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Electronics
Special Issues
AI, IoT, and NN Use in HealthCare
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AI, IoT, and NN Use in HealthCare

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4431

Special Issue Editors

Dr. Ioannis Kosmas

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 9, Omirou Str., Tavros, 17778 Athens, Greece
Interests: IoT; healthcare; e-health; NN; auditing; physical activity
Dr. Christos Michalakelis

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 9, Omirou Str., Tavros, 17778 Athens, Greece
Interests: technoeconomics; ICT markets; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI), including the Internet of Things (IoT) and Neural Networks (NNs), is becoming more commonly used in daily life. A wide spectrum of systems and applications function as tools for recording personal data that provide useful information about the physical condition of individuals. The interpretation of these data can guide the enhancement of the variables of health and well-being through the personalized adaptation of physical activity and participation in sports.

The application of similar technological innovations of AI is a challenge both in the use of relevant devices and in the collection and management of personal data. The accuracy and volume of physical factors recorded can provide one with vital information regarding the physical condition and the needs of individuals, adapting the necessary and appropriate physical activity programs needed through the same devices, which will help prevent and improve health promotion and well-being.

The aim of this Special Issue is to present the current activity in this research field and its results, linking the promotion of health and well-being resulting from healthcare, physical activity and involvement in sport with AI tools such as IoT and NN. Topics of interest include:

  • The use of AI, IoT and NN devices and systems in healthcare, physical activity and sports;
  • The accuracy and volume of data recording;
  • Privacy and security in the collection, use and communication of biomedical data;
  • Usability and users’ experience of devices;
  • Applications and systems for personal or public use in PA and healthcare;
  • Monitoring, diagnostics and prevention applications and systems;
  • Privacy, security and law of biomedical data.

Dr. Ioannis Kosmas
Dr. Christos Michalakelis
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

  • AI
  • IoT
  • NN
  • physical activity
  • sports
  • healthcare

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Published Papers (2 papers)

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Research

15 pages, 589 KiB  
Article
Predicting Consumer Service Price Evolution during the COVID-19 Pandemic: An Optimized Machine Learning Approach
by Theofanis Papadopoulos, Ioannis Kosmas, Georgios Botsoglou, Nikolaos I. Dourvas, Christoniki Maga-Nteve and Christos Michalakelis
Electronics 2023, 12(18), 3806; https://doi.org/10.3390/electronics12183806 - 8 Sep 2023
Viewed by 1777
Abstract
This research analyzes the impact of the COVID-19 pandemic on consumer service pricing within the European Union, focusing on the Transportation, Accommodation, and Food Service sectors. Our study employs various machine learning models, including multilayer perceptron, XGBoost, CatBoost, and random forest, along with [...] Read more.
This research analyzes the impact of the COVID-19 pandemic on consumer service pricing within the European Union, focusing on the Transportation, Accommodation, and Food Service sectors. Our study employs various machine learning models, including multilayer perceptron, XGBoost, CatBoost, and random forest, along with genetic algorithms for comprehensive hyperparameter tuning and price evolution forecasting. We incorporate coronavirus cases and deaths as factors to enhance prediction accuracy. The dataset comprises monthly reports of COVID-19 cases and deaths, alongside managerial survey responses regarding company estimations. Applying genetic algorithms for hyperparameter optimization across all models results in significant enhancements, yielding optimized models that exhibit RMSE score reductions ranging from 3.35% to 5.67%. Additionally, the study demonstrates that XGBoost yields more accurate predictions, achieving an RMSE score of 17.07. Full article
(This article belongs to the Special Issue AI, IoT, and NN Use in HealthCare)
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29 pages, 4823 KiB  
Article
Leveraging IoT to Address Separation Anxiety in Preschoolers: A Techno-Psychological Approach
by Reham Alabduljabbar and Raseel Alsakran
Electronics 2023, 12(16), 3479; https://doi.org/10.3390/electronics12163479 - 17 Aug 2023
Cited by 2 | Viewed by 1757
Abstract
Separation anxiety disorder (SAD) is a prevalent psychological disorder among preschoolers, characterized by excessive fear or anxiety related to separation from a primary attachment figure. The COVID-19 pandemic likely exacerbated the problem due to the transition to online schooling. While some attention has [...] Read more.
Separation anxiety disorder (SAD) is a prevalent psychological disorder among preschoolers, characterized by excessive fear or anxiety related to separation from a primary attachment figure. The COVID-19 pandemic likely exacerbated the problem due to the transition to online schooling. While some attention has been given to treating SAD, most current solutions are non-technical and based on behavior analytic research which can be costly and time-consuming. Mediated social touch, which uses technology to simulate physical touch and deliver it remotely, has been extensively studied for its potential to promote wellbeing, enhance social connectedness, and improve affective experiences in various contexts. However, no research has focused on the use of such technology to manage SAD in preschoolers. To address this gap, this work presents the design, development, and evaluation of a novel mediated social touch system aimed at managing separation anxiety in preschoolers. Specifically, the study investigates the effectiveness of using IoT in huggable interfaces and game-based applications in improving children’s emotional state and adaptation to the kindergarten environment. Through experiments conducted on a sample of nearly 30 preschoolers, the results have shown that the system is effective in helping preschoolers adapt to kindergarten, with the best results achieved when using the huggable interface and the developed game together. The implications of this study may be beneficial to parents, educators, and mental health professionals who work with preschoolers who experience SAD. Full article
(This article belongs to the Special Issue AI, IoT, and NN Use in HealthCare)
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