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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76885-2
Cover image: Pixabay.Com
Cover design by Russell Richardson
Acknowledgments
This book is based on research conducted at various leading technical institutions on different topics regarding the machine and deep learning platforms used throughout India. We are grateful to the many authors and co-authors who contributed their research work for use in the successful completion of this book. We are also grateful to our colleagues for their encouragement to start the process of working on the book, persevere throughout it, and finally publish it.
We gratefully acknowledge NIT Warangal and the Faculty of Science & Technology, ICFAI Foundation for Higher Education, Hyderabad, for providing the use of their facilities to carry out the work on this book. Finally, we would like to acknowledge with gratitude, the support and love from our parents, life partners and children.
Preface
Nowadays, machine learning has become an essential part of many commercial and industrial applications and research developments. It has expanded its roots in areas ranging from automatic medical diagnosis in healthcare to product recommendations in social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we try to show you how you yourself can easily adopt machine learning to build solutions for small applications and the best way to go about it. With the knowledge presented herein, you can build your own system to find the faults in a company’s manufactured products and the fake profiles in social networks. This book clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, fake profile detection in social media, farming sectors, etc.
For the ambitious machine learning specialists of today who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results, including comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program.
The Editors
June 2020
1
A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services
Priyank Jain* and Gagandeep Kaur†
Dept. of CSE & IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
Abstract
Air pollution has become a major concern in many developing countries. There are various factors that affect the quality of air. Some of them are Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), Sulfur Dioxide (SO2), and Carbon Monoxide (CO). The Government of India under the Open Data Initiative provides data related to air pollution. Interpretation of this data requires analysis, visualization, and prediction. This study proposes machine learning and visualization techniques for air pollution. Both supervised and unsupervised learning techniques have been used for prediction and analysis of air quality at major places in India. The data used in this research contains the presence of six major air pollutants in a given area. The work has been extended to study the impact of lockdown on air pollution in Indian cities as well.
Keywords: Open Data, JSON API, OpenAQ, clustering, SVM, LSTM, prediction, Heat Map visualizations
1.1 Introduction
1.1.1 Open Government Data Initiative
These days, Open Government Data (OGD) is gaining momentum in providing sharing of knowledge by making public data and information of governmental bodies freely available to private citizens in system processable formats so as to reuse it for mutual benefits. OGD is a global movement and has its roots in the initiative started in 2009 by the US President as a Memorandum on Transparency and Open Government providing transparency in government projects and collaborations
