Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

作     者：Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Mohammed Rizwanullah 

作者机构：Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial EngineeringCollege of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information SystemsCollege of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research CentreFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information SystemCollege of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia 

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

年 卷 期：2022年第73卷第11期

页      面：3039-3055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] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43) Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network 

摘      要：Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and *** CSVM modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.