Feature Papers in Artificial Intelligence

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 1029

Special Issue Editors


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School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: machine learning; pattern recognition; computer vision
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BISITE Research Group, University of Salamanca, Edificio Multiusos I + D + I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
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Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, 310130 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; biometrics
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Special Issue Information

Dear Colleagues,

Artificial intelligence has revolutionized numerous aspects of our society. Foundation models and embodied intelligence are expanding the frontiers of AI research, and the convergence of AI with fundamental sciences has accelerated scientific discovery across disciplines. This feature paper Special Issue aims to showcase cutting-edge research that advances both the theoretical understanding and practical applications of AI technologies.

We welcome high-quality contributions spanning the full spectrum of AI research, from novel methodologies and theoretical frameworks to transformative applications. The scope also encompasses research on AI system development challenges, such as scaling approaches, low-cost deployment, system reliability, trustworthiness, and human–AI interaction paradigms. We also encourage submissions that bridge AI with other scientific fields, such as drug discovery and materials science. The topics of interest for this Special Issue, include but are not limited to, the following:

  • Machine learning;
  • Optimization and statistical learning;
  • Deep learning and neural networks;
  • Knowledge representation and reasoning;
  • Foundation models;
  • Autonomous systems and robotics;
  • AI for science;
  • AI for societal impact.

Prof. Dr. Xin Geng
Prof. Dr. George Papakostas
Dr. Fernando De la Prieta Pintado
Prof. Dr. Valentina E. Balas
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
  • deep learning
  • neural networks
  • autonomous systems
  • robotics

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Published Papers (1 paper)

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Research

35 pages, 6431 KiB  
Article
Delving into YOLO Object Detection Models: Insights into Adversarial Robustness
by Kyriakos D. Apostolidis and George A. Papakostas
Electronics 2025, 14(8), 1624; https://doi.org/10.3390/electronics14081624 - 17 Apr 2025
Viewed by 373
Abstract
This paper provides a comprehensive study of the security of YOLO (You Only Look Once) model series for object detection, emphasizing their evolution, technical innovations, and performance across the COCO dataset. The robustness of YOLO models under adversarial attacks and image corruption, offering [...] Read more.
This paper provides a comprehensive study of the security of YOLO (You Only Look Once) model series for object detection, emphasizing their evolution, technical innovations, and performance across the COCO dataset. The robustness of YOLO models under adversarial attacks and image corruption, offering insights into their resilience and adaptability, is analyzed in depth. As real-time object detection plays an increasingly vital role in applications such as autonomous driving, security, and surveillance, this review aims to clarify the strengths and limitations of each YOLO iteration, serving as a valuable resource for researchers and practitioners aiming to optimize model selection and deployment in dynamic, real-world environments. The results reveal that YOLOX models, particularly their large variants, exhibit superior robustness compared to other YOLO versions, maintaining higher accuracy under challenging conditions. Our findings serve as a valuable resource for researchers and practitioners aiming to optimize YOLO models for dynamic and adversarial real-world environments while guiding future research toward developing more resilient object detection systems. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)
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