Handbook of Research on Deep Learning Innovations and Trends

Handbook of Research on Deep Learning Innovations and Trends

Aboul Ella Hassanien, Ashraf Darwish, Chiranji Lal Chowdhary

Copyright: © 2019 |Pages: 355

ISBN13: 9781522578628|ISBN10: 1522578625|EISBN13: 9781522578635

DOI: 10.4018/978-1-5225-7862-8

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MLA

Hassanien, Aboul Ella, et al., editors. Handbook of Research on Deep Learning Innovations and Trends. IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7862-8

APA

Hassanien, A. E., Darwish, A., & Chowdhary, C. L. (Eds.). (2019). Handbook of Research on Deep Learning Innovations and Trends. IGI Global. https://doi.org/10.4018/978-1-5225-7862-8

Chicago

Hassanien, Aboul Ella, Ashraf Darwish, and Chiranji Lal Chowdhary, eds. Handbook of Research on Deep Learning Innovations and Trends. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7862-8

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Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient.

The Handbook of Research on Deep Learning Innovations and Trends is an essential scholarly resource that presents current trends and the latest research on deep learning and explores the concepts, algorithms, and techniques of data mining and analysis. Highlighting topics such as computer vision, encryption systems, and biometrics, this book is ideal for researchers, practitioners, industry professionals, students, and academicians.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Computational Intelligence and Robotics (ACIR) Book Series

Editorial Advisory Board

Preface

Chapters

Deep Learning Applications

Chapter 1

Detection of Blood-Related Diseases Using Deep Neural Nets (pages 1-15)

Rajithkumar B. K., Shilpa D. R., Uma B. V.

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine...

Chapter 2

Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks (pages 16-39)

Sergei Savin, Aleksei Ivakhnenko

In this chapter, the problem of finding a suitable foothold for a bipedal walking robot is studied. There are a number of gait generation algorithms that rely on having a set of obstacle-free regions where the robot can step to and...

Chapter 3

CNN Customizations With Transfer Learning for Face Recognition Task (pages 40-58)

Chantana Chantrapornchai, Samrid Duangkaew

Several kinds of pretrained convolutional neural networks (CNN) exist nowadays. Utilizing these networks with the new classification task requires the retraining with new data sets. With the small embedded device, large network...

Chapter 4

Application of Deep Learning in Speech Recognition (pages 59-71)

Rekh Ram Janghel, Satya Prakash Sahu, Yogesh Kumar Rathore, Shraddha Singh, Urja Pawar

Speech is the vocalized form of communication used by humans and some animals. It is based upon the syntactic combination of items drawn from the lexicon. Each spoken word is created out of the phonetic combination of a limited set...

Chapter 5

Sensation of Deep Learning in Image Processing Applications (pages 72-96)

Ramgopal Kashyap

This chapter will address challenges with IoT and machine learning including how a portion of the difficulties of deep learning executions while planning the arrangement and choice of right calculation. Existing research in deep...

Chapter 6

Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification (pages 97-113)

Sahil Sharma, Vijay Kumar

Face depth image has been used for occlusion presence and gender prediction by transfer learning. This chapter discusses about the overfitting problem and how augmentation helps overcoming it. Pre-processing of the dataset includes...

Chapter 7

Deep Learning: Architectures and Applications (pages 114-130)

Kamaljit I. Lakhtaria, Darshankumar Modi

Deep learning is a subset of machine learning. As the name suggests, deep learning means more and more layers. Deep leaning basically works on the principle of neurons. With the increase in big data or large quantities of data, deep...

Chapter 8

A Survey on Deep Learning Techniques Used for Quality Process (pages 131-152)

Vanyashree Mardi, Naresh E., Vijaya Kumar B. P.

In the current era, software development and software quality has become extensively important for implementing the real-world software application, and it will enhance the software functionality. Moreover, early prediction of...

Chapter 9

Deep Learning in Early Detection of Alzheimer's: A Study (pages 153-162)

Anitha S. Pillai, Bindu Menon

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There...

Advanced Deep Learning Techniques

Chapter 10

Deep Clustering (pages 164-179)

M. Parimala Boobalan

Clustering is an unsupervised technique used in various application, namely machine learning, image segmentation, social network analysis, health analytics, and financial analysis. It is a task of grouping similar objects together...

Chapter 11

Deep Reinforcement Learning for Optimization (pages 180-196)

Md Mahmudul Hasan, Md Shahinur Rahman, Adrian Bell

Deep reinforcement learning (DRL) has transformed the field of artificial intelligence (AI) especially after the success of Google DeepMind. This branch of machine learning epitomizes a step toward building autonomous systems by...

Chapter 12

A Similarity-Based Object Classification Using Deep Neural Networks (pages 197-219)

Parvathi R., Pattabiraman V.

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training...

Chapter 13

Cognitive Deep Learning: Future Direction in Intelligent Retrieval (pages 220-231)

Chiranji Lal Chowdhary, Ashraf Darwish, Aboul Ella Hassanien

Deep learning states the scientific algorithms that are accustomed to come through a particular assignment. Such tidy issues could also be meteorology or brain diagnosing wherever records are obtainable as text. In such a state of...

Security in Deep Learning

Chapter 14

Malware Classification and Analysis Using Convolutional and Recurrent Neural Network (pages 233-255)

Yassine Maleh

Over the past decade, malware has grown exponentially. Traditional signature-based approaches to detecting malware have proven their limitations against new malware, and categorizing malware samples has become essential to...

Chapter 15

Anomaly Detection Using Deep Learning With Modular Networks (pages 256-290)

Manu C., Vijaya Kumar B. P., Naresh E.

In daily realistic activities, security is one of the main criteria among the different machines like IOT devices, networks. In these systems, anomaly detection is one of the issues. Anomaly detection based on user behavior is very...

Back Materials

Glossary

Compilation of References

Related References

About the Contributors

Index

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