Publications Scientifiques
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Item Tourism Investment in Algeria: Bridging the Gap Between Potential and Performance in the SDAT 2030 Framework(Université de Bordj Bou Arréridj, 2025) Badreddine, Amina; Telkhoukh, SaidaThis study focuses on tourism investment environment in Algeria and its potential to drive economic diversification beyond hydrocarbon reliance. The study used a mixed methodology, reviewing the official tourism statistics (2018-2025) and legislative frameworks and the Tourism Development Master Plan (SDAT 2030) to evaluate the current performance in relation to the strategic goals. Even with recent legislative changes under the 2022 Investment Law and the ambitious target of reaching 12 million visitors annually rather than 2.5 million by 2030, research indicates that there are still ingrained issues: tourism has become a mere contributor to GDP at 1.47% in 2023 compared to the Mediterranean average of 10%, accommodation facilities are critically inadequate at 0.1 hotel rooms per 100 inhabitants and 66% of registered tourism projects are either not started or uncommented. The study concludes that although Algeria holds considerable comparative advantages in desert, coastal and heritage tourism, the opportunities in the country could only be actualised by overcoming the shortcomings in infrastructure, institutional coordination and international marketing.Item Deformable Transformer-Based Object Detection for Robust Perception in Autonomous Driving(IEEE, 2025) Kezzal, Chahira; Benderradji, Selsabil; Benlamoudi, Azeddine; Bekhouche, Salah Eddine; Taleb, Abdel; Hadid, AbdenourAutonomous driving demands robust and real-time object detection to safely navigate in complex environments. While Convolutional neural network (CNN)-based detectors have been widely adopted, they face challenges such as limited receptive fields and inefficiencies in handling small or occluded objects. This paper presents a deformable Transformer based object detection framework designed to address these limitations. By leveraging deformable attention mechanisms, the model dynamically focuses on relevant spatial regions, significantly enhancing detection accuracy. Evaluated on the benchmark KITTI dataset, our proposed approach achieves an interesting mAP@50 of 96.6%, surpassing many state-of-the-art methods, at the cost of slower inference speed (7.0 FPS). The experimental results also demonstrate the framework’s superior precision and adaptability in autonomous driving scenarios. This work underscores the potential of deformable transformers to advance perception systems, balancing high accuracy with the demands of real-world applications.Item Efficient Real-Time Multi-Class Object Tracking with YOLO11 and ByteTrack in Real-World Driving Scenes(IEEE, 2025) Benderradji, Selsabil; Kezzal, Chahira; Benlamoudi, Azeddine; Bekhouche, Salah Eddine; Taleb, AbdelAccurate and real-time multi-object tracking (MOT) is essential for autonomous driving systems to ensure safe navigation and decision making in dynamic environments. This paper introduces a tracking-by-detection pipeline that integrates YOLOv11 a high speed, high-accuracy object detector with ByteTrack, a robust data association algorithm capable of lever-aging both high and low confidence detections. The proposed framework addresses key challenges in MOT such as frequent occlusions, fluctuating lighting, and dense traffic by combining efficient detection with motion-consistent identity tracking. Evaluated on the KITTI benchmark, our method demonstrates superior performance across multiple metrics, including HOTA, AssA, and MOTA, for both cars and pedestrians. Additionally, the system achieves an average runtime of 60.4 FPS, supporting its real-time applicability. The results confirm that the proposed YOLOv11 + ByteTrack integration provides a scalable, accurate, and deployment ready solution for complex urban driving scenarios.Item Achievable Rates of Full Duplex Cooperative Relay Selection-Based Machine Learning(IEEE, 2025) Belaoura, Widad; Althunibat, Saud; Mazen, Hasna; Qaraqe, Khalid; Ammuri, RulaMachine learning (ML) is an advanced artificial intelligence technology that addresses the ever-growing complexity in communication signal processing. In this paper, the concept of ML-based classification model to choose the best relay is investigate in a full duplex (FD) cooperative system. Specifically, a K-nearest neighbors (KNN)-based relay selection is applied to accurately predict and evaluate the achievable rate of the optimal FD relay. The core idea of the multi-class KNN is to identify the optimal relay that yields the highest achievable rate performance by utilizing a large set of offline training data derived from the channel state information (CSI), ensuring that no further training is required during system processing. The results indicate that the KNN-based FD relay selection can achieve an achievable rate comparable to the optimal exhaustive search method with lower