Academic & Scientific Articles

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    Rapport sur la Maladie d’Alzheimer
    (CERIST, 2025-02) Atik, Ali
    La maladie d'Alzheimer est une affection neurodégénérative progressive qui constitue la principale cause de démence chez les personnes âgées. Elle se caractérise par une détérioration cognitive progressive affectant la mémoire, le langage, le raisonnement et les capacités fonctionnelles. Bien que les causes exactes restent incertaines, des facteurs génétiques, environnementaux et biologiques sont impliqués, notamment l'accumulation anormale des protéines bêta-amyloïde et tau dans le cerveau. À ce jour, aucun traitement curatif n'existe, mais des approches thérapeutiques visent à ralentir la progression des symptômes et à améliorer la qualité de vie des patients. La recherche actuelle explore de nouvelles pistes, notamment les biomarqueurs pour un diagnostic précoce et les thérapies ciblées contre les mécanismes pathologiques de la maladie.
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    Better Space-Time-Robustness Trade-Offs for Set Reconciliation
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2024-07) Belazzougui , Djamal; Kucherov, Gregory; Walzer, Stefan
    We consider the problem of reconstructing the symmetric difference between similar sets from their representations (sketches) of size linear in the number of differences. Exact solutions to this problem are based on error-correcting coding techniques and suffer from a large decoding time. Existing probabilistic solutions based on Invertible Bloom Lookup Tables (IBLTs) are time-efficient but offer insufficient success guarantees for many applications. Here we propose a tunable trade-off between the two approaches combining the efficiency of IBLTs with exponentially decreasing failure probability. The proof relies on a refined analysis of IBLTs proposed in (Bæk Tejs Houen et al. SOSA 2023) which has an independent interest. We also propose a modification of our algorithm that enables telling apart the elements of each set in the symmetric difference.
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    Usage des ressources numériques à valeur pédagogique par des élèves des cycles moyen et secondaire
    (Institut National de Recherche en Education, 2024-10) Bebbouchi, Dalila
    L’avènement du Web 2.0 et la prolifération de la pandémie du Covid 19 ont facilité la production et l’accès aux ressources pédagogiques numériques pour les enseignants et les élèves. La présente communication concerne une étude exploratoire qui tente de comprendre à travers les résultats d’entretiens menés auprès d’élèves des cycles moyen et secondaire, les habitudes de recherche documentaire de ces élèves et d’examiner leurs usages qu’ils font des ressources pédagogiques numériques produites et diffusées sur Internet par des enseignants algériens. Nous présenterons également une évaluation non exhaustive effectuée sur les ressources proposées par le panel d’élèves interviewés sur le plan de la qualité et de la pertinence de la ressource.
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    L’entraide d’étudiants dans l’apprentissage en ligne : le rôle joué par le sentiment d’appartenance à un groupe et par l’autodétermination de la motivation
    (Université de Montréal, 2022) Bebbouchi, Dalila; Jézégou, Annie
    La recherche à l’origine de cet article visait à étudier les comportements d’entraide spontanée entre des étudiants inscrits dans un dispositif d’apprentissage en ligne. Il s’agissait en particulier d’examiner si le sentiment d’appartenance à un groupe exerçait une influence sur ces comportements d’entraide. Un autre objectif, lié au précédent, était de vérifier si de tels comportements avaient, à leur tour, une influence sur le degré d’autodétermination de la motivation de ces étudiants à l’égard de la formation. Les résultats de cette recherche révèlent que, pour ces étudiants, le sentiment d’appartenance à un groupe constitue un levier motivationnel pour développer des comportements d’entraide principalement basés sur l’altruisme et le réconfort. Ces comportements constituent, à leur tour, un soutien motivationnel pour poursuivre en formation.
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    The Future of BPM in the era of industry 4.0 : exploring new opportunities for innovation
    (CERIST, 2025-02) Khider, Hadjer; Meziane, Abdelkrim; Hammoudi, Slimane
    In today's digital age, the fourth industrial revolution has given rise to Industry 4.0. This new paradigm has brought new challenges for organizations, through a digital transformation. This digital transformation has profoundly impacted the way businesses operate, leading to a fundamental shift in the Business Process Management (BPM), affecting business models, processes, products, relationships and competencies. This transformation is based on the use of cyber-physical systems and information and communication technologies, in particular artificial intelligence and the Internet of Things. This paper aims to identify and define the main challenges, limitations, and opportunities of BPM in the era of Industry 4.0. Furthermore, it aims to identify potential future research directions. in addition to analyzing the impact of Industry 4.0 concepts and related technologies on the management of organizations and their business processes.
