124 results on '"Internet of Medical Things"'
Search Results
2. Techniques of medical image encryption taxonomy
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Mustafa A. Al-Fayoumi, Ammar Odeh, Ismail Keshta, and Ashraf Ahmad
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Control and Optimization ,Computer Networks and Communications ,Hardware and Architecture ,Control and Systems Engineering ,Digital medical images ,Internet of medical things ,Computer Science (miscellaneous) ,Genetic algorithms ,Electrical and Electronic Engineering ,S-box ,Instrumentation ,Information Systems - Abstract
Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.
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- 2022
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3. Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things
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Hong, Sidra Abbas, Gabriel Avelino Sampedro, Mideth Abisado, Ahmad Almadhor, Iqra Yousaf, and Seng-Phil
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harris hawk optimizer ,internet of medical things ,cyber-attacks ,machine learning ,deep learning - Abstract
The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies.
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- 2023
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4. Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model
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Mohamed Hammad, Mohammed ElAffendi, Abdelhamied A. Ateya, and Ahmed A. Abd El-Latif
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Cancer Research ,Oncology ,Internet of Medical Things ,CNN ,brain tumor detection ,deep learning ,security - Abstract
In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system’s complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.
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- 2023
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5. Visualization of Remote Patient Monitoring System Based on Internet of Medical Things
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Mudassar Ali Khan, Ikram Ud Din, Byung-Seo Kim, and Ahmad Almogren
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visualization ,remote patient monitoring ,Internet of Medical Things ,usability ,Renewable Energy, Sustainability and the Environment ,Geography, Planning and Development ,Building and Construction ,Management, Monitoring, Policy and Law - Abstract
Remote patient monitoring (RPM) has become a crucial tool for healthcare professionals in the monitoring and management of patients, particularly for patients with chronic illnesses. RPM has undergone improvements in its capability to deliver real-time data and information to healthcare practitioners as the Internet of Medical Things (IoMT) devices have become more widely available. However, managing and analyzing such a large volume of data still remains a difficult task. The visualization method suggested in this article enables healthcare professionals to examine data gathered by IoMT devices in real-time. Healthcare professionals may monitor patient health status and identify any data irregularities thanks to the system’s dashboard. To assess the system’s usability and user satisfaction, we employed both the Post-Study System Usability Questionnaire (PSSUQ) and the System Usability Scale (SUS). The outcomes of the PSSUQ and SUS assessments revealed that the suggested visualization system scored higher than the control group, demonstrating the system’s usability, accuracy, and dependability as well as its user-friendliness and intuitive interface. The visualization system can boost the effectiveness and efficiency of remote patient monitoring, resulting in better patient care and lower healthcare costs.
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- 2023
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6. A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0
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Rajesh Natarajan, Gururaj Harinahallo Lokesh, Francesco Flammini, Anitha Premkumar, Vinoth Kumar Venkatesan, and Shashi Kant Gupta
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General Materials Science ,Building and Construction ,Internet of Medical Things ,energy optimization ,security ,Geotechnical Engineering and Engineering Geology ,wireless sensor networks ,privacy ,Computer Science Applications ,Civil and Structural Engineering - Abstract
Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.
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- 2023
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7. Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices
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Gangireddy Narendra Kumar Reddy, M. Sabarimalai Manikandan, and N. V. L. Narasimha Murty
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objective distortion measures ,PPG data compression ,General Computer Science ,Photoplethysmogram (PPG) ,PPG signal analysis ,General Engineering ,General Materials Science ,Internet of Medical Things ,Electrical engineering. Electronics. Nuclear engineering ,PPG monitoring devices ,TK1-9971 - Abstract
Real-time photoplethysmogram (PPG) denoising and data compression has become most essential requirements for accurately measuring vital parameters and efficient data transmission but that may introduce different kinds of waveform distortions due to the lossy processing techniques. Subjective quality assessment tests are the most reliable way to assess the quality, but they are time expensive and also cannot be incorporated with quality-driven compression mechanism. Thus, finding a best objective distortion measure is highly demanded for automatically evaluating quality of reconstructed PPG signal that must be subjectively meaningful and simple. In this paper, we present four types of objective distortion measures and evaluate their performance in terms of quality prediction accuracy, Pearson correlation coefficient and computational time. The performance evaluation is performed on different kinds of PPG waveform distortions introduced by the predictive coding, compressed sampling, discrete cosine transform and discrete wavelet transform. On the normal and abnormal PPG signals taken from five standard databases, evaluation results showed that different subjective quality evaluation groups (5-point, 3-point and 2-point rating scale) had different best objective distortion measures in terms of prediction accuracy and Pearson correlation coefficient. Moreover, selection of a best objective distortion measure depends upon type of PPG features that need to be preserved in the reconstructed signal.
