Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

作     者：Wafaa Alsaggaf Felwa Abukhodair Amani Tariq Jamal Sayed Abdel-Khalek Romany F.Mansour 

作者机构：Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer ScienceFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics and StatisticsCollege of ScienceTaif UniversityTaif21944Saudi Arabia Department of MathematicsFaculty of ScienceNew Valley UniversityEl-Kharga72511Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：1189-1203页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research work was funded by Institutional Fund Projects under grant no(IFPHI-050-611-2020) Therefore,authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University,Jeddah,Saudi Arabia 

主　　题：Intelligent models healthcare systems disease diagnosis internet of things cloud computing clustering deep learning 

摘      要：In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly *** use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud ***,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT *** the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision *** this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC *** proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease *** addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic *** COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN *** order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI *** experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.