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Advances in Machine Learning and Intelligent Systems
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Advances in Machine Learning and Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4428

Editors

Dr. Salman Khalid

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Interests: prognostics and health management; artificial intelligence; composite structures; intelligent machines
Special Issues, Collections and Topics in MDPI journals
Dr. Aydin Azizi

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, Oxford Brookes University, Headington Campus, Oxford OX3 0BP, UK
Interests: mechatronics; control and automation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) and intelligent systems have become foundational to modern science and engineering, driving breakthroughs in perception, prediction, optimization, and autonomous decision-making. As the complexity of real-world problems grows, there is an increasing demand for robust, interpretable, and adaptive learning frameworks capable of integrating data, physics, and human knowledge.

This Special Issue aims to bring together cutting-edge research and comprehensive reviews on emerging theories, algorithms, and applications of ML and intelligent systems. We particularly welcome studies that explore the intersection of deep learning, reinforcement learning, and hybrid physics-informed or knowledge-guided models, as well as their applications in smart infrastructure, robotics, healthcare, and industrial systems. Contributions advancing the mathematical foundations, convergence analysis, and computational efficiency of intelligent algorithms are also encouraged.

Relevant topics include (but are not limited to) the following:

  • Physics-informed and hybrid learning frameworks;
  • Reinforcement and transfer learning for decision systems;
  • Intelligent sensing, prognostics, and digital twins;
  • Edge/federated AI and trustworthy intelligent systems;
  • Optimization, control, and human–AI collaboration;
  • Generative and self-evolving autonomous systems.

We welcome high-quality research and review papers that advance the mathematical and computational frontiers of intelligent systems.

Dr. Salman Khalid
Dr. Aydin Azizi
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 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
  • intelligent systems
  • physics-informed artificial intelligence
  • reinforcement learning
  • digital twins
  • optimization and control
  • trustworthy and explainable AI
  • edge and federated learning
  • prognostics and health management
  • autonomous decision-making
  • intelligent optimization
  • fuzzy systems
  • engineering applications
  • hybrid intelligent systems
  • adaptive control
  • reinforcement learning
  • optimal control
  • robot control
  • model-free adaptive contro
  • containment control
  • multi-agent systems

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

Published Papers (7 papers)