computation complexity.Item Valuation of Physical Layer Security Under Jamming Attacks Utilizing RIS(2025) Refas, Souad; Meraihi, Yassine; Ivanova, Galina; Baiche, Karim; Cherif, Amar Ramdane; Acheli, DalilaVehicular visible light communication (V VLC) systems, when combined with reconfigurable intelligent surfaces (RIS), present promising opportunities for improving communication reliability and efficiency in vehicle to vehicle (V2V) environments. Nevertheless, safeguarding these systems at the physical layer remains a critical challenge, particularly given their exposure to jamming threats. In this study, we investigate the physical layer security performance of RIS assisted V2V VLC systems under jamming scenarios, employing realistic V2V VLC channel models. We develop a methodology to examine the impact of various security strategies in mitigating the adverse effects of jamming. Our analysis examines key parameters including the signal to noise ratio SNR, the secure communication rate and the count of RIS units. Simulation results confirm that the proposed security systems significantly enhance the resilience of V2V VLC networks in the presence of jamming attacks. These results offer useful perspectives for the reliable design structure and deployment of RIS based V2V VLC systems in practical vehicular communication settings.Item أثر الدين الخارجي على النمو الاقتصادي في تونس خلال الفترة(1980-2019) باستعمال طريقة المربعات الصغرى المصححة كليا (fmols)(جامعة أمحمد بوقرة بومرداس, 2021) خميس, كريم; بوشنب, موسىتهدف هذه الدراسة إلى توضيح تأثير الدين الخارجي على النمو الاقتصادي في تونس خلال الفترة (1980-2019)، بعد التخلص من متغيرتي خدمة الدين الخارجي و سعر الصرف الأجنبي، تم تطبيق طريقة المربعات الصغرى المصححة كليا بالإبقاء على متغيرتي الدين الخارجي والاحتياطات الأجنبية، وقد خلصت هذه الدراسة إلى أن الدين الخارجي يؤثر تأثيرا إيجابيا على النمو الاقتصادي في تونس خلال فترة الدراسة. الكلمات المفتاحية : النمو الاقتصادي الدين الخارجي ؛ تونس ؛ دراسة إقتصادية قياسيItem Enhancing sustainability in CNC turning of POM-C polymer using MQL with vegetable-based lubricant: machine learning and metaheuristic optimization approaches(Springer Science and Business Media, 2025) Hakmi, Tallal; Abderazek, Hammoudi; Yapan, Yusuf Furkan; Hamdi, Amine; Uysal, AlperSustainable machining of polymer parts, which is still less advanced than metal machining, aims not only to improve machinability but also to address environmental and economic challenges. Therefore, this study analyzes the sustainability of polyoxymethylene copolymer (POM-C) turning by incorporating minimum quantity lubrication (MQL) parameters (Q: flow rate, θ: nozzle angle, and d: nozzle distance) and conventional cutting parameters (Vc: cutting speed, f: feed, and ap: depth of cut), while replacing conventional oil with a biodegradable and environmentally friendly lubricant derived from Eraoil KT/2000. Additionally, the methodology relies on sustainability indicators such as surface roughness (Ra), total energy consumption (Etotal), total carbon emissions (CEtotal), and overall cost (Ctotal). To achieve this, several approaches are employed, including analysis of variance (ANOVA), artificial neural networks (ANN), k-fold cross-validation (k-fold CV), and two multi-objective metaheuristic optimization algorithms, namely SHAMODE (success history-based multi-objective adaptive differential evolution) and RPBILDE (real-code population-based incremental learning and differential evolution), are used to identify significant factors, establish mathematical models, and determine optimal conditions. The multi-objective optimization highlights trade-offs between the four sustainability criteria. Thus, a low feed value and a low MQL flow rate, combined with significant angle and distance, as well as moderate cutting speed and depth of cut, provide minimal surface roughness (Ra = 1008 µm), low energy consumption (Etotal = 0.0947 MJ), low carbon emissions (CEtotal = 0.0583 kgCO₂) but with a slightly higher cost (Ctotal = 1701 $). These results confirm a Pareto front where the improvement of one criterion negatively impacts another, guiding industrial decisions based on prioritiesItem A Comprehensive Review of Pesticide Elimination Methods from Fruits and Vegetables Over the Past Two Decades: Optimizing Produce Safety for Sustainable Food Systems(Research Institute of Food Science and Technology, 2025) Meghlaoui, Zoubeida; Remini, Hocine; Remini-Sahraoui, Yasmine; Mellal, Mohamed Khalil; Boudalia, Sofiane; Brahimi, Yasmine; Negrichi, Samira; Allam, Ayoub; Medouni-Haroune, Lamia; Messaoudene, LyndaThe increasing use of pesticides in agriculture, valued at approximately 43.2 billion USD, has raised significant concerns regarding food safety and human health. This study reviews the effectiveness of various pesticide residue removal methods applied to fruits and vegetables (F & V). A total of 57 studies published between 2005 and 2022 were analyzed, categorizing the methods into 28 household techniques, 19 advanced methods, and 10 combined