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Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 614

Special Issue Editors


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Guest Editor
Department of Information and Communication Engineering, Mokpo National University, Cheonggye-myeon, Muan-gun, Republic of Korea
Interests: cognitive radio; smart grid; artificial intelligence algorithm; nature-inspired algorithm; 6G communication
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Convergence Science, Kongju National University, Gongju 32588, Republic of Korea
Interests: cryptology; applied algebra; system security; network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

This Special Issue is a continuation of our previous Special Issue, Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions.

Sustainable solutions across various industries have become pivotal in the era of global digital transformation, while the transformative impacts of Artificial Intelligence (AI), Big Data, and Information and Communication Technology (ICT) promise to enhance and promote a brighter, more sustainable future for humanity.

In recent years, the convergence of emerging technologies has opened up a rapidly expanding field of study. Specifically, the integration of these technologies into systems across various sectors, such as healthcare, smart cities, manufacturing, and Urban Air Mobility (UAM), has enhanced their efficiency, performance, and sustainability, making them indispensable tools in various engineering applications. This Special Issue will feature research and case studies that examine how Artificial Intelligence (AI), Big Data, and Information and Communication Technology (ICT) can drive innovation, optimize performance, and tackle challenges across varied applications, ranging from environmental management to the development of smart infrastructure.

Multidisciplinary contributions that provide a holistic view of the technological, economic, and environmental benefits of these integrations are welcome. This Special Issue will serve as a vital resource for researchers, engineers, and policymakers, showcasing diverse applications and innovative approaches.

Prof. Dr. Seongsoo Cho
Prof. Dr. Yeonwoo Lee
Prof. Dr. Changho Seo
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. Applied Sciences 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 (AI)
  • artificial intelligence and fuzzy systems
  • bio-inspired ai technology
  • big data analytics
  • information and communication technology (ICT)
  • smart cities
  • sustainable engineering
  • healthcare technology
  • manufacturing innovation
  • urban air mobility (UAM)
  • environmental management
  • smart infrastructure
  • digital transformation
  • performance optimization
  • renewable energy systems
  • data-driven decision making
  • IoT (Internet of Things) integration
  • security
  • visualization
  • logistics
  • telecommunication

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Published Papers (1 paper)

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Research

24 pages, 4988 KB  
Article
Performance Evaluation of the SCN++ Model for Structural Crack Detection in Edge Computing Environments
by Sang-Hyun Lee and Myeong-Hoon Oh
Appl. Sci. 2026, 16(9), 4375; https://doi.org/10.3390/app16094375 - 29 Apr 2026
Viewed by 323
Abstract
This study proposes a lightweight crack-segmentation model optimized for industrial and edge-computing environments, where both high accuracy and real-time inference are required. Conventional convolution-based and U-Net-based crack segmentation models offer relatively simple architectural designs, but often suffer from limited boundary precision or an [...] Read more.
This study proposes a lightweight crack-segmentation model optimized for industrial and edge-computing environments, where both high accuracy and real-time inference are required. Conventional convolution-based and U-Net-based crack segmentation models offer relatively simple architectural designs, but often suffer from limited boundary precision or an unfavorable accuracy–efficiency trade-off. Swin Transformer-based approaches can model broader contextual information but may still show poor segmentation quality relative to their computational cost in fine crack analysis. To address these limitations, we propose the Stabilized Crack Network++ (SCN++), a U-Net backbone crack segmentation network that integrates edge fusion, hybrid loss with deep supervision, exponential moving average (EMA)-based stabilization, and lightweight post-processing. The model was trained and evaluated on 40,000 concrete surface images, including 20,000 crack images and 20,000 non-crack images, using quantitative metrics such as intersection over union (IoU), Dice coefficient, frames per second (FPS), giga floating-point operations (GFLOPs), and the number of parameters, together with overlay-based qualitative analysis. Compared with the CNN, U-Net, and Swin Transformer baselines, SCN++ achieved the best overall balance between segmentation accuracy and computational efficiency, with an IoU of 0.7346, a Dice coefficient of 0.8457, 35.09 FPS, 8.45 GFLOPs, and only 2.22 M parameters. These results demonstrate that SCN++ effectively mitigates the conventional accuracy–efficiency trade-off and is a strong candidate for practical structural crack segmentation in edge-computing environments. Full article
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