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Mathematics
Special Issues
Applications of Machine Learning and Pattern Recognition
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Applications of 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 July 2026 | Viewed by 2587

Special Issue Editor

Dr. Ka-Hou Chan

E-Mail Website
Guest Editor
1. Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
2. Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence, Macao Polytechnic University, Macau, China
Interests: algorithm analysis and optimization of video coding; image processing; parallel computing; neural networks; computer graphics

Special Issue Information

Dear Colleagues,

The integration of machine learning (ML) and pattern recognition techniques into mathematical modeling has opened new frontiers across scientific and engineering disciplines. From data-driven inference to intelligent decision-making, these methods have demonstrated remarkable capabilities in extracting structure, identifying trends, and enabling predictive analytics in complex systems.

This Special Issue aims to highlight recent advances in the mathematical foundations, algorithmic innovations, and practical applications of machine learning and pattern recognition. We welcome contributions that explore theoretical models, optimization strategies, and performance analysis, as well as interdisciplinary applications in areas such as signal processing, computer vision, biomedical engineering, and financial mathematics.

Topics of interest include, but are not limited to, the following:

  • Mathematical modelling of learning algorithms;
  • Pattern recognition theory and applications;
  • Optimization and convergence analysis;
  • Neural architectures and interpretability;
  • Multi-task and transfer learning;
  • Statistical learning and probabilistic inference;
  • Applications in image, speech, and time-series data;
  • Hybrid systems and modular design frameworks.

We invite original research articles, review papers, and short communications that advance the understanding and application of ML and pattern recognition from a mathematical perspective.

Dr. Ka-Hou Chan
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
  • pattern recognition
  • mathematical modeling
  • optimization algorithms
  • neural networks
  • statistical inference
  • feature extraction
  • multi-task learning
  • signal processing
  • computer vision
  • time series analysis
  • probabilistic models
  • deep learning
  • interpretability
  • modular architectures

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

Published Papers (2 papers)

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Research

29 pages, 2471 KB  
Article
MISA-GMC: An Enhanced Multimodal Sentiment Analysis Framework with Gated Fusion and Momentum Contrastive Modality Relationship Modeling
by Zheng Du, Yapeng Wang, Xu Yang, Sio-Kei Im and Zhiwen Wang
Mathematics 2026, 14(1), 115; https://doi.org/10.3390/math14010115 - 28 Dec 2025
Viewed by 508
Abstract
Multimodal sentiment analysis jointly exploits textual, acoustic, and visual signals to recognize human emotions more accurately than unimodal models. However, real-world data often contain noisy or partially missing modalities, and naive fusion may allow unreliable signals to degrade overall performance. To address this, [...] Read more.
Multimodal sentiment analysis jointly exploits textual, acoustic, and visual signals to recognize human emotions more accurately than unimodal models. However, real-world data often contain noisy or partially missing modalities, and naive fusion may allow unreliable signals to degrade overall performance. To address this, we propose an enhanced framework named MISA-GMC, a lightweight extension of the widely used MISA backbone that explicitly accounts for modality reliability. The core idea is to adaptively reweight modalities at the sample level while regularizing cross-modal representations during training. Specifically, a reliability-aware gated fusion module down-weights unreliable modalities, and two auxiliary training-time regularizers (momentum contrastive learning and a lightweight correlation graph) help stabilize and refine multimodal representations without adding inference-time overhead. Experiments on three benchmark datasets—CMU-MOSI, CMU-MOSEI, and CH-SIMS—demonstrate the effectiveness of MISA-GMC. For instance, on CMU-MOSI, the proposed model improves 7-class accuracy from 43.29 to 45.92, reduces the mean absolute error (MAE) from 0.785 to 0.712, and increases the Pearson correlation coefficient (Corr) from 0.764 to 0.795. This indicates more accurate fine-grained sentiment prediction and better sentiment-intensity estimation. On CMU-MOSEI and CH-SIMS, MISA-GMC also achieves consistent gains over MISA and strong baselines such as LMF, ALMT, and MMIM across both classification and regression metrics. Ablation studies and missing-modality experiments further verify the contribution of each component and the robustness of MISA-GMC under partial-modality settings. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Pattern Recognition)
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20 pages, 1122 KB  
Article
Advancing Link Prediction with a Hybrid Graph Neural Network Approach
by Siwar Gharsallah, Samah Yahia, Wided Bouchelligua and Tahani Bouchrika
Mathematics 2025, 13(22), 3594; https://doi.org/10.3390/math13223594 - 9 Nov 2025
Viewed by 1825
Abstract
Social media platforms produce extensive user–item interaction data that demand advanced analytical models for effective personalization. This study investigates the link prediction task within social recommendation systems using Graph Neural Networks (GNNs). A hybrid framework is proposed that integrates Graph Convolutional Networks (GCNs) [...] Read more.
Social media platforms produce extensive user–item interaction data that demand advanced analytical models for effective personalization. This study investigates the link prediction task within social recommendation systems using Graph Neural Networks (GNNs). A hybrid framework is proposed that integrates Graph Convolutional Networks (GCNs) with dual similarity metrics combining cosine and dot product measures to enhance link prediction accuracy. Experiments conducted on the Ciao and Epinions datasets using the Graph Convolutional Network (GCN) demonstrate superior performance compared with baseline models such as GraphRec and GraphSAGE. The proposed approach effectively captures latent interaction patterns, providing a robust foundation for more accurate and personalized recommendation systems on social media platforms. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Pattern Recognition)
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