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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: 28 February 2026 | Viewed by 7043

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

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  • 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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

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Research

24 pages, 7537 KB  
Article
A Mathematical Methodology for the Detection of Rail Corrugation Based on Acoustic Analysis: Toward Autonomous Operation
by César Ricardo Soto-Ocampo, Juan David Cano-Moreno, Joaquín Maroto and José Manuel Mera
Mathematics 2025, 13(17), 2815; https://doi.org/10.3390/math13172815 - 1 Sep 2025
Viewed by 437
Abstract
In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that [...] Read more.
In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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23 pages, 994 KB  
Article
A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times
by Yu-Yan Zhang, Shih-Hsin Chen, Yen-Wen Wang, Chia-Hsuan Liao and Chen-Hsiang Yu
Mathematics 2025, 13(16), 2672; https://doi.org/10.3390/math13162672 - 19 Aug 2025
Viewed by 436
Abstract
This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely SGARF, to solve the permutation flow-shop scheduling problem with order acceptance under the conditions of limited capacity, weighted-tardiness, and past-sequence-dependent (PSD) [...] Read more.
This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely SGARF, to solve the permutation flow-shop scheduling problem with order acceptance under the conditions of limited capacity, weighted-tardiness, and past-sequence-dependent (PSD) setup times (PFSS-OAWT with PSD). To the best of our knowledge, this is the first study to investigate this problem. Our proposed algorithm increases the setup time for each successive job by a constant proportion of the cumulative processing time of preceding jobs to capture the progressive slowdown that often occurs on real production lines. In the developed algorithm with maximum 105 fitness evaluations, the RF surrogate model predicts objective function values and guides crossover and mutation. On the PFSS-OAWT with PSD benchmark (up to 500 orders and 20 machines, 160 instances), SGARF represents improvements of 0.9% over SGA, 0.8% over SGALS, and 5.6% over SABPO. Although the surrogate incurs additional runtime, the gains in both profit and order-acceptance rates justify its use for high-margin, offline planning. Overall, the results of this study suggest that integrating evolutionary search into data-driven prediction is an effective strategy for solving complex capacity-constrained scheduling problems. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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18 pages, 1043 KB  
Article
A Methodology to Extract Knowledge from Datasets Using ML
by Ricardo Sánchez-de-Madariaga, Mario Pascual Carrasco and Adolfo Muñoz Carrero
Mathematics 2025, 13(11), 1807; https://doi.org/10.3390/math13111807 - 28 May 2025
Viewed by 692
Abstract
This study aims to verify whether there is any relationship between the different classification outputs produced by distinct ML algorithms and the relevance of the data they classify, to address the problem of knowledge extraction (KE) from datasets. If such a relationship exists, [...] Read more.
This study aims to verify whether there is any relationship between the different classification outputs produced by distinct ML algorithms and the relevance of the data they classify, to address the problem of knowledge extraction (KE) from datasets. If such a relationship exists, the main objective of this research is to use it in order to improve performance in the important task of KE from datasets. A new dataset generation and a new ML classification measurement methodology were developed to determine whether the feature subsets (FSs) best classified by a specific ML algorithm corresponded to the most KE-relevant combinations of features. Medical expertise was extracted to determine the knowledge relevance using two LLMs, namely, chat GPT-4o and Google Gemini 2.5. Some specific ML algorithms fit much better than others for a working dataset extracted from a given probability distribution. They best classify FSs that contain combinations of features that are particularly knowledge-relevant. This implies that, by using a specific ML algorithm, we can indeed extract useful scientific knowledge. The best-fitting ML algorithm is not known a priori. However, we can bootstrap its identity using a small amount of medical expertise, and we have a powerful tool for extracting (medical) knowledge from datasets using ML. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing, Machine Learning and Pattern Recognition)
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19 pages, 452 KB  
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
Cited by 1 | Viewed by 1343
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 KB  
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 819
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 KB  
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 822
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 KB  
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 1637
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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