Impact of AI and Data Science in Response to Coronavirus Pandemic (Algorithms for Intelligent Systems) 9811627851, 9789811627859

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Algorithms for Intelligent Systems Series Editors: Jagdish Chand Bansal · Kusum Deep · Atulya K. Nagar

Sushruta Mishra · Pradeep Kumar Mallick · Hrudaya Kumar Tripathy · Gyoo-Soo Chae · Bhabani Shankar Prasad Mishra   Editors

Impact of AI and Data Science in Response to Coronavirus Pandemic

Algorithms for Intelligent Systems Series Editors Jagdish Chand Bansal, Department of Mathematics, South Asian University, New Delhi, Delhi, India Kusum Deep, Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Atulya K. Nagar, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

This book series publishes research on the analysis and development of algorithms for intelligent systems with their applications to various real world problems. It covers research related to autonomous agents, multi-agent systems, behavioral modeling, reinforcement learning, game theory, mechanism design, machine learning, meta-heuristic search, optimization, planning and scheduling, artificial neural networks, evolutionary computation, swarm intelligence and other algorithms for intelligent systems. The book series includes recent advancements, modification and applications of the artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy system, autonomous and multi agent systems, machine learning and other intelligent systems related areas. The material will be beneficial for the graduate students, post-graduate students as well as the researchers who want a broader view of advances in algorithms for intelligent systems. The contents will also be useful to the researchers from other fields who have no knowledge of the power of intelligent systems, e.g. the researchers in the field of bioinformatics, biochemists, mechanical and chemical engineers, economists, musicians and medical practitioners. The series publishes monographs, edited volumes, advanced textbooks and selected proceedings.

More information about this series at http://www.springer.com/series/16171

Sushruta Mishra Pradeep Kumar Mallick Hrudaya Kumar Tripathy Gyoo-Soo Chae Bhabani Shankar Prasad Mishra •

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Editors

Impact of AI and Data Science in Response to Coronavirus Pandemic

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Editors Sushruta Mishra School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Pradeep Kumar Mallick School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Hrudaya Kumar Tripathy School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Gyoo-Soo Chae Division of Information and Communication Baekseok University Cheonan, Korea (Republic of)

Bhabani Shankar Prasad Mishra School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

ISSN 2524-7565 ISSN 2524-7573 (electronic) Algorithms for Intelligent Systems ISBN 978-981-16-2785-9 ISBN 978-981-16-2786-6 (eBook) https://doi.org/10.1007/978-981-16-2786-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

It is no secret that the novel coronavirus 2019 (COVID-19) outbreak is deeply affecting the lives of millions around the world. It is one of the most contagious diseases to have hit our green planet in the past decades. While governments across the globe are working in collaboration with local authorities and healthcare providers to track, respond to, and prevent the spread of disease caused by the pandemic, health experts are turning to advanced analytics to augment current efforts to prevent further infection. It is believed that machine learning and data analytics along with other advanced technologies can help accelerate solutions and minimize the impacts of the virus in conjunction with all the other great research and planning going. This book presents AI-based technologies in dealing with COVID-19 outbreak, and it delivers an in-depth analysis of various technical aspects related to COVID-19 risk factors. Chapter 1 surveys different aspects of data science as one critical technique in the field of screening, anticipating, determining, contact following, and medication advancement for COVID-19 pandemic. Chapter 2 deals with the role of artificial intelligence in forecasting COVID-19 outbreak. Chapter 3 deals with the application of AI-based methods and technologies to successfully screen affected patients in short period at healthcare centers. Chapter 4 employs a supervised machine learning approach to identify the relevant features predicting COVID-19 disease diagnoses with high accuracy. Chapter 5 reviews the identification and classification of COVID-19 risk factors using machine learning approach on text-based clinical data instances. Chapter 6 stressed on the role of COVID-19 clusters using machine learning in efficient tracing of patients and avoiding unnecessary tests, thereby reducing unnecessary lockdowns. Chapter 7 discusses the use of deep learning algorithms like SMOTE-ENN tool to handle the imbalance COVID-19 dataset and XGBoost with sparse autoencoder techniques for feature selection as well as for classification of disease risks. Chapter 8 analyzes the role of big data in effective analysis and management of the pandemic crisis. Chapter 9 surveys existing sentiment analysis methods of Twitter data and provides comparative study of mass sentiment outcome during the initial months of pandemic. Chapter 10 draws an analytical survey of the impact of Internet of Things (IoT) in v

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handling of COVID-19 crisis. Chapter 11 focuses on the use of smart technology in issuing alerts, early tracking, diagnosis and social control. Chapter 12 aims to review the role of AI and IoT as a decisive technology to analyze, prepare us for prevention, and fight with COVID-19 and other pandemics. Chapter 13 discusses the emerging role of chatbots to deal with urgency in COVID-19 pandemic. Chapter 14 addresses the impact of computer vision technology in handling pandemic issues. Finally, Chap. 15 highlights some futuristic technologies and approaches that may be helpful in tackling pandemics. Bhubaneswar, India Bhubaneswar, India Bhubaneswar, India Cheonan, Korea (Republic of) Bhubaneswar, India

Sushruta Mishra Pradeep Kumar Mallick Hrudaya Kumar Tripathy Gyoo-Soo Chae Bhabani Shankar Prasad Mishra

Contents

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An Overview of Significant Role of Data Science and Its Associated Methodologies in COVID-19 Handling . . . . . . . Aditi Singh and Pushpak Das

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Role of Artificial Intelligence in Forecast Analysis of COVID-19 Outbreak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adarsh Sandhu and Kiran Manisha Sahu

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Application of Artificial Intelligence (AI) for the Effective Screening of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selvakumar Samuel, Vazeerudeen Abdul Hameed, and Kesava Pillai Rajadorai Machine Learning Approach for Analyzing Symptoms Associated with COVID-19 Risk Factors . . . . . . . . . . . . . . . . . . . . Srestha Rath, Roshan Mohanty, and Lambodar Jena Detection of COVID-19 Using Textual Clinical Data: A Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . Reenu Batra, Manish Mahajan, Virendra Kumar Shrivastava, and Amit Kumar Goel

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Application of Machine Learning Algorithms for Effective Determination of COVID-19 Clusters . . . . . . . . . . . . . . . . . . . . . . . 111 Vazeerudeen Abdul Hameed, Selvakumar Samuel, and Kesava Pillai Rajadorai

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A Deep Learning Application for Prediction of COVID-19 . . . . . . . 127 Aradhana Behura

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Application of Big Data in Analysis and Management of Coronavirus (COVID-19) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Deepa Benny and Komaldeep Virdi

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Sentiment Analysis of Twitter Data Related to COVID-19 . . . . . . . 169 Gargi Saha, Sinjan Roy, and Prasenjit Maji

10 IoT for COVID-19: A Descriptive Viewpoint . . . . . . . . . . . . . . . . . 193 Srishty Singh Chandrayan, Shubham Suman, and Tahira Mazumder 11 Smart Technology Application for COVID-19 Detection, Control, Prediction and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Aradhana Behura, Shibani Sahu, and Manas Ranjan Kabat 12 Impact of Artificial Intelligence and Internet of Things in Effective Handling of Coronavirus Crisis . . . . . . . . . . . . . . . . . . 235 Karan Jaju and Hiren Thakkar 13 Fight Against COVID-19 Pandemic Using Chat-Bots . . . . . . . . . . . 253 Akshay Pratap Singh, Komaldeep Virdi, and Aadarsh Choudhary 14 Computer Vision in COVID-19: A Study . . . . . . . . . . . . . . . . . . . . 285 Oisika Paul, Naveen Singh Rajput, and Chinmaya Dehury 15 Futuristic Intelligence-Based Treatment Methods to Handle COVID-19 Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Sanya Raghuwanshi and Saurav Bhaumik

Editors and Contributors

About the Editors Dr. Sushruta Mishra is from Odisha, India. He has completed B.Tech. from ITER, BBSR in 2009; M.Tech. degree from IIIT, BBSR in 2012. He has completed Ph.D. from KIIT University, BBSR in 2017. Currently he is working as Assistant Professor in KIIT University, BBSR in Department of School of Computer Engineering. He has one year of software industry experience at Infosys and more than 7 years of teaching experience. He teaches several academic subjects such as Data Mining, Computer Networks, Database Management System, Software Engineering, Internet and Web technology and many other courses. His prime research area of interest includes Machine learning, Sentiment Analysis, Cognitive Computing and Robotics. Dr. Mishra has published more than 30 research and development articles in various reputed journals, conferences and book chapters. Dr. Pradeep Kumar Mallick is currently working as Senior Associate Professor in the School of Computer Engineering, Kalinga Institute of Industrial technology (KIIT) Deemed to be University, Odisha, India .He has also served as Professor and Head Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad. He has completed his Post Doctoral Fellow (PDF) in Kongju National University South Korea, Ph.D. from Siksha Ó’ Anusandhan University, M.Tech. (CSE) from Biju Patnaik University of Technology (BPUT), and MCA from Fakir Mohan University Balasore, India. Besides academics, he is also involved various administrative activities, Member of Board of Studies, Member of Doctoral Research Evaluation Committee, Admission Committee etc. His area of research includes Algorithm Design and Analysis, and Data Mining, Image Processing, Soft Computing, and Machine Learning. Now he is the editorial member of Korean Convergence Society for SMB .He has published 14 books and more than 90 research papers in National and international journals and conference proceedings in his credit.

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Dr. Hrudaya Kumar Tripathy is an Associate Professor of School of Computer Engineering, KIIT University, Bhubaneswar, India. He has completed Doctorate in Computer Science from Berhampur University, Master in Computer Science Engineering from Indian Institute of Technology, Guwahati and received Post Doctoral Fellowship from Ministry of Higher Education Malaysia. He is having 19 years of teaching experience with post-doctorate research experience in the field of Soft Computing, Machine Learning, Speech Processing, Mobile Robotics, and Big Data Analysis. He had been a visiting faculty for a couple of years in Asia Pacific University, Kuala Lumpur, Malaysia and the University of Utara Malaysia, Sintok, Malaysia. Dr. Tripathy has awarded as Young IT professional award in 2013 on a regional level from Computer Society of India (CSI). He is having more than 100 research publications in different national/international journals and conferences and received many certificates of merits and highly applauded in a presentation of research papers. Dr. Gyoo-Soo Chea has received Ph.D. (Electrical Engineering) from Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA. After receiving Ph.D., he worked at Amphenol Mobile, Korea as a RF manager from June 2001 to February 2003. He has been with Baekseok University in Korea as a Professor since 2003. He has granted department scholarship (Kyungpook National University grant) during March 1991–February 1993 and associated with IEEE Antennas and Propagation Society, January 2001–present. He serves Editor-inChief of The Korea Institute of Information, Electronics, and Communication Technology since 2013. His research interests are antennas and dual-band inverted-F antenna. Dr. Bhabani Shankar Prasad Mishra born in Talcher, Odisha, India in 1981. He received the B.Tech. in Computer Science and Engineering from Biju Pattanaik Technical University, Odisha in 2003, M.Tech. degree in Computer Science and Engineering from the KIIT University, in 2005, Ph.D. degree in Computer Science from F. M. University, Balasore, Odisha, India, in 2011 and Post Doc in 2013 from Soft Computing Laboratory, Yansei University, South Korea. Currently he is working as an Associate Professor and Dean at School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. His research interest includes Pattern Reorganization, Data Mining, Soft Computing, Big Data and Machine Learning. He has published more than 80 research articles in reputed Journal and Conferences, has edited more than five books of current importance. Under his guidance, two Ph.D. scholars are already been awarded. Dr. Mishra was the recipient of the Gold Medal and Silver Medal during his M.Tech. for the best Post Graduate in the University. He is the member of different technical bodies ISTE, CSI and IET.

Editors and Contributors

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Contributors Reenu Batra Department of CSE, SGT University, Gurugram, India Aradhana Behura Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India Deepa Benny School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India Saurav Bhaumik Bachelor of Computer Application Department, Inspiria Knowledge Campus, Siliguri, West Bengal, India Srishty Singh Chandrayan School of Electronics Engineering, Kalinga Institute of Industrial Technology, Deemed To Be University, Bhubaneswar, India Aadarsh Choudhary Xformics Inc., Bangalore, India Pushpak Das Department of CSE, NIT Meghalaya, Shillong, India Chinmaya Dehury University of Tartu, Tartu, Estonia Amit Kumar Goel Department of CSE, Galgotia University, Greater Noida, India Vazeerudeen Abdul Hameed Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia Karan Jaju School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Lambodar Jena Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed To Be) University, Bhubaneswar, Odisha, India Manas Ranjan Kabat Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India Manish Mahajan Department of CSE, SGT University, Gurugram, India Prasenjit Maji Computer Science Engineering Department, Bengal College of Engineering and Technology, Durgapur, West Bengal, India Tahira Mazumder Ajay Kumar Garg Engineering College, Ghaziabad, India Roshan Mohanty School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Oisika Paul School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Sanya Raghuwanshi School of Electronics and Computer Science Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

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Kesava Pillai Rajadorai Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia Naveen Singh Rajput School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Srestha Rath School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Sinjan Roy School of Computer Science Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India Gargi Saha School of Computer Science Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India Kiran Manisha Sahu School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India Shibani Sahu Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India Selvakumar Samuel Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia Adarsh Sandhu School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India Virendra Kumar Shrivastava Department of CSE, SGT University, Gurugram, India Aditi Singh School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India Akshay Pratap Singh School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, India Shubham Suman School of Electronics Engineering, Kalinga Institute of Industrial Technology, Deemed To Be University, Bhubaneswar, India Hiren Thakkar Department of Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, India Komaldeep Virdi School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India

Chapter 1

An Overview of Significant Role of Data Science and Its Associated Methodologies in COVID-19 Handling Aditi Singh and Pushpak Das

1 Introduction The present-day has suffered from some major epidemics, a number of which turned out to be pandemics, have taken away the lives of million of people. These were usually caused by diseases such as cholera, plague, and yellow fever. Some of them like Severe Acute Respiratory Syndrome (SARS), Human Immunodeficiency Virus (HIV), Ebola, Middle-Eastern Respiratory Syndrome (MERS), have emerged recently. The pandemics like SARS-CoV and MERS-CoV had infected 8096 and 2494 individuals causing deaths of 774 and 858 people respectively. The human lungs are mostly affected by these viruses. Some communicable diseases can add up to significant threats in the community leading to an outbreak. A disease is said to be an epidemic when there is a sudden hike in the number of people affected above the normal rate in an area. Outbreak is somewhat similar to an epidemic, it only spreads in a limited region. Pandemic is when an epidemic has outspread over several countries in a wide area and has affected a large number of people. Epidemics and pandemics can be: • transmitted by air, droplets, or aerosols • transmitted through contact, including blood exchange, mother to child in utero, and sexual activity • transmitted through contaminated water

A. Singh (&) School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] P. Das Department of CSE, NIT Meghalaya, Shillong, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_1

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• transmitted between animals and people, through any contact • transmitted by being bitten by insects like mosquitos, fleas, ticks, etc. • transmitted by preparing and eating something (Table 1). In late 2019, a new type of pneumonia was first detected in the city of Wuhan which was then identified as a new strain of coronavirus on Jan of 2020. Coronaviruses are a huge family of viruses which are zoonotic which means they can also be transmitted between humans and animals [1]. It can cause some serious illnesses such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). World Health Organization (WHO) had declared the novel coronavirus (COVID-19) outbreak a worldwide pandemic by March 11 ’20. The COVID-19 pandemic has become the greatest test we have encountered since World War Two, influencing million of lives and causing an enormous number of passings. Since the time the main COVID-19 cases were distinguished, the sickness has spread to pretty much every other nation, causing deaths of more than 712,619 individuals among almost 19,054,364 affirmed cases by Aug 2020 (Fig. 1). SARS-CoV-2 particles look spherical in shape and have spikes like structures attached or coming out of their surface. These particles have protein and their spikes hold of to the cells of their host and change their structure which allows the viral

Table 1 Worst epidemics and pandemics before COVID-19 Name

Time period

Death toll

Plague of Athens Antonine Plague Plague of Cyprian American Plagues Great Plague of London Great Plague of Marseille Russian plague Flu pandemic Spanish Flu Third Plague HIV/AIDS Hong Kong Flu West African Ebola epidemic Asian Flu Swine Flu SARS MERS

430 BC AD 165–180 AD 250–271 16th century 1665–1666 1720–1723 1770–1772 1889–1890 1918–1920 1985 1981-Present 1968–1970 2014–2016 1957–1958 2009–2010 2002–2003 2015-Present

100,000 5M 5000 per day 90% of the population 100,000 100,000 100,000 1M 500 M 12 M 25–35 M 1M 11,325 deaths 1.1 M 200,000 774 858

1 An Overview of Significant Role of Data …

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Fig. 1 The structure of Coronavirus

membrane to fuse with the cell membrane. These genes then enter the host cell and multiply, producing similar viral bodies like them. Recent work shows that the spikes on SARS-CoV-2 is 20 times more likely to stick to the host cells compared to that of SARS 2002 which makes it spread more easily from one to another. Inspite of their similar structure of spikes, the antibodies used earlier couldn’t bind the new coronavirus. This means that the potential vaccine has to be made from scratch with new strategies.

