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Electronics
Special Issues
AI for Science: Advanced Techniques and Interdisciplinary Applications
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AI for Science: Advanced Techniques and Interdisciplinary Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 678

Special Issue Editors

Dr. Yanbin Liu

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1025, New Zealand
Interests: computer vision; deep learning; spatial–temporal modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxia Wu

E-Mail Website
Guest Editor
School of Computing and Information Systems, Singapore Management University, Singapore 188065, Singapore
Interests: recommender system; conversational AI; AI for science
Dr. Xiaolin Zhang

E-Mail Website
Guest Editor
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Interests: computer vision; deep learning; 3D digital human

Special Issue Information

Dear Colleagues,

In recent years, the rapid development of AI technologies has revolutionized scientific research and the application of engineering across multiple disciplines. This Special Issue aims to showcase the latest advancements in and application of AI techniques, focusing on innovative methodologies, interdisciplinary approaches, and novel solutions to scientific challenges. We welcome submissions that highlight the transformative impact of AI in fields such as weather, traffic, healthcare, agriculture, marine science, computational biology, environmental science, and industrial optimization.

This Special Issue welcomes contributions that explore the use of advanced AI techniques, such as deep learning, reinforcement learning, graph neural networks, and hybrid models, to solve real-world scientific problems. Submissions that investigate the application of AI in the integration of multimodal data, the optimization of complex systems, or the development of novel computational models for predicting and simulating scientific phenomena are particularly welcome.

This Special Issue welcomes original research articles, comprehensive reviews, and case studies that address topics including, but not limited to, the following:

  • Advanced AI methodologies for scientific research.
  • AI-driven modeling, optimization, and prediction across diverse scientific fields.
  • The application of AI in computational biology, bioinformatics, and chemical engineering.
  • Interdisciplinary AI methods for addressing complex scientific challenges.
  • Graph neural networks and graph-based learning for scientific and engineering problems.
  • AI for real-world challenges in environmental science, energy systems, and industrial processes.
  • Multimodal data integration and analysis for scientific research.
  • Machine learning and deep learning for scientific discovery.
  • Explainable and interpretable AI models in scientific research.
  • Real-world AI applications and case studies.

We look forward to receiving your contributions.

Dr. Yanbin Liu
Dr. Yuxia Wu
Dr. Xiaolin Zhang
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. Electronics 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

  • AI for science
  • deep learning
  • interdisciplinary AI applications
  • multimodal data analysis

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

Published Papers (1 paper)

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Research

22 pages, 3427 KiB  
Article
A Multimodal Artificial Intelligence Model for Depression Severity Detection Based on Audio and Video Signals
by Liyuan Zhang, Shuai Zhang, Xv Zhang and Yafeng Zhao
Electronics 2025, 14(7), 1464; https://doi.org/10.3390/electronics14071464 - 4 Apr 2025
Viewed by 369
Abstract
In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models [...] Read more.
In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models are often too large for lightweight deployment, restricting their real-time monitoring capabilities, especially in resource-constrained environments. To address these challenges, this study proposes a lightweight and accurate multimodal method for detecting depression severity, aiming to provide effective support for smart healthcare systems. Specifically, we design a multimodal detection network based on speech and video signals, enhancing the recognition of depression severity by optimizing the cross-modal fusion strategy. The model leverages Long Short-Term Memory (LSTM) networks to capture long-term dependencies in speech and visual sequences, effectively extracting dynamic features associated with depression. Considering the behavioral differences of respondents when interacting with human versus robotic interviewers, we train two separate sub-models and fuse their outputs using a Mixture of Experts (MOE) framework capable of modeling uncertainty, thereby suppressing the influence of low-confidence experts. In terms of the loss function, the traditional Mean Squared Error (MSE) is replaced with Negative Log-Likelihood (NLL) to better model prediction uncertainty and enhance robustness. The experimental results show that the improved AI model achieves an accuracy of 83.86% in depression severity recognition. The model’s floating-point operations per second (FLOPs) reached 0.468 GFLOPs, with a parameter size of only 0.52 MB, demonstrating its compact size and strong performance. These findings underscore the importance of emotion and facial recognition in AI applications for mental health, offering a promising solution for real-time depression monitoring in resource-limited environments. Full article
(This article belongs to the Special Issue AI for Science: Advanced Techniques and Interdisciplinary Applications)
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