Research Reports
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Item Rapport sur la Maladie d’Alzheimer(CERIST, 2025-02) Atik, AliLa 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.Item The Future of BPM in the era of industry 4.0 : exploring new opportunities for innovation(CERIST, 2025-02) Khider, Hadjer; Meziane, Abdelkrim; Hammoudi, SlimaneIn 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.Item Two-tower neural network for personalizing service recommendation in cloud environment(CERIST, 2025-02) Lebib, Fatma Zohra; Meziane, AbdelkrimEffective 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.Item Matrix Factorization for cloud service recommendation based on social trust(CERIST, 2024-04) Lebbib, Fatma Zohra; Djebrit, Ichrak; Mahmoudi, KhadidjaRecommending 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.Item Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments(CERIST, 2024-04) Djelliout, Toufik; Aliane, HassinaThe 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.Item Applied Gaming-Based Emotion- Driven on Disaster Resilience Training(CERIST, 2024-11) Hadjar, Hayette; Hemmje, Matthias; Hadjadj, Zineb; Meziane, AbdelkrimManaging 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.Item Toward an Approach for Job Recommender System: Leveraging Hybrid Techniques(CERIST, 2024-11) Khider, Hadjer; Meziane, Abdelkrim; Hammoudi, SlimaneThe 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.Item A Rhetorical Relations-Based Framework for Tailored Multimedia Document Summarization(CERIST, 2024-05) Maredj, Azze-eddine; Sadallah, MadjidIn the rapidly evolving landscape of digital content, the task of summarizing multimedia documents, which encompass textual, visual, and auditory elements, presents intricate challenges. These challenges include extracting pertinent information from diverse formats, maintaining the structural integrity and semantic coherence of the original content, and generating concise yet informative summaries. This paper introduces a novel framework for multimedia document summarization that capitalizes on the inherent structure of the document to craft coherent and succinct summaries. Central to this framework is the incorporation of a rhetorical structure for structural analysis, augmented by a graph-based representation to facilitate the extraction of pivotal information. Weighting algorithms are employed to assign significance values to document units, thereby enabling effective ranking and selection of relevant content. Furthermore, the framework is designed to accommodate user preferences and time constraints, ensuring the production of personalized and contextually relevant summaries. The summarization process is elaborately delineated, encompassing document specification, graph construction, unit weighting, and summary extraction, supported by illustrative examples and algorithmic elucidation. This proposed framework represents a significant advancement in automatic summarization, with broad potential applications across multimedia document processing, promising transformative impacts in the field.Item Resource allocation approaches for business processes in the era of digitalization: A Survey(CERIST, 2024-06) Khider, HadjerResource allocation is a critical component of business process management (BPM) that directly impacts the efficiency and effectiveness of organizational operations. It involves the strategic assignment of resources, including personnel, equipment, and materials, to various activities and tasks within a business process. Effective resource allocation is imperative for inhancing productivity, minimizing costs, and ensuring seamless process execution. This paper presents a review of existing research on resource allocation for business processes associated in the context of digitalization, identifying different approaches of resource allocation for business processes in the era of digitalization.Item Information agronomique au cœur du Portail National de Signalement des Thèses(CERIST, 2016-11) Mebtouche, NawelAnalyser l’activité de recherche fondée sur les thèses réalisées dans les établissements académiques nationaux constitue un repérage valorisant de la recherche, une aide pour les chercheurs, ainsi que pour les responsables du secteur pour la mise en place des politiques et stratégies d’avenir. C’est dans cette optique que le Centre de Recherche sur L’information Scientifique et Technique (CERIST) a développé le portail national de signalement des thèses ( PNST) . Notre présente étude est une tentative d’évaluation de la production scientifique nationale, en matière de thèse, dans le domaine de l’agronomie. La réalisation de ce travail nécessite l’utilisation la base de données du portail PNST. Cette dernière est source fiable qui récence les travaux de recherche de la post-graduation (magistère, doctorat, doctorat LMD), à l’échelle nationale, durant la période 2010-2016. Cette base de données suit la chaine de réalisation de la thèse, depuis le signalement du sujet jusqu'à la diffusion de la thèse soutenue.