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Your search keyword '"Bellatreche, Ladjel"' showing total 4 results
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4 results on '"Bellatreche, Ladjel"'

1. Evaluating Contrastive and Non-contrastive Explanations for Language Models

Author

Hadj Azzem, Yousra Chahinez, Harrag, Fouzi, Bellatreche, Ladjel, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barhamgi, Mahmoud, editor, Wang, Hua, editor, and Wang, Xin, editor

Published
2025
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2. Concepts and Relations Features Are All You Need for Embedding-Based Ontology Matching

Author

Oulefki, Samira, Berkani, Lamia, Boudjenah, Nassim, Bellatreche, Ladjel, Mokhtari, Aicha, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barhamgi, Mahmoud, editor, Wang, Hua, editor, and Wang, Xin, editor

Published
2025
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3. Advances on data management systems

Author

Bellatreche, Ladjel, Dumas, Marlon, Karras, Panagiotis, Matulevičius, Raimundas, Chiusano, Silvia, Cerquitelli, Tania, Wrembel, Robert, Bellatreche, Ladjel, Dumas, Marlon, Karras, Panagiotis, Matulevičius, Raimundas, Chiusano, Silvia, Cerquitelli, Tania, and Wrembel, Robert

Abstract

In the age of data science and advanced analytics, there is a rapid push for developing intelligent, data-centric systems in various fields such as business, finance, healthcare, environment, and cyber security. The abundant availability of data fuels this development and aims to improve the decision-making capabilities of companies and individuals. Harnessing the potential of data and knowledge is critical to achieving these goals. However, the effectiveness of these methods depends heavily on the quality of input data, data management strategies, and compliance with legal, ethical, and economic considerations. Developing innovative and robust data-centric systems, new data management strategies, and innovative data analysis solutions remains a major challenge for the research community.

Published
2025

4. Optimization of Restricted Container Relocation Using the Monte Carlo Tree Search Method.

Author

Chaabane, Abdelali, Yachba, Khadidja, and Bellatreche, Ladjel

Subjects
REINFORCEMENT learning, GENETIC algorithms, CONTAINER ships, HARBORS, LITERATURE
Abstract

This article explores how to improve operational performance in maritime ports by managing the flow of goods effectively. This study proposes an innovative approach based on Reinforcement Learning (RL), specifically the Monte Carlo Tree Search (MCTS) method, to address the restricted container relocation problem (RCRP). This method aims to determine an optimal sequence for container retrieval based on their respective priorities, in order to minimize the number of necessary relocations. By employing precise actions and a defined reward function, MCTS is guided towards the best possible solution. The efficiency and relevance of this method are demonstrated through various solved scenarios and compared to a literature-based approach using genetic algorithms. The results show that the MCTS approach is effective in addressing the complex challenges of goods flow management in maritime ports. [ABSTRACT FROM AUTHOR]

Published
2025
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