Deep Learning : Theory, Architectures and Applications in Speech, Image and Language Processing / Gyanendra Verma, Rajesh Doriya.

Deep Learning: History and Evolution --
Application of Artificial Intelligence in Medical Imaging -- Classification Tool to Predict the Presence of Colon Cancer Using Histopathology Images -- Deep Learning For Lung Cancer Detection -- Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization -- Analyzing the Performances of Different ML Algorithms on the WBCD Dataset -- Application and Evaluation of Machine Learning Algorithms in Classifying Cardiotocography(CTG) Signals -- Deep SLRT: The Development of Deep Learning based Multilingual and Multimodal Sign Language Recognition and Translation Framework -- Hybrid Convolutional Recurrent Neural Network for Isolated Indian Sign Language Recognition -- A Proposal of an Android Mobile Application for Senior Citizen Community with Multi-lingual Sentiment Analysis Chatbot -- Technology Inspired-Elaborative Education Model (TI-EEM): A futuristic need for a Sustainable Education Ecosystem -- Knowledge Graphs for Explanation of Black-Box Recommender System -- Universal Price Tag Reader for Retail Supermarket -- The Value Alignment Problem: Building Ethically Aligned Machines -- Cryptocurrency Portfolio Management Using Reinforcement Learning.

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. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. 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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.