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Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
- Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications.
- Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks
- Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Computer/data scientists, biomedical engineers, researchers and software engineers in the areas of deep learning, data analytics, big data, and intelligent systems; research scientists and practitioners in medical and biological sciences
Section I Deep Learning Basics and Mathematical Background
1. Introduction to Deep Learning
2. Probability and information Theory
3. Deep Learning Basics
4. Deep Architectures
5. Deep Auto-Encoders
6. Multilayer Perceptron
7. Artificial Neural Network
8. Deep Neural Network
9. Deep Belief Network
10. Recurrent Neural Networks
11. Convolutional Neural Networks
12. Restricted Boltzmann Machines
Section II Deep Learning in Data Science
13. Data Analytics Basics
14. Enterprise Data Science
15. Predictive Analysis
16. Scalability of deep learning methods
17. Statistical learning for mining and analysis of big data
18. Computational Intelligence Methodology for Data Science
19. Optimization for deep learning (e.g. model structure optimization, large-scale optimization, hyper-parameter optimization, etc)
20. Feature selection using deep learning
21. Novel methodologies using deep learning for classification, detection and segmentation
Section III Deep Learning in Engineering Applications
22. Deep Learning for Pattern Recognition
23. Deep Learning for Biomedical Engineering
24. Deep Learning for Image Processing
25. Deep Learning for Image Classification
26. Deep Learning for Medical Image Recognition
27. Deep learning for Remote Sensing image processing
28. Deep Learning for Image and Video Retrieval
29. Deep Learning for Visual Saliency
30. Deep Learning for Visual Understanding
31. Deep Learning for Visual Tracking
32. Deep Learning for Object Segmentation and Shape Models
33. Deep Learning for Object Detection and Recognition
34. Deep Learning for Human Actions Recognition
35. Deep Learning for Facial Recognition
36. Deep Learning for Scene Understanding
37. Deep Learning for Internet of Things
38. Deep Learning for Big Data Analytics
39. Deep Learning for Clinical and Health Informatics
40. Deep Learning foe Sentiment Analysis
- No. of pages:
- © Academic Press 2020
- 29th May 2020
- Academic Press
- eBook ISBN:
- Paperback ISBN:
Himansu Das is working as an as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He has received his B. Tech and M. Tech degree from Biju Pattnaik University of Technology (BPUT), Odisha, India. He has published several research papers in various international journals and conferences. He has also edited several books of international repute. He is associated with different international bodies as Editorial/Reviewer board member of various journals and conferences. He is a proficient in the field of Computer Science Engineering and served as an organizing chair, publicity chair and act as member of program committees of many national and international conferences. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc., His research interest includes Grid Computing, Cloud Computing, and Machine Learning. He has also 10 years of teaching and research experience in different engineering colleges.
Assistant Professor, Kalinga Institute of Industrial Technology University, Bhubaneswar, Odisha, India
Chittaranjan Pradhan is working at School of Computer Engineering, KIIT University, India. He obtained his Bachelors, Masters and PhD degree in Computer Science & Engineering stream. His research are includes Information Security, Image Processing, Data Analytics and Multimedia Systems. Dr. Pradhan has published more than 40 articles in the national and international journals and conferences. Also, he has been associated to a number of events organized at national and international level. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc. He has also experience of more than 10 years in teaching and research activities.
School of Computer Engineering, KIIT University, India
Nilanjan Dey is Associate Professor in the Department of Computer Science and Engineering, JIS University, Kolkata, India. He is a visiting fellow of the University of Reading, UK. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD from Jadavpur University in 2015. He has authored/edited more than 70 books with Elsevier, CRC Press, and SpringerNature, and published more than 300 papers. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence (IGI Global), Associated Editor of IEEE Access, and International Journal of Information Technology (Springer). He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer), Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier), Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal Processing and Data Analysis (CRC). His main research interests include medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Indian Ambassador of the International Federation for Information Processing—Young ICT Group and Senior member of IEEE.
Assistant Professor, Department of Information Technology, Techno India College of Technology, Rajarhat, Kolkata, India
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