Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Advances in Artificial Intelligence, Machine Learning and Optimization
share announcement

Advances in Artificial Intelligence, Machine Learning and Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2821

Special Issue Editors

Prof. Dr. Zne-Jung Lee

E-Mail Website
Guest Editor
School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
Interests: artificial intelligence; machine learning; big data; data mining; computational intelligence
Prof. Dr. Liang-Hung Wang

E-Mail Website
Guest Editor
Department of Microelectronics, Fuzhou University, Fuzhou 350116, China
Interests: low-power biological signal acquisition; detection ICs design; automatic identification; classification of heart disease analysis; ECG images features; brain–computer interface; EEG signal analysis; cardiac medical information; brain science heterogeneous data processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on “Advances in Artificial Intelligence, Machine Learning and Optimization”. This Special Issue aims to bring together the latest research and developments at the intersection of these three dynamic fields. Artificial Intelligence (AI), Machine Learning (ML), and Optimization play pivotal roles across various domains, shaping the future of technology, industry, medicine, and society. The potential for synergistic advancements in these areas is vast, and we are excited to explore the cutting-edge contributions driving progress in this space.

We welcome submissions that delve into, but are not limited to, the following topics:

  1. Advanced machine learning algorithms for optimization;
  2. Integration of AI techniques in optimization problems;
  3. Optimization methods for enhancing machine learning models;
  4. AI-driven approaches for large-scale optimization;
  5. Deep learning applications in solving complex optimization challenges;
  6. Metaheuristic and evolutionary algorithms in machine learning and AI;
  7. Optimization for neural network training and architecture design;
  8. Reinforcement learning for optimization and decision-making;
  9. Novel applications of AI and machine learning in optimization problems across various domains;
  10. Integrating multi-attribute decision-making techniques with AI, machine learning, and optimization methodologies in the context of advanced manufacturing, smart factories, industrial automation, and related domains.

We encourage researchers and practitioners to contribute their original research, reviews, and perspectives on these and related topics. Submissions should aim to uncover new insights, present state-of-the-art methodologies, and offer practical applications in Artificial Intelligence, Machine Learning, and Optimization.

We look forward to your valuable contributions to this Special Issue, and we are confident that the collective expertise of the research community will lead to an impactful and informative compilation.

Prof. Dr. Zne-Jung Lee
Prof. Dr. Liang-Hung Wang
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
  • optimization
  • biocomputing
  • intelligent control
  • intelligent computing
  • data mining
  • deep learning
  • intelligent technologies and applications in engineering

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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 (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 279 KiB  
Article
Belief Update Through Semiorders
by Theofanis Aravanis
Mathematics 2025, 13(13), 2102; https://doi.org/10.3390/math13132102 - 26 Jun 2025
Viewed by 63
Abstract
Belief change is a core component of intelligent reasoning, enabling agents to adapt their beliefs in response to new information. A prominent form of belief change is belief revision, which involves altering an agent’s beliefs about a static (unchanging) world in light of [...] Read more.
Belief change is a core component of intelligent reasoning, enabling agents to adapt their beliefs in response to new information. A prominent form of belief change is belief revision, which involves altering an agent’s beliefs about a static (unchanging) world in light of new evidence. A foundational framework for modeling rational belief revision was introduced by Alchourrón, Gärdenfors, and Makinson (AGM), who formalized revision functions based on total preorders over possible worlds—that is, orderings that encode the relative plausibility of alternative states of affairs. Building on this, Peppas and Williams later characterized AGM-style revision functions using weaker preference structures known as semiorders, which, unlike total preorders, permit intransitive indifference between alternatives. In this article, we extend the framework of Peppas and Williams to the context of belief update. In contrast to belief revision, belief update concerns maintaining coherent beliefs in response to actual changes in a dynamic, evolving environment. We provide both axiomatic and semantic characterizations of update functions derived from semiorders, establishing corresponding representation theorems. These results essentially generalize the classical belief-update framework of Katsuno and Mendelzon, which relies on total preorders, thereby offering a broader and more flexible perspective. The intransitivity of indifference inherent in semiorders plays a central role in our framework, enabling the representation of nuanced plausibility distinctions between possible states of affairs—an essential feature for realistically modeling belief dynamics. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

