Trends in Deep Learning Methodologies

Trends in Deep Learning Methodologies

by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava
Released November 2020
Publisher(s): Academic Press
ISBN: 9780128232682

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Book description

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

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. Chapter 1. An introduction to deep learning applications in biometric recognition
    1. 1. Introduction
    2. 2. Methods
    3. 3. Comparative analysis among different modalities
    4. 4. Further advancement
    5. 5. Conclusion
  8. Chapter 2. Deep learning in big data and data mining
    1. 1. Introduction
    2. 2. Overview of big data analysis
    3. 3. Introduction
    4. 4. Applications of deep learning in data mining
    5. 5. Conclusion
  9. Chapter 3. An overview of deep learning in big data, image, and signal processing in the modern digital age
    1. 1. Introduction
    2. 2. Discussion
    3. 3. Conclusions
    4. 4. Future trends
  10. Chapter 4. Predicting retweet class using deep learning
    1. 1. Introduction
    2. 2. Related work and proposed work
    3. 3. Data collection and preparation
    4. 4. Research set-up and experimentation
    5. 5. Results
    6. 6. Discussion
    7. 7. Conclusion
  11. Chapter 5. Role of the Internet of Things and deep learning for the growth of healthcare technology
    1. 1. Introduction to the Internet of Things
    2. 2. Role of IoT in the healthcare sector
    3. 3. IoT architecture
    4. 4. Role of deep learning in IoT
    5. 5. Design of IoT for a hospital
    6. 6. Security features considered while designing and implementing IoT for healthcare
    7. 7. Advantages and limitations of IoT for healthcare technology
    8. 8. Discussions, conclusions, and future scope of IoT
  12. Chapter 6. Deep learning methodology proposal for the classification of erythrocytes and leukocytes
    1. 1. Introduction
    2. 2. Hematology background
    3. 3. Deep learning concepts
    4. 4. Convolutional neural network
    5. 5. Scientific review
    6. 6. Methodology proposal
    7. 7. Results and discussion
    8. 8. Conclusions
    9. 9. Future research directions
  13. Chapter 7. Dementia detection using the deep convolution neural network method
    1. 1. Introduction
    2. 2. Related work
    3. 3. Basics of a convolution neural network
    4. 4. Materials and methods
    5. 5. Experimental results
    6. 6. Conclusion
  14. Chapter 8. Deep similarity learning for disease prediction
    1. 1. Introduction
    2. 2. State of the art
    3. 3. Materials and methods
    4. 4. Results and discussion
    5. 5. Conclusions and future work
  15. Chapter 9. Changing the outlook of security and privacy with approaches to deep learning
    1. 1. Introduction
    2. 2. Birth and history of deep learning
    3. 3. Frameworks of deep learning
    4. 4. Statistics behind deep learning algorithms and neural networks
    5. 5. Deep learning algorithms for securing networks
    6. 6. Performance measures for intrusion detection systems
    7. 7. Security aspects changing with deep learning
    8. 8. Conclusion and future work
  16. Chapter 10. E-CART: An improved data stream mining approach
    1. 1. Introduction
    2. 2. Related study
    3. 3. E-CART: proposed approach
    4. 4. Experiment
    5. 5. Conclusion
  17. Chapter 11. Deep learning-based detection and classification of adenocarcinoma cell nuclei
    1. 1. Introduction
    2. 2. Basics of a convolution neural network
    3. 3. Literature review
    4. 4. Proposed system architecture and methodology
    5. 5. Experimentation
    6. 6. Conclusion
  18. Chapter 12. Segmentation and classification of hand symbol images using classifiers
    1. 1. Introduction
    2. 2. Literature review
    3. 3. Hand symbol classification mechanism
    4. 4. Proposed work
    5. 5. Results and discussion
    6. 6. Conclusion
  19. Index

Product information

  • Title: Trends in Deep Learning Methodologies
  • Author(s): Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava
  • Release date: November 2020
  • Publisher(s): Academic Press
  • ISBN: 9780128232682