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Mathematics
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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 1453

Special Issue 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-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
  • 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 (2 papers)

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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
Viewed by 543
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 504
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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