26 pages, 1906 KiB  
Article
Context-Aware Markov Sensors and Finite Mixture Models for Adaptive Stochastic Dynamics Analysis of Tourist Behavior
by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong and Zhengchun Song
Mathematics 2025, 13(12), 2028; https://doi.org/10.3390/math13122028 - 19 Jun 2025
Viewed by 230
Abstract
We propose a novel framework for adaptive stochastic dynamics analysis of tourist behavior by integrating context-aware Markov models with finite mixture models (FMMs). Conventional Markov models often fail to capture abrupt changes induced by external shocks, such as event announcements or weather disruptions, [...] Read more.
We propose a novel framework for adaptive stochastic dynamics analysis of tourist behavior by integrating context-aware Markov models with finite mixture models (FMMs). Conventional Markov models often fail to capture abrupt changes induced by external shocks, such as event announcements or weather disruptions, leading to inaccurate predictions. The proposed method addresses this limitation by introducing virtual sensors that dynamically detect contextual anomalies and trigger regime switches in real-time. These sensors process streaming data to identify shocks, which are then used to reweight the probabilities of pre-learned behavioral regimes represented by FMMs. The system employs expectation maximization to train distinct Markov sub-models for each regime, enabling seamless transitions between them when contextual thresholds are exceeded. Furthermore, the framework leverages edge computing and probabilistic programming for efficient, low-latency implementation. The key contribution lies in the explicit modeling of contextual shocks and the dynamic adaptation of stochastic processes, which significantly improves robustness in volatile tourism scenarios. Experimental results demonstrate that the proposed approach outperforms traditional Markov models in accuracy and adaptability, particularly under rapidly changing conditions. Quantitative results show a 13.6% improvement in transition accuracy (0.742 vs. 0.653) compared to conventional context-aware Markov models, with an 89.2% true positive rate in shock detection and a median response latency of 47 min for regime switching. This work advances the state-of-the-art in tourist behavior analysis by providing a scalable, real-time solution for capturing complex, context-dependent dynamics. The integration of virtual sensors and FMMs offers a generalizable paradigm for stochastic modeling in other domains where external shocks play a critical role. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

24 pages, 10876 KiB  
Article
Adaptive Stylized Image Generation for Traditional Miao Batik Using Style-Conditioned LCM-LoRA Enhanced Diffusion Models
by Qingqing Hu, Yiran Peng, Jing Xu, Zichun Shao, Zhen Tian and Junming Chen
Mathematics 2025, 13(12), 1947; https://doi.org/10.3390/math13121947 - 12 Jun 2025
Viewed by 488
Abstract
As a national intangible cultural heritage in China, traditional Miao batik has encountered obstacles in contemporary dissemination and design due to its reliance on manual craftsmanship and other reasons. Existing generative models are difficult to fully capture the complex semantic and stylistic attributes [...] Read more.
As a national intangible cultural heritage in China, traditional Miao batik has encountered obstacles in contemporary dissemination and design due to its reliance on manual craftsmanship and other reasons. Existing generative models are difficult to fully capture the complex semantic and stylistic attributes in Miao batik patterns, which limits their application in digital creativity. To address this issue, we construct the structured CMBP-9 dataset to facilitate semantic-aware image generation. Based on stable diffusion v1.5, Low-Rank Adaptation (LoRA) is used to effectively transfer the structure, sign, and texture features that are unique to the Miao people, and the Latent Consistency model (LCM) is integrated to improve the inference efficiency. In addition, a Style-Conditioned Linear Fusion (SCLF) strategy is proposed to dynamically adjust the fusion of LoRA and LCM outputs according to the semantic complexity of input prompts, thereby overcoming the limitation of static weighting in existing frameworks. Extensive quantitative evaluations using LPIPS, SSIM, PSNR, FID metrics, and human evaluations show that the proposed Batik-MPDM framework achieves superior performance in terms of style fidelity and generation efficiency compared to baseline methods. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

21 pages, 533 KiB  
Article
Angle-Based Dual-Association Evolutionary Algorithm for Many-Objective Optimization
by Xinzi Wang, Huimin Wang, Zhen Tian, Wenxiao Wang and Junming Chen
Mathematics 2025, 13(11), 1757; https://doi.org/10.3390/math13111757 - 26 May 2025
Viewed by 301
Abstract
As the number of objectives increases, the comprehensive processing performance of multi-objective optimization problems significantly declines. To address this challenge, this paper proposes an Angle-based dual-association Evolutionary Algorithm for Many-Objective Optimization (MOEA-AD). The algorithm enhances the exploration capability of unknown regions by associating [...] Read more.
As the number of objectives increases, the comprehensive processing performance of multi-objective optimization problems significantly declines. To address this challenge, this paper proposes an Angle-based dual-association Evolutionary Algorithm for Many-Objective Optimization (MOEA-AD). The algorithm enhances the exploration capability of unknown regions by associating empty subspaces with the solutions of the highest fitness through an angle-based bi-association strategy. Additionally, a novel quality assessment scheme is designed to evaluate the convergence and diversity of solutions, introducing dynamic penalty coefficients to balance the relationship between the two. Adaptive hierarchical sorting of solutions is performed based on the global diversity distribution to ensure the selection of optimal solutions. The performance of MOEA-AD is validated on several classic benchmark problems (with up to 20 objectives) and compared with five state-of-the-art multi-objective evolutionary algorithms. Experimental results demonstrate that the algorithm exhibits significant advantages in both convergence and diversity. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

