Data Management, Analytics and Innovation

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Beschreibung

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 1719 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 Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia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.
Presents cutting-edge research in the fields of data management, analytics, and innovation Gathers the outcomes of ICDMAI 2020, held in New Delhi, India Offers a valuable reference resource for researchers and practitioners in academia and industry
Autorentext
Neha Sharma is working with Tata Consultancy Services and is a Founder Secretary, Society for Data Science, India. She has previously served as Director of ZIBACAR, Pune, India. Holding a Ph.D. from the prestigious Indian Institute of Technology, Dhanbad, she is a senior IEEE member and Executive Body member of the IEEE Pune Section. She has received a "Best PhD Thesis Award" and "Best Paper Presenter at International Conference Award" at the National Level from the Computer Society of India. Her research interests include data mining, database design, artificial intelligence, big data, cloud computing, blockchain and data science. Amlan Chakrabarti is a Full Professor at the School of IT, University of Calcutta. He was a Postdoctoral Fellow at Princeton University, USA, from 2011 to 2012. With nearly 20 years of experience in Engineering Education and Research, he is a recipient of the prestigious DST BOYSCAST Fellowship Award in Engineering Science (2011), JSPS Invitation Research Award (2016), Erasmus Mundus Leaders Award from the EU (2017), and a Hamied Visiting Professorship from the University of Cambridge (2018). He is an Associate Editor of the Journal of Computers and Electrical Engineering, senior member of the IEEE and ACM, IEEE Computer Society Distinguished Visitor, Distinguished Speaker of the ACM, Secretary of the IEEE CEDA India Chapter and Vice President of the Data Science Society. Prof. Valentina Emilia Balas is currently a Full Professor at the Department of Automatics and Applied Software, "Aurel Vlaicu" University of Arad, Romania. The author of more than 300 research papers, her research interests include intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling and simulation. She is the Editor-in-Chief of the Inderscience journals IJAIP and IJCSysE. She is the Director of the Department of International Relations and Head of the Intelligent Systems Research Centre at Aurel Vlaicu University of Arad. Dr. Jan Martinovic is currently Head of the Advanced Data Analysis and Simulation Lab at IT4Innovations National Supercomputing Center, VSB-TUO, Czech Republic. His research activities focus on HCP, cloud and big data convergence, HPC-as-a-Service, traffic management and data analysis. He is the coordinator of the H2020 ICT EU-funded project LEXIS (www.lexis-project.eu). He has previously coordinated contracted research activities with international and national companies and been involved in the H2020 projects ANTAREX and ExCAPE. He has been a HiPEAC member since January 2020.
Inhalt
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.