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Data Management, Analytics and Innovation : Proceedings of ICDMAI 2020, Volume 2 / edited by Neha Sharma, Amlan Chakrabarti, Valentina Emilia Balas, Jan Martinovic.
Format
Book
Language
English
Εdition
1st ed. 2021.
Published/Created
Springer Singapore 2021
Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
Description
1 online resource (XII, 462 p. 225 illus., 171 illus. in color.)
Availability
Available Online
Springer Nature - Springer Intelligent Technologies and Robotics eBooks 2021 English International
Details
Subject(s)
Computational intelligence
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Telecommunication
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Big data
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Artificial intelligence
—
Data processing
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Artificial intelligence
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Editor
Sharma, Neha
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Series
Advances in Intelligent Systems and Computing, 1175
[More in this series]
Advances in Intelligent Systems and Computing, 2194-5365 ; 1175
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Summary note
This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. .
Bibliographic references
Includes bibliographical references.
Contents
Automatic Standardization of Data based on Machine Learning and Natural Language Processing
Scoring Algorithm Identifying Anomalous Behavior in Enterprise Network.-APPLICATION OF BAYESIAN AUTOMATED HYPERPARAMETER TUNING ON CLASSIFIERS PREDICTING CUSTOMER RETENTION IN BANKING INDUSTRY
Quantum Machine Learning: A Review and Current Status
Survey of Transfer Learning and a Case Study of Emotion Recognition using Inductive Approach
An Efficient Algorithm for Complete Linkage Clustering with a Merging Threshold.
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ISBN
981-15-5619-9
Doi
10.1007/978-981-15-5619-7
Statement on responsible collection description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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