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Electronics
Special Issues
Digital Intelligence Technology and Applications
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Digital Intelligence Technology and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1896

Special Issue Editors

Dr. Xinyu Zhang

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Guest Editor
Xiangjiang Laboratory, Hunan University of Technology and Business, Changsha 410205, China
Interests: pattern recognition; artificial intelligence; data processing and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Dan He

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Guest Editor
School of Civil Engineering, University of Queensland, St Lucia, QLD 4072, Australia
Interests: intelligent transportation systems; spatial-temporal data management and data mining
Dr. Yang-Tao Wang

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Guest Editor
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: deep learning; image recognition; graph neural network and multimedia content analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Len Gelman

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Guest Editor
Department of Engineering, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: condition monitoring; structural health monitoring; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in digital intelligence technology have revolutionized various fields, driving significant progress and innovation. Digital intelligence encompasses a spectrum of technologies, including advanced algorithms, machine learning, data analytics, and artificial intelligence, which enhance our ability to process, analyze, and leverage digital data effectively. These technologies are becoming indispensable in domains such as healthcare, smart cities, smart transportation, industrial informatization, and security.

This Special Issue, titled “Digital Intelligence Technology and Applications”, aims to promote high-quality research that addresses critical issues, emerging challenges, and innovative applications in the development, analysis, and implementation of digital intelligence solutions across various sectors. We invite original research articles and comprehensive reviews on topics including, but not limited to, the following:

  1. Advances in digital data preprocessing and cleaning;
  2. Representation learning for digital data;
  3. Multimodal data fusion and analysis;
  4. Applications of digital intelligence in intelligent transportation and smart cities;
  5. Advancements in deep learning and machine learning for digital intelligence;
  6. Digital intelligence in healthcare and biomedical applications;
  7. AI-driven decision making and automation;
  8. Digital intelligence in natural environment monitoring and disaster management;
  9. Human–computer interaction and user experience in digital intelligence systems;
  10. Industrial applications of digital intelligence.

Dr. Xinyu Zhang
Dr. Dan He
Dr. Yang-Tao Wang
Prof. Dr. Len Gelman
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

  • digital intelligence
  • artificial intelligence
  • machine learning
  • data analysis
  • digital intelligence technology and applications

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

Published Papers (2 papers)

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Research

20 pages, 1930 KiB  
Article
Automated Selection of Time Series Forecasting Models for Financial Accounting Data: Synthetic Data Application
by Rokas Štrimaitis, Simona Ramanauskaitė and Pavel Stefanovič
Electronics 2025, 14(7), 1253; https://doi.org/10.3390/electronics14071253 - 22 Mar 2025
Viewed by 304
Abstract
The continuous forecasting of anticipated trends in company accounting helps to prepare for possible challenges and investment possibilities. The forecasting of performance indicators for each partner and metric in a company requires a high volume of resources, as each forecasting model requires some [...] Read more.
The continuous forecasting of anticipated trends in company accounting helps to prepare for possible challenges and investment possibilities. The forecasting of performance indicators for each partner and metric in a company requires a high volume of resources, as each forecasting model requires some supervised adjustment. To solve the challenge of manual work in this task, AutoML solutions can be used. In this study, we propose an automated forecasting model selection option. The time series data are summarized by 21 statistical features and different experiments are performed to find the best-suited forecasting method. Different classifier models are tested with the dataset, estimating the impact of data sampling and synthetic data generation. The results indicate that the undersampling approach is more suitable, as it helps in balancing the classes. Random forest methods usually show the best performance, achieving about 74% accuracy. The usage of a synthetic data-based dataset for model training reduced the accuracy by almost 20%, while the integration of synthetic and real time series data allowed us to achieve balance between both classes. This confirms that synthetic time series data generation might increase the accuracy of forecasting method selection, but it should be used in combination with real data. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications)
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20 pages, 2139 KiB  
Article
Hypergraph Neural Network for Multimodal Depression Recognition
by Xiaolong Li, Yang Dong, Yunfei Yi, Zhixun Liang and Shuqi Yan
Electronics 2024, 13(22), 4544; https://doi.org/10.3390/electronics13224544 - 19 Nov 2024
Cited by 1 | Viewed by 1028
Abstract
Deep learning-based approaches for automatic depression recognition offer advantages of low cost and high efficiency. However, depression symptoms are challenging to detect and vary significantly between individuals. Traditional deep learning methods often struggle to capture and model these nuanced features effectively, leading to [...] Read more.
Deep learning-based approaches for automatic depression recognition offer advantages of low cost and high efficiency. However, depression symptoms are challenging to detect and vary significantly between individuals. Traditional deep learning methods often struggle to capture and model these nuanced features effectively, leading to lower recognition accuracy. This paper introduces a novel multimodal depression recognition method, HYNMDR, which utilizes hypergraphs to represent the complex, high-order relationships among patients with depression. HYNMDR comprises two primary components: a temporal embedding module and a hypergraph classification module. The temporal embedding module employs a temporal convolutional network and a negative sampling loss function based on Euclidean distance to extract feature embeddings from unimodal and cross-modal long-time series data. To capture the unique ways in which depression may manifest in certain feature elements, the hypergraph classification module introduces a threshold segmentation-based hyperedge construction method. This method is the first attempt to apply hypergraph neural networks to multimodal depression recognition. Experimental evaluations on the DAIC-WOZ and E-DAIC datasets demonstrate that HYNMDR outperforms existing methods in automatic depression monitoring, achieving an F1 score of 91.1% and an accuracy of 94.0%. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications)
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