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

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发表于 2025-3-21 17:25:13 | 显示全部楼层 |阅读模式
书目名称Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
编辑Aboul Ella Hassanien,Ashraf Darwish
视频video
概述Presents 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
丛书名称Studies in Big Data
图书封面Titlebook: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges; Aboul Ella Hassanien,Ashraf Darwish Book 2021 Th
描述.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 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. .
出版日期Book 2021
关键词Machine Learning and Data Mining Applications; Deep Learning Techniques and applications; Deep Learnin
版次1
doihttps://doi.org/10.1007/978-3-030-59338-4
isbn_softcover978-3-030-59340-7
isbn_ebook978-3-030-59338-4Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Bio-inspired Machine Learning Mechanism for Detecting Malicious URL Through Passive DNS in Big Data tial or full system control to the attackers. To overcome these issues, researchers have applied machine learning techniques for malicious URL detection. However, these techniques fall to identify distinguishable generic features that are able to define the maliciousness of a given domain. Generally
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Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques andes have shown the demand for the recommender systems and their growing place in our lives. More steps deeper, we noticed that the severity of the quality and accuracy of these recommendation systems is very high to match users with same interests. For that reason and for being in competitive positio
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