Recent Advances in Artificial Intelligence and Machine Learning, 3rd 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: 31 October 2026 | Viewed by 737

Special Issue Editors


E-Mail Website
Guest Editor
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent detection; machine learning; data mining

E-Mail
Guest Editor
School of Information Engineering, Ningxia University, Yinchuan 750021, China
Interests: AI for biomedicine; knowledge-based biomedical discovery; bioinformatics

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 and are 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 to mitigate these challenges.

This Special Issue focuses on state-of-the-art research related 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 big data understanding;

(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. Meng Lei
Dr. Zhenhua Yu
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

  • artificial intelligence
  • machine learning
  • computer vision
  • natural language processing
  • intelligent speech
  • interpretable algorithms
  • deepfake

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.

Related Special Issues

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 1017 KB  
Article
Multi-Rate Sampling-Based H LFC for Networked Power Systems: An Area-Information-Fusion Method
by Liteng Yin, Lu Wang, Zhilin Yi and Chao Zhang
Mathematics 2026, 14(7), 1122; https://doi.org/10.3390/math14071122 - 27 Mar 2026
Viewed by 347
Abstract
This study explores the multi-rate sampling-based H load frequency control (LFC) problem for networked power systems by using an area-information-fusion method. This problem is addressed for two reasons: (1) most of networked control methods for LFC are focused on the one-rate sampling [...] Read more.
This study explores the multi-rate sampling-based H load frequency control (LFC) problem for networked power systems by using an area-information-fusion method. This problem is addressed for two reasons: (1) most of networked control methods for LFC are focused on the one-rate sampling scheme and (2) the previous looped function cannot be directly applied within the multi-rate sampling scheme. Here, the multi-rate sampling scheme involves each area sampling rate being reliant on its own sensor. Namely, all area sampling rates are different from each other. In the presence of a multi-rate sampling scheme, a new sampling instants sequence is established by using an area-information-fusion method. It contributes to constructing a corresponding closed-loop model by adding virtual state variables. In addition, a new looped-function approach is devised to capture the sampling information from diverse area sensors. Based on Lyapunov stability theory, less conservative LMI conditions are derived to guarantee the H performance of the multi-rate LFC system. Additionally, a co-designed method for determining the control gain and maximum sampling frequency is established. Finally, simulation studies are conducted to validate the efficacy and features of the proposed control strategy. Full article
Show Figures

Figure 1

Back to TopTop