Advanced Analytics and Deep Learning Models - Группа авторов

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of multi-domain technology creates new problems for researchers. Therefore, in order to help meet the challenge, this book series concentrates on next generation computing and communication methodologies involving smart and ambient environment design. It is an effective publishing platform for monographs, handbooks, and edited volumes on Industry 4.0, agriculture, smart city development, new computing and communication paradigms. Although the series mainly focuses on design, it also addresses analytics and investigation of industry-related real-time problems.

      Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

      Advanced Analytics and Deep Learning Models

      Edited by

       Archana Mire

       Computer Engineering Department, Terna Engineering College, Navi Mumbai, India

       Shaveta Malik

       Computer Engineering Department, Terna Engineering College, Nerul, India

      and

       Amit Kumar Tyagi

       Vellore Institute of Technology (VIT), Chennai Campus, India

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       Library of Congress Cataloging-in-Publication Data

      ISBN 978-1-119-79175-1

      Cover image: Pixabay.Com Cover design by Russell Richardson

      Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

      Printed in the USA

      10 9 8 7 6 5 4 3 2 1

      Preface

      Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are the high centres of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data.

      In fact, it is a key benefit of big data. It is also effective for big data. Moreover, it is collecting an unlabelled and uncategorized raw data. There are some challenges also in big data related to the extraction complex patterns from the large amount of data, retrieving of fast information, tagging of data etc, deep learning can be used to deal these kinds of problems or challenges.

      The purpose of this book is to help teachers to instruct the concepts of analytics in deep learning and how big data technologies are managing massive amounts of data with the help of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) etc. In this book one will find the utility and challenges of big data. Those who are keen to learn the different models of deep learning, the connection between AI, ML and DL will definitely find this book as a great source of knowledge.

      This book contains chapters on artificial intelligence, machine learning, deep learning and their uses in many useful sectors like stock market prediction, recommendation system for better service selection, ehealthcare, telemedicine, transportation. In last few interesting chapter like innovations or issue or future opportunities with fog computing/cloud computing or artificial intelligence are being discussed in this work for future readers/researchers.

      Dr. Archana MireDr. Shaveta MalikDr. Amit Kumar Tyagi January 2022

Part 1 INTRODUCTION TO COMPUTER VISION

      1

      Artificial Intelligence in Language Learning: Practices and Prospects

       Khushboo Kuddus

       School of Humanities (English), KIIT Deemed to be University, Bhubaneswar, Odisha, India

       Abstract

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