1.1

Purpose of the Study

Pandemic is a situation which if not dealt with at the right time can lead to unnecessary suffering and death. The figures have shown that the virus is spreading cautiously and surreptitiously.

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The main goal is to flatten the curve and stop the virus from spreading at a massive rate. Asymptomatic carriers, scarcity of test kits complicate efforts in trying to contain the transmission. The challenges range from early detection, containment, and isolation, and ultimately to the end of the disease. To deal with this outbreak, the government of all the suffering countries came out with various strategies to stop the spreading. Decisions like a complete lockdown, importing/ exporting of the particular medicines, wearing of masks in public places, and many more were brought into action. In the span of six months, many people lost their job, the health workers are working under pressure and stress, many health workers have become the victim of public violence and disrespect, more than 90% of the people are feeling anxious and depressed after the lockdown was introduced. The WHO, right from the beginning released plenty of guidelines, and scientists around the globe are working day and night to find a vaccine to curb this virus. Doctors are confronting outrageous trouble in the diagnosis of COVID-19 due to its rapid spread [2]. Therefore, apart from the traditional ways of treating the disease, this situation demands the use of data science and machine learning technologies for dealing faster with this virus. Science have contributed significantly during this bizarre time. For example, the various digital platforms allow people to consult doctors and perform self-assessments online, robots used for food delivery to infected people, and many more. Computer science, in particular, is contributing to detecting the early symptoms of the virus by imaging such as computed tomography (CT) and x-ray.

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Table 2 Various technologies used in health care Technology

Application in Healthcare

Artificial intelligence

Drug creation, managing medical records, digital consultation, virtual nurses, medication management, health monitoring, healthcare system analysis, treatment design Identifying diseases and diagnosis, personalized medicine, outbreak prediction, medical imaging diagnosis, drug discovery and manufacturing, machine learning-based behavioral modification Molecular diagnostics comprising pharmacogenomics and identification of pathogenic variants, in experimental data interpretation comprising DNA sequencing and gene splicing, in protein structure classification and prediction, in biomedical imaging, drug discovery, medical informatics Implantable glucose monitoring systems, activity trackers, heart monitors with reporting, medical alert systems, ingestible sensors, medication dispensers, wireless sensors, remote monitoring Monitoring patient vitals, smoother hospital administration, fraud prevention, and detection, healthcare intelligence, healthcare data solutions Telepresence, surgical assistants, rehabilitation robots, medical transportation robots, sanitation, and disinfection robots, robotic prescription dispensing systems Cancer screening, disease diagnostics, surgical assistance technology, radiology

Machine learning Deep neural network

Internet of things Big data Robots

Computer vision

1.2

Technologies Used

See Table 2.

2 Role of AI to Identify, Track, and Forecast Coronavirus Outbreaks 2.1

Tracking

The first step to combat a pandemic is to track it down. These days many AI-based technologies are used in observing and predicting an outbreak around the globe. AI can learn to predict an outbreak or epidemic by analyzing the various reports, government reports, hospital reports, social media updates, online information, satellites, etc. It is very important to forecast such outbreaks for the countries that doesn’t have enough medical facilities and educational systems [3]. Using this method, Canadian start-up BlueDot which uses big data analytics to track and anticipate the spread, predicted the outbreak days before the World Health Organization issued its public warnings.

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An exclusive software-as-a-service-based company BlueDot is designed to detect, track, and forecast the spread of any communicable disease. The BlueDot collects data on over 100+ diseases around the globe. Instead of fully relying on the data published by the Centers for Disease Control and Prevention, the AI-driven algorithm analyzes billions of data from the various other sources like the travel history of billions of people, the population of humans and other species, information from the healthcare workers, and thousands of articles in various languages. It manually classifies the data and then uses ML algorithms and natural language processing to coach the system. When China reported its first 27 pneumonia cases associated with a seafood market in Wuhan, the system started working on collecting all the information related to the disease and identifying the cities that are linked to Wuhan like the flight ticketing data was gathered to forecast the countries that are likely to show the same symptoms. The cities on the top of their list were the ones to witness the cases first. BlueDot’s mission is to not just detect a outspread but how it would affect people around the globe and its consequences.

2.2

Forecast

After the identification of the outbreak, it's important to keep track of the hotspots or the containment zone or forecast the next location. Flu forecasting is even trickier than tracking it. The machine learning techniques are applied to many uses like social active websites, online sources, aor data from any source which confirms that the spread of the disease has started elsewhere too. These data are then examined to check if there is any particular signs, like breathing problems or fever, in an area where the cases have been registered before. Algorithms of Natural language processing is used to examine the content published on any online social media apps, for instance, to distinguish if someone is discussing the situation or grumbling about how it makes them suffer. To date, there exist many online tracker websites and apps that have been released by the govt and various groups of people to keep a track of the outspread. Some of these (shown in Fig. 2) are WHO Dashboard, Microsoft’s Bing COVID-19 Tracker, HealthMap, and many more. This tracker collects information through various sources, news, and countries and keeps updating their data to view the numbers and how different communities are reacting to it. The tracker uses modern UI and several data viewing tools to grasp the current situation effectively. It also shows the active number of reports and deaths, and the changes which took over time a particular region on the chart. Apart from identifying new reports, this could help in the study of the virus and its behavior. Through this technique, by knowing a person’s age or their gender and their location, we might quickly find those which are at more risk and control the virus from spreading.

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Fig. 2 The data of the outbreak

3 Data Science Technologies in Diagnosis of Corona Virus When there is a new pandemic, diagnosing each and everyone becomes difficult. Testing on a large scale can be expensive in the beginning because of which there can be a shortage of kits. Rather than taking samples and waiting for each results using gathered data on a large scale can be much cheaper and faster. As the time is passing and the situation is getting serious day by day, researchers should invest more in advanced tools of AI and ML to improve treatment and diagnosis. Researchers have found a method for rapid and accurate diagnosis of patients by using imaging combined with artificial intelligence and data from the clinic to analyze patients with COVID-19 [4]. They can identify COVID-19 based on the CT scans of chest on how the lungs look if the person is infected. AI has the ability to intensify the images of the chest to find for even minute details in the lungs that give any signs of COVID-19. The team collected all the data from the CT scan to develop an efficient AI algorithm. IT works the same as a human would use the diagnosis and give its prediction for positive or negative. Many samples from the patients were taken and fed to the system. The data was then used to examine the situation of the remaining patients. It showed better results than those who were completely dependant on the clinical data. Patients who don’t show any lung disease in their scans and show non-specific symptoms are difficult to diagnose (shown in Fig. 3). “This finding is important as it gives the signs that an AI algorithm can be coached in order to help early finding of COVID-19, and can be used to help in the evaluation of sick patients on time,” said the Team. Likewise, the goal of Italian COVID-19 Lung Ultrasound project is to make a diagnosis system for the COVID-19 patients with the help of artificial intelligence used to the analysis of ultrasound images. Ultrasound imaging (ultrasonography)

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Fig. 3 CT scans of chest of patients

scrutinizes specific patterns to diagnose patients, determine their condition, and chooses the most suitable treatment. It was said, many COVID-19 patients that resulted with a negative chest x-ray were found to have interstitial pneumonia on lung ultrasound. And when patients can stay in the emergency room and take the lung ultrasound, the risk of nosocomial infection is more easily controlled.

4 Potential Use of Robots and Drones to Sterilize, Food Delivery, and Perform Emergency Tasks A machine that has an engine, a versatile physical structure, distinctive sensor frameworks, some force gracefully to run, and a little PC part to control the entirety of its pieces is known as a Robot. It’s an easy task to build a machine that reacts to a particular situation by giving a single output. But as ML algorithms improve, robots will acknowledge their surroundings in a completely different manner. As the chances of COVID-19 becoming a pandemic increased, the value of robots and drones in providing facilities was also realized by the various health organizations.

4.1

Used as a Disinfectant

The Chinese govt, looking at the situation ordered the Danish company UVD Robots to supply robots to china sterilization (shown in Fig. 4). The robots used UV lights to disinfect the virus which is operated by a worker from miles apart with the help of remote control. These robots have a portable base furnished with various lidar sensors and a variety of ground-breaking short frequency bright C (UVC) lights. The robot filters the climate utilizing its lidars and makes a

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Fig. 4 The UVD robots

computerized map. The robot at that point drives in the territory while discharging 20 joules for each square meter every second of 254-nanometer light to eliminate microbes and other unsafe microorganisms. Therefore, the zone can ensure a 99.99% sterilization rate.

4.2

Remote Delivery

Due to the contagious virus, transportation of food items, medicines, and various transportation work has become difficult. To deal with this situation drones have been used to deliver medical-kit to people in isolation and remote area. The robots have also been used to transport food to the affected patients (shown in Fig. 5). Cargo drones were used in a variety of ways in China during the height of the pandemic. When the vehicle arrives at its objective, it communicates a code to a client’s cellphone, permitting the client to recover it. The Chinese company, Antwork, has been using its delivery drones to transport medical samples and quarantine supplies in response to COVID-19. In South Korea, a hydrogen-controlled automaton has conveyed 15,000 defensive facemasks to occupants on many Islands in the pandemic.

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Fig. 5 Drone carrying food, medicine to remote places

4.3

Maintain Order

With the proper treatment of the patients, it is also important to implement social distancing and lockdown of the most affected cities to stop the virus from spreading. To do this apart from the concerned authorities, drones are being used. The US uses drones to monitor its people’s activities, to check if people are wearing masks, or maintaining social distancing or not. Drones are even used for taking thermal images of the site. The drones are also seen with a loudspeaker, broadcasting to encourage physical distancing and staying home.

5 Drug Discovery and Pattern Matching of Protein Structure Using Machine Learning There have been many hit and trial methods for the discovery of the vaccine but none of them were successful and it also consumes a lot of time. Protein-protein connections among infections and human cells decide our body’s responses to microorganisms. The use of Data science can fasten up this process. AI has been used for many research papers before and used to create drugs (little particles, peptides, antibodies, or more current modalities including short RNAs or cell treatments) models to deal with diseases of different kinds. Using AI can enhance the understanding of the virus and treating it. It can build models from its

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huge source of data to predict the structure of the protein that will help with the functionality of the virus and to come up with a vaccine. Utilizing artificial neural networks, research bunches have effectively fabricated models that can anticipate protein structures, at long last creation it plausible to recognize protein structures utilizing computational strategies. Present-day science is progressively wealthy in information. Notwithstanding, these multi-dimensional informational collections require suitable expository techniques to yield factually legitimate models that can make expectations for target ID, and this is the place where ML can be utilized. AI can assist us with isolating medication competitors a lot quicker via naturally: 1. Building information charts and 2. Predicting communications among drugs and viral proteins. A group of specialists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is utilizing a combinational AI framework that chooses peptides (short strings of amino acids) that are anticipated to give huge inclusion to an immunization. The layout they use is the ‘OptiVax’ algorithm. It searches for optimal binding pairs of peptides and human cell surface receptor proteins. It combines 11 different already existing programs designed to test combinations of peptides and receptors. Optimax uses the technique of beam search. It forms a decoder in the software. Beam search estimate a host of possible combinations of elements to find the most likely combination. The end result is, OptiVax came up with some vaccine designs consisting of peptide-receptor pairs that have better coverage than designs that were presented before (Fig. 6). A London-based drug company, BenevolentAI in response to the COVID-19 has applied AI for drug discovery and understanding of the body’s reaction to this infection. Instead of focusing just on the drug, they focused on the cellular mechanism that the virus uses to attack and multiple itself in the body of the host. With the utilization of natural language processing preparing (AI applied to message) to peruse and decipher countless logical articles, they assemble the biomedical knowledge graph, which are organized networks that genuinely interface various elements, (for example, medications and proteins) by which they could discover the association and the controllers. Updating the BenevolentAI Knowledge Graph after the human team worked on the selection of drug to inhibit regulation, baricitinib was identified as a possible vaccine which has both enemy of viral and hostile to cytokine properties, with 1 h 30 min of processing time, in three days of extra human work.

6 Deep Neural Networks in Screening and Tracking of Coronavirus Affected Individuals As the disease is growing and expanding, medical researchers around the world are rushing to make use of available data. Hospitals are seeking all possible ways to screen a patient by online checking methods, or by self-analysis. Deep learning is a

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Fig. 6 The working of Optivax in drug discovery

sort of ML that utilizes a layered algorithmic engineering to consider information. In its models, information is handled through a course of numerous layers, with each progressive layer utilizing the yield from the past layer to get its outcomes. Its models can turn out to be increasingly more exact as they channel more information, basically gaining from past outcomes to refine their capacity to make relationships and associations. This various layered system let profound learning models to finish grouping assignments, for example, distinguishing unobtrusive irregularities in clinical pictures, bunching patients with comparative qualities into hazard-based accomplices, or underline the connections among side effects and results inside immense amounts of unstructured information. While fundamental ML requires a software engineer to recognize if an end is right, deep learning can gauge the exactness of its answers all alone because of the idea of its multi-layered structure (Fig. 7).

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Fig. 7 The processing of CT scan images

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Case Study

An aggregate of 618 CT tests was gathered, incorporating 110 patients with positive COVID-19 results. Each COVID-19 affected patient was affirmed with RT-PCR testing unit and those cases that didn’t have a picture display on the chest CT pictures were prohibited. Likewise, there had at any rate 2 days hole between CT datasets whenever taken from similar patient to guarantee the assortment of tests. The staying 399 CT tests were gathered as the reason for controlled examination gathering. Out of those, 224 CT tests were taken from patients who had Influenza-A viral pneumonia including H1N1, H7N9, H3N2, H5N1, H7N9, and so on, and 175 CT tests from solid individuals. There were in total 198 COVID-19 and 196 Influenza-A cases from beginning phases and the rest 9.6 and 13.4% cases from intense stage individually. In addition, Influenza-A viral pneumonia CT cases were consolidated as it was imperative to recognize them from suspected patients with COVID-19, with the current circumstance in China. A sum of 528 CT tests (85.4%) was utilized for preparing and approval reason, including 189 examples of patients with COVID-19, 194 examples from patients with Influenza-A viral pneumonia, and 145 examples from unaffected individuals. The staying 90 CT sets (14.6%) were utilized as the test set, including 30 COVID-19, 30 Influenza-A viral pneumonia, and 30 solid cases. Moreover, the experiments of CT set were chosen from individuals who had not been remembered for the preparation stage. To summarize, first, the CT pictures were pre-handled to extricate viable aspiratory locales. At that point, a 3D CNN model was utilized to partition different competitor picture 3D squares. The middle picture, along with the two neighbors of each block, was gathered for additional assessment. Next, a picture order model was utilized to group all picture patches into one of 3 kinds: COVID-19, Influenza-A-viralpneumonia, and inconsequential to-contamination. Picture patches from a similar block decided in favor of the sort and certainty score of the up-and-comer overall. At last, the full examination report for one CT test was assessed utilizing the Noisy-or Bayesian capacity [5, 6].

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7 Use of Chatbot to Share Information A robot is a computerized reasoning programming intended to do a progression of undertakings all alone and without the assistance of a human. The most persistent model is that of a chatbot. It is a software that uses natural language to communicate with the user to fulfill a task or to help them or inform them.

7.1

How Does the Chatbot Works?

Chatbots are fed with thousands of manuals or specific data according to the purpose they have to fulfill. The chatbots work by embracing three order strategies: 1. Pattern matches: They use patterns to perceive the content and produce an appropriate response for which the degree of structure is given by Artificial Intelligence Markup Language. 2. Algorithms: For every question, there is a unique pattern stored in the database which can become complex when encountered with a series of questions. For this, there are algorithms present to classify and generate more manageable data. 3. Artificial neural networks: Neural Networks are a technique for finding out the yield from the data using weighted affiliations which are resolved from reiterated cycles while setting up the data. Each movement through the arrangement data changes the heaps achieving the yield with precision. The prepared information of neural organizations is an equivalent calculation and less of coding [7–9]. In a situation where people are vulnerable to wrong information and theories, the govt has taken many measures to spread awareness about COVID-19 through online means. One among them is by the use of chatbots. Chatbots are appropriate for indication screening in a pandemic since individuals with disparaged conditions frequently abstain from looking for medical care (shown in Fig. 8). MyGov Corona Helpdesk chatbot is the authority chatbot of the Indian government to react to all COVID-19-related questions. The bot offers a wide assortment of data about the pandemic in a conversational and straightforward, easy-to-use way. The data incorporates indications, how the contamination is communicated, preventive measures, wellbeing and tourism warnings, and authority government helplines for additional help. Microsoft Azure Cognitive Services can be utilized by huge organizations and medical care associations to construct venture-level bots that can talk, comprehend, and tune in. Numerous medical clinics have just begun utilizing Microsoft’s tech to precipitously answer individuals’ questions about the indications of COVID-19, and screen patients basically before proposing them to desire for in-person assessments. The stage incorporates open-source SDK, and lets organizations keep control of their information.

