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Mathematics
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Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications
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Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications

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

Deadline for manuscript submissions: 10 April 2026 | Viewed by 3366

Special Issue Editors

Dr. Ashutosh Mishra

E-Mail Website
Guest Editor
Department of Electrical & Electronics Engineering, Birla Institute of Technology and Science, Pilani, Dubai, United Arab Emirates
Interests: artificial intelligence; intelligent systems; future mobility; convergence technologies
Dr. Shodhan Rao

E-Mail Website
Guest Editor
Center for Biosystems and Biotech Data Science, Ghent University, Global Campus, Incheon 21985, Republic of Korea
Interests: mathematical system theory

Special Issue Information

Dear Colleagues,

Applied mathematics in artificial intelligence (AI) leverages mathematical methods to develop and understand AI algorithms and their applications, equipping intelligent systems with real-world problem-solving capabilities. Recognizing the transformative potential of this field, we aim to aggregate cutting-edge research in applied mathematics in AI to drive the socio-economic advancement of our emerging intelligent society.

This Special Issue invites high-quality papers that provide comprehensive reviews, innovative implementations, and groundbreaking applications of applied mathematics in AI. We seek contributions that address engineering challenges and propose solutions relevant to real-world problems.

We welcome submissions in the following areas:

  1. Mathematical foundations:
  • Linear algebra;
  • Calculus;
  • Probability and statistics;
  • Optimization techniques (gradient descent, convex optimization, combinatorial optimization).
  1. Machine learning algorithms:
  • Supervised learning;
  • Unsupervised learning;
  • Reinforcement learning.
  1. Deep learning:
  • Neural networks;
  • Convolutional neural networks (CNNs);
  • Recurrent neural networks (RNNs);
  • Generative models.
  1. Algorithmic techniques:
  • Dimensionality reduction;
  • Regularization techniques (L1, L2, dropout, data augmentation);
  • Ensemble methods.
  1. Computational methods:
  • Numerical methods;
  • Monte Carlo methods;
  • Graph theory.
  1. Applications:
  • Computer vision;
  • Natural language processing (NLP);
  • Robotics;
  • Healthcare;
  • Finance;
  • Autonomous systems.
  1. Emerging areas:
  • Quantum machine learning;
  • Fairness and ethics in AI;
  • Explainable AI.

We encourage researchers, practitioners, and academics to contribute their original research and insights to this Special Issue. Join us in advancing the frontiers of applied mathematics in artificial intelligence, fostering innovations that will shape the future of intelligent systems and their impact on society.

Submit your manuscripts for consideration and be part of this pivotal discourse in AI research. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere.

Dr. Ashutosh Mishra
Dr. Shodhan Rao
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

  • artificial intelligence applications
  • application in computing
  • artificial intelligence
  • information management
  • management science
  • AI for science
  • image classification, object detection, image segmentation, facial recognition
  • NLP, text classification, sentiment analysis, machine translation, etc.
  • robotics, path planning, control systems, sensor fusion
  • healthcare, medical image analysis, predictive modeling, drug discovery
  • finance, algorithmic trading, risk management, fraud detection
  • autonomous systems, self-driving cars, drones
  • automated decision making
  • quantum machine learning
  • fairness and ethics in AI
  • explainable AI
  • data management, data pre-processing techniques
  • case studies and practical applications
  • industry applications
  • statistics applications
  • optimization
  • mathematical modeling
  • reliability
  • decision analysis

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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.
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  • 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 (3 papers)

