Trends in Deep Learning Methodologies : Algorithms, Applications, and Systems
Language: English
Published by Elsevier Inc, 2020
Series: Book 3 of 3 - Hybrid Computational Intelligence for Pattern Analysis and Understanding
- Softcover
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nach der Bestellung gedruckt Neuware - Printed after ordering - Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.…
Seller Inventory # 9780128222263
- Title
- Trends in Deep Learning Methodologies : Algorithms, Applications, and Systems
- Author
- Vincenzo Piuri
- Publisher
- Elsevier Inc
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128222263
- ISBN 13
- 9780128222263
- Item weight
- 415 grams
- Dimensions
- 229x152x17 mm
- Series
- Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and Understanding
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more.
In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
- Provides insights into the theory, algorithms, implementation and the application of deep learning techniques
- Covers a wide range of applications of deep learning across smart healthcare and smart engineering
- Investigates the development of new models and how they can be exploited to find appropriate solutions
"Synopsis" may belong to another edition of this title.
About the Author
His main research interests include: artificial intelligence, computational intelligence, intelligent systems, machine learning, pattern analysis and recognition, signal and image processing, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, dependability, and cloud computing infrastructures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society.
He is Editor-in-Chief of the IEEE Systems Journal (2013-19), and Associate Editor of the IEEE Transactions on Cloud Computing and IEEE Access, and has been Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.
Sandeep Raj has been an Assistant Professor with the Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Sabour, India since 2018. Prior to this position (2012 - 20180, he was a was a Visiting Faculty with the National Institute of Technology Patna, India. His current research interests include digital signal processing, biomedical engineering, machine learning, internet-of-things (IoT), embedded systems design, and fabrication. He received the B. Tech. degree in Electrical and Electronics engineering from Allahabad Agricultural Institute – Deemed University, Allahabad, India, in 2009, the M. Tech. degree in electrical engineering (Gold-Medalist) and received DST INSPIRE Fellowship for pursuing the Ph.D. degree in Electrical Engineering from Indian Institute of Technology Patna, Bihta, India, 2018.
He is a member of IEEE and has published more than 11 SCI/Scopus journal articles, 5 conference papers and 1 book chapter. He is serving as a reviewer for several journals including IEEE Transactions on Industrial Electronics, IEEE Journal of Biomedical and Health Informatics, IEEE Signal Processing letters, IEEE Transactions on Instrumentation and Measurement, IEEE Access, Computer Methods and Programs in Biomedicine – (Elsevier), Computers in Biology and Medicine – (Elsevier), Australasian Physical & Engineering Sciences in Medicine – (Springer), Journal of King Saud University - Computer and Information Sciences - (Elsevier), Biomedical Engineering: Applications, Basis and Communications (BME), IETE Journal of Research.
Angelo Genovese received B.Sc., M.Sc., and Ph.D. degrees in Computer Science in 2007, 2010, and 2014 respectively, from Università degli Studi di Milano, Italy. From 2014 to 2019, he was a Postdoctoral Research Fellow and since 2015 he is a member of the Industrial, Environmental, and Biometric Informatics Laboratory (IEBIL) at the Università degli Studi di Milano, Italy. From June to August 2017, he was a Visiting Researcher at the University of Toronto, ON, Canada. Since 2019, he is Assistant Professor at Università degli Studi di Milano, Italy, Department of Computer Science.
His research interests include signal and image processing, three-dimensional reconstruction, computational intelligence technologies, and design methodologies and algorithms for self-adapting systems, applied to industrial and environmental monitoring systems and biometric recognition. In the biometrics field, his focuses are on highly usable touch-based and touchless fingerprint and palmprint recognition, as well as recognition based on soft biometric traits.
He is an Associate Editor of the Journal of Ambient Intelligence and Humanized Computing and Array. He has served as Program Chair/Co-Chair for the 2019 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2019), the 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS 2018), the 2018 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018), and the 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS 2017). He is a Member of the IEEE, the IEEE Biometrics Council, the IEEE Computational Intelligence Society, the IEEE Italy Section Systems Council Chapter, and the GRIN (Gruppo di Informatica).
Rajshree Srivastava is an Assistant Professor at DIT University Dehradun in the department of Computer Science and Engineering. She has completed her M. Tech. from JIIT Noida in CSE-IS, B. Tech. from RTU in Computer Science and Engineering. She is a life time member of (IEAE), a member of IEEE, CSI, ACM, ACM-W, IAENG, Internet of Things. Her area of research is in machine learning, big data, biomedical, privacy security. She has published book chapters; Scopus indexed papers and many in IEEE/Springer Conferences. Currently she is also session chair holder of PDGC 2018, ICETIT 2019. Reviewer of the Journal entitled International Journal of Handheld Computing Research (IJHCR), IGI Global Publisher. She has guided many undergraduate students’ projects. She has attended various FDP, Short term courses, Workshops from IIT’s, NIT’S. She has also edited some of the Springer, de-Gruyter edited book in the field of AI, Health Care Informatics.
"About the title" may belong to another edition of this title.
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