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Fig. 8 Working of chatbots in COVID-19

With an end goal to educate the world about CODIV-19 better, the WHO has dispatched a Facebook Messenger variant of its WHO Health Alert stage offering brisk and precise data about COVID-19 through Facebook’s worldwide reach.

8 Symptom Analysis Using Machine Learning Approach ML can be utilized for the detection of locations of novel Covid. It can likewise anticipate the idea of the infection. Notwithstanding, ML requires a lot of information for arranging or estimating the infections. Directed machine learning calculations need clarified information for characterizing the content or picture into various classes. ML and natural language processing make use of enormous data-based models for acceptance of example, identify, and forecast. NLP has escalated a lot of enthusiasm for ongoing years, majorly in the field of text investigation, Classification is one of the important assignments in text mining and can be performed utilizing various calculations. ML and deep learning can trade

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Fig. 9 Machine learning usefulness in pandemic

people by giving an exact analysis. As well as can be expected spare radiologists’ time and can be savvy than customary tests for COVID-19. X-beams and processed tomography (CT) sweeps can be utilized for preparing the ML model (Fig. 9). A group in Wuhan utilized ML-based technology to develop a prognostic forecast calculator to predict the mortality risk of a person that has been affected, using information from 29 patients at Tongji Hospital in Wuhan, China. Another group proposed an ML model that can anticipate an individual impacted by COVID-19 and has the probability to make extreme respiratory torment conditions (ARDS). The proposed model achieved 80% of precision. An illustration of 53 patients were used for setting up the model and are bound to 2 Chinese clinical facilities. ML can be used to dissect COVID-19 which needs a lot of study effort anyway isn’t yet commonly operational [10, 11]. An ML based model is created which is helping the Centers for Disease Control and Prevention conjecture the spread of COVID-19. This model is special since it doesn’t depend on the detailed cases. Rather, it focuses to the quantity of untested and unreported cases from the model’s information examination and uses those outcomes to foresee how fast the sickness will spread. This is called a “pandemic model” as it takes the different variables that influence the pace of ailment spread into account.

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Case Study

A team of researchers at King’s College London (London, UK) used a machine learning algorithm to analyze data from a subset of about 1600 users in the UK and US with certified COVID-19 who used to sign up using the app in March and April. The analysis revealed a breakdown of six specific symptoms that occur during factor periods in the progression of the disease. They found that people who were experiencing certain signaling conditions may need breathing support in the form of air or extra oxygen. They then developed a model that included data about age, gender, BMI, and existing conditions and symptoms collected in just five days from the onset of the illness. Although constant cough, fever, and loss of smell are often highlighted as the three most important signs of COVID-19, information collected from the app shows that people may experience symptoms including headache, muscle aches, fatigue, diarrhea, confusion, loss of appetite, shortness of breath more or less. These findings are important in the care and attention of those at high risk of the acute COVID-19.

9 Futuristic AI-Based Treatment Methods In medical care industry, the effect of AI, through NLP and ML, is changing the conveyance of care. As in different enterprises, it is normal that these innovations will keep on developing at a progressing pace throughout for a long period of time. There are mainly two applications of AI in healthcare (shown in Fig. 8): 1. Patient-oriented AI 2. Clinician-oriented AI. 1. Patient-oriented AI: The first thing we need is AI arrangements that help the move from clinic to locally established consideration, for example, distant observing, AI-controlled cautioning frameworks, or menial helpers, as patients take expanding responsibility for care. Wise self-administration shelters ML and NLP by breaking down data, redoing tolerant encounters, and speeding up cycles to additional expansion accommodation and productivity. Tolerant self-administration can likewise help in moving patients directly to an arrangement or to go to the enlistment work area, in view of predefined rules, consequently permitting the enrollment group to focus on a specific gathering of patients needing additional operations. 2. Clinician-oriented AI: Many clinician-situated applications can use NLP and ML. In the wake of examining a colossal number of pictures and related clinical information to accomplish a sufficient degree of learning, CAD frameworks can utilize the information they have figured out how to tentatively distinguish territories of the anomaly on imaging studies and produce a differential conclusion for the finding. Computer-aided designs are being inspected for use for

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Fig. 10 The futuristic treatment methods

various imaging examines, including mammography to distinguish territories dubious for breast cancer for the radiologist to additionally survey, and high-goal CT scans the chest to investigate large number of pictures in an examination to recognize zones suspicious for a cellular breakdown in the lungs that need extra radiologist audit. We can look through the chance of prescient investigation and huge information in crisis care. By evaluating the information in a few clinical records, information science devices can increase the expectations of crisis care through clinical forecasts dependent on patterns distinguished among comparative cases. An Individual’s risks could be found more precisely with huge pools of information and lower asset necessities for contrasting each clinical experience with those that preceded it, profiting clinical dynamic and wellbeing framework activities. So also, prescient examination and huge information can be utilized to upgrade drug results. A prescient examination that utilizes enormous information will probably turn into a crucial apparatus for clinicians in planning mediations and improving patient results (Fig. 10).

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Computer Vision to Detect Coronavirus Infection

Computer vision is an AI-based innovation that permits computers to see the world. By breaking down visual information, this innovation can impeccably comprehend a specific circumstance, and without missing any component, locate the best arrangements or the most discerning choices. Artificial intelligence empowered Computer vision advances are fit for spotting side effects related to COVID-19—for example, fevers and anomalous respiratory structures—with high precision and progressively.

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Fig. 11 A classification of computer vision approaches for controlling COVID-19

For the time being, the most utilized strategy for checking the temperature at air terminals or fringe intersections is through thermometer weapons. Shockingly, this technique needs human help (ordinarily wellbeing officials) and is tedious considering the number of individuals who go through those points (Fig. 11).

10.1

CT Scans

A chest CT check is a non-obtrusive test designed to get an exact picture of a patient’s chest. It uses an advanced type of x-ray innovation, which gives more point-by-point pictures of the chest than a standard x-ray. It produces pictures that incorporate bones, fats, muscles, organs, and appendages giving specialists a superior viewpoint, which is significant when making precise diagnosis. A 3D CT picture contains 200–400 cuts of pictures. This can set aside a long effort for an expert to diagnose. Since COVID-19 has similar attributes as different sorts of pneumonia, it would take an accomplished doctor in any event, 10 min to diagnose a single patient. In this manner, an AI-helped analysis utilizing computer vision is much needed. One of the latest advancements, created in China, is a calculation that can distinguish patients with Covid from chest CT checks in 20 s. The precision has been 95%. Clinical offices would now save their important time as this cycle used to take in any event 15 min for medical care experts. In addition, a computer vision-based framework can recognize Covid from regular pneumonia. In China, this new innovation has been utilized in numerous medical clinics [12, 13].

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X-Ray Imagery

The problem with CT imaging is the requirement for higher patient portion and improved expense. It makes advanced chest x-beam radiography as an ease imaging mode and more extensive accessibility for recognizing chest pathology. In this manner, a programmed conclusion of COVID-19 highlights in CXR will make it an extremely valuable analytic device against the infection. It is hard to recognize delicate tissue against a helpless difference in X-beam symbolism, so the correlation upgrade is utilized as pre-handling step. Versatile chest X-beam is the most generally performed radiological examination regarding practicality and cost viability even in created nations. In crown, filtration fixates where on a normal over hundred presumed patients are being screened for COVID 19, convenient chest X-beam is the best radiological screening instrument.

10.3

Temperature Check

A contextual analysis of Taiwan IN DISEASE PREVENTION AND MITIGATION: Taiwan has revealed less than 481 instances of the Covid and only seven passings till 14 Aug. The nation’s capacity to control the spread of the illness is credited to measures set up after the SARS plague, which incorporates the utilization of temperature observed in air terminals to screen explorers for fever (shown in Fig. 12). Numerous air terminals in Taiwan utilize high-goal warm cameras and profound learning. When the cameras catch infrared pictures of the travelers, the picture information is shipped to the frameworks like the Premio VCO-6020-1050TI or VCO-6022C-2PWR mechanical GPU PCs. The pixels are prepared by on-chip Intel® or NVIDIA® illustrations motors, while most CPUs execute PC vision calculations.

Fig. 12 Temperature check with the help of cameras on stations and public places

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AI Models to Detect Mask-Wearing Detection to Developers

Artificial intelligence models using image recognition can find people who are not wearing masks while being on a public transportation or in station, air terminals, markets, and so forth. Besides, the framework is forward to the point that it can likewise recognize the individuals who don’t wear their covers accurately. The Face Mask Detection System can be utilized at office to recognize if representatives are keeping up with security rules at work. It screens representatives without covers and sends them a suggestion to wear a mask.

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Using AI to Screen COVID-19 Patients

One way AI recognizes Covid is with warm detecting cameras. Incorporated Health Information Systems (IHiS), the public HIT organization in Singapore, has banded together with nearby medical care AI startup KroniKare to drive iThermo—an AI-fueled temperature screening arrangement that screens and distinguishes those having or demonstrating fever side effects. iThermo diminishes the requirement for manual temperature screening, and sets aside less effort to direct manual screening and it likewise limits the introduction to the dangers of the infections for bleeding-edge clinical staff. iThermo utilizes a cell phone outfitted with warm and 3D laser cameras. The AI application measures and dissects the pictures from the cell phone camera (which catches facial highlights), planning them to pictures from the warm camera (which estimates temperature) and from the laser camera (which estimates separation). The Smart Health arrangement utilizing AI can perceive human facial highlights from warm pictures to gauge the temple temperature, including people wearing displays, careful veils, caps, and other headgear, in any event, when they are strolling. The arrangement can carry out this responsibility for up to 5000 individuals in a single day. An AI framework created by Chinese tech organization Baidu that utilizes an infrared sensor and AI to anticipate individuals’ temperatures is presently being used in Beijing’s Qinghe Railway Station. Baidu’s AI infrared vision innovation takes care of the issue of quickly recognizing the internal heat levels of enormous quantities of individuals in thickly populated regions with high volumes of traffic (shown in Fig. 13). As the framework includes no human reach, it can immediately screen groups to improve effectiveness of recognition and precision with insignificant disturbance to general society. Critically, it does as such while keeping individuals at a protected separation, accordingly, diminishing the danger of cross-defilement. This methodology joins PC vision and infrared to recognize the brow temperature of up to 200 individuals in a moment inside scope of 0.5 °C. The framework cautions specialists in the event that it recognizes an individual with a temperature above 37.3 °C (99.1 °F) in light of the fact that the fever is an indication of COVID-19.

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Fig. 13 Sensors screening people

BMC in Mumbai is expected to scrutinize COVID-19 residents according to their sound waves. The results of the test can detect the presence of the virus in 30 s, and those who have been diagnosed will undergo a transcript-polymerase chain reaction (RT-PCR) swab test to determine if they are infected. Using artificial intelligence, Dr. RN Cooper Municipal General Hospital will conduct a study of 2000 patients. A rapid antigen test may give results within 30 min, but chances are, this method may give a higher rate of false results. When symptoms of COVID-19 are detected, the patient develops respiratory problems, which affect the amount of air exit, thus involving the burning muscles in the process of producing speech or voice. This interaction affects the tone of the measurable attributes that form the basis of their biomarker. The app will be used to analyze the voice of three types of people—suspicious, positive and negative patients. Depending on their biomarkers (VB), they will be available.

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Opinion Mining and Sentimental Analysis for COVID Information Tracking

Sentiment analysis additionally alluded to as opinion mining, is a way to deal with Natural Language Processing that recognizes the passionate tone behind a body of the content. It is a frequently used way for associations to decide and arrange opinions about an item, administration, or thought. It includes the utilization of data

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mining, ML, and AI to dig text for opinion and emotional data. Slant investigation frameworks assist associations with get-together experiences from sloppy and unstructured content that originates from online sources, for example, messages, blog entries, uphold tickets, webchats, web-based media channels, gatherings, and remarks. Calculations supplant manual information preparing by executing either manage, or via programmed implies, or by half and half techniques. Rule-based frameworks perform conclusion examination dependent on predefined, vocabulary-based guidelines while programmed frameworks gain from information with AI procedures. In this day and age, web-based media stages like Twitter are critical to individuals’ regular day-to-day existence. We need to manage the appearances on these stages, and as AI turns out to be increasingly mainstream and significant simply like the NLP, we need to manage this and examine and research the feelings on these stages. Appropriately prepared model examinations with a recently mined dataset that coordinates the current pattern (Covid subjects now) and dataset fabricate number, (number of scratched tweets) which narrowing the hover of a bigger measure of information into a smaller theme. Along these lines, we don’t just show that the information should be positive or negative, yet we likewise give a more nitty-gritty breakdown of the passionate levels. This can give more exact information than dissecting bigger datasets, as crisp mining is consistently accessible, so you can get a lot quicker and more precise outcomes as the end-product than prior bigger examples and different surveys. Alongside the pandemic, another emergency has introduced itself as mass dread and frenzy wonders, fuelled by deficient and frequently uncertain data. There is accordingly an enormous need to address and better comprehend the pandemic’s enlightening emergency and check public feeling, with the goal that appropriates informing and strategy choices can be executed. There is enormous disappointment among everyday citizens due to the proceeded with physical, material, and psychological wellness challenges introduced by the Lockdown, proven by the developing number of fights far and wide. There gives off an impression of being a critical conclusion, and a powerful urge in individuals to return to work, fulfill the essential physical, mental and social needs, and energy to earn cash [14, 15].

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Case Study

Tweets with catchphrase COVID-19 from the initial nine days of May 2020 were broke down to appraise public opinion along with eight key elements of outrage, expectation, disturb, dread, euphoria, pity, shock, and trust. Conclusion examination with Tweets presents a rich exploration opportunity, as countless Twitter clients express their messages, thoughts, opinions, emotions, comprehension, and convictions through Twitter posts. Assumption examination has accomplished ascent in research with the improvement of cutting edge etymological displaying structures and can be accomplished utilizing numerous all around perceived

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techniques, and instruments like R with promptly accessible libraries and capacities, and with the utilization of watched literary investigation programming to distinguish predominant notion, conduct or trademark attribute. The quantity of death in the only us is more than 89,000 passings and 1.4 million COVID-19 cases in total (In May 2020). There have been numerous monetary misfortunes and mental trouble because of the activity loss of millions of individuals. This examination was done to find the public notion of resuming the US economy. The scaling up of processing advancements over the previous decade has made it workable for immense amounts of unstructured information to be dissected for designs, including the recognizable proof of human slant communicated in literary information. Opinion investigation is one of the fundamental exploration experiences profits by printed examination as it extricates conceivably planned 10 slants significance from the content being dissected. The beginning phase past examination utilized custom strategies and analyst de-fined conventions to recognize both slant and character attributes, for example, predominance, through the investigation of electronic visit information, and normalized techniques to relegate positive and negative opinion scores. Supposition examination doles out feeling scores and classes, by coordinating watchwords and word groupings in the content being investigated, with prewritten vocabularies and comparing scores or classes. R and R packages Syuzhet was utilized to group and score the resuming Tweets dataset. The R bundle Syuzhet was utilized to characterize the Tweets into eight estimation classes. Syuzhet additionally quantifies positive and negative estimation by a basic aggregate of unit positive and negative qualities allocated to the content, and subsequently, a solitary complex enough Tweet may at the same time be scored as having a positive score of 2 and a negative score of 1. The last supposition score from the assessment bundle is a summation of slant esteems relegated to parts of the sentence (or printed field) and can be less than −1 or more than 1 [16] (Table 3). The results of the study showed people’s sentiment reflecting profound worries about Coronavirus, prompting the distinguishing proof of development in dread slant and negative notion. Enterprises and private ventures can likewise exploit through such investigations and AI models to comprehend shopper assumptions and desires better. Through such investigates, establishments laid to dissect bigger volumes of new information which are relied upon to help manufacture models to help the financial recuperation measure in the time ahead [2, 17, 18].

