Advances in Machine Learning for Big Data Analysis: Intelligent Systems Reference Library, cartea 218
Editat de Satchidananda Dehuri, Yen-Wei Chenen Limba Engleză Hardback – 25 feb 2022
In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
| Toate formatele și edițiile | Preț | Express |
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| Paperback (1) | 1132.50 lei 43-57 zile | |
| Springer Nature Singapore – 26 feb 2023 | 1132.50 lei 43-57 zile | |
| Hardback (1) | 1138.66 lei 43-57 zile | |
| Springer Nature Singapore – 25 feb 2022 | 1138.66 lei 43-57 zile |
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Specificații
ISBN-13: 9789811689291
ISBN-10: 9811689296
Pagini: 239
Ilustrații: XIX, 239 p. 97 illus., 72 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.54 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Seria Intelligent Systems Reference Library
Locul publicării:Singapore, Singapore
ISBN-10: 9811689296
Pagini: 239
Ilustrații: XIX, 239 p. 97 illus., 72 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.54 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Seria Intelligent Systems Reference Library
Locul publicării:Singapore, Singapore
Cuprins
Deep Learning for Supervised Learning.- Deep Learning for Unsupervised Learning.- Support Vector Machine for Regression.- Support Vector Machine for Classification.- Decision Tree for Regression.- Higher Order Neural Networks.- Competitive Learning.- Semi-supervised Learning.- Multi-objective Optimization Techniques.- Techniques for Feature Selection/Extraction.- Techniques for Task Relevant Big Data Analysis.- Techniques for Post Processing Task in Big Data Analysis.- Customer Relationship Management.
Notă biografică
Dr. Satchidananda Dehuri is working as Professor in the Department of Information and Communication Technology, Fakir Mohan University, Balasore, Odisha, India, since 2013. He received his M.Tech. and Ph.D. degrees in Computer Science from Utkal University, Vani Vihar, Odisha, in 2001 and 2006, respectively. He visited as a BOYSCAST Fellow to the Soft Computing Laboratory, Yonsei University, Seoul, South Korea, under the BOYSCAST Fellowship Program of DST, Govt. of India, in 2008. In 2010, he received Young Scientist Award in Engineering and Technology for the year 2008 from Odisha Vigyan Academy, Department of Science and Technology, Govt. of Odisha. He was at the Center for Theoretical Studies, Indian Institute of Technology Kharagpur, as Visiting Scholar in 2002. During May–June 2006, he was Visiting Scientist at the Center for Soft Computing Research, Indian Statistical Institute, Kolkata. His research interests include evolutionary computation, neural networks, pattern recognition, data mining, object-oriented programming and its Applications and bioinformatics. He has already published about 200 research papers in reputed journals and referred conferences, has published five text books for undergraduate and postgraduate students, and edited more than ten books of contemporary relevance. Under his direct supervision, 17 Ph.D. scholars have been successfully awarded, two scholars have submitted their thesis, and two more are pursuing their Ph.D. work. In addition, he has successfully guided two postdoctoral scholars during the stay at Ajou University, South Korea, as Associate Professor in the Department of System Engineering for 02 years. He has completed three different research projects obtained from DST, UGC, and DRDO. His h-index is 25; i10-index is 56 and has 2379 citations (Google Scholar). As a part of academic collaboration, he has visited Ireland, New Zealand, Hong Kong, France, and Nepal. He has also successfully performed many administrative duties like: i) Head of the Department of Information and Communication Technology, Fakir Mohan University, in two different periods like June 2010 to May 31, 2012 and June 2014 to May 2017 and enjoying the third term from 1st June 2021; ii) Director of Self-Financing Courses (SFC), Fakir Mohan University, Balasore, from August 2014 to May 2015. Five Programs were running during his directorship: P.G. Odia, M. Phil. Odia, P.G. English, PMIR, IMBA, M. Phil. History; iii) Director of Center of Distance and Continuing Education, Fakir Mohan University, from July 30, 2011, to August 28, 2012; iv) Director of Software Development Cell of Fakir Mohan University, from January 2007 to August 28, 2012, and v) Director of the Center of Instrumentation and Maintenance Facility, Fakir Mohan University, from March 2007 to March 2008 and from March 2009 to August 28, 2010; v) From December 31, 2017, to May 31, 2019, he has successfully completed the tenure of Controller of Examinations of Fakir Mohan University in addition to his normal duties; vi) He has also successfully completed the term of Chairman, Post Graduate Council from 1st June 2019 to 31st May 2021.
Prof. Yen-Wei Chen received his B.E. degree in 1985 from Kobe University, Kobe, Japan. He received his M.E. degree in 1987, and his D.E. degree in 1990, both from Osaka University, Osaka, Japan. He was Research Fellow at the Institute of Laser Technology, Osaka, from 1991 to 1994. From October 1994 to March 2004, he was Associate Professor and Professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently Professor at the College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan. He was Visiting Scholar at Oxford University, Oxford, UK, in 2003 and at Pennsylvania State University, Pennsylvania, USA, in 2010. His research interests include applications of machine learning, medical image analysis, and pattern recognition. He has published more than 300 research papers. He has received many distinguished awards including Best Scientific Paper Award of ICPR2013 and Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science. He is Principal Investigator of several research projects, funded by the Japanese Government.
Prof. Yen-Wei Chen received his B.E. degree in 1985 from Kobe University, Kobe, Japan. He received his M.E. degree in 1987, and his D.E. degree in 1990, both from Osaka University, Osaka, Japan. He was Research Fellow at the Institute of Laser Technology, Osaka, from 1991 to 1994. From October 1994 to March 2004, he was Associate Professor and Professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently Professor at the College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan. He was Visiting Scholar at Oxford University, Oxford, UK, in 2003 and at Pennsylvania State University, Pennsylvania, USA, in 2010. His research interests include applications of machine learning, medical image analysis, and pattern recognition. He has published more than 300 research papers. He has received many distinguished awards including Best Scientific Paper Award of ICPR2013 and Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science. He is Principal Investigator of several research projects, funded by the Japanese Government.
Textul de pe ultima copertă
This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems.
In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
Caracteristici
Focuses on research aspects of ensemble approaches of machine learning techniques Serves as a resource for various advances in the field of machine learning and data science Offers a reference guide for researchers and practitioners in academia and industry