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  • Open Access

    ARTICLE

    Reinforcement Learning for Solving the Knapsack Problem

    Zhenfu Zhang1, Haiyan Yin2, Liudong Zuo3, Pan Lai1,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 919-936, 2025, DOI:10.32604/cmc.2025.062980 - 09 June 2025

    Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >

  • Open Access

    ARTICLE

    Multiple Perspective of Multipredictor Mechanism and Multihistogram Modification for High-Fidelity Reversible Data Hiding

    Kai Gao1, Chin-Chen Chang1,*, Chia-Chen Lin2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 813-833, 2024, DOI:10.32604/csse.2024.038308 - 20 May 2024

    Abstract Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics, which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram. More >

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