CERIST DL
CERIST Digital Library is the institutional repository of the Algerian Research Centre on Scientific and Technical Information (CERIST). It provides access to the entire production of CERIST in terms of journal and conference papers, technical and research reports, theses, course materials, etc. Within CERIST DL, you can:
- Browse the scientific outputs produced at CERIST by communities, collections, authors, etc.
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Recent Submissions
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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.
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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.
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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.
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Towards Big Data Analytics over Mobile User Data using Machine Learning
(IEEE, 2023-01) Ichou, Sabrina; Hammoudi, Slimane; Cuzzocrea, Alfredo; Meziane, Abdelkrim; Benna, Amel
Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.
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A cooperative framework for automated segmentation of tumors in brain MRI images
(Springer, 2023-03) Hadjadj, Zineb
Brain tumor segmentation from 2D Magnetic Resonance Images (MRI) is an important task for several applications in the field of medical analysis. Commonly, this task is performed manually by medical professionals, but it is not always obvious due to similarities between tumors and normal tissue and variations in tumor appearance. Therefore, the automation of medical image segmentation remains a real challenge that has attracted the attention of several researchers in recent years. Instead of choosing between region and contour approaches, in this article, we propose a region-edge cooperative method for brain tumor segmentation from MRI images. The region approach used is support vector machines (SVMs), one of the popular and highly motivated classification methods, the method distinguishes between normal and abnormal pixels based on some features: intensity and texture. To control and guide the segmentation region, we take advantage of the Ron Kimmel geodesic Active Contour Model (ACM) which produces a good delimitation of the boundaries of the object. The two methods have been cooperated sequentially in order to obtain a flexible and effective system for brain tumor segmentation. Experimental studies are performed on synthetic and real 2D MRI images of various modalities from the radiology unit of the university hospital center in Bab El Oued Algeria. The used MRI images represent various tumor shapes, locations, sizes, and intensities. The proposed cooperative framework outperformed SVM-based segmentation and ACM-based segmentation when executed independently.