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- 2022
8. BULUT DESTEKLİ MEDİKAL NESNELERİN İNTERNETİ TABANLI UZAKTAN SAĞLIK İZLEME SİSTEMİ
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Murtaza CİCİOĞLU, Ali ÇALHAN, Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü., and Cicioğlu, Murtaza
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Computer Science, Information System ,Technology ,Medikal nesnelerin interneti ,cloud computing ,remote health monitoring ,Bilgisayar Bilimleri, Yazılım Mühendisliği ,uzaktan sağlık i̇zleme ,Computer Science, Software Engineering ,wireless communication ,Engineering (General). Civil engineering (General) ,Medikal Nesneleri İnterneti,Bulut Bilişim,Uzaktan Sağlık İzleme,Kablosuz Haberleşme ,bulut bilişim ,Uzaktan sağlık izleme ,Internet of Medical Things,Cloud Computing,Remote Health Monitoring,Wireless Communication ,medikal nesneleri i̇nterneti ,Bilgisayar Bilimleri, Bilgi Sistemleri ,kablosuz haberleşme ,TA1-2040 ,internet of medical things - Abstract
Nesnelerin interneti kavramı günümüzde kendinden sıkça söz ettiren bir kavram olmakla birlikte yeni nesil heterojen haberleşme ağları olarak ifade edilmektedir. Özellikle sağlık alanı başta olmak üzere, yerleşim, tarım, taşıma, endüstri vb. alanlarında her geçen gün çözümler üretmekte ve bulut teknolojileriyle birlikte anılmaktadır. Sağlık alanında Medikal Nesnelerin İnterneti olarak isimlendirilen Nesnelerin İnterneti farklı tipte sağlık uygulamaları ve cihazların birbirleri ile haberleşmesini ön plana çıkarmaktadır. Çalışmamızda bir bireyin çeşitli fizyolojik ölçümlerinin kablosuz haberleşme ile merkezi bir düğümde toplanması ve bu düğümün bir ağ geçidine verileri göndermesi şeklinde bir senaryo Riverbed Modeler benzetim programında gerçekleştirilmiştir. Ağ geçidi aldığı verileri anlık olarak buluta aktarması ve ardından bir mobil uygulama sayesinde eşzamanlı olarak sağlık personelinin ekranında görüntülenmesini sağlayacak bir altyapı tasarlanmıştır. Bu sayede gerçek zamanlı bir Medikal Nesnelerin İnterneti uygulaması önerilmektedir. Kalp ritmi, kan basıncı, oksijen miktarı, vücut sıcaklığı ve solunum oranı verileri anlık olarak birey ve sağlık personeli arasında paylaşılmış olup özellikle pandemi süreçlerinde kullanılabilecek bir uzaktan sağlık izleme sistemi kurulmuştur., The concept of the Internet of Things as a new generation heterogeneous communication networks is frequently mentioned today. It produces solutions especially in the field of health, settlement, agriculture, transportation, industry, etc. associated with cloud technologies. The Internet of Things, which is called the Medical Internet of Things in the field of health, highlights the communication of different types of health applications and devices with each other. In our study, a scenario has been realized in the Riverbed Modeler simulation program in which an individual's various physiological measurements are collected in a central node with wireless communication and it sends data to a gateway. An infrastructure has been designed to allow the gateway to instantly transfer the data it receives to the cloud and then simultaneously display it on the screen of healthcare personnel thanks to a mobile application. In this way, a real-time Medical Internet of Things application is recommended. Heart rate, blood pressure, oxygen amount, body temperature and respiratory rate data are instantly shared between individuals and healthcare personnel, and a remote health monitoring system has been established that can be used especially in pandemic processes.
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- 2021
9. IoMT with Deep CNN: AI-Based Intelligent Support System for Pandemic Diseases
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Sujithra Thandapani, Mohamed Iqbal Mahaboob, Celestine Iwendi, Durai Selvaraj, Ankur Dumka, Mamoon Rashid, and Senthilkumar Mohan
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Computer Networks and Communications ,Hardware and Architecture ,Control and Systems Engineering ,Signal Processing ,Internet of Medical Things ,COVID-19 ,treatment support system ,artificial intelligence ,deep convolution neural network ,Electrical and Electronic Engineering - Abstract
The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.
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- 2023
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10. Nurse, Give Me the News! Understanding Support for and Opposition to a COVID-19 Health Screening System
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Soojeong Yoo, Audrey P Wang, and Natalia Gulbransen-Diaz
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Health, Toxicology and Mutagenesis ,Public Health, Environmental and Occupational Health ,COVID-19 ,self-service technology ,health screening ,health services ,hospital ,design ,implementation ,digital health ,qualitative study ,Internet of Things ,Internet of Medical Things - Abstract
Helping the sick and protecting the vulnerable has long been the credo of the health profession. In response to the coronavirus-disease-2019 (COVID-19 pandemic), hospitals and healthcare institutions have rapidly employed public health measures to mitigate patient and staff infection. This paper investigates staff and visitor responses to the COVID-19 eGate health screening system; a self-service technology (SST) which aims to protect health care workers and facilities from COVID-19. Our study evaluates the in situ deployment of the eGate, and employs a System Usability Scale (SUS) and questionnaire (n = 220) to understand staff and visitor’s acceptance of the eGate. In detailing the themes relevant to those who advocate for the system and those who oppose it, we contribute towards a more detailed understanding of the use and non-use of health-screening SSTs. We conclude with a series of considerations for the design of future interactive screening systems within hospitals.
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- 2023
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11. MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors
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Roseline Oluwaseun Ogundokun, Sanjay Misra, Akinyemi Omololu Akinrotimi, Hasan Ogul, and MDPI AG (Basel, Switzerland)
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deep convolutional neural network ,breast cancer histology ,Internet of Medical Things ,Electrical and Electronic Engineering ,internet of medical things ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
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- 2023
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12. Användar-acceptans för Internet of Medical Things (IoMT) inom svensk distansvård
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Kotka, David and Hegestrand Ganesh, Tom
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E-health ,IoMT ,user acceptance ,Användaracceptans ,distansvård ,e-hälsa ,Internet of Medical Things ,remote healthcare ,UTAUT ,Systemvetenskap, informationssystem och informatik ,Information Systems - Abstract
The increasing life expectancy of an aging population has caused a higher demand for healthcare services, highlighting a shortage within the healthcare system due to insufficient projected growth of healthcare personnel. One solution to this issue is the utilization of the Internet of Medical Things (IoMT). This study aims to describe the general acceptance level amongst healthcare recipients of adopting IoMT in Swedish remote healthcare and how the acceptance level differs amongst different age groups depending on the application of IoMT. Acceptance and implementation of IoMT in remote healthcare are discussed, with an emphasis on factors such as user-friendliness and practical hardware design. In this study, a digital survey was conducted with 103 respondents, which demonstrated that the majority were positive about using IoMT for monitoring and treating health conditions remotely. Although awareness of security risks exists, respondents generally expressed a low number of concerns regarding unauthorized access to information and modification of IoMT devices in general. The study's conclusion highlights the notable acceptance of IoMT in remote healthcare in Sweden. The results indicate network-connected devices are integrated into people's daily lives, and user-friendliness and cybersecurity influence the adoption of IoMT. Older age groups demonstrated less concern about cybersecurity than younger age groups. When analysing specific applications, the results show some age-related differences in acceptance, which also varied depending on the application, particularly when comparing more life-critical devices such as IoMT pacemakers with IoMT fall detectors. Den ökande livslängden hos en ökande befolkning har skapat ett ökat vårdbehov och detta har påvisat en brist inom sjukvården då den förväntade utvecklingen av sjukvårdspersonal är otillräcklig. En lösning på detta är användandet av Internet of Medical Things (IoMT) och denna studie har som syfte att beskriva den allmänna acceptansnivån hos vårdtagare för användandet av IoMT inom den svenska distansvården samt hur acceptansnivån skiljer sig för olika åldrar beroende på tillämpning av IoMT. Acceptans och implementering av IoMT i distanssjukvården diskuteras, och faktorer som användarvänlighet och praktisk hårdvarudesign betonas. I denna studie utfördes en digital enkätundersökning innehållande 103 respondenter som visade att majoriteten var positiva till att använda IoMT för att övervaka och behandla hälsotillstånd på distans. Även om medvetenheten om säkerhetsrisker finns, var respondenternas oro för obehörig åtkomst till information samt modifiering av IoMT-utrustning generellt låg. Studiens slutsats framhäver att distansvård med IoMT har en hög acceptans i Sverige. Resultaten indikerar att nätverksanslutna enheter är integrerade i människors vardag och att användarvänlighet samt cybersäkerhet påverkar användningen av IoMT. Äldre åldersgrupper visade mindre oro för cybersäkerheten än yngre åldersgrupper. Vid analys av specifika tillämpningar visar resultaten att det finns vissa åldersrelaterade skillnader i acceptans, vilka också varierade beroende på tillämpning, särskilt i jämförelse med mer livskritiska enheter såsom IoMT-pacemakers i jämförelse med IoMT-falldetektorer.