Table 3 Values assigned for different sentiments

Word

Sentiment

Good Great Terrible Alright

0.5 0.8 −0.8 0.1

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Image Processing of COVID-19 Pictorial Samples

As technology has improved, we can see real images of a cell and the virus. Electron microscopy and X-beam crystallography are two different ways through which specialists can take amazing pictures of infections, for example, SARS-CoV-2, HIV-1, MERS-CoV, flu, the human metapneumovirus, or the respiratory syncytial infection by utilizing a checking electron magnifying lens. Identifying the effect on human lungs due to COVID-19 in a radiological image is difficult for a common man, which can only be observed by medical experts. So using some image processing techniques we can easily visualize the effect of COVID-19 on human lungs. Thresholding is an easy method of segmenting images. Segmentation is a core part of picture processing and computer vision applications such as medical image segmentation, disease recognition, handwriting recognition, traffic control system, video surveillance, etc. Picture division algorithms are generally founded on any of the two properties of power esteems, irregularity, and closeness. Brokenness segments a picture dependent on the unexpected changes in the force, for example, edges of a picture. While the likeness depends on dividing a picture into areas that are comparative as per a lot of predefined models. Thresholding is the all the time utilized picture improvement procedure utilized for partition a picture into article and foundation. Pixel values above the threshold value are “object” and below are considered as “background” and eliminates unimportant shading variations. In thresholding, the objects from their background are extracted using a threshold value T. At that point any point (x, y) for which f(x, y) > T is called an object point, and f(x, y) < T is known as a background point. The urgent worth that is utilized to choose whether any given pixel is to be dark or white is the ‘edge’. By actualizing the proposed calculation the assessment of the limit relies solely upon the property of the pixel and the dim level estimation of the picture [19, 20].

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Case Study

In this, a sample of two radiological images of lungs of a woman was taken, who has a cough, and respiratory distress for one year and now she got affected with COVID-19 (shown in Fig. 14). After applying the thresholding technique, it becomes easy to analyze the image and predict the outcome. In Fig. 15, the Grey value of COVID-19 affected lung image is found to be high when compared with that of healthy lung image, and the virus effected part is clearly visualized.

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Fig. 14 Image of lungs before and after COVID-19

Fig. 15 Sample of COVID-19 affected lungs image after processing-gray value (0.5098)

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Data Science Norms and Guidelines

Today, the world is fighting a new enemy we cannot see, hear, or touch, yet it’s wiping out more than 15,000 thousand people as the global loss of life rises. People are walking around in a daze with anxiety and nervousness after the pandemic. Stockpiling food, toiletries, and personal care products—feels like a horror movie

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the universe is living in. Towns deserted, streets empty, store shelves empty and roads without cars. The epic COVID-19, which is gradually losing its novelty as the time of defilement is expanding step by step, is exceptionally infectious. So far there is no useful medication to fix the found illness. So it has become necessary to adapt to some new norms in the field of technology for obtaining any possible medication or vaccination. Contact Tracing: It has become very important to trace every individual who gets infected. Some contact tracking applications with over 1,000,000 downloads are India’s Arogya Setu, Singapore’s Trace Together, and South Korea’s Corona Watch. These applications by using Bluetooth and GPS innovation of the cell phone sends alarms to clients when they penetrate social distancing standards. As soon as an infected person comes near you, the app detects it and alerts the user, and insists them to get tested. Updated Information: In a pandemic situation, rumors are very likely to spread and creates fear among the people. Johns Hopkins University in relationship with Centers for Disease Control and Prevention of US, China, and Europe, and the World Health Organization (WHO) has delivered intuitive data sets that utilize genuine realistic data framework for checking insights concerning the patient contaminated, recouped, or died because of the infection. Better Outcomes with Preventive Care Treatments: Companies like Biotia a health tech startup giving AI-based remedies to diagnose and control infectious diseases. DNA sequencing-based techniques and exclusive AI-powered software are used to quickly and correctly identify pathogens and antimicrobial resistance. AI technology, machine learning, and data analytics—rapidly identify patients and patterns in medical data and who are at risk of severe health problems. The main focus should always be to flatten the curve so the virus can be eliminated. It can be done by using various analysis.

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Intelligent Predictive Frameworks for COVID-19 Diagnosis

Covid virus, for example, SARS-Coronavirus and MERS-Coronavirus that have risen up out of the creature supplies caused a worldwide scourge with troubling grimness and mortality. These infections have a place with the subfamily of Coronavirinae that is an aspect of the Coronaviridae family. Because of the brisk spread of the SAR-CoV-2, doctors are confronting extraordinary trouble in the determination of COVID-19. Despite the fact that the Reverse Transcription Polymerase Chain Reaction (RT-PCR) technique is the standard strategy utilized in the analysis of COVID-19, because of a pandemic, it experiences restrictions, for example, low affectability requires additional time, and short in gracefully. As advanced innovations are assuming a significant function in the avoidance of ailment, the overall wellbeing crisis is looking for the help of computerized

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innovation, for example, the utilization of measurable, AI, and profound learning models for the proficient analysis of clinical pictures to stop the spread of the illness. A few bits of examination have utilized various ways to deal with ML, for example, Support Vector Machine, Regression, Random Forest, K-implies, etc. in the forecast and finding of SARS-CoV-2. The irregular backwoods calculation is utilized as a segregation instrument to investigate patients with COVID-19 indications. The proposed strategy accomplished better results in the expectation of COVID-19 with a precision of 0.9795 for the cross-approval set and 0.9697 for the test set. Further, the apparatuses additionally accomplished better execution regarding affectability, particularity, and by and large exactness of 0.9512, 0.9697, and 0.9595, individually on an outer approval set [21]. Other than irregular backwoods and backing vector machines, different methodologies of AI approaches, for example, straight and strategic relapse, XGBoost, K-implies, neural organizations have additionally been utilized in the clinical and general wellbeing approach. A superior depiction of the investigation populace could likewise assist us with understanding the watched inconstancy in the revealed results across considers, for example, Coronavirus-related mortality. The changeability in the overall frequencies of the anticipated results presents a significant test to the expectation modeler. An expectation model applied in a setting with an alternate relative recurrence of the result may create forecasts that are miscalibrated and might be refreshed before it can securely be applied in that new setting. Such an update may regularly be required when expectation models are moved to various medical care frameworks, which requires information from patients with Coronavirus to be accessible from that framework.

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Case Study

With given information of patients that tried positive for Covid, a few highlights were distinguished like age, sex, signs, and indications, length of side effects, and so forth Subsequent to thinking about the highlights, the information pre-preparing techniques and the predictive analysis was done on the person. In predictive analysis, highlight designing is the cycle of algorithmically decreasing the dimensionality of the element space to a littler arrangement of highlights with higher prescient force versus the predictive mark. There are two significant sorts of highlight choice techniques: filter methods and wrapper methods. Filter strategies utilized in this investigation depend on entropy that is generally applied in data hypothesis. On account of this test, entropy is a metric that estimates how much data a component embodies to help foresee the last class mark ARDS of the sample. The higher the entropy of an element, the more fluctuation that highlights shows, and hence the more probable that element contains important data for foreseeing the last name.

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This examination shows that predictive analysis can assume a function in increasing clinical abilities in recognizing “wiped out” from not “debilitated”. The model features that a few bits of clinical information might be undervalued by clinicians, for example, mellow increments in ALT and hemoglobin just as myalgias. Key qualities prescient of finding, including fever, lymphopenia, chest imaging, were not as prescient of seriousness. Further refinement of these models with more information, from various settings with various ranges of seriousness, would fortify the prescient intensity of the model and permit it to be a helpful instrument in recognizing right on time from the numerous with COVID-19, who will build up the more genuine malady and require nearer clinical consideration in future [22, 23].

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Application for Big Data in Coronavirus Analysis

Big Data innovation has the capability to store a gigantic measure of data about the individuals suffering with this COVID-19 infection. It helps in understanding the possibility of this disease in detail. The data got can moreover be set up over again for making future preventive methods. This development is used to store the data of a wide scope of cases (spoiled, recovered, and passed) affected by COVID-19. Enormous data gives a tremendous proportion of information to the specialists, prosperity workers, sickness transmission specialists and encourages them to make a good choice to fight the COVID-19 disease. This data can be used to follow the contamination on an overall reason perseveringly and to make headway in the clinical field. It can help with assessing the impact of COVID-19 on each particular domain and the whole people. It helps in the imaginative work of new treatment systems. Enormous information can moreover give likely sources and opportunities to people and, along these lines, help to manage the horrendous condition [24]. Generally, this advancement offers data to grasp assessment of the disease transmission, improvement, and prosperity noticing and expectation framework. Enormous information examination will go probably as a mode for following, controlling, assessment, and evasion of COVID-19 as a pandemic [25, 26]. It will widen manufacturing, overhaul vaccination headway on more huge techniques, with all out data (Table 4).

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Integration of IOT and Cloud Computing in COVID-19 Detection and Diagnosis

Internet of Things engaged gadgets have made distant observing possible in the medical administrations area, by keeping patients protected and strong, and empowering specialists to pass on standout consideration. It has moreover extended

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Table 4 Applications of big data in COVID-19 pandemic [27] Applications

Description

Identification of infected cases

It is equipped for putting away the total clinical history, all things considered, because of its capacity of putting away a monstrous measure of information By giving the caught information, this innovation helps in identification of the contaminated cases and embrace further examination of the degree of dangers Used to store the movement history of individuals to investigate the danger Assists with distinguishing individuals who might be in contact with the contaminated patient of this infection Enormous information can keep the record of fever and different side effects of a patient and propose if clinical consideration is required Assists with recognizing the dubious cases and other falsehood with the proper information Rapidly assists with distinguishing the contaminated patient at a beginning phase Assists with examining and recognize people who can be contaminated by this infection in future Serves to effectively investigate the quick-moving illness as efficiently as could be expected under the circumstances Potential to deal with suitable data in regards to the sickness This innovation gathers data with respect to this infection during the lockdown Track and screen the development of individuals and whole wellbeing the board It assists with examining the quantity of individuals entered or leaving from the affected city With these tremendous measure of information, wellbeing pro can rapidly recognize the odds of the infection in those individuals Aid optimizing the improvement of new prescriptions and gear required for current and future restorative requirements Gives past information of infection occupied or spread and, consequently, helps in increasing a giving bit of leeway over fresher pandemic/plague with recently examined results

Travel records

Fever symptoms

Identification of the virus at an early stage

Identification and analysis of fast-moving disease Information during lockdown

People entered or leaving the affected area

Faster development of medical treatments

patient incorporation and satisfaction as participations with specialists have gotten easier and more powerful. In addition, far off checking of patient’s prosperity helps in diminishing the length of clinical center remain and prevents re-insistences. IoT furthermore significantly influences reducing clinical consideration costs by and large and improving treatment outcomes. Diagnose of COVID-19 can be improved with the assistance Internet of Things. Also, Cloud registering can help with constant online communication.

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Case Study

In Chinese case findings whose focus is to analyze Coronavirus earlier and to improve its treatment by applying clinical development, the “Coronavirus Intelligent Diagnosis and Treatment Assistant Program (nCapp)” in light of the Internet of Things, the utilization of IOT and distributed computing was actualized in nCapp to analyze COVID-19 patients [2]. The IoT was initially alluded to as radio-recurrence recognizable proof innovation and gear joined with the Internet-dependent on the concurred correspondence convention to accomplish savvy the executives of thing data. Today, this idea has been extended and developed, that is, the utilization of correspondence advances, for example, nearby organizations or the Internet to associate sensors, machines, individuals, and things to accomplish the association among individuals and things, things and things, individuals situated informationization, and controller and keen administration. The most essential useful component of the IoT is “omnipresent availability.” Its three fundamental cycles are far-reaching discernment ! solid transmission ! wise handling. The utilization of the IoT to medication is alluded to as the clinical IoT (MIoT) which plans to build up a choice arranged large information investigation model upheld by data innovation, for example, correspondence, gadgets, science, and medication. MIoT can likewise be utilized for the anticipation and control of COVID-19. We can set up a three-level linkage nCapp framework dependent on the clinical hypothesis and innovation of the IoT to analyze and treat COVID-19. The IoT nCapp cloud clinical framework stage contains the fundamental elements of the IoT and has center illustrations handling unit (GPU). Distributed computing frameworks associated with existing electronic clinical records, picture chronicling, and picture documenting and correspondence can more readily aid profound mining and keen finding. Among them, the elements of internet observing, area following, alert linkage, and subsequent timetables are helpful for online revelation, checking, the executives, and treatment help for COVID-19. Plan the executives, far off upkeep, order the board, and factual dynamic capacities can grow the monstrous data mining of COVID-19 and complete administration and ideal treatment of COVID-19 by applying preset rules or normalized standards. Security protection and online redesign capacities are nCapp ensure that can guarantee the typical activity of the IoT cloud in addition to the terminal framework [28]. It can likewise help with posing inquiries; enlisting patients’ subtleties; organizing with patients, network specialists, and specialists; and giving safe determination treatment projects and two-way references (Fig. 16).

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Fig. 16 The eight functions of nCapp in a smartphone

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Limitations of Data Science Technologies

Coronavirus is a gigantic general medical condition that has brought about numerous passings and demonstrating how we structure our general public with regards to significant things like the accessibility and moderateness of medical services, specialist’s privileges, and even opportunity of development [29]. As we are progressing in innovation, AI has not yet been effective against COVID-19. Its utilization is hampered by an absence of information, and by a lot of information. The impediments can be found in the following ways: 1. Tracking and prediction: number of promising activities, notwithstanding, have been begun to accumulate and share information - both existing information, new information, and to prepare new AI models. As an aftereffect of an absence of information, loud online media and exception information, huge information hubris, and algorithmic elements, AI figures of the spread of COVID-19 are not yet extremely exact or solid. 2. Diagnosis and prognosis: Largely, the capability of AI is conclusion isn’t yet continued into training, despite the fact that it has been accounted for that various Chinese clinics have sent “computer based intelligence helped” radiology innovations. Radiologists from different spots have demonstrated their anxiety that there isn’t adequate information accessible to prepare AI models, that the greater part of the accessible COVID-19 pictures originate from Chinese medical clinics and may confront issue of determination predisposition, and that utilizing CT-outputs and X-beams may degenerate the hardware and spread the infection further. 3. Treatments and vaccine: The supposition that is AI can quicken both the cycles of finding new medications just as for repurposing existing medications. Various scientists have just detailed finding drugs for repurposing. It isn’t likely that these medicines (specifically an immunization) will be accessible sooner rather than later, in any event, to be very useful during the current pandemic. The explanation is that the clinical and logical checks, trails, and controls that should be performed before these medications will be endorsed, when they have been distinguished and screened, will require some investment as indicated by gauges as long as a year and a half for an immunization.

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4. Social control: AI has been contended to be important to deal with the pandemic by utilizing warm imaging to filter public spaces for individuals possibly contaminated, and by implementing social separating and lockdown measures. The issue is before the flare-up is finished, the disintegration of information security would not be moved back and that administrations would keep on utilizing their improved capacity to study their populaces and utilize the information acquired in the battle against COVID-19 for different purposes [30]. Adaptability to accumulate and dissect big data quickly is basic in combatting the pandemic, regardless of whether it might necessitate that the specialists gather more close to home information than numerous individuals would feel great with. Subsequently, it is vital that the specialists take specific consideration in their treatment of such data and their avocations and interchanges to the general population on the loose. The peril is that individuals could lose trust in government.

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Conclusion

These days, COVID-19 disease brought about by SARS-CoV-2 is at the top of all researched areas because it’s having a huge impact on millions of people in various countries in a less period of time. The whole political, social, monetary, and budgetary crisis has been disrupted by the outbreak. The providence of the world’s leading countries is on the verge of destruction. With more than 100 countries already involved in the prevention of virus, business worldwide is working in terror of collapse of global markets. Areas, for example, the graceful chain, exchange, transport, the travel industry, and the inn business have been seriously influenced by the pandemic. Different areas, for example, Apparel and material, development, and development have been hit hard because of staff deficiencies and the accessibility of crude materials. Another area that has been seriously influenced is the aeronautics business as both global and homegrown flights have been dropped because of lockdown in numerous nations. Even though the locking impact is lower for a noteworthy retailer, a few retailers, for example, stores and markets are vigorously affected by the circumstance. Subsequently, instructive organizations have additionally been seriously influenced and prompted the conclusion of foundations which has disturbed understudy learning and inner and outside appraisal needed to get an understudy certificate. Albeit a few areas have been influenced, there are explicit areas, for example, Digital and Internet Economy, Food-based retail, Chemicals, and Pharma areas that have seen development during the elimination time frame. Information Science techniques are additionally useful in foreseeing the effect of COVID-19 on different areas that can help the government in actualizing fitting arrangements to conquer monetary troubles [2]. Because of the absence of time and its quick spread, medical services associations have a dire requirement for dynamic innovation to deal with the infection and help them in discovering ongoing suggestions to prevent its spread, in the battle

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against the current pandemic. On the off chance that we accept this opportunity to gather information, consolidate our insight, and join our abilities, we can save numerous lives—presently and later on [20].

References 1. Faisal M (2020) COVID-19 pandemic is not the end of the world: a global perspective. Jundishapur J Health Sci 2. Swapnarekha H, Behera HS, Nayak J, Naik B (2020) Role of intelligent computing in COVID-19 prognosis: a state-of-the-art review. Chaos, Solitons & Fractals 3. Dogra S, Gupta A, Goyal V, Chib AS, Kataria V (2020) Recent trends, therapeutic applications, and future trends of nanomaterials in dentistry. Elsevier BV 4. Computational intelligence methods in COVID- 19: surveillance, prevention, prediction and diagnosis. Springer Science and Business Media LLC 5. Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K (2020) Deep neural network to detect COVID-19: one architecture for both CT scans and chest X-rays. Springer Science and Business Media LLC 6. Wang L, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv:2003.09871 7. Mallick PK, Mishra S, Chae G-S (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput:1–16 8. Mishra S, Tripathy HK, Mallick P, Bhoi AK, Barsocchi P (2020) EAGA-MLP—An enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20:4036 9. Mishra S, Mallick PK, Jena L, Chae G-S (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Heal 8:274 10. How do chatbots work? a guide to the chatbot architecture. https://marutitech.com/chatbotswork-guide-chatbot-architecture/ 11. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10:244– 256 12. Khanday AMUD, Rabani ST, Khan QR et al (2020) Machine learning based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol 12:731–739. https://doi.org/10. 1007/s41870-020-00495-9 13. Ulhaq A, Born J, Khan A, Gomes DP, Chakraborty S, Paul M (2020) COVID-19 control by computer vision approaches: a survey. IEEE Access 14. Ali N, Samuel Y, Pelaez A, Chong PH, Yakubov M (2020) Feeling positive about reopening? new normal scenarios from COVID-19 reopen sentiment analytics. Cold Spring Harbor Laboratory 15. Mishra S, Tripathy HK, Mishra B, Sahoo S (2017) Implementation of classification rule mining to minimize liver disorder risks. Int J Control Theory Appl 10:117–124 16. Samuel J, Ali GG, Rahman M, Esawi E, Samuel Y (2020) COVID-19 public sentiment insights and machine learning for tweets classification. Cold Spring Harbor Laboratory 17. Sarkar A, Kumar R (2017) Chapter 1 study of various image segmentation methodologies. IGI Global 18. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494 19. Jiang X, Coffee M, Bari A, Wang J et al (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Mater Continua

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20. Haleem A, Javaid M, Khan IH, Vaishya R (2020) Significant applications of big data in COVID-19 pandemic. Indian J Orthopaed 21. Internet of medical things for smart healthcare. Springer Science and Business Media LLC, 2020 22. Ray C, Tripathy HK, Mishra S (2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, Hyderabad, India, 9–11 Jan 2019, pp 209–219 23. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms. IGI Global, Pennsylvania, PA, USA, pp 413–432 24. Bai L, Yang D, Wang X, Tong L et al (2020) Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19). Clin eHealth 25. Callaghan S (2020) COVID-19 is a data science issue. Patterns 26. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. https://www.ncbi. nlm.nih.gov/pmc/articles/PMC7186767/ 27. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201 28. Chaudhury P, Mishra S, Tripathy HK, Kishore B (2016) Enhancing the capabilities of student result prediction system. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies, Udaipur, India, 4–5 March 2016, pp 1–6 29. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486 30. Hussain AA, Bouachir O, Al-Turjman F, Aloqaily M (2020) AI techniques for COVID-19. IEEE Access

Chapter 2

Role of Artificial Intelligence in Forecast Analysis of COVID-19 Outbreak Adarsh Sandhu and Kiran Manisha Sahu

1 Introduction Corona viruses are a large class of viruses that are usually found among animals. They can be sent from animals to humans (in the rare case). The spikes sticking out from the virus’s outer surface seems like the sun’s corona, it is from this that ‘coronavirus’ was named. It affects the respiratory tract, its symptoms can vary from respiratory illness to severe conditions like SARS. It was said by World Health Organization (WHO) that the novel coronavirus (nCoV) is a new strain of the virus that has not been previously found in humans. BlueDot, a company got an indication of the possibility of “unusual pneumonia” and warned its client of possible indication of a pandemic by December 31, 2019. COVID-19 is a disease caused by the infection of the novel coronavirus, which can be easily transmitted from human to human in close contact. World Health Organization named this virus “Covid-19”. This virus has spread its influence in more than hundreds of countries in the world and most of them are still troubled with it. In Fig. 1 we can observe that worldwide cases have increased exponentially. In Fig. 1 we can observe that worldwide cases have increased exponentially and how the number of active cases has been increasing from January 2020 globally, however, few countries have done very well in restricting the spread of the virus such as New Zealand, Taiwan, Germany. Also, some countries have been struggling for a long duration with the spread of the virus such as the USA, Brazil, India. We can see the number of cases in countries who were struggling for a long duration in combating the COVID-19 virus in Figs. 2 and 3. We can see cases in A. Sandhu (&)
K. M. Sahu (&) School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India e-mail: [email protected] K. M. Sahu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_2

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Fig. 1 COVID-19 cases worldwide

Fig. 2 Active cases in the US, Brazil, and India

the countries who have been successful in combating this COVID-19 virus, we can observe that the curve is flattened so, it can be concluded that for successfully controlling the spread of the virus, the country should be able to flatten the curve of active cases.

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Fig. 3 Active cases in New Zealand and Taiwan

New Zealand, Taiwan has effectively controlled COVID-19 active cases. There are some factors like low population density, effective government functioning, and better infrastructure which helped these countries in combating this virus. In today’s world new technology especially artificial intelligence has made human life easier and in various places, artificial intelligence is used for solving a real-world problem. The main objective of this chapter is to know about how artificial intelligence with other technology such as robotics, big data, image processing, data mining, etc. has helped us in different stages of this pandemic. This chapter will highlight the technology which has helped humans in fighting against COVID-19.

2 A Survey of AI Against COVID-19 A method based on three-dimensional deep learning, namely COVID-19 detection neural network (COVNet), is introduced in [1] for the detection of COVID-19 based on volumetric chest CT images. Three kinds of CT images which include COVID-19, community-acquired pneumonia (CAP), and other cases of non-pneumonia, are combined to test the effectiveness of the proposed model. Another deep learning-based methodology on the concatenation between the three dimensional CNN ResNet-18 network and the location-attention mechanism is proposed in [2] to discover corona virus infection using pulmonary CT images. In studies [3, 4], Distinct manifestations of CT images of COVID-19 were found and

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their differences with other types of viral pneumonia such as influenza-A are exploited through the proposed neural network-based model. A modified stacked auto-encoder deep learning methodology is used in [5] to forecast the confirmed cases of COVID-19 across China in real-time. This modified auto-encoder network consists of four layers, i.e., input layer then first latent layer, second latent layer, and the output layer, with the no. of nodes correspondingly is 8, 32, 4, and 1. Along with this, a prototype of an AI-enabled system, namely a-Satellite, is introduced in [6] to evaluate the infectious risk of a provided geographical area at community levels. The system gathers various kind of real-time and large-scale data from heterogeneous sources, such as demographic data, no. of deaths and cases, social media data, and traffic density. Chang et al. [7] modify a stochastic agent and discrete-time based model, namely ACEMod (Australian Census based Epidemic Model), previously used for simulation of influenza pandemic [8, 9], for modeling Australia with the COVID-19 pandemic over time. A framework for detection of COVID-19 using data collected from smart phones’ on board sensors such as microphones, cameras, temperature, and inertial sensors is introduced in [10]. Machine learning algorithms are engaged in learning and acquiring knowledge about the disease symptoms based on the collected data. Likewise, Story et al. [11] use smartphone’s videos for nausea symptoms prediction Lawanont et al. [12] use images and sensors’ measurements for monitoring and human headache level prediction. For cough type detection in [13, 14], audio data is obtained from smartphone’s microphone. A phone-based online survey is proposed in [15] where individuals’ basic travel history and their common manifestations are collected. A hybrid AI model is used for COVID-19 infection rate prediction which is proposed in [17], and is a combination of deep learning tools, epidemic susceptible infected (SI) model, and NLP. In another work, Lopez et al. [18] recommend the use of NLP and text mining to analyze a multilanguage Twitter dataset to understand the common responses to the COVID-19 outbreak across countries and the changing policies. Likewise, three machine learning models including Naive Bayes, support vector machine (SVM), and random forest are used in [19] to classify 3000–4000 COVID-19 related posts gathered from Sina Weibo, a Chinese equivalent of Twitter, into seven types of situational information. To predict structures of several proteins associated with COVID-19 based on their related amino acid sequences, Google DeepMind is using the latest version of their protein structure prediction system, named AlphaFold [20, 21]. This is being expected that these type of predictions will help to understand how the coronavirus functions and lead to future development of therapeutics against COVID-19. Using genomic signatures and a decision tree approach, Randhawa et al’s. [22] objective is to predict the classification of COVID-19 based on an alignment-free machine learning method [23].

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3 Role of Artificial Intelligence (AI) in Handling Pandemic AI has been used in this pandemic for various purpose, currently, we are living in a smart world and our approach to solving a problem is becoming smarter day by day, during this pandemic various technologies especially Artificial Intelligence is used to identify, track and forecast outbreaks of the COVID-19 infection along with this it has also been used in the diagnose the COVID-19 patients. In Fig. 4 we can see AI is helping us in fighting against COVID-19 in multiple ways. Some samples use cases are discussed below. • To get recovery from the loss which has been caused due to pandemic AI is helping in tracking the economic recovery, WeBank has used AI to detect solar panel installation from satellite imagery and used it to track China’s economic recovery. • During this pandemic, it was strictly advised to maintain social distance and follow some protocols such that transmission can be prevented. AI-based UVD robots helped in disinfecting hospitals and public places and several AI-enabled chatbots helped people in getting assistance related to this pandemic. • For preventing people AI has been used, Aragoya Setu and several other AI-based apps were developed which helps in monitoring and determining the probability of infection of a person.

Fig. 4 AI in different phases of COVID-19

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• AI has been used in detecting if a person is infected or not, several apps were made which help people in getting early warnings, computer vision helped in determining if a person is having COVID-19 by using the image of chest X-ray.

4 Artificial Intelligence in Identifying the COVID-19 Patients During outgrowth of the novel coronavirus, many researchers tried to identify the main reason behind it. During the early pandemic, a researcher from Montreal University tried to collect radiology scans to train the Artificial Intelligence model to accept warning signals of patients affected through COVID-19 [24, 25]. After a few months, many companies and researchers tried to help in the identification of COVID-19 patients using lungs ultrasound picture, CT scans, chest X-rays with some prior information about the patient. From this modeling kind of training to AI models, it can be predicted whether the patients need more attention or can be taken back based upon the severity of the disease [26].

Fig. 5 Journey of COVID-19

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Figure 5, depicts the journey of COVID-19, during the journey at various stages Artificial Intelligence has helped in combating against this virus. Initially, a swab is taken from a person in the testing site and its data is updated then sway is sent to the testing lab after the declaration of the result the data is sent to the state database and gets updated then result is sent back to the testing site and a spreadsheet is validated before sending it to the health department, after all these processes the patient is notified. There is an Application called COVID Voice Detector which gives results by taking the user’s voice as input. For getting a valid output the user has to go through many steps such as the person has to cough and record his voice so that that voice can be compared to the COVID patients by the researchers, then the person has to hold out vowels as long as he could so that his/her lungs capacity can be measured. Finally, the result will tell if the person has any symptoms of COVID-19 or not. Based on the data (travel history and physical conditions) given by the user, prediction can be made upon the risk associated with the person. In Fig. 6 we can

Fig. 6 A decision tree for identifying symptoms of COVID-19

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see how a simple decision tree can help make predictions related to the risk associated with a person. There are various mobile applications based on the concept of a decision tree for predicting the risk factor of a person.

5 AI in Tracking the Spread of the Virus Artificial Intelligence and various other techniques have been used for tracking the hot-spot and infectious patients which can spread the virus. By tracking the person having COVID-19 related symptoms further, transmission can be avoided [27]. Many technologies have been introduced for tracking infectious patients, some of them are. COVID Near You: It is a website developed by GOOGLE in which people who are getting COVID-19 related symptoms are asked to describe their symptoms, travel history, and if they have been in contact with someone having or diagnosed with COVID-19. Based on these data it helps in avoiding further transmission. AROGYA SETU: This is a mobile application which helps in tracking the COVID-19 patients for avoiding its transmission. This app helps to prevent the risk of having COVID-19 infection and detects the presence of other people by using Bluetooth as proximity detection. When this app detects the interaction of two peoples, both the phones securely exchange some data which is stored on devices of all individuals. Based on the data, this app calculates your risk of infection. BLUE DOT: This is a Canadian start-up who have made a very early prediction about the spread of the virus, by using the global airline ticketing data, the next COVID-19 infected place was predicted. They have used natural language processing and machine learning algorithms for recognizing the outbreaks. DATA SCIENCE AND DATA MINING: Professors with some industry experts and students at IIT BBSR have used this technology to track the inflow of people from a different places in ODISHA to predict the future outbreaks. With the help of data provided by the government about the people who are traveling to ODISHA, the prediction was made about the future outbreaks in different districts of ODISHA. In the Fig. 7 we can see how data is helping in identifying potential COVID-19 patients [28]. There are many other apps available for tracking the COVID-19 infection. In Fig. 7, it is explained how intelligence plays role in giving warnings and preventing further transmission of the virus [16]. In Fig. 7 we can see on day 1 person A has been in contact with various people during the day, the data of contact with persons is saved in mobile phones of each person. On day 2 person A reports some symptoms and found himself COVID positive then an instant signal is sent to all of the persons who have been in contact with person A.

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Fig. 7 Basic working of COVID-19 apps

Fig. 8 Big Data in finding potential COVID-19 transmitter

In Fig. 8 we can see for knowledge mapping various government and police database along with data related to infection are used, with the help of these data mapping between confirmed cases people, and potential transmitter is done and as a result of this multi-source heterogeneous big data analysis, we get the details of people who may be a potential transmitter of COVID-19.

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6 AI in Forecasting COVID-19 Cases There are some set of models which has been used for the prediction of the spread of this pandemic. All model belonging to that set has been used to predict the percentage of an active case concerning the total population for ten COVID-19 infected countries [29]. More specifically, 6 various time series models were developed, namely Auto-Regressive Integrated Moving Average (ARIMA), Facebook’s Prophet, The Holt–Winters additive model (HWAAS), TBAT, DeepAR, and N-Beats, and compared for the following countries: USA, France, Spain, Italy, UK, Germany, Russia, Iran, Brazil, and Turkey. For evaluating the models’ root mean square error (RMSE) is considered. In Table 1 we can see the result obtained by doing the forecasting of this pandemic.

Table 1 Model performance in terms of (RMSE) for the different nations ARIMA

PHOPHET

HWAAS

US 0.007421 0.013877 0.172957 SPAIN 0.080094 0.065433 0.031497 ITALY 0.005628 0.019217 0.006616 UK 0.005484 0.007634 0.004366 FRANCE 0.060824 0.044482 0.011007 GERMANY 0.006431 0.037139 0.004586 RUSSIA 0.001536 0.014681 0.002295 TURKEY 0.004442 0.044595 0.000887 BRAZIL 0.004194 0.009279 0.005717 IRAN 0.002628 0.016281 0.001046 Source: Bold number highlights the least RMSE value

Fig. 9 Friedman statistical test ranking plot

NBEATS 0.036958 0.050492 0.008645 0.037623 0.004220 0.013192 0.027078 0.018265 0.010870 0.003745 for a country

GLUONTS

TBAT

0.044805 0.108842 0.043551 0.046134 0.010549 0.057523 0.034479 0.093839 0.002836 0.002277

0.009873 0.029295 0.005810 0.004310 0.007003 0.003389 0.002193 0.001946 0.005621 0.000425

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For finding rank among those models Friedman’s non-parametric multiple group tests were performed with a significance level of a = 0.02 was applied and the result is plotted in Fig. 9. Ensemble model combines prediction from different models such as Linear, Ridge, Lasso, ARIMA, SVR to produce the best prediction from dataset under consideration. First of all processing of COVID-19 data is done to make it best suitable for the model, after that ensemble model uses various models for making prediction such as: • Linear: It’s a simple model that describes a continuous response variable (no. of COVID cases) as a function of one or more predictor variables. • Ridge: This model is used for analyzing multiple regression data that suffer from multicollinearity. • Lasso: This model deals with simple linear regression that uses shrinkage. Shrinkage is where data values are shrunk at a central point (like mean, median). It is suitable for a simple, sparse model (with fewer parameters). • ARIMA: This is an autoregressive integrated moving average model that is mostly used in time series analysis. It uses both current and lagged values of the feature as a parameter for prediction. • SVR: This model supports the basic idea of Support Vector Machine (SVM, a classification algorithm), but applies it to predict real values (Fig. 10). After getting results from each model, ensemble model finds best model and makes predictions. For determining prediction error, Mean absolute error (MAE), Root mean square error (RMSE) are not suited here as cases are rising each day

Fig. 10 Flowchart of the ensemble model

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Table 2 RPE comparison among various countries Total Linear Ridge Lasso ARIMA SVR Ensemble Mean RPE

Russia

UAE

US

Iran

India

UK

Italy

Brazil

106,498 6.9 6.79 6.89 8.09 5 5 6.73

12,481 9.16 9.17 9.2 11.49 7.08 7.08 9.21

1,069,424 12.96 12.92 12.96 7.49 11.42 7.49 11.55

94,640 16.23 16.14 16.22 8.66 10.68 8.66 13.58

34,863 8.78 8.75 8.77 8.81 17.07 8.75 10.43

172,481 15.31 15.27 15.31 12.48 13.39 12.48 14.35

205,463 16.08 16.04 16.08 17.23 14.73 14.73 16.03

87,187 17.18 17.21 17.18 24.75 15.99 15.99 18.46

rapidly during pandemic period. For example, if number of cases increases from 100 to 130, the MAE is 30, while if it increases from 1000 to 1300, MAE is 300. Percentage error in prediction in both cases is 30%. So it is concluded that relative percentage error (RPE) is most appropriate for prediction. In Table 2 we can compare RPE for various countries. From Table 2 it is found that the ensemble model is giving accurate results by combining different models with less than 10% error in prediction for some countries.

