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Advances in Artificial Intelligence and Computer Vision Based on Deep...
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Advances in Artificial Intelligence and Computer Vision Based on Deep Learning

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

Deadline for manuscript submissions: closed (20 November 2025) | Viewed by 1690

Special Issue Editors

Dr. Pedro Javier Herrera Caro

E-Mail Website
Guest Editor
Department of Computer Systems and Software Engineering, School of Computer Engineering, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: computer vision; pattern recognition; artificial intelligence; image processing; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Jaime Duque-Domingo

E-Mail Website
Guest Editor
Department of Systems Engineering and Automatics, University of Valladolid, 47002 Valladolid, Spain
Interests: indoor positioning; WPS; RGB cameras; WiFi; fingerprint map; trajectory; IPS; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is a very powerful branch of machine learning, within the frame of artificial intelligence, and represents a promising field of knowledge that is continually on the rise, in part thanks to technological advances which allow the processing of enormous amounts of data with complex structures. The fullest realization of these can be seen in neural networks—and, more specifically, deep neural networks. It is evident that AI is a promising field with many diverse practical applications, representing a very active area of research, in which abundant intelligent systems have flourished and facilitate and automate routine work.

Deep learning has proven its usefulness in many disciplines, including computer vision, virtual reality, voice and audio processing, natural language processing, robotics, bioinformatics, video games, search engines, and finance, among others, all included within the general field of artificial intelligence. Emphasis, in this Special Issue, is placed on the field of computer vision, an area in which deep learning models play a very important role, as well as virtual reality. However, given the essentially transversal and multidisciplinary nature of computer vision, its interference with other areas and disciplines is very common, which broadens the range of possible researchers and scholars that may find interest in this issue.

The following is a list of the main topics covered by this Special Issue concerned with computer vision based on deep learning models:

  • Voice and audio processing;
  • Natural language processing;
  • Robotics;
  • Bioinformatics;
  • Video games;
  • Search engines;
  • Economy and finance.

The Special Issue will not be limited to these topics. Papers must present innovative methods and approaches, or novel applications of existing tools.

Dr. Pedro Javier Herrera Caro
Dr. Jaime Duque-Domingo
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. 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

  • deep learning
  • artificial intelligence
  • neural networks
  • computer vision
  • 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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21 pages, 26916 KB  
Article
HDCA: Heterogeneous Dual-Path Contrastive Architecture for Action Recognition
by Shilu Kang, Hua Huo, Lan Ma, Jinxuan Wang and Aokun Mei
Electronics 2025, 14(23), 4730; https://doi.org/10.3390/electronics14234730 - 30 Nov 2025
Viewed by 114
Abstract
We propose Heterogeneous Dual-path Contrastive Architecture (HDCA) for action recognition. Our model involves a spatial pathway and a temporal pathway; these two pathways employ distinct backbone networks and input formats, tailored to the specific properties of spatial features and temporal features. The spatial [...] Read more.
We propose Heterogeneous Dual-path Contrastive Architecture (HDCA) for action recognition. Our model involves a spatial pathway and a temporal pathway; these two pathways employ distinct backbone networks and input formats, tailored to the specific properties of spatial features and temporal features. The spatial pathway processes super images to capture spatial semantics while the temporal pathway operates on frame sequences to model motion patterns. This targeted design can precisely capture the scenes and motions depicted in videos while improving parameter efficiency. To establish a cross-modality complementary enhancement mechanism, we develop cross-modality contrastive loss and intra-group contrastive loss to train the HDCA. These contrastive losses work synergistically to maximize the similarity of feature representations among videos belonging to the same class while minimizing similarity across different classes, achieving cross-modality alignment through cross-modality contrastive loss and enhancing intra-group compactness via intra-group contrastive loss. HDCA fully exploits the complementary strengths of spatial features and temporal features in action recognition. Systematic experiments on three benchmark datasets validate the effectiveness and superiority of our approach, which support the motivation and hypothesis of our model design. The experimental results demonstrate that our model achieves competitive performance compared to existing state-of-the-art approaches for action recognition. Notably, performance gains increase with dataset complexity, indicating that discriminative correlation information between modalities learned by HDCA yield greater performance gains in the recognition tasks of complex videos. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Computer Vision Based on Deep Learning)
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14 pages, 657 KB  
Article
Pretrained Models Against Traditional Machine Learning for Detecting Fake Hadith
by Jawaher Alghamdi, Adeeb Albukhari and Thair Al-Dala’in
Electronics 2025, 14(17), 3484; https://doi.org/10.3390/electronics14173484 - 31 Aug 2025
Viewed by 1174
Abstract
The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) [...] Read more.
The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) are increasingly strained by the sheer volume in circulation. To combat this issue, machine learning (ML) and natural language processing (NLP) techniques, specifically through transfer learning, are explored to automate Hadith classification into Genuine and Fake categories. This study utilizes an imbalanced dataset of 8544 Hadiths, with 7008 authentic and 1536 fake Hadiths, to systematically investigate the collective impact of both linguistic and contextual features, particularly the chain of narrators (Isnad), on Hadith authentication. For the first time in this specialized domain, state-of-the-art pre-trained language models (PLMs) such as Multilingual BERT (mBERT), CamelBERT, and AraBERT are evaluated alongside classical algorithms like logistic regression (LR) and support vector machine (SVM) for Hadith authentication. Our best-performing model, AraBERT, achieved a 99.94% F1score when including the chain of narrators, demonstrating the profound effectiveness of contextual elements (Isnad) in significantly improving accuracy, providing novel insights into the indispensable role of computational methods in Hadith authentication and reinforcing traditional scholarly emphasis. This research represents a significant advancement in combating misinformation in this important field. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Computer Vision Based on Deep Learning)
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