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Titlebook: Machine Learning, Image Processing, Network Security and Data Sciences; Select Proceedings o Rajesh Doriya,Badal Soni,Xiao-Zhi Gao Conferen

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书目名称Machine Learning, Image Processing, Network Security and Data Sciences
副标题Select Proceedings o
编辑Rajesh Doriya,Badal Soni,Xiao-Zhi Gao
视频videohttp://file.papertrans.cn/621/620729/620729.mp4
概述Includes outstanding research papers presented at MIND 2021.Discusses new findings in big data, machine learning, and computational intelligence.Serves as a reference resource for researchers and prac
丛书名称Lecture Notes in Electrical Engineering
图书封面Titlebook: Machine Learning, Image Processing, Network Security and Data Sciences; Select Proceedings o Rajesh Doriya,Badal Soni,Xiao-Zhi Gao Conferen
描述.This book constitutes the refereed proceedings of the Third International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cybersecurity. This book aims to develop an understanding of image processing, networks, and data modeling by using various machine learning algorithms for a wide range of real-world applications. In addition to providing basic principles of data processing, this book teaches standard models and algorithms for data and image analysis. .
出版日期Conference proceedings 2023
关键词Machine Learning; Image Processing; Computer Security; Data Science; MIND Proceedings; Artificial Intelli
版次1
doihttps://doi.org/10.1007/978-981-19-5868-7
isbn_softcover978-981-19-5870-0
isbn_ebook978-981-19-5868-7Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Machine Learning, Image Processing, Network Security and Data Sciences978-981-19-5868-7Series ISSN 1876-1100 Series E-ISSN 1876-1119
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Lecture Notes in Electrical Engineeringhttp://image.papertrans.cn/m/image/620729.jpg
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Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methodsrning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experime
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Prediction of Polycystic Ovarian Syndrome Using Machine Learning Techniquesis condition leads to hormonal and metabolic imbalance that affects the overall health and appearance of the patient. The existing PCOS predictors use features that have medical connotations associated with them, these features are costly and time consuming to procure. The aim of this paper is to el
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