7 AI in Treatment COVID-19 Cases Not only has AI has helped in tracking COVID-19 but also helped man to speed up things and find the drug that is most suitable for getting success against the deadly disease. Since the outbreak of COVID-19, with the integration of AI and medical work 20 AI systems have been used in many hospitals for suspected and confirmed patients [30]. In the early stages of the outbreak of this pandemic, Some reposition drugs such as Hydroxychloroquine (HCQ), Dexamethasone, Remdesivir, Avifavir/Favipiravir, etc. have also been used for curing COVID-19. IIIT-Delhi tried to find the best possible dose for the handling COVID-19 so they followed the approach called Drug Target Interaction (DTI) prediction. This approach follows computational drug repositioning i.e. reusing a drug that was developed for treating certain diseases before. AI does this by computing the similarity between chemical structures of drugs. In the later stages, researchers found that the drug which proved to be successful in treating COVID-19 in early stages (December 2019) and the ones selected for the latest stage (June 2020) are considerably different. In such a case, the AI model can aid clinicians in selecting sets of drugs that are used for the strain under investigation [31–35].

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Fig. 11 Impact of technologies in development of vaccine

Infervision, an enterprise that uses AI and Deep Learning in the medical field has helped to identify multiple diseases from one set of chest scans. Before AI was discovered, patients had to wait a few hours to get the result of a CT Scan. But during this pandemic every minute is important and every minute saved can decrease the chance of increasing contamination. Infervision AI is helping for patient triage, abnormal and severe case analysis, prior case comparisons, and treatment assessments. Artificial intelligence has not only helped in developing drugs, it has also helped in the development of the COVID-19 vaccine. Before 2020 for any new experiment, a long phase has to be followed which was actually time-consuming but not so effective. So with the advancement of time and technology many things improved and with help of AI works can be done in hours which took months to complete. In Fig. 11 we can see how the duration of development of vaccines has changed with the advancement of technology. Traditional vaccine development involves planning, enlisting freshmen for trial, and executing each of three clinical trial stage, and generating the essential information. For showing a vaccine to be safe in any stage it takes around 5–11 months of data grouping after each patient is treated. After getting approval for the vaccine the manufacturing process starts for commercial purposes. The entire process generally takes more than 5 years to get completed. Currently for the development of the COVID-19 vaccine companies rapidly arranged dedicated resources and support needed at hospitals to rapidly medicate and acquire data from patients. Clinical trials, FDA review and approval, and manufacturing phases are overlapped (parallel execution) to facilitate the rapid development of COVID vaccine. Because of all such efforts for the rapid development of the vaccine, it is predicted that it may take around 12–18 months for the development of vaccines.

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8 Conclusion By using different technology especially artificial intelligence and data mining prediction, detection, and diagnosis of COVID-19 have been done across the globe which helped in combating this virus. Different machine learning and statistical approaches have been used in predicting future cases which helped the government in making social and health care policies. For detection of COVID-19 infection, several application was made which takes symptoms of a patient as input and predicts the risk of infection and if a person is found infectious then the warning is sent to all other people who have been in contact with the infected person in recent time, by this way further transmission of disease can be prohibited. Due to the advancement of technology, the time taken for finding drugs and developing vaccines has been reduced as compared with the traditional approach. Robots and Drones have also been used in this pandemic for disinfecting the place and contactless delivery of goods. The technology has helped us a lot in fighting against COVID-19 in various stages without artificial intelligence and other technology humans will take much longer time to get recovered from it.

References 1. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, …, Cao K (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology:200905 2. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, …, Li Y (2020) Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv preprint arXiv:2002.09334 3. Kanne JP (2020) Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiology:200241 4. Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, …, Jacobi A (2020) CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology:200230 5. Hu Z, Ge Q, Jin L, Xiong M (2020) Artificial intelligence forecasting of Covid-19 in China. arXiv preprint arXiv:2002.07112 6. Ye Y, Hou S, Fan Y, Qian Y, Zhang Y, Sun S, …, Laparo K (2020) a-satellite: an AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19. arXiv preprint arXiv:2003.12232 7. Chang SL, Harding N, Zachreson C, Cliff OM, Prokopenko M (2020) Modelling transmission and control of the COVID-19 pandemic in Australia. arXiv preprint arXiv:2003.10218 8. Zachreson C, Fair KM, Cliff OM, Harding N, Piraveenan M, Prokopenko M (2018) Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: results of a census-calibrated model. Sci Adv 4(12):eaau5294 9. Cliff OM, Harding N, Piraveenan M, Erten EY, Gambhir M, Prokopenko M (2018) Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: an agent-based modelling approach. Simul Model Pract Theory 87:412–431 10. Maghdid HS, Ghafoor KZ, Sadiq AS, Curran K, Rabie K (2020) A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study. arXiv preprint arXiv:2003.07434

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11. Story A, Aldridge RW, Smith CM, Garber E, Hall J, Ferenando G, …, Abubakar I (2019) Smartphone-enabled video observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial. The Lancet 393 (10177):1216–1224 12. Lawanont W, Inoue M, Mongkolnam P, Nukoolkit C (2018) Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept. IEEJ Trans Electr Electron Eng 13(10):1501–1510 13. Nemati E, Rahman MM, Nathan V, Vatanparvar K, Kuang J (2019) A comprehensive approach for cough type detection. In: 2019 IEEE/ACM international conference on connected health: applications, systems and engineering technologies (CHASE), pp 15–16. IEEE 14. Vhaduri S, Van Kessel T, Ko B, Wood D, Wang S, Brunschwiler T (2019) Nocturnal cough and snore detection in noisy environments using smartphone-microphones. In: 2019 IEEE international conference on healthcare informatics (ICHI), pp 1–7. IEEE 15. Rao ASS, Vazquez JA (2020) Identification of COVID 19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/ towns are under quarantine. Infect Control Hosp Epidemiol:1–18. https://doi.org/10.1017/ice. 2020.61 16. Allam Z, Jones DS (2020) On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare 8(1):46. MDPI 17. Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, …, Yuan Q (2020) Predicting COVID-19 using hybrid AI model. Available at http://dx.doi.org/10.2139/ssrn.3555202 18. Lopez CE, Vasu M, Gallemore C (2020) Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset. arXiv preprint arXiv:2003.10359 19. Li L, Zhang Q, Wang X, Zhang J, Wang T, Gao TL, …, Wang FY (2020) Characterizing the propagation of situational information in social media during COVID-19 epidemic: a case study on Weibo. IEEE Trans Comput Social Syst. https://doi.org/10.1109/tcss.2020.2980007 20. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, …, Penedones H (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706– 710 21. Jumper J, Tunyasuvunakool K, Kohli P, Hassabis D, The AlphaFold Team (2020) Computational predictions of protein structures associated with COVID-19. DeepMind website, 5 March 2020. https://deepmind.com/research/open-source/computationalpredictions-of-proteinstructures-associated-with-COVID-19 22. Randhawa GS, Soltysiak MP, El Roz H, de Souza CP, Hill KA, Kari L (2020) Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. bioRxiv, https://doi.org/10.1101/2020.02.03.932350 23. Randhawa GS, Hill KA, Kari L (2019) MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz918 24. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14): 4036 25. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 26. Li H, Liu SM, Yu XH, Tang SL, Tang CK (2020) Coronavirus disease 2019 (COVID-19): current status and future perspectives. Int J Antimicrob Agents 55(5):105951 27. Ivanov D, Dolgui A (2020) Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int J Prod Res 58(10):2904–2915

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28. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10(3): 244–256 29. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 30. Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E: Logistics Transp Rev 136:101922 31. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 32. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494 33. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201 34. Mishra M, Mishra S, Mishra BK, Choudhury P (2017) Analysis of power aware protocols and standards for critical e-health applications. In: Internet of things and big data technologies for next generation healthcare. Springer, Cham, pp 281–305 35. Mishra S, Mishra BK, Tripathy HK, Dutta A (2020) Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering. Academic Press, pp 1–23

Chapter 3

Application of Artificial Intelligence (AI) for the Effective Screening of COVID-19 Selvakumar Samuel, Vazeerudeen Abdul Hameed, and Kesava Pillai Rajadorai

1 Introduction The outbreak of COVID-19 is turned into a worldwide concern. These days, in numerous regions, the quick development of the cases is beyond the ability of doctors and medical clinics that could handle them. Numerous patients, they might be COVID-19 patients, or other sicknesses are accumulated in the hospitals. One of the critical difficulties in this emergency is to screen and monitor the COVID-19 patients rapidly and effectively to encourage opportune decisions for their treatment, examination, and management. It is fundamentally critical to immediately screen out COVID-19 patients and take early isolation and treatment, to evade further cross infections and take advantage of the lucky break to save lives. The reverse transcription-polymerase chain reaction (RT-PCR) is the current benchmark method of COVID-19 detection. The main disadvantage of this method is the long screening time and low sensitivity [1]. Medical experts and Scientists globally are working very hard to create fast detection methods for COVID-19. Artificial Intelligence (AI) based methods, solutions, tools, or systems are promising to screen the COVID-19 quicker than the current clinical methods. AI applications are relatively easy to deploy and very efficient.

S. Samuel (&)
V. A. Hameed
K. P. Rajadorai Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia e-mail: [email protected] V. A. Hameed e-mail: [email protected] K. P. Rajadorai e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_3

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In this chapter, most of the current AI applications for COVID-19 rapid screening are discussed. Since AI methods are robotized, AI applications could help to screen thousands of patients rapidly.

2 AI-Based Recent Works for COVID-19 Screening There are two types of tests as Diagnostics tests and Antibody Tests can be used to screen COVID-19. Due to some medical facts, Antibody test is not actively used to diagnose COVID-19 [2]. Currently, Diagnostic tests such as molecular tests (RT-PCR tests) and antigen tests are commonly used to diagnose. The details of these tests are summarized in Table 1. Currently, the Reverse transcription-polymerase chain reaction (RT-PCR)— based methods are mostly used by medical physicians to diagnose COVID-19. It is a time-consuming method. To overcome the current critical situations, rapid methods to diagnose COVID-19 are required. Particularly to substitute or to complement RT-PCR-based methods. There are many recent AI-based studies are published to fill this gap. Many efficient Deep learning models are introduced. These AI models providing promising solutions for screening COVID-19 cases using CT Scans, Chest X-rays, and other possible ways. Deep learning algorithms are efficiently detecting COVID-19 cases. In Table 2 some of the recent research publications which are related to AI-based COVID-19 screening using CT Scans or Chest X-ray methods or techniques are summarized.

3 AI-Based System or Tools for COVID-19 Detection and Monitoring The AI approach was first introduced in 1956 and trained to learn from the experience of machines in the field of computer science. Machine learning (ML) is a subdivision of AI and is achieved by using mathematical models to compute sample datasets. The current ML uses deep learning with neural network algorithms, which can be achieved with more rapid and increased accuracy of complex tasks to identify patterns. In addition to pathological applications, ML mechanisms are both foretelling and diagnostic applications in many clinical specialties such as radiology, dermatology, ophthalmology, and cardiology. In the future, AI is predicted to affect almost every aspect of health care [15]. In COVID-19 screening, the role of AI in radiology is dominating and provides promising solutions. Analyzing the Computerized Tomography (CT) scans and Chest X-rays are the great alternatives to molecular tests for rapid screening of

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Table 1 Different types of Coronavirus tests [2] Also known as…

How the example is taken…

Molecular test

Antigen test

Antibody test

Diagnostic test, viral test, molecular test, nucleic acid amplification test (NAAT), RT-PCR test, LAMP test Nasopharyngeal (the part of the throat behind the nose), nasal or throat swab (most test) Saliva (a few tests) Same day (some location) or up to week (longer in some locations with many tests)

Diagnostic test

Serological test, serology, blood test, serology test

Nasal or nasopharyngeal swab (most tests)

Finger stick or blood draw

Some may be very fast (15–30 min), depending on the test

Same day (many locations) or 1–3 days

Positive results are highly accurate, but false positive can happen, especially in areas where very few people have the virus. Negative result mat need to be confirmed with a molecular test Diagnoses active coronavirus infection

Sometimes a second antibody test is needed for accurate result

How long it takes to get results… Is another test needed…

This test is typically highly accurate and usually does not need to be repeated

What its shows…

Diagnoses active coronavirus infection

What it can’t do…

Show if you ever had COVID-19 or were infected with the virus that causes COVID-19 in the past

Antigen tests are more likely to miss an active COVID-19 infection compared to molecular tests. Your health care provider may order a molecular test if your antigen test shows negative result but you have symptoms of COVID-19

Shows if you’ve been infected by coronavirus in the past Diagnoses COVID-19 at the time of the test or show that you do not have COVID-19

COVID-19 infections. There are many AI-based systems or tools available to screen COVID-19 infections using CT Scans or Chest X-rays. Some of the notable solutions are discussed in the following sections.

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Table 2 AI-based recent research works for COVID-19 screening Wang et el. [3] Xu et al. [4] Zheng et al. [5] Asnauoi et al. [6] Ozturk et al. [7] Waheed et al. [8] Chouhan et al. [9] Che Azemin et al. [10] Ucar et al. [11] Apostolopoulos et al. [12] Pereira et al. [13] Rahimzadeh et al. [14]

3.1

CT image scans Pneumonia image scans CT image scans Chest X-Rays and CT scans Chest X-rays Chest X-rays

Deep learning methods Deep learning methods

Mullti-class classifiers

90.1% accuracy with 0.5 threshold probability 92.18% accuracy

Mullti-class classifiers Auxiliary classifier generative adversarial network Transfer learning method

98.08% accuracy 95% accuracy with combined augmented images 96.4% test accuracy

Radiologist chest images 3-class X-ray scans 7-class X-ray scans Chest X-rays

Binary classifier

71.9% accuracy

Bayes Optimization

98.26% accuracy

Convolution neural network Hierarchical learners

99.18% accuracy

Chest X-rays

Multi-class clasisfiers

91.4% accuracy

Chest X-rays

Deep learning methods

Internal: 82.9%, External: 71.1% 86.7% accuracy

89% F-score

AI-Based Screening Using Computerized Tomography (CT) Scans

Alternate to the RT-PCR tests, the Computerized Tomography (CT) based diagnosis is suggested as the most important medical assessment. Even though a CT scan takes only a few minutes to capture the images, analyzing the images for the precise decision takes a long time. This is one of the immediate challenges is to help the doctors to make a quick decision and to support a large number of peoples. The application of AI methods or algorithms can help to diagnose a huge number of coronavirus cases from lung images in a short period. Figure 1 below depicts the interface of an AI system with an example case. An AI-based system developed by the researchers from RAD Logics, Tel-Aviv University, New York Mount Sinai Hospital, and University of Maryland School of Medicine to screen COVID-19 patients by examining thoracic CT scans. They have proposed a system that combines deep learning models and clinical understanding of 2D and 3D analysis and training data from international databases available, including from affected areas in China. Researchers also used basic data augmentation techniques. Figure 2 below is a sample case.

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Fig. 1 Interface of the AI system with an anonymous case [15]

Fig. 2 Multi-time point tracking of patient disease progression [16]

This team has conducted many retrospective tests. The results showed developed deep learning model was able to detect COVID-19 19 cases excellently with 98.2% sensitivity and 92.2% specificity. Their application also uses 3D volume analysis to assess the condition of the disease and create a “Corona score” metric as shown in Fig. 2, which can be used to track the progression of the disease in COVID-19 patients.

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Fig. 3 Four COVID-19 lung CT scans (top) with corresponding-colored maps showing coronavirus abnormalities (bottom) [17]

Alibaba Group introduced an AI-based system, that could detect suspected patients with 96% accuracy within 20 s. As of February 2020, this algorithm has helped to diagnose more than 30,000 cases in China [17]. This AI system will be able to provide immediate results upon health care staff upload CT scans into the cloud. A sample case from this system is given below in Fig. 3.