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- 2023
13. FedSepsis : A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
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Rahim Rahmani and Mahbub Ul Alam
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multi-modality ,Computer and Information Sciences ,federated learning ,deep learning ,Data- och informationsvetenskap ,Biochemistry ,Atomic and Molecular Physics, and Optics ,smart healthcare ,Analytical Chemistry ,early sepsis detection ,clinical decision support system ,electronic health records ,Internet of Medical Things ,natural language processing ,Electrical and Electronic Engineering ,Instrumentation - Abstract
The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.
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- 2023
14. Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment
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Rana Alabdan, Abdulrahman Alruban, Anwer Mustafa Hilal, and Abdelwahed Motwakel
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Internet of Medical Things ,oral cancer ,biomedical imaging ,artificial intelligence ,Inception model ,hybrid deep learning ,Health Information Management ,Leadership and Management ,Health Policy ,Health Informatics - Abstract
Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.
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- 2022
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15. Collaborative Screening of COVID-19-like Disease from Multi-Institutional Radiographs: A Federated Learning Approach
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Mohamed Abdel-Basset, Hossam Hawash, and Mohamed Abouhawwash
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General Mathematics ,Computer Science (miscellaneous) ,deep learning ,internet of medical things ,COVID-19-like pandemics ,federated learning ,domain adaption ,Engineering (miscellaneous) - Abstract
COVID-19-like pandemics are a major threat to the global health system have the potential to cause high mortality across age groups. The advance of the Internet of Medical Things (IoMT) technologies paves the way toward developing reliable solutions to combat these pandemics. Medical images (i.e., X-rays, computed tomography (CT)) provide an efficient tool for disease detection and diagnosis. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it complicated to obtain large numbers of samples from a single institution. However, owing to the necessity to preserve the privacy of patient data, it is challenging to build a centralized dataset from many institutions, especially during a pandemic. Moreover, heterogeneity between institutions presents a barrier to building efficient screening solutions. Thus, this paper presents a fog-based federated generative domain adaption framework (FGDA), where fog nodes aggregate patients’ data necessary to collaboratively train local deep-learning models for disease screening in medical images from different institutions. Local differential privacy is presented to protect the local gradients against attackers during the global model aggregation. In FGDA, the generative domain adaptation (DA) method is introduced to handle data discrepancies. Experimental evaluation on a case study of COVID-19 segmentation demonstrated the efficiency of FGDA over competing learning approaches with statistical significance.
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- 2022
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16. Physical Unclonable Function and Machine Learning Based Group Authentication and Data Masking for In-Hospital Segments
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Pintu Kumar Sadhu, Venkata P. Yanambaka, and Ahmed Abdelgawad
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Internet of Things ,Internet of Medical Things ,smart city ,security and privacy ,authentication framework ,group device authentication ,physical unclonable function ,machine learning ,Computer Networks and Communications ,Hardware and Architecture ,Control and Systems Engineering ,Signal Processing ,Electrical and Electronic Engineering - Abstract
The involvement of the Internet of things (IoT) in the development of technology makes systems automated and peoples’ lives easier. The IoT is taking part in many applications, from smart homes to smart industries, in order to make a city smart. One of the major applications of the IoT is the Internet of medical things (IoMT) which deals with patients’ sensitive information. This confidential information needs to be properly transferred and securely authenticated. For successful data protection and preserving privacy, this paper proposes multidevice authentication for the in-hospital segment using a physical unclonable function (PUF) and machine learning (ML). The proposed method authenticates multiple devices using a single message. Most of the protocols require PUF keys to be stored at the server, which is not required in the proposed framework. Moreover, authentication, as well as data, is sent to the server in the same message, which results in faster processing. Furthermore, a single ML model authenticates a group of devices at the same time. The proposed method shows 99.54% accuracy in identifying the group of devices. Moreover, the proposed method takes 2.6 ms and 104 bytes to complete the authentication of a device and takes less time with the increment of devices in the group. The proposed algorithm is analyzed using a formal analysis to show its resistance against various vulnerabilities.
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- 2022
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17. The SSTeP-KiZ System—Secure Real-Time Communication Based on Open Web Standards for Multimodal Sensor-Assisted Tele-Psychotherapy
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Jonas Primbs, Winfried Ilg, Annika Thierfelder, Björn Severitt, Carolin Sarah Hohnecker, Annika Kristin Alt, Anja Pascher, Ursula Wörz, Heinrich Lautenbacher, Karsten Hollmann, Gottfried Maria Barth, Tobias Renner, and Michael Menth
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sensor networks ,internet of medical things ,healthcare monitoring ,OCD ,telepsychotherapy ,WebRTC ,security ,open standards ,performance evaluation ,Electrical and Electronic Engineering ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system’s security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance.
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- 2022
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18. An Intelligent Platform for Threat Assessment and Cyber-Attack Mitigation in IoMT Ecosystems
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Nicholas Kolokotronis, Maria Dareioti, Stavros Shiaeles, and Emanuele Bellini
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Intrusion response ,Machine learning ,Internet of medical things ,Intrusion detection ,Cyber-security - Abstract
The increasing connectivity of medical devices along with the growing complexity, heterogeneity and attack surface of healthcare ecosystems has lead to numerous severe cyber-attacks. This paper proposes a novel collaborative security platform for threat assessment, intelligent detection and autonomous mitigation. The solution leverages machine learning (ML) and federated learning for detecting and preventing sophisticated multi-stage attacks, as well as blockchain for supporting integrity verification and accountability to defend against advanced persistent threats. The solution uses a distributed edge approach, performing intensive computations at the edge of the network, where information is generated, to achieve real-time processing of security events. The prevention capabilities employ autonomous decision-making with optimal response strategies towards cyber-attacks and run-time adaptation; these rely on dynamic risk-based models that use real-time information about security incidents.