approaches. Household methods, such as washing under running water, achieved removal rates of up to 90%, while peeling ensured complete (100%) elimination of residues. The addition of salt or vinegar solutions improved removal efficiency, reaching 92%. Advanced methods, notably ozonation, demonstrated high efficacy with up to 95% removal. The most effective approaches were combined techniques, integrating washing, ultrasound, and ozonation, which achieved residue elimination rates of up to 99%. Despite their efficiency, advanced methods face limitations due to high costs and technological constraints, reducing their accessibility for widespread use. This review underscores the necessity of an integrated approach to enhance food safety. Additionally, it highlights the need for further research on the long-term impact of these removal methods on the nutritional quality of F & V. These findings provide essential insights for consumers, farmers, and the food industry, contributing to the development of more effective and practical food safety strategiesItem Environmental Impacts and Behavioral Adaptations of Honeybees in Algeria: A Review of Apis mellifera intermissa and Apis mellifera sahariensis Characteristics(Multidisciplinary Digital Publishing Institute, 2025) Haider, Yamina; Adjlane, Noureddine; Haddad, NizarHoneybees are vital for pollination and the overall health of ecosystems. Since the 18th century, the intricate biology of honeybees has been a subject of scientific inquiry. Understanding their biological and behavioral characteristics is essential for effective beekeeping, honey production, and ecosystem sustainability. This review examines the environmental impact and management practices on the health of local honeybees in Algeria, focusing on Apis mellifera intermissa and Apis mellifera sahariensis. We summarize research findings on genetic diversity, morphometric traits, behavioral characteristics, and adaptation of local honeybees. Additionally, we discuss the threats posed by abiotic and biotic stressors and highlight the importance of conservation and sustainable management. The reviewed studies indicate that environmental factors significantly influence the behavioral characteristics and adaptation of local honeybees. Notably, the hygienic behavior of A. m. intermissa contributes to their resistance against diseases and the Varroa destructor mite. Further research in these areas is important for enhancing our understanding of honeybee health and population dynamics in Algeria, thereby informing strategies for sustainable beekeeping practicesItem Design and implementation of a self-driving car using deep reinforcement learning: A comprehensive study(Elsevier, 2025) Djerbi, Rachid; Rouane, Anis; Taleb, Zineb; Saradouni, SafiaThis paper presents a groundbreaking and comprehensive study on the design, implementation, and evaluation of a self-driving car utilizing deep reinforcement learning, showcasing significant advancements in autonomous vehicle technology. Our robust framework integrates three innovative AI models for essential functionalities: road detection, traffic sign recognition, and obstacle avoidance. The system architecture, structured around a three layers “DDD” (Data, Detection, Decision) approach, involves meticulous data preprocessing for traffic signs and road data, followed by specialized Deep Learning models for each detection task, including a CNN for traffic signs, a CNN for road detection, and the pre-trained MobileNet-SSD for obstacle detection. A reinforcement learning agent in the Decision Layer processes these outputs for real-time control (steering, acceleration, braking) through a continuous learning process with environmental feedback. The research encompasses both extensive simulation in Unity, leveraging the ML-Agents toolkit for agent training across diverse environments, and crucial real-world deployment. Our reward/punishment system in the simulation environment, based on collisions with road markers and obstacles, refined the agent's decision-making. The trained AI models were successfully exported and deployed onto a physical prototype, controlled by a Raspberry Pi and equipped with a camera and ultrasonic sensors. Real-world testing affirmed the robust performance of the physical model in detecting roads, recognizing traffic signs, and effectively avoiding obstacles. Quantitative results demonstrate compelling performance, including over 90% accuracy in obstacle detection and a 15% improvement in navigation efficiency compared to traditional algorithms under controlled simulation conditions. Model evaluation metrics show a 98% accuracy, 12% loss, and a prediction rate exceeding 77%. This study not only contributes a comprehensive framework for autonomous vehicle development but also highlights the transformative potential of deep reinforcement learning for creating intelligent and adaptable autonomous systems in both virtual and real-world scenarios, paving the way for safer and more efficient transportation technologies