16 pages, 12134 KiB  
Article
Intelligent Dynamic Multi-Dimensional Heterogeneous Resource Scheduling Optimization Strategy Based on Kubernetes
by Jialin Cai, Hui Zeng, Feifei Liu and Junming Chen
Mathematics 2025, 13(8), 1342; https://doi.org/10.3390/math13081342 - 19 Apr 2025
Viewed by 491
Abstract
In this paper, we tackle the challenge of optimizing resource utilization and demand-driven allocation in dynamic, multi-dimensional heterogeneous environments. Traditional containerized task scheduling systems, like Kubernetes, typically rely on default schedulers that primarily focus on CPU and memory, overlooking the multi-dimensional nature of [...] Read more.
In this paper, we tackle the challenge of optimizing resource utilization and demand-driven allocation in dynamic, multi-dimensional heterogeneous environments. Traditional containerized task scheduling systems, like Kubernetes, typically rely on default schedulers that primarily focus on CPU and memory, overlooking the multi-dimensional nature of heterogeneous resources such as GPUs, network I/O, and disk I/O. This results in suboptimal scheduling and underutilization of resources. To address this, we propose a dynamic scheduling method for heterogeneous resources using an enhanced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm that adjusts weights in real time and applies nonlinear normalization. Leveraging parallel computing, approximation, incremental computation, local updates, and hardware acceleration, the method minimizes overhead and ensures efficiency. Experimental results showed that, under low-load conditions, our method reduced task response times by 31–36%, increased throughput by 20–50%, and boosted resource utilization by over 20% compared to both the default Kubernetes scheduler and the Kubernetes Container Scheduling Strategy (KCSS) algorithm. These improvements were tested across diverse workloads, utilizing CPU, memory, GPU, and I/O resources, in a large-scale cluster environment, demonstrating the method’s robustness. These enhancements optimize cluster performance and resource efficiency, offering valuable insights for task scheduling in containerized cloud platforms. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

19 pages, 4910 KiB  
Article
A Novel SHAP-GAN Network for Interpretable Ovarian Cancer Diagnosis
by Jingxun Cai, Zne-Jung Lee, Zhihxian Lin and Ming-Ren Yang
Mathematics 2025, 13(5), 882; https://doi.org/10.3390/math13050882 - 6 Mar 2025
Viewed by 777
Abstract
Ovarian cancer stands out as one of the most formidable adversaries in women’s health, largely due to its typically subtle and nonspecific early symptoms, which pose significant challenges to early detection and diagnosis. Although existing diagnostic methods, such as biomarker testing and imaging, [...] Read more.
Ovarian cancer stands out as one of the most formidable adversaries in women’s health, largely due to its typically subtle and nonspecific early symptoms, which pose significant challenges to early detection and diagnosis. Although existing diagnostic methods, such as biomarker testing and imaging, can help with early diagnosis to some extent, these methods still have limitations in sensitivity and accuracy, often leading to misdiagnosis or missed diagnosis. Ovarian cancer’s high heterogeneity and complexity increase diagnostic challenges, especially in disease progression prediction and patient classification. Machine learning (ML) has outperformed traditional methods in cancer detection by processing large datasets to identify patterns missed by conventional techniques. However, existing AI models still struggle with accuracy in handling imbalanced and high-dimensional data, and their “black-box” nature limits clinical interpretability. To address these issues, this study proposes SHAP-GAN, an innovative diagnostic model for ovarian cancer that integrates Shapley Additive exPlanations (SHAP) with Generative Adversarial Networks (GANs). The SHAP module quantifies each biomarker’s contribution to the diagnosis, while the GAN component optimizes medical data generation. This approach tackles three key challenges in medical diagnosis: data scarcity, model interpretability, and diagnostic accuracy. Results show that SHAP-GAN outperforms traditional methods in sensitivity, accuracy, and interpretability, particularly with high-dimensional and imbalanced ovarian cancer datasets. The top three influential features identified are PRR11, CIAO1, and SMPD3, which exhibit wide SHAP value distributions, highlighting their significant impact on model predictions. The SHAP-GAN network has demonstrated an impressive accuracy rate of 99.34% on the ovarian cancer dataset, significantly outperforming baseline algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), and XGBoost. Specifically, SVM achieved an accuracy of 72.78%, LR achieved 86.09%, and XGBoost achieved 96.69%. These results highlight the superior performance of SHAP-GAN in handling high-dimensional and imbalanced datasets. Furthermore, SHAP-GAN significantly alleviates the challenges associated with intricate genetic data analysis, empowering medical professionals to tailor personalized treatment strategies for individual patients. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop