A Study Towards Recent Trends, Issues and Research Challenges of Intelligent IoT Healthcare Techniques: IoMT and CIoMT

Verma, Garima; Shahi, Aditya Pratap; Prakash, Shiva

doi:10.1007/978-981-16-8826-3_16

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…However, their implementation also comes with challenges, including high costs, complexity, ethical considerations, integration issues, limited autonomy, patient acceptance, security risks, job impact, and accessibility. Addressing these challenges requires collaboration and careful consideration of ethical, legal, and social implications to ensure medical robots' responsible and effective integration into healthcare settings [12].…”

Section: Healthcare Fieldmentioning

confidence: 99%

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Abed¹,

Al-Doori²,

Abdullah³

et al. 2023

IJSSE

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The recent digital revolution has resulted in robots being integrated more than ever into various domains, such as agriculture, healthcare, and the military. Robots are dedicated to serving, facilitating, and improving human life. However, the growing prevalence of robotics has brought to light the need for robust security measures. While unintentional accidents are inevitable, this paper focuses on the increasingly challenging problem of malicious cyber-attacks against robotic systems. One specific incident highlighting the severity of this issue occurred in 2022 when a healthcare robot was hacked, resulting in the misadministration of medication to several patients, leading to severe health complications and unnecessary loss of human lives. Such instances underscore the urgency of understanding the robotics domain's security vulnerabilities, threats, and consequences. In conclusion, this paper highlights the critical aspects of securing robotic systems in today's technologically advanced world. By identifying and analyzing the primary security vulnerabilities, this paper examines the primary security vulnerabilities, the type of application, then the impact of vulnerabilities; we can pave the way for effective security measures and ultimately ensure the safety and reliability of robotic systems.

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Section: Healthcare Fieldmentioning

confidence: 99%

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Abed¹,

Al-Doori²,

Abdullah³

et al. 2023

IJSSE

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“…The Heart Failure Clinical Records of UCI Repository dataset are utilized to determine the occurrence of heart disease in the general population [9]. These systems usually monitor parameters [10] [11] like blood pressure, blood sugar, body temperature, heart rate, and so on [12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Maurya

Prakash

2023

Preprint

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Nowadays, heart disease is considered to be the main cause of sickness. Since the majority of people are unaware of their own kind and severity of heart disease, heart disease is now a significant problem that affects people of all ages. On the other hand, manual approach of prediction is challenging and often requires the capability to choose the relevant approach. To resolve these issues, various machine-learning models are playing a vital role in automatic disease prediction in medical field. In this study, we have calculated and made a comparison of accuracy of various machine learning models such as SVM, KNN, Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, AdaBoost, Extra Tree Classifier and Gradient Boosting for prediction of heart disease using UCI repository dataset for training and testing of models. Among all the models used, the highest accuracy of 95.08% obtained by the Gradient Boosting model The major aim of the paper is to get a reliable, computationally effective machine learning algorithm for heart disease prediction.

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“…Patients can communicate with their healthcare providers over long distances using telemedicine, receive care in the comfort of their own homes while having their health continuously monitored, and allow doctors to monitor the hospitalization process while providing suggestions to patients and their doctors. Today, the most promising technology in the business and medical industries is IoT (Verma et al, 2022). One of the goals of IoT is to enable, share, and gather data anonymously from physical and intelligent devices, such as home appliances, cars, and other physical objects (Gaur & Prakash, 2021).…”

Section: Introductionmentioning

confidence: 99%

Smart Contract-Based Secure Decentralized Smart Healthcare System

Raj

Prakash

2022

International Journal of Software Innovation

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Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.

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A Study Towards Recent Trends, Issues and Research Challenges of Intelligent IoT Healthcare Techniques: IoMT and CIoMT

Cited by 6 publications

References 27 publications

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Smart Contract-Based Secure Decentralized Smart Healthcare System

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