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Entropy in Machine Learning Applications, 2nd Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 481

Special Issue Editor


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Guest Editor
1. Zhuhai Sub Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, Zhuhai College of Science and Technology, Zhuhai 519041, China
2. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: machine learning methods in computational biology; optimization problems solving using evolutionary algorithms; hybrid evolutionary algorithms; deep learning models and algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include, but not be limited to, applications using machine learning methods, including the construction and application of managed pressure drilling knowledge graphs with extended cross-entropy loss, the computational characterization of undifferentially expressed genes with altered transcription regulations, entropy-weighted water quality prediction methods based on long short-term memory and data correlation analysis, convolutional networks that use cross-entropy as the loss function based on transfer learning for rice disease detection, and the semantic disambiguation of advertising vocabulary based on knowledge graphs.

Prof. Dr. Yanchun Liang
Guest Editor

Manuscript Submission Information

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Keywords

  • knowledge graph
  • data correlation
  • differential expression
  • long short-term memory
  • semantic disambiguation
  • advertising vocabulary
  • entity relationship extraction
  • semi-supervised learning
  • cross-entropy loss
  • semantic entropy

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Published Papers (1 paper)

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Research

21 pages, 7233 KiB  
Article
Advancing Traditional Dunhuang Regional Pattern Design with Diffusion Adapter Networks and Cross-Entropy
by Yihuan Tian, Tao Yu, Zuling Cheng and Sunjung Lee
Entropy 2025, 27(5), 546; https://doi.org/10.3390/e27050546 - 21 May 2025
Viewed by 253
Abstract
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. [...] Read more.
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. However, existing text-to-image methods are computationally intensive and struggle to capture fine details and complex semantic relationships in text and images. To address these challenges, this paper proposes the Diffusion Adapter Network (DANet). It employs a lightweight adapter module to extract visual structural information, enabling the diffusion model to generate Dunhuang patterns with high accuracy, while eliminating the need for expensive fine-tuning of the original model. The attention adapter incorporates a multihead attention module (MHAM) to enhance image modality cues, allowing the model to focus more effectively on key information. A multiscale attention module (MSAM) is employed to capture features at different scales, thereby providing more precise generative guidance. In addition, an adaptive control mechanism (ACM) dynamically adjusts the guidance coefficients across feature layers to further enhance generation quality. In addition, incorporating a cross-entropy loss function enhances the model’s capability in semantic understanding and the classification of Dunhuang patterns. The DANet achieves state-of-the-art (SOTA) performance on the proposed Diversified Dunhuang Patterns Dataset (DDHP). Specifically, it attains a perceptual similarity score (LPIPS) of 0.498, a graph matching score (CLIP score) of 0.533, and a feature similarity score (CLIP-I) of 0.772. Full article
(This article belongs to the Special Issue Entropy in Machine Learning Applications, 2nd Edition)
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