybridized versions. It also examines the various applications of the MRFO algorithm in several domains of problems such as classification, feature selection, scheduling, robotics, photovoltaic power systems, optimal parameter control, and clustering. Furthermore, the results of the MRFO algorithm are compared with some well-regarded optimization meta-heuristics such as Differential Evolution (DE), Harmony Search (HS), Bat Algorithm (BA), Multi-Verse Optimizer (MVO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Henry Gas Solubility Optimization (HGSO), and White Shark Optimizer (WSO). Finally, the paper proposes some potential future research directions to further advance the MRFO’s capability and applicabilityItem A Monadic Second-Order Temporal Logic framework for hypergraphs(Springer Nature, 2024) Bhuyan, Bikram Pratim; Singh, Thipendra P.; Tomar, Ravi; Meraihi, Yassine; Ramdane-Cherif, AmarThis study introduces a novel computational framework integrating monadic second-order temporal logic (MSOTL) with hypergraph models to enhance the predictive analysis and prediction of complex systems, with a specific focus on urban agriculture. Traditional graph-based models often fail to capture the intricate, high-order temporal dynamics inherent in such systems. By leveraging the expressive power of MSOTL within a hypergraph context, our approach enables a more nuanced representation of temporal and relational data, leading to improved predictive accuracy and deeper analytical insights. The framework was applied to a comprehensive dataset of urban agricultural practices, incorporating data from diverse farming sites across multiple countries. Our results demonstrate the model’s capability to outperform existing methods in predicting agricultural outcomes by effectively capturing both the spatial and temporal complexities of urban farming data. The study not only advances the theoretical understanding of hypergraph-based temporal logic modeling but also offers an application for urban agricultural planning and management.Item Solving the LEDs placement problem in indoor VLC system using a hybrid coronavirus herd immunity optimizer(Springer Nature, 2024) Benayad, Abdelbaki; Boustil, Amel; Meraihi, Yassine; Yahia, Selma; Mekhmoukh Taleb, Sylia; Ait Saadi, Amylia; Ramdane-Cherif, AmarVisible light communication (VLC) is a developing technology enabling simultaneous illumination and communication between users. This is achieved by employing light emitting diodes (LEDs) as transmitters and photo-detectors (PDs) as receivers. In indoor visible light communication (VLC) systems, a significant challenge is the deployment of a various number of LEDs that accommodate different numbers of users. This particular problem falls under the category of Non-deterministic polynomial-time hard (NP-hard), making it difficult to find exact solutions in a reasonable amount of time. As a result, employing approximation approaches, particularly meta-heuristics, proves to be a suitable and effective way to address this challenge. In this paper, we propose a hybrid approach (ICHIO-FA) based on the combination of improved coronavirus herd immunity optimizer (ICHIO) with firefly algorithm (FA) for solving the LEDs placement problem in an indoor VLC system. In the proposed ICHIO-FA algorithm, the chaotic map concept is adopted to increase the chaotic stochastic behavior of the CHIO. Moreover, the opposition-based learning (OBL) mechanism is applied to enhance the convergence speed of CHIO and explore the search space effectively. Finally, FA is used as a local search method for ICHIO to avoid trapping into local optima. The effectiveness of the proposed ICHIO-FA algorithm is tested on several scenarios under different settings, taking into account the throughput and user coverage metrics. Simulation results demonstrate the accuracy and superiority of the ICHIO-FA approach in finding optimal LEDs positions when compared with the standard CHIO, FA, particle swarm optimization (PSO), genetic algorithm (GA), marine predators algorithm (MPA), whale optimization algorithm (WOA), manta ray foraging optimization (MRFO), bat algorithm (BA), grey wolf optimizer (GWO), and simulated annealing (SA).