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- 2022
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19. An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications
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Rahmat Ullah, Ikram Asghar, and Mark G. Griffiths
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Electrical and Electronic Engineering ,bibliometrics ,internet of medical things ,internet of things for medical devices ,healthcare applications ,bibliometric study ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.
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- 2022
20. PhysioEnabler: Intelligent sensor system to aid motor rehabilitation with a web application
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Batoca, Pedro Miguel Assis, Postolache, Octavian Adrian, and Correia, Américo Manuel Carapeto
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Internet das coisas médicas ,Internet of things ,Smart sensors ,Aplicação web ,Sensores inteligentes ,Internet of medical things ,Web application ,Reabilitação física ,Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática [Domínio/Área Científica] ,Physical rehabilitation ,Cloud - Abstract
Physical rehabilitation is a current topic due to the global aging population and an increase in chronic diseases and there are several initiatives and studies to bring new and innovative solutions in this area. One of the main challenges is the lack of data that can help to diagnose and provide more adequate treatments to patients. The current walking aids help improve the patients’ day-to-day by providing greater independence in their activities but don’t allow the extraction of any objective data for analysis. With the advances in IoT technologies, it is possible to enhance the aids’ functionality with sensors and other devices to extract information that can help physiotherapists improve their decisions and influence the patients’ course of treatment. The prototype presented in this dissertation proposes to add force, IMU (sensor that combines accelerometer, gyroscope, and magnetometer), and RFID (allows the identification with radio signals) sensors to a crutch and to use a microcontroller connected to these sensors to extract the data and send it to an endpoint in the cloud via MQTT protocol. The data processing takes place with cloud functions that also store the results. The information is available for patients and physiotherapists to view and analyze in a front-end developed in Python. The application also allows the creation of custom exercise plans according to the patient’s needs and is available for physiotherapists and patients to view. A reabilitação física é um tema atual devido ao envelhecimento da população em geral, mas também ao aumento de doenças crónicas, havendo já diversas iniciativas e estudos para encontrar soluções inovadoras nesta área. Um dos maiores desafios é a falta de dados que possam ajudar a diagnosticar e tratar de forma mais adequada os pacientes. Os dispositivos de ajuda à mobilidade mais comuns permitem melhorar o dia a dia dos pacientes, na medida que lhes providenciam uma maior independência nas suas atividades, mas, não permitem a recolha de dados para análise. Com os avanços nas tecnologias de IoT, é possível dotar estas ajudas com sensores e outros dispositivos de modo a extrair dados que permitam aos fisioterapeutas tomar melhores decisões e influenciar positivamente o tratamento de um paciente. O protótipo apresentado nesta dissertação propõe o uso de sensores de força, IMU (sensor que combina acelerômetro, giroscópio e magnetómetro) e RFID (permite a identificação a partir de sinais rádio) a uma canadiana e utilizar um microcontrolador ligado aos sensores para extrair esta informação de modo a enviá-la para um endpoint na cloud via protocolo MQTT. O processamento destes dados é feito por cloud functions que também armazenam o resultado. A informação é disponibilizada tanto para fisioterapeutas como para pacientes num front-end desenvolvido em Python. A aplicação permite também que sejam criados planos de tratamento customizados de acordo com as necessidades de cada paciente que também podem ser consultados pelos vários utilizadores incluindo o fisioterapeuta e o paciente, utilizador da canadiana inteligente.
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- 2022
21. A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things
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Rajasekhar Chaganti, Azrour Mourade, Vinayakumar Ravi, Naga Vemprala, Amit Dua, and Bharat Bhushan
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Renewable Energy, Sustainability and the Environment ,Geography, Planning and Development ,internet of medical things ,cyber security ,intrusion detection system ,particle swarm optimization ,deep learning ,deep neural network ,network attacks ,Building and Construction ,Management, Monitoring, Policy and Law - Abstract
Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models.
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- 2022
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22. A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV
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Amir Ali, Shibli Nisar, Muhammad Asghar Khan, Syed Agha Hassnain Mohsan, Fazal Noor, Hala Mostafa, and Mohamed Marey
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UAV ,dengue ,internet of medical things ,privacy ,tracking systems ,Control and Systems Engineering ,Mechanical Engineering ,Electrical and Electronic Engineering - Abstract
Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.
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- 2022
23. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
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Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, Yuriy Izotov, and Dmitry Korzun
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COVID-19 ,biochemical and hematological biomarkers ,routine blood values ,feature selection method ,LogNNet neural network ,machine learning sensors ,Internet of Medical Things ,IoT ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Internet of Things ,Cholesterol, HDL ,FOS: Physical sciences ,Biochemistry ,Physics - Medical Physics ,Quantitative Biology - Quantitative Methods ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry ,Machine Learning (cs.LG) ,Machine Learning ,FOS: Biological sciences ,Amylases ,Humans ,Medical Physics (physics.med-ph) ,Electrical and Electronic Engineering ,Instrumentation ,Triglycerides ,Quantitative Methods (q-bio.QM) - Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service., 30 pages, 9 figures, 8 tables, 1 algorithm
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- 2022
24. RAMi: A New Real-Time Internet of Medical Things Architecture for Elderly Patient Monitoring
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Olivier Debauche, Jean Bertin Nkamla Penka, Saïd Mahmoudi, Xavier Lessage, Moad Hani, Pierre Manneback, Uriel Kanku Lufuluabu, Nicolas Bert, Dounia Messaoudi, and Adriano Guttadauria
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real-time architecture ,internet of things ,internet of medical things ,healthcare internet of things ,edge AI ,edge computing ,data ,apache ,real-time ,blockchain ,Information Systems - Abstract
The aging of the world’s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.
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- 2022
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25. Development of a Low-Power IoMT Portable Pillbox for Medication Adherence Improvement and Remote Treatment Adjustment
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Dimitrios Karagiannis, Konstantinos Mitsis, and Konstantina S. Nikita
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Internet ,3D printing ,drug interactions ,image recognition ,internet of medical things ,IoMT ,low-power device ,medication adherence ,personalized medicine ,pillbox ,Humans ,Electrical and Electronic Engineering ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Medication Adherence ,Analytical Chemistry - Abstract
Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug–drug, food–drug, and supplement–drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.
