Deep Learning for Data Analytics
View on ScienceDirect

Deep Learning for Data Analytics

Foundations, Biomedical Applications, and Challenges

1st Edition - May 29, 2020

Write a review

  • Editors: Himansu Das, Chattaranjan Pradhan, Nilanjan Dey
  • eBook ISBN: 9780128226087
  • Paperback ISBN: 9780128197646

Purchase options

Purchase options
DRM-free (PDF, EPub, Mobi)
eBook Format Help
Available
Sales tax will be calculated at check-out

Institutional Subscription

Support CenterReturns & Refunds
Free Global Shipping
No minimum order

Description

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.

Key Features

  • 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

Readership

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

Table of Contents

  • 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

Product details

  • No. of pages: 218
  • Language: English
  • Copyright: © Academic Press 2020
  • Published: May 29, 2020
  • Imprint: Academic Press
  • eBook ISBN: 9780128226087
  • Paperback ISBN: 9780128197646

About the Editors

Himansu Das

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.

Affiliations and Expertise

Assistant Professor, Kalinga Institute of Industrial Technology University, Bhubaneswar, Odisha, India

Chattaranjan Pradhan

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.

Affiliations and Expertise

School of Computer Engineering, KIIT University, India

Nilanjan Dey

Nilanjan Dey is an 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, and also holds the position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held the honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He got his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research (Springer Nature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is an associate editor of IET Image Processing (Wiley) and an editorial board member of Complex & Intelligent Systems (Springer Nature), Applied Soft Computing (Elsevier), and more. He has written 110 books and over 300 other publications in the areas of medical imaging, machine learning, computer aided diagnosis, data mining, etc. His works have been cited over 15,000 times. He is India’s Ambassador to the International Federation for Information Processing—Young ICT Group and a senior member of IEEE.

Affiliations and Expertise

Department of Computer Science & Engineering, Maulana Abul Kalam Azad JIS University, Agarpara, Kolkata, India.

Ratings and Reviews

Write a review

There are currently no reviews for "Deep Learning for Data Analytics"