Editors: Gyanendra Verma, Rajesh Doriya

Deep Learning: Theory, Architectures, and Applications in Speech, Image, and Language Processing

eBook: US $59 Special Offer (PDF + Printed Copy): US $101
Printed Copy: US $71
Library License: US $236
ISBN: 978-981-5079-22-7 (Print)
ISBN: 978-981-5079-21-0 (Online)
Year of Publication: 2023
DOI: 10.2174/97898150792101230101

Introduction

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries.

The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented.

The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management.

The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Audience: Computer science academicians, professionals, and students; researchers and engineers working on AI models.

Foreword

- Pp. i
Shitala Prasad
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Preface

- Pp. ii-iii (2)
Gyanendra Verma, Rajesh Doriya
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List of Contributors

- Pp. iv-v (2)

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Deep Learning: History and Evolution

- Pp. 1-18 (18)
Jaykumar Suraj Lachure*, Gyanendra Verma, Rajesh Doriya

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Application of Artificial Intelligence in Medical Imaging

- Pp. 19-32 (14)
Sampurna Panda, Rakesh Kumar Dhaka, Babita Panda*

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Classification Tool to Predict the Presence of Colon Cancer Using Histopathology Images

- Pp. 33-46 (14)
Saleena Thorayanpilackal Sulaiman*, Muhamed Ilyas Poovankavil, Abdul Jabbar Perumbalath

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Deep Learning For Lung Cancer Detection

- Pp. 47-59 (13)
Sushila Ratre*, Nehha Seetharaman, Aqib Ali Sayed

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Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization

- Pp. 60-72 (13)
Jayalakshmi Ramachandran Nair*, Sumathy Pichai Pillai, Rajkumar Narayanan

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Analyzing the Performances of Different ML Algorithms on the WBCD Dataset

- Pp. 73-89 (17)
Trupthi Muralidharr*, Prajwal Sethu Madhav, Priyanka Prashanth Kumar, Harshawardhan Tiwari

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Application and Evaluation of Machine Learning Algorithms in Classifying Cardiotocography (CTG) Signals

- Pp. 90-102 (13)
Srishti Sakshi Sinha, Uma Vijayasundaram*

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Deep SLRT: The Development of Deep Learning based Multilingual and Multimodal Sign Language Recognition and Translation Framework

- Pp. 103-128 (26)
Natarajan Balasubramanian, Elakkiya Rajasekar*

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Hybrid Convolutional Recurrent Neural Network for Isolated Indian Sign Language Recognition

- Pp. 129-145 (17)
Elakkiya Rajasekar, Archana Mathiazhagan, Elakkiya Rajalakshmi*

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A Proposal of an Android Mobile Application for Senior Citizen Community with Multi-lingual Sentiment Analysis Chatbot

- Pp. 146-166 (21)
Harshee Pitroda*, Manisha Tiwari, Ishani Saha

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Technology Inspired-Elaborative Education Model (TI-EEM): A futuristic need for a Sustainable Education Ecosystem

- Pp. 167-182 (16)
Anil Verma, Aman Singh*, Divya Anand, Rishika Vij

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Knowledge Graphs for Explaination of Black-Box Recommender System

- Pp. 183-205 (23)
Mayank Gupta, Poonam Saini*

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Universal Price Tag Reader for Retail Supermarket

- Pp. 206-219 (14)
Jay Prajapati*, Siba Panda

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The Value Alignment Problem: Building Ethically Aligned Machines

- Pp. 220-233 (14)
Sukrati Chaturvedi*, Chellapilla Vasantha Lakshmi, Patvardhan Chellapilla

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Cryptocurrency Portfolio Management Using Reinforcement Learning

- Pp. 234-248 (15)
Vatsal Khandor*, Sanay Shah, Parth Kalkotwar, Saurav Tiwari, Sindhu Nair

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Subject Index

- Pp. 249-253 (5)
Gyanendra Verma, Rajesh Doriya
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