Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019.Due to the similarity in initial symptoms with vi...Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019.Due to the similarity in initial symptoms with viral fever,it is challenging to identify this virus initially.Non-detection of this virus at the early stage results in the death of the patient.Developing and densely populated countries face a scarcity of resources like hospitals,ventilators,oxygen,and healthcare workers.Technologies like the Internet of Things(IoT)and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage.To minimize the spread of the pandemic,IoT-enabled devices can be used to collect patient’s data remotely in a secure manner.Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus.In this work,the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot,IoT,and deep learning technology.In phase one,an artificially assisted chatbot can guide an individual by asking about some common symptoms.In case of detection of even a single sign,the second phase of diagnosis can be considered,consisting of using a thermal scanner and pulse oximeter.In case of high temperature and low oxygen saturation levels,the third phase of diagnosis will be recommended,where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body.The proposed model reduces human intervention through chatbot-based initial screening,sensor-based IoT devices,and deep learning-based X-ray analysis.It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.展开更多
Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and pati...Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare management.However,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator end.In this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable attacks.Initially,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)tree.The authentications prevent unauthorized users from accessing or misuse the data.After that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the attackers.Finally,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health status.The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT.Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.展开更多
Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing va...Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing values in medical datasets.Imputation methods are used to predict these missing values.In this paper,a new imputation method called hybrid imputation optimized by the classifier(HIOC)is proposed to predict missing values efficiently.Design/methodology/approach-The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations(MICE),K nearest neighbor(KNN),mean and mode imputation methods in an optimum way.Performance of HIOC has been compared to MICE,KNN,and mean and mode methods.Four classifiers support vector machine(SVM),naive Bayes(NB),random forest(RF)and decision tree(DT)have been used to evaluate the performance of imputation methods.Findings-The results show that HIOC performed efficiently even with a high rate of missing values.It had reduced root mean square error(RMSE)up to 17.32%in the heart disease dataset and 34.73%in the breast cancer dataset.Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases.It increased classification accuracy up to 18.61%in the heart disease dataset and 6.20%in the breast cancer dataset.Originality/value-The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.展开更多
文摘Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019.Due to the similarity in initial symptoms with viral fever,it is challenging to identify this virus initially.Non-detection of this virus at the early stage results in the death of the patient.Developing and densely populated countries face a scarcity of resources like hospitals,ventilators,oxygen,and healthcare workers.Technologies like the Internet of Things(IoT)and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage.To minimize the spread of the pandemic,IoT-enabled devices can be used to collect patient’s data remotely in a secure manner.Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus.In this work,the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot,IoT,and deep learning technology.In phase one,an artificially assisted chatbot can guide an individual by asking about some common symptoms.In case of detection of even a single sign,the second phase of diagnosis can be considered,consisting of using a thermal scanner and pulse oximeter.In case of high temperature and low oxygen saturation levels,the third phase of diagnosis will be recommended,where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body.The proposed model reduces human intervention through chatbot-based initial screening,sensor-based IoT devices,and deep learning-based X-ray analysis.It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.
基金This project has been funded by the Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number BA-2105.
文摘Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare management.However,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator end.In this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable attacks.Initially,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)tree.The authentications prevent unauthorized users from accessing or misuse the data.After that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the attackers.Finally,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health status.The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT.Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.
文摘Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing values in medical datasets.Imputation methods are used to predict these missing values.In this paper,a new imputation method called hybrid imputation optimized by the classifier(HIOC)is proposed to predict missing values efficiently.Design/methodology/approach-The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations(MICE),K nearest neighbor(KNN),mean and mode imputation methods in an optimum way.Performance of HIOC has been compared to MICE,KNN,and mean and mode methods.Four classifiers support vector machine(SVM),naive Bayes(NB),random forest(RF)and decision tree(DT)have been used to evaluate the performance of imputation methods.Findings-The results show that HIOC performed efficiently even with a high rate of missing values.It had reduced root mean square error(RMSE)up to 17.32%in the heart disease dataset and 34.73%in the breast cancer dataset.Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases.It increased classification accuracy up to 18.61%in the heart disease dataset and 6.20%in the breast cancer dataset.Originality/value-The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.
