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Big AI Models in 5G Communication Technology, Cybersecurity, Metaverse, and...
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Big AI Models in 5G Communication Technology, Cybersecurity, Metaverse, and Healthcare Services

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1658

Special Issue Editors

Dr. Saurav Mallik

E-Mail Website
Guest Editor
Research Scientist, University of Arizona, Tucson, AZ 85721, USA
Interests: machine learning; biostatistics; computational biology; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ruifeng Hu

E-Mail Website
Guest Editor
Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: medical genomics; database construction; omics-based data integration
Special Issues, Collections and Topics in MDPI journals
Dr. Aimin Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: machine learning; computation mathematics; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue mainly focuses on 5G technology it is already developed, it must be improved to meet the growing demands of modern society for applications like augmented and virtual reality and constant mobile communication. Over the previous few years, engineers have made considerable progress in inventing solutions that will push wireless networking and communication to new heights in the future. Green Internet of Things, digital twins, AI, semantic/goal-oriented communications, smart holographic/reflecting surfaces, and millimetre and terahertz spectrum are just a few examples. The advent of next-generation communication technology is expected to significantly boost the capacity and speed of our networks, allowing for more rapid and comprehensive data interchange and connectivity.

New methods for exploring these wireless data with specific forms of artificial intelligence (AI) are becoming available thanks to developments in machine learning and deep learning, paving the way for the successful completion of a wide range of Big, computation- or communication-oriented tasks, such as intelligent mobile edge computing, environment/object sensing, and intelligent wireless communication. Traditional techniques necessitate a unique AI model for each task because of the significant HW/SW overheads and the inability to delve deeply into the underlying correlation inside data and across jobs. Therefore, in light of the recent uptick in study in this area, the goal of this Special Issue (SI) is to bring together academic and industry experts to present cutting-edge developments in big AI models to the wireless communication community and to highlight numerous exciting prospects for future study across various disciplines especially biomedical and healthcare services. Here are some of the potential themes for this issue:

  • Big AI model's effect on the cost of energy and networking.
  • Big AI model architecture development for next-generation wireless systems.
  • Developing wireless network protocols for Big AI adoption.
  • Wireless networked distributed training of Big AI models that minimizes energy consumption.
  • Big AI model's wireless network performance is thoroughly analyzed.
  • Big data artificial intelligence model experiments, testbeds, and deployments over mobile networks.
  • Concerns with massive AI models in wireless networks' privacy and security.
  • Big artificial intelligence model for wireless semantic communications.
  • Cooperative wireless sensing and communication using a Big AI model.
  • Big AI model for intelligent mobile edge computing with several tasks.
  • AI-based Zero Trust Models.
  • Decentralized Zero Trust Models.
  • Machine Learning and IOT in Wireless Networks.
  • Cyberattacks and Risk Assessment, Standards in Wireless Networks
  • Intelligent Mobile Edge Computing in Wireless Communications.
  • AI/ML-based design and processing of intelligent/tensorial surfaces-aided communications.
  • 5G and 6G Standardization.
  • Blockchain and Cryptography.
  • Security on Wireless and Mobile Networks.
  • Threat and Vulnerability Analysis in Wireless Communication.
  • Energy-Efficient Techniques for 5G Wireless Communication Technologies.
  • Green Internet of Things Standards, Policy and Regulation.
  • AI and optimization technologies in biomedical and healthcare services.
  • 5G models and metaverse in remote healthcare services.

Dr. Saurav Mallik
Dr. Ruifeng Hu
Dr. Aimin Li
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • machine learning
  • wireless communication technology
  • vulnerability
  • mobile network
  • artificial intelligence
  • cyber attacks
  • biomedical and health informatics

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Published Papers (1 paper)

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23 pages, 2804 KiB  
Article
Exploring Determinants and Predictive Models of Latent Tuberculosis Infection Outcomes in Rural Areas of the Eastern Cape: A Pilot Comparative Analysis of Logistic Regression and Machine Learning Approaches
by Lindiwe Modest Faye, Cebo Magwaza, Ntandazo Dlatu and Teke Apalata
Information 2025, 16(3), 239; https://doi.org/10.3390/info16030239 - 18 Mar 2025
Viewed by 277
Abstract
Latent tuberculosis infection (LTBI) poses a significant public health challenge, especially in populations with high HIV prevalence and limited healthcare access. Early detection and targeted interventions are essential to prevent the progression of active tuberculosis. This study aimed to identify the key factors [...] Read more.
Latent tuberculosis infection (LTBI) poses a significant public health challenge, especially in populations with high HIV prevalence and limited healthcare access. Early detection and targeted interventions are essential to prevent the progression of active tuberculosis. This study aimed to identify the key factors influencing LTBI outcomes through the application of predictive models, including logistic regression and machine learning techniques, while also evaluating strategies to enhance LTBI awareness and testing. Data from rural areas in the Eastern Cape, South Africa, were analyzed to identify key demographic, health, and knowledge-related factors influencing LTBI outcomes. Predictive models utilized, included logistic regression, decision trees, and random forests, to identify key determinants of LTBI positivity based on demographic, health, and knowledge-related factors in rural areas of the Eastern Cape, South Africa. The models evaluated factors such as age, HIV status, and LTBI awareness, with random forests demonstrating the best balance of accuracy and interpretability. Additionally, a knowledge diffusion model was employed to assess the effectiveness of educational strategies in increasing LTBI awareness and testing uptake. Logistic regression achieved an accuracy of 68% with high precision (70%) but low recall (33%) for LTBI-positive cases, identifying age, HIV status, and LTBI awareness as significant predictors. The random forest model outperformed logistic regression in accuracy (59.26%) and F1-score (0.63), providing a better balance between precision and recall. Feature importance analysis revealed that age, occupation, and knowledge of LTBI symptoms were the most critical factors across both models. The knowledge diffusion model demonstrated that targeted interventions significantly increased LTBI awareness and testing, particularly in high-risk groups. While logistic regression offers more interpretable results for public health interventions, machine learning models like random forests provide enhanced predictive power by capturing complex relationships between demographics and health factors. These findings highlight the need for targeted educational campaigns and increased LTBI testing in high-risk populations, particularly those with limited awareness of LTBI symptoms. Full article
(This article belongs to the Special Issue Big AI Models in 5G Communication Technology, Cybersecurity, Metaverse, and Healthcare Services)
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