3.2

AI-Based Screening Using Chest X-Rays

The deep learning-based AI systems able to screen COVID-19 patients on X-rays as well. AI-based screening of chest scans can lessen the increasing workload on radiologists, who ought to examine and prioritize an increasing number of patient chest scans each day. Some companies have developed this type of tool and are used in various hospitals globally to detect and monitor COVID-19 cases. RAD Logics is one of the healthcare software companies that has developed this type of deep learning tool and it is capable to process a huge number of CT scans each day [17]. This software tool being used in China, Russia, and Italy. This software can give immediate warnings after a chest scan if a patient wants to see. The tool also “monitors a patient’s progress by providing a numerical corona score, which will measure the severity of the disease which can be used to measure the disease over time”. An example case is shown in Fig. 4. RAD logics also believes that their tool can predict a patient requires a ventilator or not. Yet another Hong Kong based company “Ping An Insurance (Group)” has introduced AI–based systems to evaluate chest scans. A sample system interface of this company is given in Fig. 5 below. This is a free system that can perform

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Fig. 4 Using three CT scans from a single coronavirus patient, the RAD Logics algorithm quantifies the amount of recovery with a “corona score” [17]

Fig. 5 Ping an smart healthcare’s smart image-reading system may conduct smart image-reading and generate smart analysis results on the frontline in epidemic area [19]

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Fig. 6 User interface of the intelligent evaluation system of chest CT for COVID-19 [19]

analysis rapidly results in around 15 s with a rating accuracy of 90% and above [18]. Likewise, a Shanghai-based company YITU AI has introduced a similar AI-based system. As per the company news, this system can complete the quantitative analysis in 2–3 s [19]. The sample interface of this system is given in Fig. 6. Another South Korean AI software company “Lunit” launched a free AI-based system for chest X-ray analysis. The results of this system showed that the sensitivity and specificity of Lunit INSIGHT CXR were recorded at 95.6%, and 88.7% respectively. “Compared to the radioactive report, there is no significant difference in the AUROC value of the AI algorithm for detecting COVID-19 with pneumonia” [20]. A sample X-ray scan from Lunit is given in Fig. 7. COVID-Net, an open-source program designed to collect and analyze the X-rays of the chest. It has a neural network development in line with the fast-growing dataset and COVID-19 risk stratification.

3.3

Case Study—Analyzing COVID-19 Cases with AI (It is a Case Extracted from Lunit Website [21])

“Our preliminary study results show that Lunit INSIGHT CXR is capable of detecting consolidation, which is a major finding that indicates pneumonia, which, in this study, uses data from coronavirus patients. The AI showed performance at a level comparable to that of radiologists. In our study, we collected six AP (anteroposterior, taking the image from the front side of the chest) x-ray images of six

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Fig. 7 Lunit INSIGHT CXR detecting COVID-19 pneumonia in chest x-ray images [20]

patients with confirmed diagnosis of coronavirus at the time of admission. All patients had pneumonia on chest CT. On x-ray, three patients had clearly visible lesions, subtle in one patient, and not visible in the remaining two patients, when interpreted by a radiologist. In the retrospective analysis of these images, our goal was to compare the detection performance of human radiologist and AI, to see if our AI would be capable of detecting at human expert-level or more, correctly finding lesions from a chest x-ray of a coronavirus infected patient. The result showed that AI was able to detect what radiologists detected, demonstrating same-level performance.

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Case 1:

There is an ill-defined patchy increased opacity in the left mid to lower lung zone, which may be unclear for interpretation. The Lunit INSIGHT CXR has correctly detected the lesion with the abnormality score of 34.0%. Case 2: The plain CXR taken on the patient in ICU care shows patchy ground-glass opacities in the bilateral lower lung zones. The Lunit INSIGHT CXR has correctly detected the lesions with the abnormality score of 72.0%, even in the setting with many tubes, central line, and external devices overlapped on the image.

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Case 3:

There are multifocal patch/nodular consolidations and ground-glass opacities around the right mid to lower lung zone. The Lunit INSIGHT CXR has correctly detected the lesions with the abnormality score of 76.0%.”

4 Other AI-Based Methods/Tools for COVID-19 Screening Although AI providing dominating solutions using X-ray and CT images to screen COVID-19 rapidly, as well as provides effective impact on the other alternative tests such as Anosmia or Ageusia test, Voice sample test, Cough sound test, and AI-powered ECG reports to detect and monitor the COVID-19 infections. Many researchers have explored and proved the benefits of these tests with AI to manage this pandemic. The following sections summarize some of the details of these tests.

4.1

AI-Powered Loss of Smell (Anosmia) and Loss of Taste (Ageusia) Tests

The Anosmia and Ageusia are highly prevalent signs of COVID-19 [22, 23]. A clinical study found that COVID-19 patients lose their sense of smell and taste about 27 times more likely than other patients [23]. Another study from King’s College London [24] noted that peoples were testing positive for symptoms of Anosmia or Ageusia in two-third of the people reported coronavirus infection. The symptom of Anosmia or Ageusia is stronger prognosticator of COVID-19 than fever [25]. So, testing for the Anosmia or Ageusia will help to save the lives as it is a crucial early symptom for a COVID-19 infection.

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Researchers [25] have developed an AI application called as “COVID Symptom Study app” (formerly recognized as “Mobile Symptom Tracking”) that can predict COVID-19 based on their signs, for instance, recurring cough, fever, fatigue, and Anosmia or Ageusia. As of May 2020, more than 3.3 million individuals worldwide have installed the app and used it to check their health status every day. Figure 8 was extracted from [25]. It clearly describes the benefit of AI in predicting COVID-19 using symptoms. As of March 27, 2020, 265,851 peoples in the UK reported any signs possibly connected to COVID-19 (top). People’s provided information on COVID-19 was examined and whether the test result was available. 1176 people tested a COVID-19 (0.4%) they said they had received. Positive (middle; n = 340) negative (bottom; n = 836) the symptom frequencies of those who tested are shown in Fig. 8.

4.2

AI-Enabled Electrocardiogram (ECG) for COVID-19

The breathlessness is a key sign of a COVID-19 illness. AI-powered electrocardiogram offers a swift and efficient technique to diagnose patients with documented COVID-19 for left ventricular systolic dysfunction (LVSD) [26]. AI-enabled ECG performance is superior to typical blood tests in detecting acute LVSD with a performance measure of 0.89 versus 0.80 [26]. A sample case outcome of the AI-enabled ECG which was extracted from [27] is shown in Fig. 9.

4.3

COVID-19 Screening Using Voice Samples with AI

Analyzing wellbeing’s voice samples is an alternate rapid screening method for COVID-19 infections. AI using voice samples screen the individuals, screen report will be given within 30 s by the voice app [28]. This test is like a breath analyzer test. The voice app will analyze voices depending on the individual’s vocal biomarkers (VB). If an individual’s vocal biomarker is below the standard point (0.5), that person is deemed not infected. But if anyone records biomarkers (VB) above the standard point, that person will be treated as suspicious and should undertake the standard test for confirmation [28, 29]. One of the popular voice analysis mobile applications from an Israeli voice tech startup screen is given below in Fig. 10. United States of America, India, Israel, and others are actively using this AI-based application for screening wellbeing COVID-19 infections. Apart from these projects, there are other teams of researchers from the UK, Saudi Arabia, and India have developed applications to diagnose COVID-19 signs in a person’s speech [30, 31].

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Fig. 8 Symptoms reported through the COVID Symptom Study app

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Fig. 9 ECGs and AI-powered ECG outcomes of a 77-year-old woman (patient #8) who developed LVSD due to COVID-19 myocarditis. EF ¼ ejection fraction [27]

Fig. 10 Vocalis Health—a voice app interface [30]

4.4

COVID-19 Screening from Cough Sounds with AI

AI can examine CT or X-ray images rapidly to screen the COVID-19 infections, but these approaches require a CT Scan or X-ray facility at the hospitals. So that alternate AI-based methods with easier approaches would help to manage the

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current challenging situations. One of the alternate methods is to screen the COVID-19 infections from cough sounds using the power of AI. MIT Researchers [32] have found that contrasting cough sounds between healthy individuals and asymptomatic individuals. These differences cannot be understood by the human ear. But they can be gotten by Artificial Intelligence. The MIT researchers have incorporated this model into an intelligible application. It is potentially a free, convenient, non-pervasive prescreening tool to identify people who are asymptomatic for COVID-19. A user login daily, cough into their phone, and immediately confirm by a systematic check to get information on whether they may be infected or not. If the system showed any suspicious result, then consequently should confirm with a standard diagnosis. A section of the Indian Council of Medical Research (ICMR) and a healthcare initiative developed by the Governments of India and Norway have launched a study to screen COVID-19 infection from people’s cough sounds using AI methods [33]. A mobile app was developed through Norwegian-India Partnership Initiative, in which people without viral infection recorded their cough sounds, though researchers have hoarded cough sounds from COVID-19 patients. The Researchers from the University of Oklahoma have distinguished the COVID-19 infected individuals cough sound with coughs due to other infections [34–36]. They have developed a mobile application named “AI4COVID-19”. This mobile application records the cough sounds. When a cough sound is received by the cough detection engine, that cough sound will be forwarded to the tri-pronged mediator-centered AI engine to diagnose between COVID-19 infected individual cough sound and non-COVID-19 coughs. The Fig. 11 showing the “picture of smartphone app at user front-end and back-end cloud. AI-powered engine blocks consisting of Cough Detector block and COVID-19 diagnosis block containing Deep Transfer Learning-based Multi-Class classifier (DTL-MC), Classical Machine Learning-based Multi-Class classifier (CML-MC) and Deep Transfer Learning-based Binary-Class classifier (DTL-BC)”.

Fig. 11 The system architecture and flow diagram of AI4COVID-19 [36]

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Similar projects have been pursued by other research teams such as “Cough Against Covid in India, funded by Bill and Melinda Gates Foundation”, “The Coughvid project in Switzerland”, and “COVID-19 Sounds project in the University of Cambridge”.

5 Summary Currently, swift screening of COVID-19 infections is the most wanted solution worldwide. A popular molecular test RT-PCR takes a long-time to detect and monitor the COVID-19 cases. However, AI-enabled tools able to screen in seconds. AI-powered systems or tools have proved the rapid and effective screen of COVID-19 infections by analyzing CT scans and chest X-rays [37–39]. Apart from that AI-enabled other alternative screening methods such as analyzing the cough sounds, loss of taste or smell tests, voice sample tests, and AI-powered ECGs have provided promising solutions for the early detection of the disease, swift screening, monitoring, and management of the COVID-19. This chapter has summarized some of the related notable research works and AI-based advances for the effective and rapid screening of COVID-19.

References 1. Cheng M (2020) AI system in expediting diagnosis of COVID-19 2. Coronavirus disease 2019 testing basics. https://www.fda.gov/consumers/consumer-updates/ coronavirus-disease-2019-testing-basics 3. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2020) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) 4. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J, Lang G, Li Y, Zhao H, Liu J, Xu K, Ruan L, Sheng J, Qiu Y, Wu W, Liang T, Li L (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6:1122–1129 5. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for COVID-19 from chest CT using weak label 6. El Asnaoui K, ChawkiY (2020) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dynam 1–12 7. Ozturk T, Talo M, Yildirim E, Baloglu U, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121: 8. Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro P (2020) CovidGAN: data augmentation using auxiliary classifier GAN for improved COVID-19 detection. IEEE Access 8:91916–91923 9. Chouhan V, Singh S, Khamparia A, Gupta D, Tiwari P, Moreira C, Damaševičius R, de Albuquerque V (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10:559 10. Che Azemin M, Hassan R, Mohd Tamrin M, Md Ali M (2020) COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings. Int J Biomed Imaging 2020:1–7

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11. Ucar F, Korkmaz D (2020) COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140: 12. Apostolopoulos I, Aznaouridis S, Tzani M (2020) Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 40:462–469 13. Pereira R, Bertolini D, Teixeira L, Silla C, Costa Y (2020) COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed 194: 14. Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inf Med Unlocked 19: 15. Borkowski A (2020) Using artificial intelligence for COVID-19 chest X-ray diagnosis. Federal Practitioner 16. AI CT scan analysis for COVID-19 detection and patient monitoring | synced. https:// syncedreview.com/2020/03/18/ai-ct-scan-analysis-for-covid-19-detection-and-patientmonitoring/ 17. Scudellari M. Full page reload. https://spectrum.ieee.org/the-human-os/biomedical/imaging/ hospitals-deploy-ai-tools-detect-covid19-chest-scans 18. Ltd P. Ping an launches COVID-19 smart image-reading system to help control the epidemic. https://www.prnewswire.com/news-releases/ping-an-launches-covid-19-smart-image-readingsystem-to-help-control-the-epidemic-301013282.html 19. YITU AI aids epidemic prevention and control of coronavirus. https://www.biospectrumasia. com/news/48/15466/yitu-ai-aids-epidemic-prevention-and-control-of-coronavirus.html 20. AI can offer fast and reliable examination to triage COVID-19 patients—a multicenter retrospective study reveals. https://lunit.prezly.com/ai-can-offer-fast-and-reliableexamination-to-triage-covid-19-patients-a-multicenter-retrospective-study-reveals 21. COVID-19 https://www.lunit.io/en/covid19/ 22. Hopkins C, Smith B (2020) Widespread smell testing for COVID-19 has limited application. The Lancet 396:1630 23. Bludau J https://www.khou.com/article/news/health/coronavirus/connect-the-dots-smell-testsmay-be-best-way-to-screen-for-covid-19/285-3455f6d4-fc7d-4e5c-b445-a2f4bace07cc 24. New AI diagnostic can predict COVID-19 without testing. https://www.sciencedaily.com/ releases/2020/05/200511112628.html 25. Menni C, Valdes A, Freidin M, Sudre C, Nguyen L, Drew D, Ganesh S, Varsavsky T, Cardoso M, El-Sayed Moustafa J, Visconti A, Hysi P, Bowyer R, Mangino M, Falchi M, Wolf J, Ourselin S, Chan A, Steves C, Spector T (2020) Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat Med 26:1037–1040 26. James Ives M. AI-enabled ECG provides a rapid and effective method to screen heart failure, https://www.news-medical.net/news/20200804/AI-enabled-ECG-provides-a-rapid-andeffective-method-to-screen-heart-failure.aspx 27. Attia Z, Kapa S, Noseworthy P, Lopez-Jimenez F, Friedman P (2020) Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19. Mayo Clin Proc 95:2464–2466 28. Chakraborty R. Mumbai civic body to soon screen patients for COVID-19 using voice samples, AI. https://www.hindustantimes.com/mumbai-news/mumbai-civic-body-to-soonscreen-patients-for-covid-19-using-voice-samples-ai/story-O9f8bhGJaXvUU10XlTtQZI.html 29. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20 (14):4036 30. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 31. Usman M, Wajid M, Zubair M, Ahmed A. Full page reload. https://spectrum.ieee.org/newsfrom-around-ieee/the-institute/ieee-member-news/this-app-could-help-detect-covid19-byanalyzing-a-persons-speech

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32. Chu J. Artificial intelligence model detects asymptomatic Covid-19 infections through cellphone-recorded coughs. https://news.mit.edu/2020/covid-19-cough-cellphone-detection1029 33. Researchers trying to identify COVID-19 case from cough sounds. https://www. moneycontrol.com/news/trends/health-trends/researchers-trying-to-identify-covid-19-casefrom-cough-sounds-5594611.html 34. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Frontiers Public Health 8:274. https://doi.org/10. 3389/fpubh.2020.00274 35. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 36. Imran A, Posokhova I, Qureshi H, Masood U, Riaz M, Ali K, John C, Hussain M, Nabeel M (2020) AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inf Med Unlocked 20: 37. Sushruta M, Hrudaya KT, Brojo KM (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts. Int J Control Theory Appl 10(295–310):91 38. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing, pp 385–390. Springer, Singapore 92 39. Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of Feature ranking using machine learning techniques for diabetes disease prediction. Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India 4–5:1–3

Chapter 4

Machine Learning Approach for Analyzing Symptoms Associated with COVID-19 Risk Factors Srestha Rath, Roshan Mohanty, and Lambodar Jena

1 Introduction Worldwide spreading of a new disease is said to be known as a pandemic. An influential pandemic happens when a new type of virus emerges and moves around the nations and everywhere [1]. Viruses that have caused past pandemics that have usually arisen from animal influenza viruses. The last pandemic which affected humanity was H1N1 virus, also known as the Swine Flu. During the February of 2009, a novel pandemic A (H1N1) virus was spread in the USA and moved around quickly across the world. This was just 10 years before the new novel Coronavirus which is wreaking havoc in the world; so from this, it is realized that pandemics are not a rare phenomenon for the mankind. On December 30, 2019, a bunch of cases of pneumonia from a not known cause was found in the city of Wuhan, Hubei province in China was reported immediately. China had made this aware to the WHO of unusual pneumonia cases in Wuhan on December 31. COVID-19 was reported to have begun in a seafood market area where wildlife was put up without acknowledging the government. The cases were then sent to WHO. In January 2020, a previously unknown new virus was identified, subsequently named the 2019 novel Coronavirus, and samples obtained from the pneumonia cases and analysis of the virus’ genetics indicated that this was the cause of the outbreak, i.e., the cluster of cases which were found in Wuhan. On the February 7, some Chinese S. Rath
R. Mohanty School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India e-mail: [email protected] R. Mohanty e-mail: [email protected] L. Jena (&) Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed To Be) University, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_4

