Deep LearningModel for Big Data Classification in Apache Spark Environment
作者机构：Department of Computer Science and EngineeringK.Ramakrishnan College of EngineeringTrichyTamilnadu620009India Department of Information and Communication EngineeringAnna UniversityChennaiTamilnadu600025India Department of Electronics and Instrumentation EngineeringKongu Engineering CollegeErodeTamilnadu638060India Department of Computer Science and EngineeringM.Kumarasamy College of EngineeringKarurTamilnadu639113India Department of Electronics and Communication EngineeringK.Ramakrishnan College of EngineeringTrichyTamilnadu620009India
出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)
年 卷 期：2023年第37卷第9期
页 面：2537-2547页
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)]
主 题：Big data apache spark classification feature selection gated recurrent unit adam optimizer
摘 要：Big data analytics is a popular research topic due to its applicability in various real time *** recent advent of machine learning and deep learning models can be applied to analyze big data with better *** big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification *** study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache *** SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of *** addition,OGRU based classification model is employed to classify the big data into appropriate ***,the hyperparameter tuning of the GRU model takes place using Adam ***,the Apache Spark platform is applied for processing big data in an effective *** order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.
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