Titlebook: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges; Aboul Ella Hassanien,Ashraf Darwish Book 2021 Th

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2197-6503 ne Learning.Exhibits various technologies to create systems .This book is intended to present the state of the art in research on machine learning and big data analytics.?The accepted chapters?covered many themes including? artificial intelligence and data mining applications,?machine learning and a

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Predicting Student Retention Among a Homogeneous Population Using Data Mining to reduce the performance bias of machine learning algorithms. Our study reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. The effectiveness of the algorithms further increases with the use of college performance data.

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges978-3-030-59338-4Series ISSN 2197-6503 Series E-ISSN 2197-6511

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Aboul Ella Hassanien,Ashraf DarwishPresents recent research in Machine Learning and Big Data Analytics.Provides an Analysis, Applications, and Challenges of Big Data and Machine Learning.Exhibits various technologies to create systems

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Book 2021es including? artificial intelligence and data mining applications,?machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications. .

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