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  1. 121

    Innovation of Teaching Method of Digital Media Art Based on Convolutional Neural Network by Yumei Liu

    Published 2022-01-01
    “…In order to improve the effect of digital media art teaching, this study combines the neural network algorithm to carry out the innovation of digital media art teaching resource management and teaching method innovation. …”
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    Article
  2. 122

    Using Color Difference Compensation Method to Balance and Repair the Image of Art Design by Hui Ji, Yang Shang

    Published 2021-01-01
    “…A white balance color compensation algorithm for fuzzy chromatic aberration based on wavelet packet decomposition is proposed. …”
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    Article
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    Iris Localization Algorithm based on Effective Area by Jinfeng Yu, Lei Zhang, Zhi Wang

    Published 2021-01-01
    “…The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.…”
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  7. 127

    A Fusion Multiobjective Empire Split Algorithm by Liang Liang

    Published 2020-01-01
    “…Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.…”
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  8. 128

    Classical and quantum algorithms for many-body problems by Ayral, Thomas

    Published 2025-01-01
    “…In particular, we present the state-of-the-art in quantum methods, distinguishing between perfect and noisy processors. …”
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    Article
  9. 129

    Label iteration-based clustering ensemble algorithm by HE Yulin, YANG Jin, HUANG Zhexue, YIN Jianfei

    Published 2024-12-01
    “…Finally, a series of persuasive experiments were conducted to validate the feasibility and effectiveness of LICE algorithm. The experimental results showed that the normalized mutual information, adjusted Rand index, Fowlkes-Mallows index and purity of LICE algorithm increased by 17.23%, 16.75%, 31.29%, and 12.37% on average at the 5th iteration compared to the initial iteration and these four indexes increased by 11.76%, 16.50%, 9.36%, and 14.20% on average for the representative datasets in comparison with seven state-of-the-art clustering ensemble algorithms and thus demonstrate that LICE algorithm is an efficient and reasonable clustering ensemble algorithm with the potential to handle large-scale data clustering problems.…”
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