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- 2022
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26. Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model
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Ashwag Albakri and Yahya Muhammed Alqahtani
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Internet of Medical Things ,smart healthcare ,blockchain ,security ,key generation ,image encryption ,Fluid Flow and Transfer Processes ,Process Chemistry and Technology ,General Engineering ,General Materials Science ,Instrumentation ,Computer Science Applications - Abstract
The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.
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- 2023
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27. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions
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Kandaraj Piamrat, GUIDO MARCHETTO, Alessio Sacco, Ons Aouedi, Software Stack for Massively Geo-Distributed Infrastructures (STACK), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), STR (LS2N - équipe STR ), Laboratoire des Sciences du Numérique de Nantes (LS2N), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Politecnico di Torino = Polytechnic of Turin (Polito)
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Federated learning ,Internet of Medical Things ,healthcare ,privacy ,Health Information Management ,Health Informatics ,[INFO]Computer Science [cs] ,Electrical and Electronic Engineering ,ComputingMilieux_MISCELLANEOUS ,Computer Science Applications - Abstract
International audience; Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.
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- 2022
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28. Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things' Data
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Ana Koren, Marko Jurcevic, and Ramjee Prasad
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eHealth Services ,Central health information system, eHealth Services, electronic health record, internet of medical things, healthcare standards ,General Computer Science ,General Engineering ,Central health information system ,General Materials Science ,healthcare standards ,electronic health record ,Electrical and Electronic Engineering ,internet of medical things - Abstract
Fitness and activity trackers are hugely popular wearable devices that monitor various health-related metrics, such as step count, heartbeat rate, or even oxygen saturation. Utilizing personal health information obtained by users- personal trackers provides promising results in the fields of telemedicine and personal well-being. However, we face challenges such as data quality, privacy and compliance with standards and regulations. This paper addresses such challenges, with the focus on the last one. Semantic constraints for healthcare datatypes are defined to ensure compliance with standards, making the information medically valid and relevant. A process of semantic verification and Schematron-based validation is proposed. The validation process suggested in this paper will enable the data to be transferred and incorporated into a formal Electronic Health Record. The process is then verified using datasets containing various health-related data types. The aim is to integrate personal health data into Electronic Health Record, which forms a part of Central Health Information System. This would provide personalized medical services to patients and help physicians to make more informed decisions.
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- 2022
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29. Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network
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Mehmet Tahir HUYUT and Andrei Velichko
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,SARS-CoV-2 ,FOS: Physical sciences ,COVID-19 ,Prognosis ,Biochemistry ,Physics - Medical Physics ,Quantitative Biology - Quantitative Methods ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry ,Machine Learning (cs.LG) ,Artificial Intelligence (cs.AI) ,FOS: Biological sciences ,Humans ,Medical Physics (physics.med-ph) ,Neural Networks, Computer ,Electrical and Electronic Engineering ,Instrumentation ,Pandemics ,Quantitative Methods (q-bio.QM) ,biochemical and hematological biomarkers ,routine blood values ,feature selection method ,LogNNet neural network ,Internet of Medical Things ,IoT - Abstract
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 dis-ease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things., Comment: 28 pages, 10 figures, 12 Tables
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- 2022
30. Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms
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Micheal Olaolu Arowolo and Y.K. Saheed
- Subjects
particle swarm optimization ,General Computer Science ,Computer science ,business.industry ,Internet of Things ,smart environment ,General Engineering ,TK1-9971 ,Recurrent neural network ,Cyber-attack ,recurrent neural network ,General Materials Science ,The Internet ,Smart environment ,Internet of Medical Things ,Electrical engineering. Electronics. Nuclear engineering ,business ,cyber-attack ,Computer network - Abstract
Information and communication technology (ICT) advancements have altered the entire computing paradigm. As a result of these improvements, numerous new channels of communication are being created, one of which is the Internet of Things (IoT). The IoT has recently emerged as cutting-edge technology for creating smart environments. The Internet of Medical Things (IoMT) is a subset of the IoT, in which medical equipment exchange information with each other to exchange sensitive information. These developments enable the healthcare business to maintain a higher level of touch and care for its patients. Security is seen as a significant challenge in whatsoever technology’s reliance based on the IoT. Security difficulties occur owing to the various potential attacks posed by attackers. There are numerous security concerns, such as remote hijacking, impersonation, denial of service attacks, password guessing, and man-in-the-middle. In the event of such attacks, critical data associated with IoT connectivity may be revealed, altered, or even rendered inaccessible to authorized users. As a result, it turns out to be critical to safeguard the IoT/IoMT ecosystem against malware assaults. The main goal of this study is to demonstrate how a deep recurrent neural network (DRNN) and supervised machine learning models (random forest, decision tree, KNN, and ridge classifier) can be utilized to develop an efficient and effective IDS in the IoMT environment for classifying and forecasting unexpected cyber threats. Preprocessing and normalization of network data are performed. Following that, we optimized features using a bio-inspired particle swarm algorithm. On the standard data for intrusion detection, a thorough evaluation of experiments in DRNN and other SML is performed. It was established through rigorous testing that the proposed SML model outperforms existing approaches with an accuracy of 99.76%.
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- 2021
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31. Framework for ethical and acceptable use of social distancing tools and smart devices during COVID-19 pandemic in Zimbabwe
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Maureen Nokuthula Sibiya, Nzuza Ndumiso, Sakhile Simelane, Stephen G. Fashoto, Boluwaji Akinnuwesi, Richard Millham, and Elliot Mbunge
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Artificial intelligence ,business.industry ,Social distance ,Internet privacy ,COVID-19 ,Transparency (behavior) ,Masking (Electronic Health Record) ,Article ,Compliance (psychology) ,Acceptable use policy ,Politics ,Internet of medical things ,Synchronization (computer science) ,Public trust ,Social distancing monitoring tools ,Business ,Ethical values - Abstract
Despite the successful development of vaccines, coronavirus disease (COVID-19) continues to present unprecedented challenges. Besides the ongoing vaccination activities, many countries still rely on measures including social distancing, contact tracing, mandatory face masking among others. Several digital technologies such as smart devices, social distancing tools, smart applications have been adopted to enhance public adherence to reduce secondary transmission. Such technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation and quarantine compliance. The use of digital technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which subsequently impact their efficacy and adoption. Also, the aggressive and mandatory use of large-scale digital technologies is not easy to implement, adhere to and subsequently difficult to practice which ultimately lead to imperfect public compliance. To alleviate these impediments, we analysed the available literature and propose an ethical framework for the use of digital technologies centred on ethical practices. The proposed framework highlights the trade-offs, potential roles and coordination of different stakeholders involved in the development and implementation of digital technologies, from various social and political contexts in Zimbabwe. We suggest that transparency, regular engagement and participation of potential users are likely to boost public trust. However, the potential violation of ethical values, poor communication, hasty implementation of digital technologies will likely undermine public trust, and as such, risk their adoption and efficacy.
