applsci-logo

Journal Browser

Journal Browser

Applications of Deep Learning and Artificial Intelligence Methods: 3rd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 288

Special Issue Editors


E-Mail Website
Guest Editor
Division of Artificial Intelligence Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: multi-agent system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Information Science, Faculty of Liberal Arts, Tohoku Gakuin University, Sendai 981-3193, Japan
Interests: Internet of Things; ubiquitous computing; multi-agent system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning and artificial intelligence have attracted great attention in almost every field in recent years. Applications of deep learning and artificial intelligence methods are now pervasive, being used in various fields beyond conventional computer engineering areas. Therefore, the goal of this Special Issue is to discuss new ideas and recent experimental results in the fields of the applications of deep learning and artificial intelligence methods.

Topics of interest include, but are not limited to, the following subjects:

  • Artificial intelligence tools and applications;
  • Automatic control;
  • Natural language processing;
  • Computer vision and speech understanding;
  • Data mining and analysis;
  • Heuristic and AI planning strategies;
  • Intelligent system;
  • Robotics.

Prof. Dr. Yujin Lim
Dr. Hideyuki Takahashi
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. Applied Sciences 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 2400 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
  • deep learning
  • artificial intelligence
  • natural language processing
  • computer vision
  • data mining
  • robotics

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (1 paper)

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

Research

18 pages, 3238 KiB  
Article
Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
by Peiwang Zhu, Chengwu Liang, Yalong Liu and Songqi Jiang
Appl. Sci. 2025, 15(9), 4768; https://doi.org/10.3390/app15094768 - 25 Apr 2025
Viewed by 139
Abstract
Skeleton-based human activity recognition is a key research topic in the fields of deep learning and computer vision. However, existing approaches are less effective at capturing short-term sub-action information at different granularity levels and long-term motion correlations, which affect recognition accuracy. To overcome [...] Read more.
Skeleton-based human activity recognition is a key research topic in the fields of deep learning and computer vision. However, existing approaches are less effective at capturing short-term sub-action information at different granularity levels and long-term motion correlations, which affect recognition accuracy. To overcome these challenges, an innovative multi-grained temporal clip transformer (MTC-Former) is proposed. Firstly, based on the transformer backbone, a multi-grained temporal clip attention (MTCA) module with multi-branch architecture is proposed to capture the characteristics of short-term sub-action features. Secondly, an innovative multi-scale spatial–temporal feature interaction module is proposed to jointly learn sub-action dependencies and facilitate skeletal motion interactions, where long-range motion patterns are embedded to enhance correlation modeling. Experiments were conducted on three datasets, including NTU RGB+D, NTU RGB+D 120, and InHARD, and achieved state-of-the-art Top-1 recognition accuracy, demonstrating the superiority of the proposed MTC-Former. Full article
Show Figures

Figure 1

Back to TopTop