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Mathematics
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Mathematical Methods in Machine Learning, Neural Networks and Computer Vision
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Mathematical Methods in Machine Learning, Neural Networks and Computer Vision

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 January 2026 | Viewed by 3758

Special Issue Editor

Prof. Dr. Zhaoxian Zhou

E-Mail Website
Guest Editor
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Dr., Hattiesburg, MS 39406, USA
Interests: pattern recognition; human activity recognition; face recognition; image processing; big data analytics; machine learning; biomechanics and health; computational finance; high-performance computing; wireless sensor networks; wearable devices; applied electromagnetics; antennas; radiowave propagation and wireless communications; high-power microwave; pulsed power and plasma science; engineering education

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Mathematical Methods in Machine Learning, Neural Networks and Computer Vision", aims to explore the intersection of advanced mathematical techniques with cutting-edge applications in artificial intelligence. It encompasses research into mathematical tools, novel algorithms, optimization strategies, and theoretical frameworks that underpin the development and enhancement of machine learning models, neural networks, and computer vision systems. Contributions to this Special Issue include studies on statistical learning theories, deep learning architectures, computational geometry, graph-based methods, and their applications in time series analysis, image recognition, video processing, natural language processing, and autonomous systems. This Special Issue seeks to foster a deeper understanding of the mathematical foundations crucial to advancing the capabilities and efficiency of AI technologies.

Prof. Dr. Zhaoxian Zhou
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning
  • deep learning
  • neural networks
  • image processing
  • pattern recognition
  • computer vision
  • mathematical methods
  • optimization algorithms
  • statistical models
  • autonomous systems

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

Published Papers (3 papers)

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Research

29 pages, 4638 KB  
Article
Semantics-Driven 3D Scene Retrieval via Joint Loss Deep Learning
by Juefei Yuan, Tianyang Wang, Shandian Zhe, Yijuan Lu, Zhaoxian Zhou and Bo Li
Mathematics 2025, 13(22), 3726; https://doi.org/10.3390/math13223726 - 20 Nov 2025
Viewed by 529
Abstract
Three-dimensional (3D) scene model retrieval has emerged as a novel and challenging area within content-based 3D model retrieval research. It plays an increasingly critical role in various domains, such as video games, film production, and immersive technologies, including virtual reality (VR), augmented reality [...] Read more.
Three-dimensional (3D) scene model retrieval has emerged as a novel and challenging area within content-based 3D model retrieval research. It plays an increasingly critical role in various domains, such as video games, film production, and immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), where automated generation of 3D content is highly desirable. Despite their potential, the existing 3D scene retrieval techniques often overlook the rich semantic relationships among objects and between objects and their surrounding scenes. To address this gap, we introduce a comprehensive scene semantic tree that systematically encodes learned object occurrence probabilities within each scene category, capturing essential semantic information. Building upon this structure, we propose a novel semantics-driven image-based 3D scene retrieval method. The experimental evaluations show that the proposed approach effectively models scene semantics, enables more accurate similarity assessments between 3D scenes, and achieves substantial performance improvements. All the experimental results, along with the associated code and datasets, are available on the project website. Full article
(This article belongs to the Special Issue Mathematical Methods in Machine Learning, Neural Networks and Computer Vision)
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18 pages, 12842 KB  
Article
Progressive Policy Learning: A Hierarchical Framework for Dexterous Bimanual Manipulation
by Kang-Won Lee, Jung-Woo Lee, Seongyong Kim and Soo-Chul Lim
Mathematics 2025, 13(22), 3585; https://doi.org/10.3390/math13223585 - 8 Nov 2025
Viewed by 757
Abstract
Dexterous bimanual manipulation remains a challenging task in reinforcement learning (RL) due to the vast state–action space and the complex interdependence between the hands. Conventional end-to-end learning struggles to handle this complexity, and multi-agent RL often faces limitations in stably acquiring cooperative movements. [...] Read more.
Dexterous bimanual manipulation remains a challenging task in reinforcement learning (RL) due to the vast state–action space and the complex interdependence between the hands. Conventional end-to-end learning struggles to handle this complexity, and multi-agent RL often faces limitations in stably acquiring cooperative movements. To address these issues, this study proposes a hierarchical progressive policy learning framework for dexterous bimanual manipulation. In the proposed method, one hand’s policy is first trained to stably grasp the object, and, while maintaining this grasp, the other hand’s manipulation policy is progressively learned. This hierarchical decomposition reduces the search space for each policy and enhances both the connectivity and the stability of learning by training the subsequent policy on the stable states generated by the preceding policy. Simulation results show that the proposed framework outperforms conventional end-to-end and multi-agent RL approaches. The proposed method was demonstrated via sim-to-real transfer on a physical dual-arm platform and empirically validated on a bimanual cube manipulation task. Full article
(This article belongs to the Special Issue Mathematical Methods in Machine Learning, Neural Networks and Computer Vision)
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18 pages, 3928 KB  
Article
Frequency-Domain Hybrid Model for EEG-Based Emotion Recognition
by Jinyu Liu, Naidan Feng and Yongquan Liang
Mathematics 2025, 13(7), 1072; https://doi.org/10.3390/math13071072 - 25 Mar 2025
Viewed by 1345
Abstract
Emotion recognition based on Electroencephalogram (EEG) signals plays a vital role in affective computing and human–computer interaction (HCI). However, noise, artifacts, and signal distortions present challenges that limit classification accuracy and robustness. To address these issues, we propose ECA-ResDNN, a novel hybrid model [...] Read more.
Emotion recognition based on Electroencephalogram (EEG) signals plays a vital role in affective computing and human–computer interaction (HCI). However, noise, artifacts, and signal distortions present challenges that limit classification accuracy and robustness. To address these issues, we propose ECA-ResDNN, a novel hybrid model designed to leverage the frequency, spatial, and temporal characteristics of EEG signals for improved emotion recognition. Unlike conventional models, ECA-ResDNN integrates an Efficient Channel Attention (ECA) mechanism within a residual neural network to enhance feature selection in the frequency domain while preserving essential spatial information. A Deep Neural Network further extracts temporal dependencies, improving classification precision. Additionally, a hybrid loss function that combines cross-entropy loss and fuzzy set loss enhances the model’s robustness to noise and uncertainty. Experimental results demonstrate that ECA-ResDNN significantly outperforms existing approaches in both accuracy and robustness, underscoring its potential for applications in affective computing, mental health monitoring, and intelligent human–computer interaction. Full article
(This article belongs to the Special Issue Mathematical Methods in Machine Learning, Neural Networks and Computer Vision)
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