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- 2021
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32. Risk Assessment Methodologies for the Internet of Medical Things: A Survey and Comparative Appraisal
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Thomas K. Dasaklis, Vangelis Malamas, George Stergiopoulos, Christos Douligeris, Panayiotis Kotzanikolaou, and Fotis Chantzis
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vulnerability assessment ,Knowledge management ,General Computer Science ,Computer science ,Best practice ,Context (language use) ,02 engineering and technology ,threat modeling ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,Road map ,Internet of Medical Things ,Risk management ,impact assessment ,business.industry ,General Engineering ,risk assessment ,020206 networking & telecommunications ,Usability ,medical device security ,Security controls ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,Risk assessment ,lcsh:TK1-9971 - Abstract
The Internet of Medical Things (IoMT) has revolutionized health care services by providing significant benefits in terms of patient well being and relevant costs. Traditional risk assessment methodologies, however, cannot be effectively applied in the IoMT context since IoMT devices form part of a distributed and trustless environment and naturally support functionalities that favor reliability and usability instead of security. In this work we present a survey of risk assessment and mitigation methodologies for IoMT. For conducting the survey, we assess two streams of literature. First, we systematically review and classify the current scientific research in IoMT risk assessment methodologies. Second, we review existing standards/best practices for IoMT security assessment and mitigation in order to i) provide a comparative assessment of these standards/best practices on the basis of predefined criteria (scope and/or coverage, maturity level, and relevant risk methodology applied) and ii) identify common themes for IoMT security controls. Based on the analysis, we provide various IoMT research and implementation gaps along with a road map of fruitful areas for future research. The paper could be of significant value to security assessment researchers and policymakers/stakeholders in the health care industry.
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- 2021
33. Research on Disease Prediction Based on Improved DeepFM and IoMT
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Syed Umar Amin, Zhihan Lv, Zengchen Yu, and Musaed Alhussein
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General Computer Science ,Computer science ,Sample (statistics) ,02 engineering and technology ,Recommender system ,Machine learning ,computer.software_genre ,Field (computer science) ,factorization machine ,Data modeling ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,General Materials Science ,hepatitis ,Internet of Medical Things ,Artificial neural network ,business.industry ,Deep learning ,General Engineering ,020206 networking & telecommunications ,Workload ,020201 artificial intelligence & image processing ,Artificial intelligence ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,computer ,lcsh:TK1-9971 - Abstract
In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.
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- 2021
34. Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications
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Mohammad Kamrul Hasan, Saleh Alyahya, Mohammad Safiqul Islam, Rossilawati Sulaiman, Musse Mohamed Ahmed, Sheroz Khan, Aisha Hassan Abdalla Hashim, Samar Kamil, Shabana Habib, Shayla Islam, and Arif Hassan
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General Computer Science ,Computer science ,medical image encryption ,02 engineering and technology ,Computer security ,computer.software_genre ,Encryption ,Field (computer science) ,lightweight encryption ,Image (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Redundancy (engineering) ,Cryptosystem ,General Materials Science ,Confidentiality ,Internet of Medical Things ,Authentication ,business.industry ,General Engineering ,020206 networking & telecommunications ,020201 artificial intelligence & image processing ,The Internet ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,lcsh:TK1-9971 ,computer - Abstract
The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.
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- 2021
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35. IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning
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Amir Haider, Taher M. Ghazal, MuhibUr Rahman, Shahan Yamin Siddiqui, Iftikhar Naseer, Mohammad Kamrul Hasan, Afifi Mohammed. A, Sagheer Abbas, Munir Ahmad, Karamath Ateeq, Muhammad Adnan Khan, and Junaid Ahmad Khan
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Oncology ,medicine.medical_specialty ,General Computer Science ,Lobular carcinoma ,convolutional neural network ,Cloud computing ,breast cancer prediction ,Breast cancer ,Internal medicine ,medicine ,Mucinous carcinoma ,General Materials Science ,Internet of Medical Things ,skin and connective tissue diseases ,business.industry ,Deep learning ,Mortality rate ,General Engineering ,deep learning ,Breast cancer stages ,Ductal carcinoma ,medicine.disease ,TK1-9971 ,Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business - Abstract
Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.
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- 2021
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36. FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks
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Abdu Gumaei, Mohammad Mehedi Hassan, Giancarlo Fortino, Meteb Altaf, Bader Fahad Alkhamees, Mabrook Al-Rakhami, and Khan Muhammad
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,General Computer Science ,Computer science ,Distributed computing ,Computer Science - Human-Computer Interaction ,Cloud computing ,Machine Learning (cs.LG) ,Human-Computer Interaction (cs.HC) ,edge computing ,General Materials Science ,Internet of Medical Things ,Edge computing ,Vanishing gradient problem ,Mobile edge computing ,Artificial neural network ,Wireless network ,business.industry ,Deep learning ,General Engineering ,deep learning ,healthcare system ,TK1-9971 ,fall detection ,Artificial intelligence ,Electrical engineering. Electronics. Nuclear engineering ,business ,5G - Abstract
Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and the internet of medical things (IoMT) to address this research area. Edge computing can reduce the cost of cloud communication, including high latency and bandwidth use, by moving conventional healthcare services and applications closer to end-users. Artificial intelligence (AI) techniques such as deep learning (DL) have been used recently for automatic fall detection, as well as supporting healthcare services. However, DL requires a vast amount of data and substantial processing power to improve its performance for the IoMT linked to the traditional edge computing environment. This research proposes an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications. We also propose the use of a deep gated recurrent unit (DGRU) neural network to improve the accuracy of existing DL-based fall detection methods. DGRU has the advantage of dealing with time-series IoMT data, and it can reduce the number of parameters and avoid the vanishing gradient problem. The experimental results on two public datasets show that the DGRU model of the proposed framework achieves higher accuracy rates compared to the current related works on the same datasets.
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- 2021
37. Impact of Digital Technologies on the Efficiency of Healthcare Delivery
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Osipov, Vladimir S. and Skryl, Tatiana V.
