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Applications of Machine Learning de Prashant Johri

Applications of Machine Learning: Algorithms for Intelligent Systems

Editat de Prashant Johri, Jitendra Kumar Verma, Sudip Paul
en Limba Engleză Hardback – 5 mai 2020
This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.
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Specificații

ISBN-13: 9789811533563
ISBN-10: 9811533563
Ilustrații: XXII, 394 p. 158 illus., 112 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.76 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seria Algorithms for Intelligent Systems

Locul publicării:Singapore, Singapore

Cuprins

Statistical Learning Process for the Reduction of Sample Collection Assuring a Desired Level of Confidence.- Sentiment Analysis on Google Play Store Data using Deep Learning.- Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping.- Tracking an Object using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter.- Transfer Learning and Domain Adaptation for Named Entity Recognition.- Knowledge Graph from Informal Text: Architecture, Components, Algorithms and Applications.- Neighborhood-based Collaborative Recommendations: An Introduction.- Classification of Arabic Texts Using Singular Value Decomposition and Fuzzy C-Means Algorithms.- Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System.- Melody Extraction from Music: A Comprehensive Study.- Comparative Analysis of Combined Gas Turbine-Steam Turbine Power Cycle Performance by Using Entropy Generation and Statistical Methodology.- Data Mining - A Tool for Handling Huge Voluminous Data.- Improved Training Pattern in Back Propagation Neural Networks Using Holt-Winters' Seasonal Method and Gradient Boosting Model.- Ensemble of Multi-headed Machine Learning Architectures for Time-series Forecasting of Healthcare Expenditures.- Applying Soft Computing Approaches To Investigate Software Fault Proneness in Agile Software Development Environment.

Notă biografică

Dr. Prashant Johri is a Professor at the School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He received his MCA from Aligarh Muslim University and Ph.D. in Computer Science from Jiwaji University, Gwalior, India. He has also worked as a Professor and Director (MCA), Noida Institute of Engineering and Technology, (NIET). His research interests include big data, data analytics, data retrieval and predictive analytics, information security, privacy protection, big data open platforms, etc. He is actively publishing in these areas.

Dr. Jitendra Kumar Verma is Assistant Professor (Grade III) of Computer Science & Engineering at Amity School of Engineering & Technology, Amity University Haryana, Gurugram (Manesar), India. He received the degree of Ph.D. from Jawaharlal Nehru University (JNU), New Delhi, India in 2017, degree of M.Tech from JNU in 2013 and degree of B.Tech in Computer Science & Engineering from Kamla Nehru Institute of Technology (KNIT), Sultanpur, Uttar Pradesh, India in 2008. Dr. Verma is awardee of prestigious DAAD "A new Passage to India" Fellowship (2015-16) funded by Federal Ministry of Education and Research - BMBF, Germany and German Academic Exchange Service (DAAD). He worked at JULIUS-MAXIMILIAN UNIVERSITY OF WÜRZBURG, GERMANY (mother of 14 Nobel Laureate) as a Visiting Research Scholar. Dr. Verma is member of several technical societies e.g. IEEE, IEEE IAS, and ACM. Over his short career, he published several research papers in proceedings of various international conferences and peer-reviewed International Journals of repute. He also contributed numerous book chapters to the several books published with publishers of high international repute. Apart from scholarly contribution towards scientific community, he organized several Conferences/Workshops/Seminars at the national and international levels. He voluntarily served as reviewer for various International Journals, conferences, and workshops. He also served as Guest Editor and Editorial Board Member of numerous international journals.  His research interest includes cloud computing, Mobile cloud, Machine learning, AR & VR, Soft computing, Fuzzy systems, Healthcare, Pattern recognition, Bio-inspired phenomena, and advanced optimization model & computation.

Dr. Sudip Paul is an Assistant Professor at the Department of Biomedical Engineering, School of Technology, North-Eastern Hill University (NEHU), Shillong, India. He received his Ph.D. from the Indian Institute of Technology (Banaras Hindu University), Varanasi, with a specialization in Electrophysiology and Brain Signal Analysis. He was selected as a Postdoc Fellow in 2017–18 under the Biotechnology Overseas Associateship for scientists working in the Northeastern States of India, supported by the Department of Biotechnology, Government of India. Dr. Sudip has published morethan 90 international journal and conference papers and has filed four patents. Recently, he completed three book projects and is currently serving as Editor for a further two. Dr. Sudip is a member of numerous societies and professional bodies, e.g. the APSN, ISN, IBRO, SNCI, SfN, and IEEE. He received First Prize in the Sushruta Innovation Award 2011, sponsored by the Department of Science and Technology, Government of India, and various other awards, including a World Federation of Neurology (WFN) Travelling Fellowship, Young Investigator Award, and IBRO and ISN Travel Awards. Dr. Sudip has also served as an editorial board member for a variety of international journals.

Textul de pe ultima copertă

This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Caracteristici

Discusses the applications of machine learning in artificial intelligence A valuable resource for researchers in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics Covers human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences