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Mathematics
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Advances in Intelligent Computing, Machine Learning and Pattern Recognition
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Advances in Intelligent Computing, Machine Learning and Pattern Recognition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3194

Special Issue Editors

Prof. Dr. Hwei Jen Lin

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan
Interests: deep learning; computer vision; image processing; pattern recognition; algorithms
Prof. Dr. Ching-Ting Tu

E-Mail Website
Guest Editor
Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
Interests: machine learning; deep learning; computer vision; image processing; pattern recognition

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics, entitled “Advances in Intelligent Computing, Machine Learning and Pattern Recognition”. This Special Issue aims to present the latest progress and breakthroughs in interdisciplinary fields such as intelligent computing, machine learning, and pattern recognition, and to provide a comprehensive platform for the dissemination of significant research findings and applications. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Neural Networks: Explorations of new architectures, training techniques, and applications in various domains.
  • Deep Learning: Advances in deep learning models, including convolutional networks, recurrent networks, and their implementations.
  • Algorithmic Optimization: Innovative optimization algorithms and their applications in intelligent computing and machine learning.
  • Computational Intelligence: The integration of intelligent systems, including fuzzy logic, genetic algorithms, and evolutionary computing.
  • Clustering Algorithms: Novel clustering methods and their effectiveness in pattern recognition and data mining tasks.
  • Pattern Recognition: New methodologies and systems for recognizing patterns in various types of data.
  • AI Application Systems: Development and implementation of intelligent computing, machine learning, and pattern recognition techniques in various fields, including healthcare, finance, transportation, communications, humanities, and more. Emphasis is placed on practical applications and the impact of these technologies on real-world problems.
  • Big Data Analytics: Techniques and tools for managing, analyzing, and extracting meaningful insights from large datasets.
  • Linear Algebra in Intelligent Systems: Research on the application of linear algebra, including matrix theory and transformations, in the development and optimization of intelligent systems.

We invite submissions of high-quality research articles, reviews, and case studies that address these topics. Contributions should demonstrate original theoretical, methodological, or applied advancements and should provide significant insights into the challenges and future directions of intelligent computing, machine learning, and pattern recognition.

Manuscripts will undergo a rigorous peer-review process to ensure the publication of top-tier research. We look forward to your contributions to this Special Issue.

Prof. Dr. Hwei Jen Lin
Prof. Dr. Ching-Ting Tu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural networks
  • convolutional networks
  • deep learning
  • big data analytics
  • computational intelligence
  • clustering algorithms
  • applied mathematics
  • matrix theory
  • evolutionary computing
  • optimization algorithms

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

19 pages, 452 KiB  
Article
A Surrogate-Assisted Gray Prediction Evolution Algorithm for High-Dimensional Expensive Optimization Problems
by Xiaoliang Huang, Hongbing Liu, Quan Zhou and Qinghua Su
Mathematics 2025, 13(6), 1007; https://doi.org/10.3390/math13061007 - 20 Mar 2025
Viewed by 326
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs), which combine the search capabilities of evolutionary algorithms (EAs) with the predictive capabilities of surrogate models, are effective methods for solving expensive optimization problems (EOPs). However, the over-reliance on the accuracy of the surrogate model causes the optimization performance [...] Read more.
Surrogate-assisted evolutionary algorithms (SAEAs), which combine the search capabilities of evolutionary algorithms (EAs) with the predictive capabilities of surrogate models, are effective methods for solving expensive optimization problems (EOPs). However, the over-reliance on the accuracy of the surrogate model causes the optimization performance of most SAEAs to decrease drastically with the increase in dimensionality. To tackle this challenge, this paper proposes a surrogate-assisted gray prediction evolution (SAGPE) algorithm based on gray prediction evolution (GPE). In SAGPE, both the global and local surrogate model are constructed to assist the GPE search alternately. The proposed algorithm improves optimization efficiency by combining the macro-predictive ability of the even gray model in GPE for population update trends and the predictive ability of surrogate models to synergistically guide population searches in promising directions. In addition, an inferior offspring learning strategy is proposed to improve the utilization of population information. The performance of SAGPE is tested on eight common benchmark functions and a speed reducer design problem. The optimization results are compared with existing algorithms and show that SAGPE has significant performance advantages in terms of convergence speed and solution accuracy. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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19 pages, 4395 KiB  
Article
Enhancing Zero-Shot Learning Through Kernelized Visual Prototypes and Similarity Learning
by Kanglong Cheng and Bowen Fang
Mathematics 2025, 13(3), 412; https://doi.org/10.3390/math13030412 - 26 Jan 2025
Viewed by 639
Abstract
Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems. To address these issues, we propose a novel kernelized similarity learning approach [...] Read more.
Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems. To address these issues, we propose a novel kernelized similarity learning approach that reduces intraclass similarity while increasing interclass similarity. Specifically, we utilize kernelized ridge regression to learn visual prototypes for unseen classes in the semantic vectors. Furthermore, we introduce kernel polarization and autoencoder structures into the similarity function to enhance discriminative ability and mitigate the hubness and domain-shift problems. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art ZSL and generalized zero-shot learning (GZSL) methods, highlighting its effectiveness in improving classification performance for unseen classes. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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10 pages, 221 KiB  
Article
Linear Jointly Disjointness-Preserving Maps Between Rectangular Matrix Spaces
by Ru-Jheng Li, Yu-Ju Lin, Ming-Cheng Tsai and Ya-Shu Wang
Mathematics 2025, 13(2), 305; https://doi.org/10.3390/math13020305 - 18 Jan 2025
Viewed by 536
Abstract
This paper studies pairs of linear maps that preserve the disjointness of matrices in rectangular matrix spaces. We present a complete characterization of all pairs of bijective linear maps that jointly preserve disjointness. Additionally, we apply these results to study maps that preserve [...] Read more.
This paper studies pairs of linear maps that preserve the disjointness of matrices in rectangular matrix spaces. We present a complete characterization of all pairs of bijective linear maps that jointly preserve disjointness. Additionally, we apply these results to study maps that preserve specific matrix properties, such as the double-zero product property. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
23 pages, 2968 KiB  
Article
MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks
by Hsiau-Wen Lin, Trang-Thi Ho, Ching-Ting Tu, Hwei-Jen Lin and Chen-Hsiang Yu
Mathematics 2025, 13(2), 226; https://doi.org/10.3390/math13020226 - 10 Jan 2025
Viewed by 1076
Abstract
This paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation. Traditional MMD methods align overall distributions but struggle with class-wise alignment, reducing feature [...] Read more.
This paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation. Traditional MMD methods align overall distributions but struggle with class-wise alignment, reducing feature distinguishability. MCWMMD incorporates a meta-module to dynamically learn a deep kernel for MMD, improving alignment accuracy and model adaptability. This meta-learning technique enhances the model’s ability to generalize across tasks by ensuring domain-invariant and class-discriminative feature representations. Despite the complexity of the method, including the need for meta-module training, it presents a significant advancement in UDA. Future work will explore scalability in diverse real-world scenarios and further optimize the meta-learning framework. MCWMMD offers a promising solution to the persistent challenge of domain adaptation, paving the way for more adaptable and generalizable deep learning models. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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