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Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition
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Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 7080

Special Issue Editors

Dr. Liang Zou

E-Mail Website
Guest Editor
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: deep learning; computer vision; intelligent speech
Special Issues, Collections and Topics in MDPI journals
Dr. Liang Zhao

E-Mail Website
Guest Editor
School of Software Technology, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; medical big data; multimodal machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yonghui Xu

E-Mail Website
Guest Editor
Department of Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Jinan 250101, China
Interests: artificial intelligence; big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence (AI) and machine learning (ML), various intelligent models have been developed to solve practical problems in every imaginable domain, including, but not limited to, healthcare, engineering, finance, agriculture, and remote sensing. Currently, the application of intelligent systems for real-world applications is feasible and sound. AI and ML have a significant impact on human life, helping to transform life for the better in general. However, the implementation of AI and ML technologies faces several challenges, such as limited labeled samples, class imbalance, privacy issues, and model interpretability. There is a critical need for the development of advanced AL and ML methods in order to mitigate these challenges.

This Special Issue focuses on state-of-the-art research relating to the development and application of AI and ML technologies to enhance people’s lives. Topics of interest include, but are not limited to, the following:

  1. The applications of artificial intelligence and machine learning models in various domains, such as smart health, smart cities, and smart factories;
  2. Novel artificial intelligence and machine learning methods and algorithms;
  3. Interpretable artificial intelligence and machine learning for the understanding of big data;
  4. Artificial intelligence and machine learning for computer vision, such as image classification, object detection, segmentation, understanding, and generation;
  5. Deep learning artificial intelligence and machine learning for intelligent speech (e.g., speech recognition, speaker verification, speech enhancement, and speech synthesis);
  6. Artificial intelligence and machine learning for natural language processing;
  7. Deepfake and anti-spoofing techniques.

Dr. Liang Zou
Dr. Liang Zhao
Prof. Dr. Yonghui Xu
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
  • machine learning
  • computer vision
  • natural language processing
  • intelligent speech
  • interpretable algorithms
  • deepfake
  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Neural networks
  • Natural language processing
  • Computer vision
  • Reinforcement learning
  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Transfer learning
  • Generative models
  • Data augmentation
  • Feature extraction
  • Hyperparameter tuning
  • Model interpretability
  • Explainable AI
  • Adversarial learning
  • Federated learning
  • Ethical AI
  • Meta-learning
  • Graph neural networks
  • Evolutionary algorithms
  • Swarm intelligence
  • Decision trees
  • Random forests
  • Ensemble learning
  • Bayesian networks
  • Support vector machines
  • Anomaly detection
  • Clustering algorithms
  • Dimensionality reduction
  • Active learning
  • Multi-task learning
  • Self-supervised learning
  • Data mining
  • Knowledge representation
  • Autonomous systems
  • Robotics
  • Cognitive computing

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Related Special Issue

Published Papers (5 papers)

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Research

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20 pages, 4145 KiB  
Article
Multiscale Interaction Purification-Based Global Context Network for Industrial Process Fault Diagnosis
by Yukun Huang, Jianchang Liu, Peng Xu, Lin Jiang, Xiaoyu Sun and Haotian Tang
Mathematics 2025, 13(9), 1371; https://doi.org/10.3390/math13091371 - 23 Apr 2025
Viewed by 271
Abstract
The application of deep convolutional neural networks (CNNs) has gained popularity in the field of industrial process fault diagnosis. However, conventional CNNs primarily extract local features through convolution operations and have limited receptive fields. This leads to insufficient feature expression, as CNNs neglect [...] Read more.
The application of deep convolutional neural networks (CNNs) has gained popularity in the field of industrial process fault diagnosis. However, conventional CNNs primarily extract local features through convolution operations and have limited receptive fields. This leads to insufficient feature expression, as CNNs neglect the temporal correlations in industrial process data, ultimately resulting in lower diagnostic performance. To address this issue, a multiscale interaction purification-based global context network (MIPGC-Net) is proposed. First, we propose a multiscale feature interaction refinement (MFIR) module. The module aims to extract multiscale features enriched with combined information through feature interaction while refining feature representations by employing the efficient channel attention mechanism. Next, we develop a wide temporal dependency feature extraction sub-network (WTD) by integrating the MFIR module with the global context network. This sub-network can capture the temporal correlation information from the input, enhancing the comprehensive perception of global information. Finally, MIPGC-Net is constructed by stacking multiple WTD sub-networks to perform fault diagnosis in industrial processes, effectively capturing both local and global information. The proposed method is validated on both the Tennessee Eastman and the Continuous Stirred-Tank Reactor processes, confirming its effectiveness. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition)
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26 pages, 3007 KiB  
Article
EDRNet: Edge-Enhanced Dynamic Routing Adaptive for Depth Completion
by Fuyun Sun, Baoquan Li and Qiaomei Zhang
Mathematics 2025, 13(6), 953; https://doi.org/10.3390/math13060953 - 13 Mar 2025
Viewed by 455
Abstract
Depth completion is a technique to densify the sparse depth maps acquired by depth sensors (e.g., RGB-D cameras, LiDAR) to generate complete and accurate depth maps. This technique has important application value in autonomous driving, robot navigation, and virtual reality. Currently, deep learning [...] Read more.
Depth completion is a technique to densify the sparse depth maps acquired by depth sensors (e.g., RGB-D cameras, LiDAR) to generate complete and accurate depth maps. This technique has important application value in autonomous driving, robot navigation, and virtual reality. Currently, deep learning has become a mainstream method for depth completion. Therefore, we propose an edge-enhanced dynamically routed adaptive depth completion network, EDRNet, to achieve efficient and accurate depth completion through lightweight design and boundary optimisation. Firstly, we introduce the Canny operator (a classical image processing technique) to explicitly extract and fuse the object contour information and fuse the acquired edge maps with RGB images and sparse depth map inputs to provide the network with clear edge-structure information. Secondly, we design a Sparse Adaptive Dynamic Routing Transformer block called SADRT, which can effectively combine the global modelling capability of the Transformer and the local feature extraction capability of CNN. The dynamic routing mechanism introduced in this block can dynamically select key regions for efficient feature extraction, and the amount of redundant computation is significantly reduced compared with the traditional Transformer. In addition, we design a loss function with additional penalties for the depth error of the object edges, which further enhances the constraints on the edges. The experimental results demonstrate that the method presented in this paper achieves significant performance improvements on the public datasets KITTI DC and NYU Depth v2, especially in the edge region’s depth prediction accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition)
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22 pages, 6362 KiB  
Article
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers
by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov and Diana Kalita
Mathematics 2025, 13(5), 828; https://doi.org/10.3390/math13050828 - 1 Mar 2025
Viewed by 567
Abstract
The current development of machine learning has advanced many fields in applied sciences and industry, including remote sensing. In this area, deep neural networks are used to solve routine object detection problems, satisfying the required rules and conditions. However, the growing number and [...] Read more.
The current development of machine learning has advanced many fields in applied sciences and industry, including remote sensing. In this area, deep neural networks are used to solve routine object detection problems, satisfying the required rules and conditions. However, the growing number and difficulty of such problems cause the developers to construct machine learning models with higher computational complexities, such as an increased number of hidden layers, epochs, learning rate, and rate decay. In this paper, we propose the Yolov8 architecture with decomposed layers via canonical polyadic and Tucker methods for accelerating the solving of the object detection problem in satellite images. Our positive–negative momentum approaches enabled a reduction in the loss in precision and recall assessments for the proposed neural network. The convolutional layer factorization reduces the shapes and accelerates the computations at kernel nodes in the proposed deep learning models. The advanced optimization algorithms achieve the global minimum of loss functions, which makes the precision and recall metrics superior to the ones for their known counterparts. We examined the proposed Yolov8 with decomposed layers, comparing it with the conventional Yolov8 on the DIOR and VisDrone 2020 datasets containing the UAV images. We verified the performance of the proposed and known neural networks on different optimizers. It is shown that the proposed neural network accelerates the solving object detection problem by 44–52%. The proposed Yolov8 with Tucker and canonical polyadic decompositions has greater precision and recall metrics than the usual Yolov8 with known analogs by 0.84–0.94 and 0.228–1.107 percentage points, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition)
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13 pages, 2406 KiB  
Article
Machine Learning-Enhanced Fabrication of Three-Dimensional Co-Pt Microstructures via Localized Electrochemical Deposition
by Yangqianhui Zhang, Zhanyun Zhu, Huayong Yang and Dong Han
Mathematics 2024, 12(21), 3443; https://doi.org/10.3390/math12213443 - 4 Nov 2024
Viewed by 2572
Abstract
This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face [...] Read more.
This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face significant challenges in controlling the thickness and uniformity of deposition layers, particularly in the manufacturing of magnetic materials. To address these challenges, this paper proposes a method that integrates machine learning algorithms to optimize the electrochemical deposition parameters, achieving a Co:Pt atomic ratio of 50:50. This optimized ratio is crucial for enhancing the material’s magnetic properties. The Co-Pt microstructures fabricated exhibit high coercivity and remanence magnetization comparable to those of bulk Co-Pt magnets. Our machine learning framework provides a robust approach for optimizing complex material synthesis processes, enhancing control over deposition conditions, and achieving superior material properties. This method opens up new possibilities for the fabrication of 3D microstructures with complex shapes and structures, which could be useful in a variety of applications, including micro-electromechanical systems (MEMSs), micro-robots, and data storage devices. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition)
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Review

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35 pages, 9798 KiB  
Review
Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review
by Salman Khalid, Muhammad Haris Yazdani, Muhammad Muzammil Azad, Muhammad Umar Elahi, Izaz Raouf and Heung Soo Kim
Mathematics 2025, 13(1), 17; https://doi.org/10.3390/math13010017 - 25 Dec 2024
Cited by 3 | Viewed by 2292
Abstract
Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding [...] Read more.
Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition)
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