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Research

Jump to: Review

28 pages, 3675 KB  
Article
A Proposal of a Mathematics Problem Generation Tool Using Generative AI for STACK Online Assessment System
by Prismahardi Aji Riyantoko, Nobuo Funabiki, Komang Candra Brata, Noprianto, Sischa Wahyuning Tyas and Dwi Arman Prasetya
Mathematics 2026, 14(14), 2481; https://doi.org/10.3390/math14142481 - 9 Jul 2026
Viewed by 325
Abstract
The System for Teaching and Assessment using a Computer Algebra Kernel (STACK) is an open source, computer algebra-based online assessment system for teaching and learning mathematics at university. Although the popularity is increasing around the world, its problem generation needs a complex procedure [...] Read more.
The System for Teaching and Assessment using a Computer Algebra Kernel (STACK) is an open source, computer algebra-based online assessment system for teaching and learning mathematics at university. Although the popularity is increasing around the world, its problem generation needs a complex procedure such as algebraic scripting, dynamic randomization, and grading logic, which poses a substantial workload. In this paper, we propose a mathematics problem generation tool using Generative AI for STACK. It adopts a Retrieval-Augmented Generation (RAG) framework to guide the AI to produce pedagogically aligned problems across Depth of Knowledge (DoK) levels, while a Computer Algebra System (CAS) validates mathematical precision. The output is rendered into an XML template and is imported into the STACK system. For evaluation, we measured the success rate of generating 90 problem files for STACK by the proposal and compared the completion time with their manual generation. Learning Object Review Instrument (LORI) was also evaluated for user satisfactions. The results showed that the success rate was 79% while the time was reduced by 35.71%. Furthermore, the LORI evaluations demonstrated a feasibility score of 82.1%, confirming the potential to mitigate teacher workload. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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27 pages, 427 KB  
Article
Adaptive Quine Structures for Metacognitive Evolution in Large Language Models: A Functional Framework with Gödelian Bounds and Illustrative Applications
by Ali Mohammad Saghiri
Mathematics 2026, 14(13), 2371; https://doi.org/10.3390/math14132371 - 3 Jul 2026
Viewed by 337
Abstract
How can an LLM-based agent recognize the limits of its own self-knowledge and improve that self-knowledge over time? This paper proposes the metacognitive evolutionary system (MES), a functional framework for studying metacognitive evolution at the prompt level in large language model (LLM)-based agents. [...] Read more.
How can an LLM-based agent recognize the limits of its own self-knowledge and improve that self-knowledge over time? This paper proposes the metacognitive evolutionary system (MES), a functional framework for studying metacognitive evolution at the prompt level in large language model (LLM)-based agents. MES does not modify model weights; it evolves the prompt program around a fixed base model, keeping the system readable, auditable, and easier for humans to inspect. The framework introduces a recursive metacognitive tower, Mn(P)=LLM(⟨Mn−1(P)⟩), to model layered self-evaluation. The fixed-point behavior of this tower is interpreted as a Strange Loop, with a formal analogy to Gödelian incompleteness used to describe its epistemic limits. The system is built using five primitive functions: inference, grounding, awareness, adaptive self-replication, and population-level selection. A grounded fitness function guides prompt evolution across generations, evaluating uncertainty calibration, error detection, strategy adaptation, and epistemic boundedness. Through Quine-style prompt rewriting, MES studies how agents can revise their own prompt-level structure while remaining constrained by grounded evaluation. The paper presents the formal architecture, analyzes tower dynamics using Markov chains, discusses convergence results, identifies six application domains, and proposes QuineBench as an evaluation design. Numerical case studies serve as illustrative analytical examples rather than empirical experiments, and QuineBench is presented as a structured protocol for future validation, providing a theoretical foundation and a clear path toward empirical evaluation. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
29 pages, 4248 KB  
Article
Design and Experimental Validation of a Novel Particle Swarm Optimization Algorithm Designed to Optimize Solar Power Extraction
by Asier del Rio, Oscar Barambones and Jokin Uralde
Mathematics 2026, 14(12), 2079; https://doi.org/10.3390/math14122079 - 10 Jun 2026
Viewed by 231
Abstract
As the search for sustainable energy solutions increases, Photovoltaic (PV) panels have emerged as a crucial technology, harnessing solar energy to meet the growing global demand. These devices require maximum power point tracking (MPPT) for efficient operation as a consequence of their nonlinear [...] Read more.
As the search for sustainable energy solutions increases, Photovoltaic (PV) panels have emerged as a crucial technology, harnessing solar energy to meet the growing global demand. These devices require maximum power point tracking (MPPT) for efficient operation as a consequence of their nonlinear electrical behavior. These nonlinearities cause traditional algorithms to be less than fully effective, thus creating room for improvement that can be filled by intelligent algorithm proposals, such as Particle Swarm Optimization (PSO). In this context, a new variant of the PSO algorithm based on evolutionary behavior and voltage window restrictions is presented, implemented, and validated with the aim of developing an advanced control system to operate in a real PV system for MPPT. The study covers several experiments comparing its performance with other PSO variants found in the literature. The proposed algorithm exhibits smoother transitions with fewer power shocks due to a restricted voltage window, ensuring rapid convergence through its evolutionary feature. These improvements lead to a significant reduction in energy losses during the search process, dropping from about 3.76% with the standard PSO to only 2.56%, while also halving the convergence time. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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22 pages, 1559 KB  
Article
Fuzzy-Guided Exploration for Multi-Agent Reinforcement Learning in Traffic Signal Control
by Dejan Ćiprovski, Nemanja Ilić, Boško Božilović and Miljan Vučetić
Mathematics 2026, 14(11), 1942; https://doi.org/10.3390/math14111942 - 2 Jun 2026
Viewed by 367
Abstract
Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these [...] Read more.
Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these methods treats every candidate signal phase as equally worth trying, discarding the rich domain knowledge that traffic theory already provides. This paper proposes fuzzy-guided exploration, in which a multi-criteria fuzzy inference system uses local traffic conditions, with phase pressure as its primary input, to assign each candidate phase a priority. These priorities define a sampling distribution used in place of the uniform draw. We evaluate the method across four MARL algorithms covering independent learning (IQL) and the centralized training with decentralized execution paradigm (VDN, QMIX, and QPLEX) on both a synthetic grid and a real-world network. Fuzzy-guided exploration consistently improves upon the baseline in all combinations, with tangible gains on the synthetic grid and substantially larger improvements on the real-world network. These findings demonstrate that exploration is an effective intervention point for domain-knowledge integration in cooperative MARL, and that pressure-based scoring provides a well-suited signal to serve that role in traffic signal control. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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27 pages, 4747 KB  
Article
JTA-GAN: A Physics-Informed Framework for Realistic Underwater Image Generation and Improved Object Detection
by Yung-Hsiang Chen, Li-Yen Yu and Yung-Yue Chen
Mathematics 2026, 14(4), 605; https://doi.org/10.3390/math14040605 - 9 Feb 2026
Cited by 2 | Viewed by 1023
Abstract
Accurate object detection in underwater environments is severely challenged by light attenuation, wavelength-dependent color distortion, and scattering-induced turbidity, which create a substantial domain gap between terrestrial and underwater imagery. Conventional Generative Adversarial Network(GAN)-based translation models, such as CycleGAN, attempt to mitigate this gap [...] Read more.
Accurate object detection in underwater environments is severely challenged by light attenuation, wavelength-dependent color distortion, and scattering-induced turbidity, which create a substantial domain gap between terrestrial and underwater imagery. Conventional Generative Adversarial Network(GAN)-based translation models, such as CycleGAN, attempt to mitigate this gap but often suffer from instability and unrealistic color shifts due to their black-box design. To address these limitations, we propose JTA-GAN (Joint Turbidity–Attenuation GAN), a physics-informed generative framework that explicitly disentangles underwater image formation into scene radiance (J, derived from the physical imaging model), transmission (T), and ambient light (A). By enforcing a simplified physical imaging model within the generator architecture, JTA-GAN enables spatially coherent haze and attenuation synthesis without requiring ground-truth depth supervision. An asymmetric architecture stabilizes reverse mapping, while Learned Perceptual Image Patch Similarity(LPIPS)-based perceptual loss further improves reconstruction realism. Using the JTA-GAN network, we generated 65,153 physically plausible synthetic images for training You Only Look Once(YOLO)-based detectors. Evaluation on the SUIM benchmark demonstrates consistent performance improvements; specifically, YOLOv8s trained with synthetic data from JTA-GAN achieves 17.3% mAP(mean Average Precision), outperforming the land-only baseline (13.2%) and CycleGAN-based augmentation (10.8%). These results confirm that physics-informed generative modeling provides a theoretically grounded and effective solution for underwater domain adaptation under the high-turbidity and low-light conditions represented in the study. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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23 pages, 5683 KB  
Article
Optimizing RTAB-Map Viewability to Reduce Cognitive Workload in VR Teleoperation: A User-Centric Approach
by Hojin Yoon, Haegyeom Choi, Jaehoon Jeong and Donghun Lee
Mathematics 2026, 14(3), 579; https://doi.org/10.3390/math14030579 - 6 Feb 2026
Viewed by 838
Abstract
In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accuracy, often neglecting the optimization of viewability required for human operators to [...] Read more.
In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accuracy, often neglecting the optimization of viewability required for human operators to clearly perceive object depth and structure in virtual environments. To address this, this study proposes a methodology to optimize the viewability of RTAB-Map-based 3D maps using the Taguchi method, aiming to enhance VR teleoperation efficiency and reduce cognitive workload. We identified eight key parameters that critically affect visual quality and utilized an L18 orthogonal array to derive an optimal combination that controls point cloud density and noise levels. Experimental results from a target object picking task demonstrated that the optimized 3D map reduced task completion time by approximately 9 s compared to the RGB image condition, achieving efficiency levels approaching those of the physical-world baseline. Furthermore, evaluations using NASA-TLX confirmed that intuitive visual feedback minimized situational awareness errors and substantially alleviated cognitive workload. This study suggests a new direction for constructing high-efficiency teleoperation interfaces from a Human–Robot Interaction perspective by expanding SLAM optimization criteria from geometric precision to user-centric visual quality. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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Review