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 Performance analysis of bidirectional multi-hop vehicle-to-vehicle visible light communication(Institute of Electrical and Electronics Engineers Inc, 2023) Refas, Souad; Acheli, Dalila; Yahia, Selma; Meraihi, Yassine; Ramdane-Cherif, Amar; Van, Nhan Vo; Ho, Tu DacVehicular visible light communication (VVLC) has emerged as a promising field of research, garnering considerable attention from scientists and researchers. VVLC offers a potential solution to enable connectivity and communication between travelling vehicles along the road by using their existing headlights (HLs) and taillights (TLs) as wireless transmitters and integrating photodetectors (PDs) within the car front or car-back as wireless receivers. However, VVLC encounters more challenges than indoor VLC, particularly in vehicle-to-vehicle (V2V) communication, where vehicle mobility disrupts the establishment of direct communication links. To address this, we propose a multi-hop relay system wherein intermediate vehicles act as wireless relays to maintain a line-of-sight (LoS) link. In this paper, we investigate the performance of a bidirectional multi-hop relay V2V-VLC system that operates in both the forward and backward directions. Based on realistic ray tracing channel models, we derive a closed-form expression for the full bidirectional communication range. We also analyze how the transceiver's parameters and the number of relays affect the system performance. Our results show that the proposed bidirectional multi-hop relay system can extend the direct transmission range by more than 19 m with only a hop relay.Item Mesh router nodes placement for wireless mesh networks based on an enhanced Moth–Flame optimization algorithm(Springer, 2023) Mekhmoukh Taleb, Sylia; Meraihi, Yassine; Mirjalili, Seyedali; Acheli, Dalila; Ramdane-Cherif, Amar; Benmessaoud Gabis, AsmaThis paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA)Item Performance evaluation of vehicular visible light communication based on angle-oriented receiver(Elsevier, 2022) Yahia, Selma; Meraihi, Yassine; Ramdane-Cherif, Amar; Gabis, Asma Benmessaoud; Eldeeb, Hossien B.Visible Light Communication (VLC) has emerged as a promising technology to complement radio frequency-based vehicular communications. Initial studies in Vehicle-to-Vehicle (V2V) VLC systems assumed that two vehicles follow each other with perfect alignment. Such idealistic assumption is not always maintained during traveling along the road. The lateral shift between the vehicles might strongly impact the system performance. In addition, the effect of the transceiver and system parameters on the performance of V2V-VLC systems should be taken into account. In this paper, we fill this research gap by investigating the performance of V2V-VLC systems under the impact of the lateral shift between the vehicles and transceiver parameters. Then, we introduce the use of the angle-oriented receiver (AOR) in V2V-VLC systems to enhance the system performance in terms of achievable capacity, maximum achievable distance, and packet delivery ratio (PDR). The AOR consists of multiple receiving elements oriented in different directions. We further investigate the impact of the number of AOR elements, both the field-of-view (FoV) and the aperture diameter of each receiving element, and the bandwidth on the system performance. Our results demonstrate that with a carefully chosen system and AOR parameters, a higher system capacity of up to 61 Mb/s is achieved at a communication distance of 50 mItem 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 Performance study and analysis of MIMO visible light communication-based V2V systems(Springer, 2022) Yahia, Selma; Meraihi, Yassine; Refas, Souad; Benmessaoud Gabis, Asma; Ramdane-Cherif, Amar; Eldeeb, Hossien B.Vehicular Visible Light Communication (VLC) has recently attracted much interest from researchers and scientists. This technology enables the connectivity between the vehicles and the infrastructures along the road utilizing the Lighting-Emitting-Diodes based vehicle HeadLights (HLs) and TailLights (TLs) as wireless transmitters. This paper investigates the performance of a Vehicle-to-Vehicle VLC system using a Multiple-Input Multiple-Output (MIMO) scheme. Specifically, we establish the MIMO transmission system by using the two HLs of the source vehicle as wireless transmitters and multiple receivers (RXs) installed at the rear of the destination vehicle as wireless receivers. We consider different numbers of RXs, which result in various MIMO configurations, i.e., 2 × 2 , 2 × 3 , and 2 × 4. We conduct a channel modeling study based on the non-sequential ray-tracing capabilities of the OpticStudio software to obtain the optical channel gain, considering the possibility of both horizontal and vertical displacement between vehicles. We then explore the contribution of each RX in the total received power. In addition, we investigate the effect of weather conditions, modulation orders, and artificial light sources on the bit error rate (BER) performance of the considered MIMO configurations. The obtained results demonstrate that deploying the MIMO with higher orders can significantly enhance the system performance, particularly when there is a lateral shift between the two cars. It has been drawn from our results that the required SNR to achieve a BER of 10- 4 reduces by 6 dB when 2 × 4 MIMO configuration is deployed compared to the 2 × 2 MIMO configurationItem 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 suggested