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    Two-tower neural network for personalizing service recommendation in cloud environment
    (CERIST, 2025-02) Lebib, Fatma Zohra; Meziane, Abdelkrim
    Effective cloud service recommendation necessitates a deep understanding of both user preferences and the diversity of cloud services. This paper proposes a novel two-tower neural network architecture to address this challenge. By leveraging the power of neural networks, we create concise representations of users and cloud services, enabling highly personalized recommendations. Our two-tower architecture is designed to efficiently scale to the massive scale of cloud environments. Each tower, dedicated to users and cloud services respectively, integrates relevant features and interaction data to generate dense embeddings. Cloud services are then ranked based on their similarity to the user’s embedding, ensuring accurate and tailored suggestions. The proposed two-tower model was evaluated on the WSdream dataset for both regression and classification tasks. Experimental results consistently demonstrated superior performance compared to state-of-the-art recommendation techniques, including k-nearest neighbors and matrix factorization.
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    Matrix Factorization for cloud service recommendation based on social trust
    (CERIST, 2024-04) Lebbib, Fatma Zohra; Djebrit, Ichrak; Mahmoudi, Khadidja
    Recommending trustworthy cloud services is essential to establishing credibility and ensuring better user decision-making based on their specific needs. Traditional recommendation approaches based on collaborative filtering face some challenges, including data sparsity issues. In this paper, a social trust-based recommendation approach using matrix factorization is proposed to improve recommendation accuracy and address the limitations imposed by data sparsity in recommender systems. First, the level of trustworthiness of users is inferred from their interactions on social networks. Then, the social trust model is integrated with the matrix factorization technique to generate reliable recommendations for users. The results of experiments conducted on the Epinions and WSdream datasets demonstrate that social trust significantly improves recommendation accuracy compared to state-of-the-art recommendation systems that do not take trust into account.
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    Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments
    (CERIST, 2024-04) Djelliout, Toufik; Aliane, Hassina
    The use of convolutional neural networks (CNN) in the preservation of cultural heritage monuments, especially in conflict-affected regions such as Gaza, Ukraine, Iraq and others, represents a significant advancement in heritage conservation efforts. This paper presents an approach that uses a Multi-CNN model to classify images of cultural heritage monuments into various categories, encompassing period, monument type and location. By leveraging the capabilities of CNNs, this model demonstrates a high level of accuracy in categorizing heritage monuments based on multiple attributes. The study highlights the superior performance of the Multi-CNN model compared to other popular models such as DenseNet169, GoogleNet and MnasNet, highlighting its effectiveness in accurately classifying images of cultural heritage monuments in various dimensions. According to the evaluation results, the top-performing multi-CNN model achieves a classification accuracy of 94.52%, outperforming the single CNN models. The DenseNet196 model achieves 93.70% accuracy, the MnasNet model achieves 92.80% accuracy, and the GoogleNet model achieves 88.18% accuracy.
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    Applied Gaming-Based Emotion- Driven on Disaster Resilience Training
    (CERIST, 2024-11) Hadjar, Hayette; Hemmje, Matthias; Hadjadj, Zineb; Meziane, Abdelkrim
    Managing stress in disaster response environments is a critical challenge that requires effective strategies to enhance the resilience and well-being of emergency responders. This study introduces DisasterPlay, a prototype web-based platform designed for resilience training. The prototype features a comprehensive model design, user interface, and implementation using WebXR, facial emotion monitoring, and contactless vital signs monitoring. This approach not only improves the training experience but also aids decisionmakers in selecting the most suitable candidates for high-stakes tasks, thereby enhancing resource allocation. Accessible via web browsers and utilizing cloud-based data processing, this innovative platform aims to provide a robust solution for advancing disaster response strategies.
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    Toward an Approach for Job Recommender System: Leveraging Hybrid Techniques
    (CERIST, 2024-11) Khider, Hadjer; Meziane, Abdelkrim; Hammoudi, Slimane
    The rapid evolution of the job market, driven by digitalization and changing business environment dynamics, requires the development of sufficient job recommender systems. A significant number of challenges are facing those job seekers on LinkedIn professional social network. These LinkedIn users are seeking job-maker proposals that align with their business needs. Supporting these job seekers is a real challenge. In order to address this deficiency, we propose a methodology for job recommender systems on the professional social network LinkedIn, based on the user profiles on that platform. This paper presents a user-centric design approach and recommendation process for jobs based on the social profile of the LinkedIn users. The proposed approach to job recommendation combines hybrid techniques, integrating collaborative filtering, content-based filtering, context aware recommendation. In this paper, we introduce a user-centric and interactive framework that enables job seekers to interact with our Job Recommender System to provide the most relevant and valuable recommendations. The proposed framework is designed to addressing common challenges in the field; this approach aims to enhance recommendation accuracy and user satisfaction.