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Internet of things ,Medical information systems ,Internet of medical things ,Medical services ,Health care system ,Digital technologies ,Article - Abstract
We are currently witnessing that the introduction of various digital technologies has an impact on various socioeconomic processes. The current situation with the global pandemic of 2020 has become an objective necessity for the development of levers of digital technologies in medicine. The purpose of this work is to conduct a comprehensive analysis and assessment of the main trends of digital technologies’ implementation in the healthcare system in two main areas—global (most innovative world achievements) and local (national trends). The methods of research are the following: institutional analysis and comparative analysis. The results indicate that digital technologies make it possible to protect doctors and patients from forced contacts and avoid infection with dangerous infectious diseases. The traditional “patient–doctor” model is losing its relevance, and today’s reality forces it to change significantly. Digital technology, together with medical devices, is ushering in a new era in digital health care, and the digitalization of the healthcare system itself is leading to improved delivery of health services, better quality control, and lower costs. The main conclusions are given on the results of implementation of digital technologies in the sphere of health care. Currently, the trends and innovations are evident, which in just a few years will pass from the category of innovative experiments to the rank of attributes necessary for normal functioning in the period of digital transformation. Moreover, the proposed three-level system of decision making, based on the achievements of this period, will allow creating a theoretical basis for a specific situation and minimize the risks of non-optimal outcome.
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- 2021
38. Securing Internet of Medical Things with Friendly-jamming schemes
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Qubeijian Wang, Xuran Li, Muhammad Imran, Hong-Ning Dai, and Dengwang Li
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Computer Networks and Communications ,Network security ,business.industry ,Computer science ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Data security ,020206 networking & telecommunications ,Jamming ,Eavesdropping ,Cryptography ,02 engineering and technology ,Computer security ,computer.software_genre ,Article ,Countermeasure ,Internet of medical things ,Friendly jamming ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,020201 artificial intelligence & image processing ,The Internet ,business ,computer - Abstract
The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission.
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- 2020
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39. IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process
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Venkat P. Rangan, Kamalanathan Kandasamy, Sethuraman Srinivas, and Krishnashree Achuthan
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Cybersecurity risk assessment frameworks ,lcsh:Computer engineering. Computer hardware ,Process (engineering) ,Computer science ,Context (language use) ,lcsh:TK7885-7895 ,02 engineering and technology ,Asset (computer security) ,Risk vectors ,lcsh:QA75.5-76.95 ,0202 electrical engineering, electronic engineering, information engineering ,Internet of Medical Things ,Risk management ,Risk assessment ,Risk rank ,business.industry ,Risk management framework ,020206 networking & telecommunications ,Computer Science Applications ,Risk analysis (engineering) ,Signal Processing ,020201 artificial intelligence & image processing ,The Internet ,lcsh:Electronic computers. Computer science ,business ,Threat assessment - Abstract
Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in many governmental and commercial organizations. Extending these existing frameworks to IoT systems alone will not address the new risks that have arisen in the IoT ecosystem. This study has included a review of existing popular cyber risk assessment methodologies and their suitability to IoT systems. National Institute of Standards and Technology, Operationally Critical Threat, Asset, and Vulnerability Evaluation, Threat Assessment & Remediation Analysis, and International Standards Organization are the four main frameworks critically analyzed in this research study. IoT risks are presented and reviewed in terms of the IoT risk category and impacted industries. IoT systems in financial technology and healthcare are dealt with in detail, given their high-risk exposure. Risk vectors for IoT and the Internet of Medical Things (IoMT) are discussed in this study. A unique risk ranking method to rank and quantify IoT risk is introduced in this study. This ranking method initiates a risk assessment approach exclusively for IoT systems by quantifying IoT risk vectors, leading to effective risk mitigation strategies and techniques. A unique computational approach to calculate the cyber risk for IoT systems with IoT-specific impact factors has been designed and explained in the context of IoMT systems.
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- 2020
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40. Blockchain for Internet of Medical Things: A Technical Review
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Ellouze, Fatma, Fersi, Ghofrane, and Jmaiel, Mohamed
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Blockchain ,Healthcare ,Security ,Internet of Medical Things ,Article - Abstract
The Internet of Medical Things (IoMT) represents a network of implantable or wearable medical devices that continuously collect medical data about the patient’s health status. These data are heavy, sensitive and require high level of security. With the emergence of blockchain technology, researchers are focusing on using blockchain strategies to bring security to healthcare applications. However, such integration is very difficult and challenging due to the different requirements in these two technologies. We present in this paper a technical review of existing solutions applying blockchain technology on IoMT. We analyze these studies, discuss the proposed architectures and how they managed the integration challenges. The open issues regarding the application of blockchain over IoMT are also specified.
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- 2020
41. Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?
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Dehuan Wan, Muhammad Imran, Hong-Ning Dai, Xuran Li, Bishenghui Tao, and Dengwang Li
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FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,J.3 ,Computer Networks and Communications ,Computer science ,Supply chain ,Interoperability ,Internet privacy ,02 engineering and technology ,C.2.4 ,Article ,Panacea (medicine) ,Computer Science - Computers and Society ,Blockchain ,Computers and Society (cs.CY) ,Health care ,Pandemic ,0202 electrical engineering, electronic engineering, information engineering ,Use case ,Internet of Medical Things ,Architecture ,business.industry ,COVID-19 ,020206 networking & telecommunications ,Computer Science Applications ,Computer Science - Distributed, Parallel, and Cluster Computing ,Hardware and Architecture ,Privacy ,Security ,020201 artificial intelligence & image processing ,The Internet ,Distributed, Parallel, and Cluster Computing (cs.DC) ,business ,Cryptography and Security (cs.CR) ,Software ,Information Systems - Abstract
The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area., 15 pages, 8 figures
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- 2022
42. Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers
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Mohamed Esmail Karar, Bandar Alotaibi, and Munif Alotaibi
- Subjects
Leukemia ,acute leukemia ,generative adversarial networks ,computer-aided diagnosis ,internet of medical things ,wireless microscopic imaging ,Artificial Intelligence ,Image Interpretation, Computer-Assisted ,Internet of Things ,Humans ,Electrical and Electronic Engineering ,Child ,Biochemistry ,Instrumentation ,Algorithms ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.
