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Electronics
Special Issues
Applications of Computational Intelligence, 3rd Edition
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Applications of Computational Intelligence, 3rd Edition

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1506

Special Issue Editors

Dr. Yue Wu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; image processing and pattern recognition; theory and applications of computational intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Qin

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: machine learning; evolutionary computation; computer vision; services computing; pervasive computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maoguo Gong

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding, Xidian University, Xi'an 710071, China
Interests: computational intelligence; evolutionary computation; neural networks; multi-objective optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiguang Miao

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; machine learning; high performance calculation; big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, over time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI.

This Special Issue invites researchers to contribute high-quality original research papers and surveys on any aspect of computational intelligence, especially papers that show the power and impact of applications of computational intelligence. The main topics for the Special Issue include, but are not limited to, the following keywords.

Dr. Yue Wu
Prof. Dr. Kai Qin
Prof. Dr. Maoguo Gong
Prof. Dr. Qiguang Miao
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

  • artificial intelligence
  • neural networks
  • evolutionary computation
  • fuzzy logic and systems
  • swarm intelligence
  • deep learning
  • applications of computational intelligence

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

Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 1716 KiB  
Article
Knowledge Translator: Cross-Lingual Course Video Text Style Transform via Imposed Sequential Attention Networks
by Jingyi Zhang, Bocheng Zhao, Wenxing Zhang and Qiguang Miao
Electronics 2025, 14(6), 1213; https://doi.org/10.3390/electronics14061213 - 19 Mar 2025
Cited by 1 | Viewed by 247
Abstract
Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different [...] Read more.
Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different countries and regions. However, current MOOC video processing solutions rely excessively on manual operations, resulting in low efficiency and difficulty in meeting the urgent requirement for large-scale content translation. Key technical challenges include the accurate localization of embedded text in complex video frames, maintaining style consistency across languages, and preserving text readability and visual quality during translation. Existing methods often struggle with handling diverse text styles, background interference, and language-specific typographic variations. In view of this, this paper proposes an innovative cross-language style transfer algorithm that integrates advanced techniques such as attention mechanisms, latent space mapping, and adaptive instance normalization. Specifically, the algorithm first utilizes attention mechanisms to accurately locate the position of each text in the image, ensuring that subsequent processing can be targeted at specific text areas. Subsequently, by extracting features corresponding to this location information, the algorithm can ensure accurate matching of styles and text features, achieving an effective style transfer. Additionally, this paper introduces a new color loss function aimed at ensuring the consistency of text colors before and after style transfer, further enhancing the visual quality of edited images. Through extensive experimental verification, the algorithm proposed in this paper demonstrated excellent performance on both synthetic and real-world datasets. Compared with existing methods, the algorithm exhibited significant advantages in multiple image evaluation metrics, and the proposed method achieved a 2% improvement in the FID metric and a 20% improvement in the IS metric on relevant datasets compared to SOTA methods. Additionally, both the proposed method and the introduced dataset, PTTEXT, will be made publicly available upon the acceptance of the paper. For additional details, please refer to the project URL, which will be made public after the paper has been accepted. Full article
(This article belongs to the Special Issue Applications of Computational Intelligence, 3rd Edition)
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25 pages, 69301 KiB  
Article
An Improved Image-Denoising Technique Using the Whale Optimization Algorithm
by Pei Hu, Yibo Han and Jeng-Shyang Pan
Electronics 2025, 14(1), 145; https://doi.org/10.3390/electronics14010145 - 1 Jan 2025
Cited by 1 | Viewed by 1001
Abstract
Images often suffer from various types of noise during their collection and transmission, such as salt-and-pepper, speckle, and Gaussian noise. The wavelet transform (WT) is widely utilized for denoising. However, the decomposition level and threshold significantly impact the quality of the resulting images, [...] Read more.
Images often suffer from various types of noise during their collection and transmission, such as salt-and-pepper, speckle, and Gaussian noise. The wavelet transform (WT) is widely utilized for denoising. However, the decomposition level and threshold significantly impact the quality of the resulting images, but they are difficult to set. This paper uses a modified whale optimization algorithm (MWOA) to optimize the parameters of the WT to achieve better image denoising. The MWOA is enhanced through position updates and mutation to improve the solution quality of WOA and enlarge the search space of the WT. In benchmark images, experimental comparisons with other optimization algorithms like WOA, adaptive cuckoo search (ACS), and social spider optimization (SSO) show that the proposed denoising method achieves superior results in terms of the peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index (SSIM). Full article
(This article belongs to the Special Issue Applications of Computational Intelligence, 3rd Edition)
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