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Electronics
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Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies...
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Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition

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

Deadline for manuscript submissions: 15 January 2026 | Viewed by 395

Special Issue Editors

Dr. Shuping Zhao

E-Mail Website
Guest Editor
School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
Interests: machine learning; biometrics; data mining; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Wen

E-Mail Website
Guest Editor
School of Computer Science & Technology, Harbin Institute of Technology, Shenzhen 150001, China
Interests: machine learning; data mining; pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Huang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China
Interests: anomaly detection; multimedia analysis; object detection; image/video compression; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bob Zhang

E-Mail Website
Guest Editor
Department of Computer and Information Science, University of Macau, Macau, China
Interests: biometrics; pattern recognition; image processing; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, machine learning, pattern recognition, and deep learning techniques have been successfully applied to science and engineering research. For example, biometric recognition, i.e., the recognition of palmprints, faces, and irises, has enabled personal security authentication for airports, banks, and online payments. These techniques have also allowed us to retrieve the information we are interested in from the internet. Furthermore, image processing technology can help us obtain more beautiful photos. Deep learning, in particular, has a powerful ability to extract discriminant patterns and make accurate predictions from large-scale databases. However, the performances of machine learning, pattern recognition, and deep learning algorithms rely significantly on model design, mathematical interpretation, and optimization. A good fusion of theories and models is crucial to the success of the applications listed above. The aim of this Special Issue is to highlight recent advances in machine learning, pattern recognition, and deep learning methodologies and theories. Papers with interesting/significant new applications of the abovementioned methods are also welcome. The topics of interest for this Special Issue include, but are not limited to, the following:

  1. Advanced machine intelligence methods and applications;
  2. Advanced pattern analysis methods and applications;
  3. Deep-learning-based methods and applications;
  4. Biometric recognition algorithms and applications;
  5. Multi-view/modal learning and fusion;
  6. Data mining and analysis;
  7. Hashing learning-based methods and applications;
  8. Dimensionality reduction and discriminant representation;
  9. Subspace learning and clustering;
  10. Graph learning-based methods and applications;
  11. Super-resolution/enhancement/restoration of images;
  12. Advanced models within computer vision, such as object tracking and detection;
  13. Sparse representations and their applications.

Dr. Shuping Zhao
Dr. Jie Wen
Dr. Chao Huang
Dr. Bob 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

  • artificial intelligence
  • machine learning
  • pattern recognition
  • deep learning
  • mathematical optimization

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Related Special Issue

Published Papers (3 papers)

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Research

14 pages, 1835 KiB  
Article
Cybersecurity Applications of Near-Term Large Language Models
by Casimer DeCusatis, Raymond Tomo, Aurn Singh, Emile Khoury and Andrew Masone
Electronics 2025, 14(13), 2704; https://doi.org/10.3390/electronics14132704 (registering DOI) - 4 Jul 2025
Abstract
This paper examines near-term generative large language models (GenLLM) for cybersecurity applications. We experimentally study three common use cases, namely the use of GenLLM as a digital assistant, analysts for threat hunting and incident response, and analysts for access management in zero trust [...] Read more.
This paper examines near-term generative large language models (GenLLM) for cybersecurity applications. We experimentally study three common use cases, namely the use of GenLLM as a digital assistant, analysts for threat hunting and incident response, and analysts for access management in zero trust systems. In particular, we establish that one of the most common GenLLMs, ChatGPT, can pass cybersecurity certification exams for security fundamentals, hacking and penetration testing, and mobile device security, as well as perform competitively in cybersecurity ethics assessments. We also identify issues associated with hallucinations in these environments. The ability of ChatGPT to analyze network scans and security logs is also evaluated. Finally, we attempt to jailbreak ChatGPT in order to assess its application to access management systems. Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)
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21 pages, 817 KiB  
Article
C3-VULMAP: A Dataset for Privacy-Aware Vulnerability Detection in Healthcare Systems
by Jude Enenche Ameh, Abayomi Otebolaku, Alex Shenfield and Augustine Ikpehai
Electronics 2025, 14(13), 2703; https://doi.org/10.3390/electronics14132703 - 4 Jul 2025
Abstract
The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance [...] Read more.
The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare. Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)
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16 pages, 6657 KiB  
Article
Experimental Assessment of YOLO Variants for Coronary Artery Disease Segmentation from Angiograms
by Eduardo Díaz-Gaxiola, Arturo Yee-Rendon, Ines F. Vega-Lopez, Juan Augusto Campos-Leal, Iván García-Aguilar, Ezequiel López-Rubio and Rafael M. Luque-Baena
Electronics 2025, 14(13), 2683; https://doi.org/10.3390/electronics14132683 - 2 Jul 2025
Abstract
Coronary artery disease (CAD) is one of the leading causes of mortality worldwide, highlighting the importance of developing accurate and efficient diagnostic tools. This study presents a comparative evaluation of three recent YOLO architecture versions (YOLOv8, YOLOv9, and YOLOv11) for the tasks of [...] Read more.
Coronary artery disease (CAD) is one of the leading causes of mortality worldwide, highlighting the importance of developing accurate and efficient diagnostic tools. This study presents a comparative evaluation of three recent YOLO architecture versions (YOLOv8, YOLOv9, and YOLOv11) for the tasks of coronary vessel segmentation and stenosis detection using the ARCADE dataset. Two workflows were explored: one with original angiographic images and another incorporating Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement. Models were trained for 100 epochs using the AdamW optimizer and evaluated with precision, recall, and F1-score under a pixel-based segmentation framework. YOLOv9-E achieved the highest performance in vessel segmentation with an F1-score of 0.4524, while YOLOv11-X was most effective for stenosis detection, achieving an F1-score of 0.7826. Although CLAHE improved local contrast, it did not consistently improve segmentation results and occasionally introduced artifacts that negatively affected model performance. Compared to state-of-the-art methods, the YOLO models demonstrated competitive results, especially for large, well-defined coronary segments, but showed limitations in detecting smaller or more complex pathological structures. These findings support the use of YOLO-based architectures for real-time CAD segmentation tasks and highlight opportunities for future improvement through the integration of attention mechanisms or hybrid deep learning strategies. Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)
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