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A Combinatorial Technology of AI and IoMT for Smart Health Care Systems: Current Trends and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7349

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Special Issue Editor

Prof. Dr. Fadi Al-Turjman

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Guest Editor

Artificial Intelligence Engineering Department, Research Center for AI and IoT, AI & Robotics Institute, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Turkey
Interests: IoT; AI; cloud computing; blockchain; AIoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and the Internet of Medical Things (IoMT) are two advancing techniques in sensor-based healthcare applications. These two technologies include many areas of application such as smart healthcare, smart cities, smart transportation and smart hospital, IoT enabled smart medical devices. Nowadays, there has been tremendous growth in dual applications of AI and IoMT especially in healthcare which is readily available with its intelligent devices and easily accessible to the users and customizable for the operating the functionalities. Edge computing is another emerging method for processing patient information. The edge computing ensures the data collection from the source and accountable for data collection and provides greater control over automation and managing cloud based data. The main advantages of this combinatorial technologies include minimal human efforts, affordable resource utilization, adoption of intelligent application, sensor based data collection. The proliferation of smart devices has enabled the usage of healthcare applications such as continuous observation of patients health, self monitoring by wearable devices, especially isolating themselves during the COVID-19 pandemic crisis, home medication by keeping themselves out of hospital. In this scenario, IoT has brought remarkable vision of connected health to reality. This Special Issue is envisioned to produce qualitative research papers on recent trends towards AI and IoMT and can also include the use of edge computing for health care, specifically including the techniques, methods, innovative application, its design and deployment for providing better insight in the current healthcare services and needs. Authors are requisitioned to submit the unpublished research works in the predetermined scope with below listed topics but not restricted to:

AI/IoMT based Techniques in healthcare applications
AI/IoT based deployments in COVID-19 pandemic infrastructure
IoMT: Sensor based applications in health care
Design and Applications of Edge computing in IoT enabled Methods
AI/IoMT : Data fusion in healthcare issues
IoT-Edge computing : Data Storage, Process and Analysis in health communications
Security Threats in sensor based data transformation in health care
Application of AI in IoT and edge computing for health care
Analyzing Long Term risk factors in healthcare using AI and IoMT methods AI techniques in handling smart sensors/devices in IoT/IoMT environment

Prof. Dr. Fadi Al-Turjman
Guest Editor

Manuscript Submission Information

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Keywords

AI
Smart sensors
WSNs
IoT
IoMT

Published Papers (3 papers)

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Research

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20 pages, 3407 KiB

Open AccessArticle

EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats

by B. D. Deebak and Fadi Al-Turjman

Sensors 2023, 23(6), 2995; https://doi.org/10.3390/s23062995 - 10 Mar 2023

Cited by 5 | Viewed by 1739

Abstract

Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient’s body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (

~ 98.3 %

) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease. Full article

(This article belongs to the Special Issue A Combinatorial Technology of AI and IoMT for Smart Health Care Systems: Current Trends and Applications)

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18 pages, 2021 KiB

Open AccessArticle

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

by Dhanalekshmi Prasad Yedurkar, Shilpa P. Metkar, Fadi Al-Turjman, Thompson Stephan, Manjur Kolhar and Chadi Altrjman

Sensors 2022, 22(23), 9302; https://doi.org/10.3390/s22239302 - 29 Nov 2022

Cited by 2 | Viewed by 1455

Abstract

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject’s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain. Full article

(This article belongs to the Special Issue A Combinatorial Technology of AI and IoMT for Smart Health Care Systems: Current Trends and Applications)

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Review

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27 pages, 1950 KiB

Open AccessReview

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review

by Irkham Irkham, Abdullahi Umar Ibrahim, Pwadubashiyi Coston Pwavodi, Fadi Al-Turjman and Yeni Wahyuni Hartati

Sensors 2023, 23(4), 2240; https://doi.org/10.3390/s23042240 - 16 Feb 2023

Cited by 19 | Viewed by 3185

Abstract

The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10⁻⁶), Nanomolar nM (10⁻⁹), Picomolar pM (10⁻¹²), femtomolar fM (10⁻¹⁵), and attomolar aM (10⁻¹⁸). Full article

(This article belongs to the Special Issue A Combinatorial Technology of AI and IoMT for Smart Health Care Systems: Current Trends and Applications)

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