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

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Green Sustainable Science and Technology".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 4599

Special Issue Editors

Prof. Dr. Seongsoo Cho

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea
Interests: learning/machine learning; image processing; sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yeonwoo Lee

E-Mail Website
Guest Editor
Department of Artificial Intelligence Engineering, Mokpo National University, Cheonggye-myeon, Muan-gun, Jeollanam-do, Republic of Korea
Interests: cognitive radio; smart grid; artificial intelligence algorithm; nature-inspired algorithm; 6G communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Changho Seo

E-Mail Website
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 Colleagues,

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 a wide range of 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 a diverse array of applications, 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 aims to 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 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. 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 (4 papers)

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Research

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17 pages, 4467 KiB  
Article
Predicting Risk Factors for Dyslipidemia Based on Health Behaviors by Age in Adults Using Machine Learning
by Jin-Hui Ku, Jong-Suk Kim and Kwang-Hwan Kim
Appl. Sci. 2025, 15(9), 5131; https://doi.org/10.3390/app15095131 - 5 May 2025
Viewed by 223
Abstract
According to the 2022 Korean Society of Lipidology and Atherosclerosis, in Korea, dyslipidemia is a common disease that occurs in 40.2% of adults aged 20 or older, and its prevalence increases with age. Although dyslipidemia has a high prevalence of 47.8% in adults [...] Read more.
According to the 2022 Korean Society of Lipidology and Atherosclerosis, in Korea, dyslipidemia is a common disease that occurs in 40.2% of adults aged 20 or older, and its prevalence increases with age. Although dyslipidemia has a high prevalence of 47.8% in adults aged 30 or older, it is known to be preventable and manageable through lifestyle improvements in areas including eating habits, alcohol consumption, smoking, and physical activity. In this study, we propose a model for predicting age-specific dyslipidemia risk factors according to adult health behavior characteristics and diet. By analyzing the correlation between age-specific health behaviors and diet and the presence or absence of dyslipidemia, we aimed to predict dyslipidemia risk factors through a combination of multiple factor variables. This study utilized data from the 8th National Health and Nutrition Examination Survey, and selected 12,028 adults who received a doctor’s diagnosis of dyslipidemia as the subjects. In order to compare the characteristics of the dyslipidemia diagnosis group and the non-diagnosed group, a Rao–Scott χ2 test was performed, and machine learning-based logistic regression and decision tree analyses were performed to predict the dyslipidemia risk factors. Analyzing the difference in the dyslipidemia prevalence according to the general characteristics and health status showed no significant difference between the men and women in the 19–34, 35–49, and 50–64 age groups, but there was a significant difference in the dyslipidemia prevalence in the 65 and older group. It was found that the dyslipidemia risk also increased with age. In terms of health behavior characteristics, the alcohol intake frequency and aerobic exercise frequency were found to have statistically significant effects and, in terms of eating habits, the breakfast frequency and dining out frequency were found to be significant factor variables in the dyslipidemia prevalence. As a result of the decision tree analysis, the most important dyslipidemia predictive factor showed differences according to the age group. The most important predictive variable for the presence or absence of dyslipidemia in the 19–34 age group was the BMI; for the 35–49 age group, it was gender and subjective health perception; for the 50–64 age group, it was subjective health perception and the BMI; and for the 65 and older group, it was the BMI. This suggests that healthy eating habits and behaviors such as aerobic exercise are very important for preventing and managing dyslipidemia as age increases. Full article
(This article belongs to the Special Issue Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions)
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22 pages, 16196 KiB  
Article
A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
by Yong-Joon Lee
Appl. Sci. 2024, 14(24), 11836; https://doi.org/10.3390/app142411836 - 18 Dec 2024
Viewed by 1475
Abstract
In the 4th industrial era, the proliferation of interconnected smart devices and advancements in AI, particularly big data and machine learning, have integrated various industrial domains into cyberspace. This convergence brings novel security threats, making it essential to prevent known incidents and anticipate [...] Read more.
In the 4th industrial era, the proliferation of interconnected smart devices and advancements in AI, particularly big data and machine learning, have integrated various industrial domains into cyberspace. This convergence brings novel security threats, making it essential to prevent known incidents and anticipate potential breaches. This study develops a scenario-based evaluation system to predict and evaluate possible security accidents using the MITRE ATT&CK framework. It analyzes various security incidents, leveraging attack strategies and techniques to create detailed security scenarios and profiling services. Key contributions include integrating security logs, quantifying incident likelihood, and establishing proactive threat management measures. The study also proposes automated security audits and legacy system integration to enhance security posture. Experimental results show the system’s efficacy in detecting and preventing threats, providing actionable insights and a structured approach to threat analysis and response. This research lays the foundation for advanced security prediction systems, ensuring robust defense mechanisms against emerging cyber threats. Full article
(This article belongs to the Special Issue Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions)
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22 pages, 4303 KiB  
Article
Extending WSN Lifetime with Enhanced LEACH Protocol in Autonomous Vehicle Using Improved K-Means and Advanced Cluster Configuration Algorithms
by Cheolhee Yoon, Seongsoo Cho and Yeonwoo Lee
Appl. Sci. 2024, 14(24), 11720; https://doi.org/10.3390/app142411720 - 16 Dec 2024
Cited by 2 | Viewed by 1022
Abstract
In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive [...] Read more.
In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection. Full article
(This article belongs to the Special Issue Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions)
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Review

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37 pages, 3583 KiB  
Review
AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
by Javier Arévalo-Royo, Francisco-Javier Flor-Montalvo, Juan-Ignacio Latorre-Biel, Rubén Tino-Ramos, Eduardo Martínez-Cámara and Julio Blanco-Fernández
Appl. Sci. 2025, 15(4), 2096; https://doi.org/10.3390/app15042096 - 17 Feb 2025
Viewed by 1000
Abstract
This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, [...] Read more.
This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment. Full article
(This article belongs to the Special Issue Integration of AI, Big Data, and ICT into Emerging Technologies for Sustainable Solutions)
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