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Mathematics
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AI-Based on Mathematical Modelling for IoMT Devices and Networks
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AI-Based on Mathematical Modelling for IoMT Devices and Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 6 June 2025 | Viewed by 6023

Special Issue Editor

Prof. Dr. Victor Hugo C. De Albuquerque

E-Mail Website
Guest Editor
Department of Teleinformatics Engineering (DETI), Federal University of Ceará, Fortaleza, Brazil
Interests: biomedical engineering; bioinformatics; internet of medical things; artificial intelligence; pattern recognition; signal data science; metaverse
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent times, AI enabled approaches are fundamental for the next frontier of revolution and modernization, competition, and production. Thus, AI techniques are applied in several applications including healthcare, image recognition, fraud detection, statistical arbitrage, prediction and classification, and spam filtering, among others. This Special Issue focuses on modern AI methods to deal with the massive amount of heterogeneous data. We aim to present novel mathematical models, frameworks, systems, and applications of AI and data mining for big data in Internet of Medical Things (IoMT) devices and networks. Modern networks are experiencing great enlargement and advancements of both AI and IoMT to change our lives. The integration of these two technologies along with the proper handling of big data can be used for many applications.

The goal of this Special Issue is to publish original research and review articles, comprehensive to all readers of the magazine regardless of their specialty. This Special Issue includes emerging XAI techniques and also manages the big data in several domains to fulfill the needs of the industries and academia as supported and directed by  energetic and potential researchers.

This Special Issue focuses on the following topics, but is not limited to them:

  • AI for medical neural networks;
  • AI Sensor devices/ technologies for IoMT applications;
  • Smart Sensors for Intelligent IoMT applications;
  • Wearables sensors for Intelligent IoMT with fog-cloud network;
  • Data pre-processing and transmission sensor techniques in AI for IoMT;
  • AI sensors in health informatics;
  • AI-empowered sensing for smart cities/grid/healthcare;
  • Rehabilitation and ambient monitoring for aging society

Prof. Dr. Victor Hugo C. de Albuquerque
Guest Editor

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. Mathematics 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 2600 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

  • mathematical methods in medicine
  • mathematics for intelligent systems
  • mathematics for data science

Published Papers (2 papers)

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Research

21 pages, 3141 KiB  
Article
An IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning
by Senthil Kumar Jagatheesaperumal, Snegha Rajkumar, Joshinika Venkatesh Suresh, Abdu H. Gumaei, Noura Alhakbani, Md. Zia Uddin and Mohammad Mehedi Hassan
Mathematics 2023, 11(12), 2758; https://doi.org/10.3390/math11122758 - 18 Jun 2023
Cited by 5 | Viewed by 3205
Abstract
To promote a healthy lifestyle, it is essential for individuals to maintain a well-balanced diet and engage in customized workouts tailored to their specific body conditions and health concerns. In this study, we present a framework that assesses an individual’s existing health conditions, [...] Read more.
To promote a healthy lifestyle, it is essential for individuals to maintain a well-balanced diet and engage in customized workouts tailored to their specific body conditions and health concerns. In this study, we present a framework that assesses an individual’s existing health conditions, enabling people to evaluate their well-being conveniently without the need for a doctor’s consultation. The framework includes a kit that measures various health indicators, such as body temperature, pulse rate, blood oxygen level, and body mass index (BMI), requiring minimal effort from nurses. To analyze the health parameters, we collected data from a diverse group of individuals aged 17–24, including both men and women. The dataset consists of pulse rate (BPM), blood oxygen level (SpO2), BMI, and body temperature, obtained through an integrated Internet of Things (IoT) unit. Prior to analysis, the data was augmented and balanced using machine learning algorithms. Our framework employs a two-stage classifier system to recommend a balanced diet and exercise based on the analyzed data. In this work, machine learning models are utilized to analyze specifically designed datasets for adult healthcare frameworks. Various techniques, including Random Forest, CatBoost classifier, Logistic Regression, and MLP classifier, are employed for this analysis. The algorithm demonstrates its highest accuracy when the training and testing datasets are divided in a 70:30 ratio, resulting in an average accuracy rate of approximately 99% for the mentioned algorithms. Through experimental analysis, we discovered that the CatBoost algorithm outperforms other approaches in terms of achieving maximum prediction accuracy. Additionally, we have developed an interactive web platform that facilitates easy interaction with the implemented framework, enhancing the user experience and accessibility. Full article
(This article belongs to the Special Issue AI-Based on Mathematical Modelling for IoMT Devices and Networks)
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22 pages, 2285 KiB  
Article
A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data
by Walaa N. Ismail, Fathimathul Rajeena P. P. and Mona A. S. Ali
Mathematics 2023, 11(4), 957; https://doi.org/10.3390/math11040957 - 13 Feb 2023
Cited by 13 | Viewed by 2230
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that affects a large number of people across the globe. Even though AD is one of the most commonly seen brain disorders, it is difficult to detect and it requires a categorical representation of features to [...] Read more.
Alzheimer’s disease (AD) is a neurodegenerative disease that affects a large number of people across the globe. Even though AD is one of the most commonly seen brain disorders, it is difficult to detect and it requires a categorical representation of features to differentiate similar patterns. Research into more complex problems, such as AD detection, frequently employs neural networks. Those approaches are regarded as well-understood and even sufficient by researchers and scientists without formal training in artificial intelligence. Thus, it is imperative to identify a method of detection that is fully automated and user-friendly to non-AI experts. The method should find efficient values for models’ design parameters promptly to simplify the neural network design process and subsequently democratize artificial intelligence. Further, multi-modal medical image fusion has richer modal features and a superior ability to represent information. A fusion image is formed by integrating relevant and complementary information from multiple input images to facilitate more accurate diagnosis and better treatment. This study presents a MultiAz-Net as a novel optimized ensemble-based deep neural network learning model that incorporate heterogeneous information from PET and MRI images to diagnose Alzheimer’s disease. Based on features extracted from the fused data, we propose an automated procedure for predicting the onset of AD at an early stage. Three steps are involved in the proposed architecture: image fusion, feature extraction, and classification. Additionally, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is presented as a multi-objective optimization algorithm to optimize the layers of the MultiAz-Net. The desired objective functions are imposed to achieve this, and the design parameters are searched for corresponding values. The proposed deep ensemble model has been tested to perform four Alzheimer’s disease categorization tasks, three binary categorizations, and one multi-class categorization task by utilizing the publicly available Alzheimer neuroimaging dataset. The proposed method achieved (92.3 ± 5.45)% accuracy for the multi-class-classification task, significantly better than other network models that have been reported. Full article
(This article belongs to the Special Issue AI-Based on Mathematical Modelling for IoMT Devices and Networks)
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