Ensemble Deep Learning for IoT Based COVID 19 Health Care Pollution Monitor  

作　　者：Nithya Rekha Sivakumar 

机构地区：[1]Department of Computer Sciences,College of Computer and Information Sciences,Princess Nourah Bint Abdulrahman University,Riyadh,11671,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2383-2398,共16页智能自动化与软计算（英文）

基　　金：supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R194);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：Internet of things(IoT)has brought a greater transformation in health-care sector thereby improving patient care,minimizing treatment costs.The pre-sent method employs classical mechanisms for extracting features and a regression model for prediction.These methods have failed to consider the pollu-tion aspects involved during COVID 19 prediction.Utilizing Ensemble Deep Learning and Framingham Feature Extraction(FFE)techniques,a smart health-care system is introduced for COVID-19 pandemic disease diagnosis.The Col-lected feature or data via predictive mechanisms to form pollution maps.Those maps are used to implement real-time countermeasures,such as storing the extracted data or feature in a Cloud server to minimize concentrations of air pol-lutants.Once integrated with patient management systems,this solution would minimize pollution emitted via patient’s sensors by offering spaces in the cloud server when pollution thresholds are reached.Second,the Gini Index factor infor-mation gain technique eliminates unimportant and redundant attributes while selecting the most relevant,reducing computing overhead and optimizing system performance.Finally,the COVID-19 disease prognosis ensemble deep learning-based classifier is constructed.Experimental analysis is planned to measure the prediction accuracy,error,precision and recall for different numbers of patients.Experimental results show that prediction accuracy is improved by 8%,error rate was reduced by 47%and prediction time is minimized by 36%compared to exist-ing methods.

关 键 词：Internet of Things Covid-19 ensemble deep learning framingham feature extraction 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]