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34 pages, 2306 KB  
Review
A Review of Explainable Machine Learning in Medical Thermography for Interpretable Thermal Feature Analysis and Biomarker Discovery
by Muhammad Sohail, Hikmat Yar and Heung Soo Kim
Mathematics 2026, 14(10), 1666; https://doi.org/10.3390/math14101666 - 13 May 2026
Viewed by 543
Abstract
Medical thermography is a noninvasive, contactless imaging technique that captures spatial temperature distributions across the human body, providing insights into vascular function, inflammation, metabolism, physiological regulation, and aging. Recently, machine learning has been increasingly utilized to analyze thermographic data for disease screening, functional [...] Read more.
Medical thermography is a noninvasive, contactless imaging technique that captures spatial temperature distributions across the human body, providing insights into vascular function, inflammation, metabolism, physiological regulation, and aging. Recently, machine learning has been increasingly utilized to analyze thermographic data for disease screening, functional assessment, and biomarker identification. However, the existing literature is fragmented, with varied clinical applications, feature-engineering strategies, and predictive modeling frameworks, often lacking a focus on interpretability and the reliable identification of clinically relevant thermal markers. This review offers a structured overview of explainable machine learning in medical thermography, emphasizing thermal feature representation, model interpretability, and biomarker discovery. It categorizes thermographic features into pixel-based representations, region-wise statistical descriptors, texture measures, and deep latent features. Additionally, it evaluates conventional machine learning and deep learning methods for classification, regression, and risk assessment tasks. The review pays special attention to interpretable learning strategies, such as feature importance analysis, surrogate explanation models, saliency-based visualization, and Shapley-value-based methods, which can enhance transparency and confidence in model outputs. Key challenges are critically discussed, including imaging variability, limited dataset sizes, weak protocol standardization, class imbalance, generalizability, and the gap between predictive performance and clinical trust. Overall, this review synthesizes current advancements, identifies major research gaps, and outlines future directions for developing trustworthy machine learning frameworks in medical thermography and enhancing interpretable thermal biomarker discovery. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Systems)
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