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Titlebook: Microservices in Big Data Analytics; Second International Anil Chaudhary,Chothmal Choudhary,Tapas Badal Conference proceedings 2020 Springe

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书目名称Microservices in Big Data Analytics
副标题Second International
编辑Anil Chaudhary,Chothmal Choudhary,Tapas Badal
视频video
图书封面Titlebook: Microservices in Big Data Analytics; Second International Anil Chaudhary,Chothmal Choudhary,Tapas Badal Conference proceedings 2020 Springe
描述.These proceedings gather cutting-edge papers exploring the principles, techniques, and applications of Microservices in Big Data Analytics.. The ICETCE-2019 is the latest installment in a successful series of annual conferences that began in 2011. Every year since, it has significantly contributed to the research community in the form of numerous high-quality research papers. This year, the conference’s focus was on the highly relevant area of Microservices in Big Data Analytics..
出版日期Conference proceedings 2020
关键词Big Data; Deep Learning; Machine Learning; Microservice Architecture; Service Oriented Architecture; Visu
版次1
doihttps://doi.org/10.1007/978-981-15-0128-9
isbn_softcover978-981-15-0130-2
isbn_ebook978-981-15-0128-9
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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Improved DYMO-Based ACO for MANET Using Distance and Density of Nodes,onvention is contrasted and alternate conventions of its classification on the premise of different execution parameters. Result analysis shows that proposed protocol performs superior to the different existing protocols like AODV, TORA, DYMO, M-DYMO, and ACO.
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Comparison of Execution Time of Mobile Application Using Equal Division and Profile-Based Algorithme implementation of execution of the compute-intensive mobile application on the local mobile device and mobile ad hoc cloud and compares the execution time. This paper also compares the application execution time with the application which is distributed equally and based on the profile of mobile d
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Prediction of Underwater Surface Target Through SONAR: A Case Study of Machine Learning,, and AUC came out to be 0.92. With random forest algorithm, the results are further optimized by feature selection to get the accuracy of 90%. Assuring results are found, when the fulfillment of the designed groundwork is set side by side with the standard classifiers like SVM, random forest, etc.,
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Big Data Machine Learning Framework for Drug Toxicity Prediction,of 96.20%, the results are compared with standard machine learning models like random forest, AdaBoost, Naive Bayes, etc., and are found to be much better than these classifiers. With the increase in toxicity in environment, this framework will play a significant role in improving lifestyle.
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