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Computational Intelligence for Internet of Medical Things and Big Data Analytics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 9781

Special Issue Editors

Dr. Jeong Dong Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sunmoon University, Asan-si 31460, Korea
Interests: data driven modeling; big data analysis; machine learning (interpretable DL); bioinformatics; life science (healthcare, EHR, etc.)
Dr. Bongjae Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Chungbuk National University, Chungbuk 28644, Korea
Interests: mobile computing; internet of things; deep learning; artificial intelligence
Dr. Neunghoe Kim

E-Mail Website
Guest Editor
Department of Software Convergence Engineering, Kunsan National University, Gunsan-si 54150, Korea
Interests: big data; semantic analysis; software engineering

Special Issue Information

Dear Colleagues,

The Internet of Medical Things (IoMT) is becoming a significant research issue in healthcare, which integrates medical devices, applications from home users, hospitals, and pharmaceutical services. IoMT technologies can obtain data from sensor-based devices such as clinical apparatuses, wearables, sensors, and other specific purpose equipment. Connecting these devices through wireless media enables us to monitor individual health conditions and provide personalized healthcare and medical services. The large amounts and varieties of data, however, come with many challenges. Furthermore, artificial intelligence is an important technology that has a great effect in IoMT. On the one hand it can assist medical professionals in almost every area of their proficiencies for more accurate decision-making solutions, but on the other hand it offers significant benefits for the wellbeing for users by increasing their quality of life and reducing their medical expenses.  Machine learning approaches to IoMT will increase the diversity of solutions to challenges that  presented by the incorporation of BigData into the healthcare setting.  Learning specific patterns, recommending necessary actions, and predicting the diagnosis of rare conditions are just some of the attractive elements of health monitoring.

The aim of this Special Issue is to present research emphasizing novel approaches to providing solutions in related fields such as the connectivity of various sensor devices, interoperability and interpretability of data analyzing methods, and comprehensive surveys identifying new challenges to theoretical and practical applications.

Dr. Jeong Dong Kim
Dr. Bongjae Kim
Dr. Neunghoe Kim
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

  • internet of medical things
  • wireless sensor networks
  • remote healthcare monitoring
  • wearables
  • wellness
  • machine learning
  • personalized healthcare
  • online machine learning
  • personalized medicine
  • interoperability
  • digital health
  • biosensors
  • smart healthcare
  • biomedical monitoring
  • multimodality fusion

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Published Papers (2 papers)

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Research

20 pages, 1330 KiB  
Article
Towards Design and Development of Security Assessment Framework for Internet of Medical Things
by Fahad A. Alzahrani, Masood Ahmad and Md Tarique Jamal Ansari
Appl. Sci. 2022, 12(16), 8148; https://doi.org/10.3390/app12168148 - 15 Aug 2022
Cited by 21 | Viewed by 2361
Abstract
The majority of medical equipment in use today does not have built-in security features. As a result, whether linked to a hospital system or the cloud, these devices’ built-in weaknesses make them vulnerable to a variety of cyberattacks. In hospitals and clinics, hackers [...] Read more.
The majority of medical equipment in use today does not have built-in security features. As a result, whether linked to a hospital system or the cloud, these devices’ built-in weaknesses make them vulnerable to a variety of cyberattacks. In hospitals and clinics, hackers can breach equipment, manipulate data, and disrupt facilities, putting patients’ health as well as their lives in jeopardy. A professional can manage cybersecurity threats by lowering the attack surface of the system. Security analysis, whether as a means to detect possible vulnerabilities that can be exploited by attackers or as a means to prevent cyberattacks, plays an important role in risk mitigation. In addition, throughout the pre-market and post-market phases, security checks are required. This study presents a paradigm for incorporating security check concepts into medical device design and development and healthcare big data security. The security of devices and healthcare data is tested by the integrated fuzzy AHP-TOPSIS method. After the security check of devices, with the parameters security-checked for data, the algorithm is designed and implemented. As a result, the appropriate customized security controls are prompted in order to impede the attack. Full article
(This article belongs to the Special Issue Computational Intelligence for Internet of Medical Things and Big Data Analytics)
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15 pages, 2038 KiB  
Article
DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
by In-Ae Kang, Soualihou Ngnamsie Njimbouom, Kyung-Oh Lee and Jeong-Dong Kim
Appl. Sci. 2022, 12(6), 3043; https://doi.org/10.3390/app12063043 - 16 Mar 2022
Cited by 24 | Viewed by 6340
Abstract
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. Classified as one of the most prevalent oral health issues, research on dental caries has been carried out for early detection due to pain [...] Read more.
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. Classified as one of the most prevalent oral health issues, research on dental caries has been carried out for early detection due to pain and cost of treatment. Medical research in oral healthcare has shown limitations such as considerable funds and time required; therefore, artificial intelligence has been used in recent years to develop models that can predict the risk of dental caries. The data used in our study were collected from a children’s oral health survey conducted in 2018 by the Korean Center for Disease Control and Prevention. Several Machine Learning algorithms were applied to this data, and their performances were evaluated using accuracy, F1-score, precision, and recall. Random forest has achieved the highest performance compared to other machine learnings methods, with an accuracy of 92%, F1-score of 90%, precision of 94%, and recall of 87%. The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries. Full article
(This article belongs to the Special Issue Computational Intelligence for Internet of Medical Things and Big Data Analytics)
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