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27 pages, 4238 KB  
Article
A Scalable Reinforcement Learning Framework for Ultra-Reliable Low-Latency Spectrum Management in Healthcare Internet of Things
by Adeel Iqbal, Ali Nauman, Tahir Khurshaid and Sang-Bong Rhee
Mathematics 2025, 13(18), 2941; https://doi.org/10.3390/math13182941 - 11 Sep 2025
Viewed by 112
Abstract
Healthcare Internet of Things (H-IoT) systems demand ultra-reliable and low-latency communication (URLLC) to support critical functions such as remote monitoring, emergency response, and real-time diagnostics. However, spectrum scarcity and heterogeneous traffic patterns pose major challenges for centralized scheduling in dense H-IoT deployments. This [...] Read more.
Healthcare Internet of Things (H-IoT) systems demand ultra-reliable and low-latency communication (URLLC) to support critical functions such as remote monitoring, emergency response, and real-time diagnostics. However, spectrum scarcity and heterogeneous traffic patterns pose major challenges for centralized scheduling in dense H-IoT deployments. This paper proposed a multi-agent reinforcement learning (MARL) framework for dynamic, priority-aware spectrum management (PASM), where cooperative MARL agents jointly optimize throughput, latency, energy efficiency, fairness, and blocking probability under varying traffic and channel conditions. Six learning strategies are developed and compared, including Q-Learning, Double Q-Learning, Deep Q-Network (DQN), Actor–Critic, Dueling DQN, and Proximal Policy Optimization (PPO), within a simulated H-IoT environment that captures heterogeneous traffic, device priorities, and realistic URLLC constraints. A comprehensive simulation study across scalable scenarios ranging from 3 to 50 devices demonstrated that PPO consistently outperforms all baselines, improving mean throughput by 6.2%, reducing 95th-percentile delay by 11.5%, increasing energy efficiency by 11.9%, lowering blocking probability by 33.3%, and accelerating convergence by 75.8% compared to the strongest non-PPO baseline. These findings establish PPO as a robust and scalable solution for QoS-compliant spectrum management in dense H-IoT environments, while Dueling DQN emerges as a competitive deep RL alternative. Full article
(This article belongs to the Special Issue Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications)
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34 pages, 2708 KB  
Article
Integrating Temporal Event Prediction and Large Language Models for Automatic Commentary Generation in Video Games
by Xuanyu Sheng, Aihe Yu, Mingfeng Zhang, Gayoung An, Jisun Park and Kyungeun Cho
Mathematics 2025, 13(17), 2738; https://doi.org/10.3390/math13172738 - 26 Aug 2025
Viewed by 663
Abstract
Game commentary enhances viewer immersion and understanding, particularly in football video games, where dynamic gameplay offers ideal conditions for automated commentary. The existing methods often rely on predefined templates and game state inputs combined with an LLM, such as GPT-3.5. However, they frequently [...] Read more.
Game commentary enhances viewer immersion and understanding, particularly in football video games, where dynamic gameplay offers ideal conditions for automated commentary. The existing methods often rely on predefined templates and game state inputs combined with an LLM, such as GPT-3.5. However, they frequently suffer from repetitive phrasing and delayed responses. Recent studies have attempted to mitigate the response delays by employing traditional machine learning models, such as SVM and ANN, for event prediction. Nonetheless, these models fail to capture the temporal dependencies in gameplay sequences, thereby limiting their predictive performance. To address these limitations, an integrated framework is proposed, combining a lightweight convolutional model with multi-scale temporal filters (OS-CNN) for real-time event prediction and an open-source LLM (LLaMA 3.3) for dynamic commentary generation. Our method incorporates prompt engineering techniques by embedding predicted events into contextualized instruction templates, which enables the LLM to produce fluent and diverse commentary tailored to ongoing gameplay. Evaluated in the Google Research Football environment, the proposed method achieved an F1-score of 0.7470 in the balanced setting, closely matching the best-performing GRU model (0.7547) while outperforming SVM (0.5271) and Transformer (0.7344). In the more realistic Balanced–Imbalanced setting, it attained the highest F1-score of 0.8503, substantially exceeding SVM (0.4708), GRU (0.7376), and Transformer (0.5085). Additionally, it enhances the lexical diversity (Distinct-2: +32.1%) and reduces the phrase repetition by 42.3% (Self-BLEU), compared with template-based generation. These results demonstrate the effectiveness of our approach in generating context-aware, low-latency, and natural commentary suitable for real-time deployment in football video games. Full article
(This article belongs to the Special Issue Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications)
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24 pages, 4481 KB  
Article
Towards Numerical Method-Informed Neural Networks for PDE Learning
by Pasquale De Luca and Livia Marcellino
Mathematics 2025, 13(15), 2392; https://doi.org/10.3390/math13152392 - 25 Jul 2025
Viewed by 499
Abstract
Solving stiff partial differential equations with neural networks remains challenging due to the presence of multiple time scales and numerical instabilities that arise during training. This paper addresses these limitations by embedding the mathematical structure of implicit–explicit time integration schemes directly into neural [...] Read more.
Solving stiff partial differential equations with neural networks remains challenging due to the presence of multiple time scales and numerical instabilities that arise during training. This paper addresses these limitations by embedding the mathematical structure of implicit–explicit time integration schemes directly into neural network architectures. The proposed approach preserves the operator splitting decomposition that separates stiff linear terms from non-stiff nonlinear terms, inheriting the stability properties established for these numerical methods. We evaluate the methodology on Allen–Cahn equation dynamics, where interface evolution exhibits the multi-scale behavior characteristic of stiff systems. The structure-preserving architecture achieves improvements in solution accuracy and long-term stability compared to conventional physics-informed approaches, while maintaining proper energy dissipation throughout the evolution. Full article
(This article belongs to the Special Issue Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications)
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