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- 2022
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43. CovidXplus-A New Mobile Application for Image-Guided Diagnosis of COVID-19 Patients
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Mohamed Esmail Karar and Bilal Ahmad
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Deep Learning ,COVID-19 ,Internet of Medical Things ,Computer-Aided Diagnosis - Abstract
Patients with unexplained pneumonia were discovered in Wuhan City, China, at the end of 2019, according to the World Health Organization (WHO). Chinese authorities announced on January 2020 that they discovered a new virus that causes these infections. That's why the virus was assigned the name of novel Coronavirus Disease 2019 (COVID-19). It is a new disease that affects the lungs and airways and can cause mild to severe illness, as well as pneumonia. Coronaviruses are a wide family of viruses that can infect both animals and humans. Extreme Acute Respiratory Syndrome 2 is the most recent Coronavirus to be discovered (SARS-CoV-2). According to the results, the virus spreads from person to person in close contact over a distance of about 2 meters. When someone coughs or sneezes, respiratory droplets are released, which spread the virus. As a result, we need to build an application that allows use of computer-aided diagnosis (CAD) systems for detection of the COVID-19 based on radiological techniques. This paper proposes a novel mobile application based on fine-tuned transfer learning models to boost the efficiency of CAD systems in the detection of the highly suspected COVID-19 patients using medical X-ray images. Three fine-tuned deep learning models, namely ResNet50, ResNet101, and ResNet152 are exploited in this study.
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- 2022
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44. Deep Learning Methods in the Medical Device Industry: An Overview
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Asif, Muhammad Mugees, Asif, Sana, Mubarik, Iqra, and Nasir, Shanza
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Artificial intelligence ,education ,Internet of medical things ,Deep learning ,Neural networks ,Medical device - Abstract
In this study, we conducted a literature evaluation that was separated into classes based on the domains in which deep learning methods are utilized in the medical device business, as well as an examination of the article distribution by year. Healthcare, big data and wearable technologies, biomedical signal, image processing, diagnostics, and Internet of medical things are among the six categories. As a result, deep learning approaches have gained traction in the medical device business in recent years, with the majority of studies focusing on diagnosis and image processing.
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- 2022
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45. IoT Health Data in Electronic Health Records (EHR):Security and Privacy Issues in Era of 6G
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Ana Koren and Ramjee Prasad
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Information Systems and Management ,EHR ,Computer Networks and Communications ,Management of Technology and Innovation ,healthcare ,Wearable sensors ,eHealth ,electronic health record ,internet of medical things ,internet of things ,Computer Science Applications ,Information Systems ,6G - Abstract
Millions of wearable devices with embedded sensors (e.g., fitness trackers) are present in daily lives of its users, with the number growing continuously, especially with the approaching 6G communication technology. These devices are helping their users in monitoring daily activities and promoting positive health habits. Potential integration of such collected data into central medical system would lead to more personalized healthcare and an improved patient-physician experience. However, this process is met with several challenges, as medical data is of a highly sensitive nature. This paper focuses on the security and privacy issues for such a process. After providing a comprehensive list of security and privacy threats relevant to data collection and its handling within a Central Health Information system, the paper addresses the challenges of designing a secure system and offeres recommendations, solutions and guidelines for identified pre-6G and 6G security and privacy issues.
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- 2022
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46. Octopus: A Novel Approach for Health Data Masking and Retrieving Using Physical Unclonable Functions and Machine Learning
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Sagar Satra, Pintu Kumar Sadhu, Venkata P. Yanambaka, and Ahmed Abdelgawad
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internet of medical things ,physical unclonable functions ,machine learning ,security and privacy ,Electrical and Electronic Engineering ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
Health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because devices are now linked to high-speed internet and mobile phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this paper, Octopus and Physically Unclonable Functions (PUFs) are used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
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- 2023
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47. Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning
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Mohammad (Behdad) Jamshidi, Omid Moztarzadeh, Alireza Jamshidi, Ahmed Abdelgawad, Ayman S. El-Baz, and Lukas Hauer
- Subjects
artificial intelligence ,big data ,deep learning ,drug discovery ,edge computing ,internet of things ,internet of medical things ,natural language processing ,Computer Networks and Communications - Abstract
The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine’s performance.
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- 2023
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48. Securing Access to Internet of Medical Things Using a Graphical-Password-Based User Authentication Scheme
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Mudassar Ali Khan, Ikram Ud Din, and Ahmad Almogren
- Subjects
Renewable Energy, Sustainability and the Environment ,Geography, Planning and Development ,authentication ,graphical passwords ,healthcare ,Internet of Medical Things ,security and usability ,Building and Construction ,Management, Monitoring, Policy and Law - Abstract
Digital healthcare services have seen significant growth in this decade and many new technologies have been thoroughly examined to provide efficient services through secure infrastructures. The Internet of Medical Things (IoMT) revitalizes a healthcare infrastructure by creating an interconnected, intelligent, accessible, and efficient network. While there have been many studies on possible device authentication techniques for the IoMT, there is still much work to be done in user authentication to provide sustainable IoT solutions. Graphical passwords, which use visual content such as images instead of traditional text-based passwords, can help users authenticate themselves. However, current schemes have limitations. Therefore, this paper proposes a novel graphical authentication scheme that uses multiple factors to register and authenticate users using simple arithmetic operations, machine learning for hand gesture recognition, and medical images for recall purposes. The proposed method is designed to keep the authentication process simple, memorable, and robust. To evaluate the proposed scheme, we use the Post-Study System Usability Questionnaire (PSSUQ) to compare it with PIN-based and pattern-based authentication techniques. While comparing treatment and comparison groups, system quality showed a 16.7% better score, information quality a 25% increase, interface quality a 40% increase, and overall quality showed a 25% increase. The proposed method successfully revitalizes the use of graphical passwords, specifically in the field of IoMT, by developing a user-friendly, satisfying, and robust authentication scheme.
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- 2023
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49. Federated Learning-Based Lightweight Two-Factor Authentication Framework with Privacy Preservation for Mobile Sink in the Social IoMT
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B. D. Deebak and Seong Oun Hwang
- Subjects
Computer Networks and Communications ,Hardware and Architecture ,Control and Systems Engineering ,Signal Processing ,Electrical and Electronic Engineering ,Internet of Medical Things ,eHealth ,two-factor authentication ,federated learning ,learning analysis ,device protection ,transmission efficiency - Abstract
The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches.
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- 2023
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50. Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer
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Yossra Hussain Ali, Varghese Sabu Chooralil, Karthikeyan Balasubramanian, Rajasekhar Reddy Manyam, Sekar Kidambi Raju, Ahmed T. Sadiq, and Alaa K. Farhan
- Subjects
convolutional neural networks ,deep learning ,Bioengineering ,lung cancer detection ,internet of medical things ,and particle swarm optimization - Abstract
Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.
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- 2023
- Full Text
- View/download PDF
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