Research Reports

Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/34

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Author: search.filters.author.Meziane, AbdelkrimSubject: search.filters.subject.Cloud computing

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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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    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.