CERIST DL
CERIST Digital Library is the institutional repository of the Algerian Research Centre on Scientific and Technical Information Within CERIST DL, you can:
- Browse the scientific outputs produced at CERIST by communities, collections, authors, etc.
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Recent Submissions
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.
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.
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.
A Rhetorical Relations-Based Framework for Tailored Multimedia Document Summarization
(CERIST, 2024-05) Maredj, Azze-eddine; Sadallah, Madjid
In 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.
Resource allocation approaches for business processes in the era of digitalization: A Survey
(CERIST, 2024-06) Khider, Hadjer
Resource 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.