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Mathematics
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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: 30 May 2027 | Viewed by 6634

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-anonymized 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 (5 papers)

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Research

33 pages, 2314 KB  
Article
LLM-Assisted Scoring for College English Writing Assessment: Statistical Calibration Against Teacher Standards
by Yongping Wang, Ning Liu, Xizhi Chu, Tuo Wang, Xuan Cheng and Yapeng Wang
Mathematics 2026, 14(17), 3033; https://doi.org/10.3390/math14173033 (registering DOI) - 23 Aug 2026
Abstract
Large classes in Chinese College English programmes make frequent analytic assessment of student writing difficult. Large language models (LLMs) may support more frequent formative assessment, but their scores may vary across queries and be systematically harsher or more lenient than local teacher ratings. [...] Read more.
Large classes in Chinese College English programmes make frequent analytic assessment of student writing difficult. Large language models (LLMs) may support more frequent formative assessment, but their scores may vary across queries and be systematically harsher or more lenient than local teacher ratings. Using a corpus-based, five-fold cross-validated comparative rater-evaluation design, this study examined whether statistical calibration could make LLM-assisted scores more interpretable for College English writing assessment and where their use should remain limited. Data comprised 414 timed argumentative essays written by Chinese non-English majors at one applied undergraduate institution. Two trained College English teachers independently rated the essays on a seven-dimension analytic rubric informed by China’s Standards of English Language Ability, providing the local reference standard. Three LLMs rated each essay–dimension pair on five occasions. Under five-fold cross-validation, uncalibrated scores were compared with location–scale correction, isotonic calibration, and equipercentile linking, using quadratic weighted kappa, Spearman correlation, mean absolute error, signed bias, and half-point tolerance accuracy. Agreement between models did not imply agreement with teachers: two models showed inter-model kappa values of 0.70–0.78 but an average kappa of only 0.15 with teacher ratings while rating the essays about one band more severely. Calibration removed most of this severity difference and raised pooled kappa to 0.61–0.70 depending on the method (0.63–0.64 under equipercentile linking), compared with a teacher–teacher agreement benchmark of 0.747. The three methods differed little, and the improvement mainly reflected closer alignment of score distributions rather than better judgement of writing quality. Agreement was higher for vocabulary, syntax, and grammar but remained low for cohesion and conventions. The findings suggest that LLM-assisted scoring may support low-stakes formative feedback when calibrated to local teacher standards and used under teacher supervision, while teachers retain responsibility for judging content, coherence, argumentation, and communicative quality. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Pattern Recognition)
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25 pages, 3088 KB  
Article
Research on UAV 3D Airspace Signal Strength Prediction Based on Physical Perception Feature Engineering
by Long Liu, Yapeng Wang, Xu Yang, Sio-Kei Im, Xuan Cheng, Lu Huang, Jiaqi Chen and Heng Guan
Mathematics 2026, 14(8), 1399; https://doi.org/10.3390/math14081399 - 21 Apr 2026
Viewed by 500
Abstract
With the rapid development of the low-altitude economy, constructing an accurate unmanned aerial vehicle (UAV) air-to-ground channel model is crucial for ensuring communication quality. However, due to the significant fluctuations in UAV operation altitudes and the complex propagation environment, traditional empirical models struggle [...] Read more.
With the rapid development of the low-altitude economy, constructing an accurate unmanned aerial vehicle (UAV) air-to-ground channel model is crucial for ensuring communication quality. However, due to the significant fluctuations in UAV operation altitudes and the complex propagation environment, traditional empirical models struggle to achieve universal high-precision prediction within a 3D airspace. This paper proposes a Physics-Informed Feature Engineering (PIFE) method and constructs a 3D signal strength prediction model in combination with Gradient Boosting Decision Tree (XGBoost). Unlike traditional purely data-driven methods, this paper explicitly extracts physical propagation features such as three-dimensional Euclidean distance and height-to-angle ratio, and specifically designs a height–path loss interaction term to capture the nonlinear coupling relationship of signal attenuation at different operating heights. The experimental results demonstrate that the model proposed in this paper performs excellently in multi-altitude airspace scenarios ranging from 70 m to 150 m. At the typical operation height of 70 m, the model achieves a high goodness of fit (R2) of 0.843. Ablation experiments further confirm that the introduction of physical interaction features successfully breaks through the performance bottleneck of pure geometric features, proving the necessity of explicitly modeling the height–distance coupling effect in complex three-dimensional airspace. The research in this paper demonstrates the effectiveness of integrating physical priors with machine learning algorithms, providing an important theoretical basis and technical support for future drone network planning and coverage optimization in complex low-altitude environments. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Pattern Recognition)
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18 pages, 479 KB  
Article
Unified Representation and Game-Theoretic Modelling of Online Rumour Diffusion
by Ka-Hou Chan and Sio-Kei Im
Mathematics 2026, 14(5), 854; https://doi.org/10.3390/math14050854 - 2 Mar 2026
Viewed by 601
Abstract
Rumour propagation in online social networks poses significant risks to public trust, economic stability, and crisis management. Existing models often struggle with heterogeneous feature spaces, adversarial dynamics between rumours and debunking information, and data sparsity in early outbreak stages. This study introduces a [...] Read more.
Rumour propagation in online social networks poses significant risks to public trust, economic stability, and crisis management. Existing models often struggle with heterogeneous feature spaces, adversarial dynamics between rumours and debunking information, and data sparsity in early outbreak stages. This study introduces a cross-domain framework for group behaviour prediction that integrates unified representation learning, game-theoretic adversarial modelling, and transfer adaptation. A hybrid BERT–Node2Vec encoder captures both semantic richness and structural influence, while evolutionary game theory quantifies competitive interactions between rumour-spreaders and refuters. To alleviate data scarcity, Joint Distribution Adaptation (JDA) aligns heterogeneous feature spaces across domains, enabling robust transfer learning. Evaluated on simulated and real-world social media datasets, the proposed model demonstrates improved accuracy and interpretability in predicting rumour diffusion trends under adversarial conditions. These findings highlight the value of integrating semantic, structural, and behavioural signals into a scalable architecture, offering a practical solution for safeguarding digital ecosystems against misinformation. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Pattern Recognition)
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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
Cited by 2 | Viewed by 2249
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
Cited by 3 | Viewed by 2604
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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