Applications of cognitive internet of medical things in modern healthcare

Jabbar, M. A.; Shandilya, Shishir Kumar; Kumar, Ajit; Shandilya, Smita

doi:10.1016/j.compeleceng.2022.108276

Cited by 20 publications

(6 citation statements)

References 24 publications

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“…The mist computing model outlined in Section 4 is responsible for processing data pertaining to heart patients and gives back prediction results by employing the intelligent DQN algorithm that aid in determining the prevalence of heart diseases, along with the veracity of the claim. Following the work in reference [1], the present study uses the Cleveland heart disease dataset comprising of 14 attributes and observations from 303 records. The 14 attributes account for 13 features and a target attribute which suggests prevalence of heart diseases in the patients' records i.e., 1 denoting heart disease and 0 representing no heart disease.…”

Section: B Resultsmentioning

confidence: 99%

“…The development of cutting-edge means of communication and storage, the medical, agricultural, and other fields have significantly benefited from incorporating smart gadgets into everyday life [1]. Because of the tremendous progress made in digital technology over the past few years, the Internet of Things (IoT) has had a significant influence on our everyday life.…”

Section: Introductionmentioning

confidence: 99%

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DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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The prevalence of heart disease has remained a major cause of mortalities across the world and has been challenging for healthcare providers to detect early symptoms of cardiac patients. To this end, several conventional machine learning models have gained popularity in providing precise prediction of heart diseases by taking into account the underlying conditions of patients. The drawbacks associated with these methods are a lack of generalization and the convergence rate of these methods being much slower. As the healthcare data associated with these systems scale up leading to healthcare big data issues, a Cloud-Fog computing-based paradigm is necessary to facilitate low-latency and energy-efficient computation of the healthcare data. In this paper, a DeepMist framework is suggested which exploits Deep Learning models operating over Mist Computing infrastructure to leverage fast predictive convergence, low-latency, and energy efficiency for smart healthcare systems. We exploit the Deep Q Network (DQN) algorithm for building the prediction model for identifying heart diseases over the Mist computing layer. Different performance evaluation metrics, like precision, recall, f-measure, accuracy, energy consumption, and delay, are used to assess the proposed DeepMist framework. It provided an overall prediction accuracy of 97.6714 % and loss value of 0.3841, along with energy consumption and delay of 32.1002 mJ and 2.8002 ms respectively. To validate the efficacy of DeepMist, we compare its outcomes over the heart disease dataset in convergence with other benchmark models like Q-Reinforcement Learning (QRL) and Deep Reinforcement Learning (DRL) algorithms and observe that the proposed scheme outperforms all others.

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Section: B Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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“…SS plays a crucial role in enhancing healthcare applications across various scenarios by ensuring reliable and efficient wireless communication. Here are different scenarios where spectrum sensing benefits healthcare [7][8][9] :…”

Section: Motivationmentioning

confidence: 99%

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Kumar,

Kaur,

Gaur

et al. 2024

F1000Res

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Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

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“…An efficient and successful smart healthcare system can be designed by incorporating machine learning and cognitive radio with it [35,36]. Smart healthcare devices and systems must be spectral and energy efficient while they are assisted by cognitive radio [37,38].…”

Section: Related Work: Smart Healthcare Using Machine Learning and Co...mentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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Applications of cognitive internet of medical things in modern healthcare

Cited by 20 publications

References 24 publications

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

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