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researchers speculated that the virus could have spread from an infected animal to humans through illegal trafficking of Pangolins, prized in Asia for its nutritional and medicinal values. This virus was named Coronavirus disease 2019 (COVID-19) by WHO in February 2020. This new strain of Coronavirus, which was originally detected in Wuhan, is called severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2). The virus causes the Coronavirus disease-19, i.e., (COVID-19). As of August 12, 2020, over 20,300,000 cases have been recognized globally in 188 countries with a total of over 741,100 fatalities. On the bright side, due to extensive medical care and treatment, 12,600,000 were recovered. COVID circulates in human beings and are passed on to other human beings through the sense of touch [2]. Rarely, it can also be transmitted from animals to humans and vice versa. Researchers say that the virus would spread from an infected to another through illegal trafficking of bats, prized in Asia for food and medicinal use. Locals have pointed to either bats or snakes from which are possible sources of transmission. Technology refers to methods, frameworks, and devices which are the aftermath of scientific information which gets utilized for real-life purposes [3]. Artificial intelligence can be characterized into areas like machine learning (ML), natural language processing (NLP), and computer vision applications. These capacities command computers to use huge information-based models to design, depict, and predict. Image processing has shown substantial growth in recent years and is now being used to locate people with fever at public places. Artificial intelligence is now being used to develop vaccines for the people. When a disease strikes the world, the first question that occurs to everyone around the globe is if there is a medicine to cure it or a vaccine that can be made. Today’s world faces the same problem too. It eyes a treatment that can eradicate this disease. In all of this, technology is becoming a hope. It is helping scientists make the process faster. Artificial intelligence is helping medical professionals understand the virus and build a vaccine for the same. It is also helping in faster diagnosis of patients, tracking the spread of the disease, and predicting its implications. Today, it is also being employed for creating awareness among the mass and maintain social distancing among the people [4, 5]. More young people are getting infected with COVID-19 as cases flare up throughout the world. The latest age profile data of COVID-19 shows people below 40 s are nearly 34% of total reported cases, about 28 lakh, as on August 20. However, five months ago they were only 28% of the total reported cases—1801, as on April 2. The reason for infection in young age is because of their going physically to the workplace, and another possibility would be lack of self-protection. A higher proportion of the young population compared to elderly is also one of the reason. However, in the same period, infection among the age group of 45–54 and 55–64 seems almost stable, around 18% of total cases, respectively (Fig. 1).

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Fig. 1 Number of cases around the world in each age bracket

2 Associated Symptoms with COVID-19 Most common symptoms, which are found in the affected patients, are the common flu-like symptoms. The speciality, or else it can also call it the curse COVID-19 is that it affects different people in different ways. This creates ambiguity and confusion. Figure 2 shows that there are two kinds of symptoms: asymptotic and symptomatic. It is transferable before symptoms start appearing, which is the sole reason so many people get contaminated to the infection. Infected patients can also have mild symptoms, which does not get display any indication to the body inspite of having the deadly virus in their bodies. The isolation period of COVID-19 is thought to be 14 days. Below is an analysis of the major symptom contributors faced by human beings. • Coughing is the most common symptom that clears the throat of scum or different other categorical irritations. While someone coughs to clear the throat timely, the environment can cause more recurrent coughing. A cough which lasts for lesser than two weeks is a drastic cough. Cough will be improved highly and very soon if quite a number times cough happens. If it extends more than 2 weeks and getting improved toward the last of that particular period, it is considered a sub-drastic cough. A persevering cough usually takes more than 7 weeks which is itself a long-term cough. • Sore throat is quite painful than the cough, Hoarse, or irritation of that throat often worsens when the person swallows. The prevailing cause of a sore throat (pharyngitis) is the fervid infection, examples are cold or the flu-like symptoms. When a virus causes sore throat, then it resolves on its own. In other respiratory illnesses, like the common cold, a sore throat is often an early symptom.

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Fig. 2 Various symptoms among COVID-19 patients

Because respiratory viruses are inhaled, they enter the nose and throat first. They may replicate there early on leading to throat soreness and irritation. • Fatigue is the sickness usually described as lackness or tiredness of energy. Its symptoms show as simply feeling heavy eyed or tiredness. When someone has fatigued, there is absolutely no impulse to do a certain job and feel apathetic. Being drowsy commonly may be a symptom. It is a sickness of many medical shapes that go from having less to very severe. It is an innate result of daily lifestyle options and depends from individual to individual, such as no exercise/ yoga or unhealthy diet. If fatigue does not get resolved with proper sleep and eating habits, then it is caused by an untreated physical or depression which should see the doctor to get immediate help. They can help identify the cause of fatigue and treat it well. • Chill refers to the sickness of having cold without an evident cause. They get such feel when bones frequently expand and contract and the blood vessels in the skin narrows. It takes place with a fever and cause shivering or shaking. Treatment is usually based on whether chills go along with a fever, and the gravity of the fever is checked. If fever is mild in nature and there are no other syndromes, then one does not have to go to doctor. Proper schedule of sleep and fluid can cure at the soonest.

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• Myalgia is the pharmaceutical term for muscle ache. It originates from one of the muscles. It may come to light due to excessive exertion, infections from different tissues of the body. Numerous conditions can be related with aches and pain, which are influential and are recognized to be muscle pain. It can be limited to one muscle group or spread, involving many number muscle groups. • Huge risk when shrinking SARS-CoV-2 into contact with someone who has in it, wholly if being bared to their saliva or standing next to them when they are coughing, sneezing, or talking. The deadly virus can be revealed similarly to other parameters causing viral infections: using the substance into nose blood, saliva, or tissue sample. However, RT-PCR tests which uses a cotton to take the swab to a sample from the inside of the nostrils. • Dyspnea, particularly with sheer drops in oxygen soaking thoroughly with a liquid especially with few effort, can help clinicians to more easily distinguish Coronavirus disease from other diseases. During the COVID-19, conciseness of breath generally starts in between the 3rd night and 7th day of illness, it can also be 8 days after the outbreak of the COVID depending on the immunity of the person. Patients who have COVID-related concern often describe the commotion, need oxygen cylinder to breathe properly while with SARS-CoV-2 infection dyspnea in tune to worse with exertion. • Skin color changes fast to the younger ones with less severe COVID-19 might develop symptoms like pains all over the body, itching on their hands, and feet that are similar to provocative skin conditions. Usually called COVID toes can last upto 13 days or more depending on the immunity power. • The clinical hue cycle of Coronavirus infection is quite broad, ranging from an easy catching cold to acute pneumonia. Objectively, COVID-19 initially presents as a condition like flu. Persons going through COVID-19 develops many indications and symptoms, such as very low respiratory illness and resolute fever, an average of 4–5 days after infection has caused the body. The fever is tenacious, in contrast to the continuation decline observed in cases of influenza. • In some areas Sputum testing had resulted in notably high rates of SARS-CoV-2 detection and supports the use of such type of testing as a very beneficial method for the identification and monitoring of COVID-19 patients. • Diarrhea generally happens to people. Some people may have to go through diarrhea without any other flu-like symptoms, such as fever to be precise. One of the symptom can generally be diarrhea. Research has found that lesser people have only absorption symptoms and higher had both digestive and respiratory symptoms which can lead to other problems. They analyzed all the COVID-19 clinical case studies and reports which are related to ingestion issues. Some adults have experienced vomiting, compared with the children and the younger generation.

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3 Sample Background Study Several analysis and research work implementations are being carried out using machine learning in the field of treatment of COVID-19. In this section, some notable works are highlighted. Cohen et al. [6] describe the public COVID-19 image collection consisting of X-ray and CT scans with ongoing updates. The dataset consists of more than 125 images extracted from various online publications and websites. Researcher [7] published a dataset consisting of 275 CT scans of COVID-19 positive patients. The dataset is extracted from 760 medRxiv and bioRxiv preprints about COVID-19. The authors also employed a deep convolutional network for training on the dataset to learn COVID-19 cases for new data with an accuracy of around 85%. Wang et al. [8] investigated a deep learning strategy for COVID-19 screening from CT scans. A total of 453 COVID-19 pathogen-confirmed CT scans were utilized along with typical viral pneumonia cases. Author in [9] utilized the online dataset and proposed COVIDX-Net, a deep learning framework for automatic COVID-19 detection from X-ray images. Authors in [10] propose support vector machine-based classification of X-ray images instead of predominately employed deep learning models. The authors argue that deep learning models require large datasets for training that are not available currently for COVID-19 cases. Afshar et al. [11] also negated the applicability of DNNs on small COVID-19 datasets. The authors proposed a capsule network model (COVID-CAPS) for the identification of COVID-19 based on X-ray images. Authors [12] utilized data augmentation techniques to increase the number of data points for CNN-based classification of COVID-19 X-ray images. The proposed methodology adds data augmentation to basic steps of feature extraction and classification. Tindale et al. [13] study the COVID-19 outbreak to estimate the incubation period and serial interval distribution based on data obtained in Singapore (93 cases) and Tianjin (135). The incubation period is the period between exposure to an infection and the appearance of the first symptoms. Researchers [14] described an econometric model to forecast the spread and prevalence of COVID-19. The analysis is aimed to aid public health authorities to make provisions ahead of time-based on the forecast. Dey et al. [15] analyzed the epidemiological outbreak of COVID-19 using a visual exploratory data analysis approach. Apostolopoulos and Mpesiana [16] has proposed a deep learning-based technique called deep transfer learning, which can predict patients with Coronavirus disease automatically. It uses images of chest X-ray obtained from patients with Coronavirus and from a healthy person. This paper study [17] presented automated techniques are used for classifying the X-ray of chest into pneumonia and the class of disease-free by using nine architectures of deep learning. Xu et al. [18] established a deep learning paradigm for the screening of Coronavirus patients at an early stage. The main aim of this paper is to distinguish Coronavirus from Influenza-A viral pneumonia and normal cases with the use of CT images. Hemdan et al. [19] introduced a new approach of deep learning called COVIDX-Net to help radiologists to automatically diagnose patients with Coronavirus using X-ray

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images. This technique is built on seven deep CNN classifiers. In this study [20], they present earlier detection of Coronavirus using CT scan images by using machine learning methods, and the dataset is taken from “Societa-Italiana di Radiologia Medica-e Interventistica,” which belongs to the 53 Coronavirus cases and data involving CT abdominal images of 150 and 150 CT images. Kumar and Kumari [21] used different deep learning-based techniques for Coronavirus detection diseases by using X-ray images.

4 Preprocessing Using Machine Learning As seen above, AI can help us in more ways than someone would know, so in this research paper, it is learnt how machine learning, which is a major part of artificial intelligence, can help us in developing a model that will detect the disease Coronavirus by knowing the symptoms [22]. Machine learning has been previously used in the symptom analysis of other diseases like diabetes, hepatitis, and lung-related disorders. There are many obstacles in this path, as to get a certain level of accuracy in a prediction, a huge amount of data is needed to be given as input. The data that has to be fed to the computer should be preprocessed and clean, with no missing data, redundant data, and false information. There should be a certain validation to any kind of data will be using [23] (Fig. 3).

Fig. 3 Major symptoms found in a COVID-19 positive patient

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Data Gathering: COVID-19 which is also known by the name Coronavirus, was declared a pandemic by World Health Organisation (WHO) in 2020. It was only proper for an infection spreading around the world in a rapid manner, to be declared so. Due to this situation, an analysis needed to be done. Therefore, most of the hospitals gave open and free access to the accumulated data regarding this pandemic. For us, finding the data was not an easy task. The dataset is taken from Kaggle—It is a community where people share genuine datasets from all over the world, for machine learning and data science enthusiasts to work on. Data Analyzing: The dataset acquired consists of 21 columns and 127 rows. As the chart shows, the dataset deals with the symptoms a Coronavirus infected person will face. This unique virus, once infected in a human body can show different type of symptoms at different ages. Factors like age, gender, and body temperature are also in the dataset, but it was excluded from the chart to throw light on all the major symptoms. Data Preprocessing: The block of data which is worked with is not systematic and is highly unstructured. For the analysis to start, the data is cleaned and the data by various steps. Hence, it is modified as per the requirements, so as to get better performance [24]. Splitting the dataset into test set and training set: The dataset was divided into a 80:20 ratio, wherein 80% of the data was given to the training set, on which the model will get trained on. 20% of data was given to be the test set, as the model which has been made, will be tested by using that particular set as shown in Fig. 4. Encoding of independent variables: It was initially done with the first three columns of the dataset, i.e., the factors age, gender, and body temperature. But later on, as the research progressed, it was found out that considering the first three columns in the symptoms analysis was not worthwhile, so the first three columns for the model making were removed. Feature scaling: Feature scaling was not required at all as the values were of similar type (as mentioned, the problem of having higher and lower values in a dataset was resolved by removing the first three columns).

5 Classifiers Logistic Regression: Unlike the name of the algorithm, it can be used for both classification and regression purposes. But in recent cases, it has been shown that logistic regression is mostly used for classification purposes. The response variable is binary, i.e., 0,1, and it belongs to one of the classes. It is used in the process of predicting the categorical variable by using the dependent variables given in the problem statement. The response variable can be 0,1 or true or false and Yes/No. Logistic regression algorithm works by predicting the probability of occurrence of

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Fig. 4 Spitting of the dataset in a 80:20 ratio

any event by fitting the said data to a logit function, thereby also being known as logistic regression also. The output always lies between 0 and 1.

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Decision Trees: It is one of the supervised learning algorithms and is generally used for solving problem statements pertaining to classification. A tree-structured classifier, in which the internal nodes symbolize all the features of any given type of data. And the branches represent the decision rules, while every leaf node is useful in representing the final outcome of the question. It has the capacity of working for both categorical and continuous dependent kind of variables. In this algorithm, the population of the given dataset can be split into two or more than that homogenous sets of data. Done according to what are the most significant attributes/ independent variables to help in making as distinct groups as possible. Decision trees are also known as CART—classification and regression tree. The procedure is that the decision tree will pose a problem, and based on the answer (Yes/No), it further splits into many sub-trees, which in turn to many more sub-trees. Support Vector Machines: It is another classification method in which, we plot each data item as a point figure in an n-dimensional space, where n is the number of features available. Here, each value of feature is the value of a particular coordinate. For the separation of any two classes of data points, a number of hyperplanes are available from which the suitable one can be selected. The main purpose of applying the SVM algorithm to any problem is to find the best fitted line in two dimensions or the best hyperplane in more than two dimensions. The hyperplane is found by calculating the maximum distance between any two data points of both the classes. Maximizing the margin distance is always favored as it gives the security of future data points being added in the confidence interval. Therefore, a large margin is always more favorable than a smaller margin, which would not be able to accommodate newer dataset points. This algorithm has a very high computational power. In SVM, for efficient delivery, one should have a large margin and that must help in maximizing the margin which is used as the loss function.
cðx; y; f ðxÞÞ ¼

0; if y f ðxÞ  1  1  y f ðxÞ; else



There will also be an addition of regularization parameter to the cost function, to make it more flexible. The regularization parameter will help in balancing the loss accompanied with the margin maximization. After that process, the modified cost functions look as: min kjjxjj2 W

n X

ð1  g;\x; w [ Þ þ

i¼1

Naive Bayes: A classification technique is derived from the Bayes’ theorem. It is based on an assumption of independence between the predictor variables. Bayes’ theorem can be stated as:

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A Naive Bayes classifier works on the principle that it assumes that the presence of a singular particular feature in any class is not related to the presence of all the other features in the problem statement. Naive Bayesian model is comparatively easy to construct and in particular useful for large datasets. Along with how simple it is to implement, surprisingly, Naive Bayes can also outperform even highly sophisticated classification methods, in some problem statements. Bayes theorem calculates posterior probability P(c|x) from P(c), P(x), and P(x|c), as shown:

It gives good performance in small datasets. In most of the practical applications, it hardly fits [25]. Naive Bayes algorithm gives low variance and high bias. This creates a limitation in the applications. Random Forest: Random forest classifier is a part of the ensemble learning process, that is basically an accumulation of several decision trees. It trains them in parallel with bootstrapping, and then the process of aggregation of all the decision trees takes place; this is known as bagging. Bootstrapping signifies that several individual decision trees are in continuous process of being trained parallely on different subsets of the training dataset using different subsets of attainable features. Bootstrapping makes sure that, every singular decision tree within a random forest is particular and exclusive, causing the variance of the RF classifier to be reduced. In the last step, the RF classifier accumulates and aggregates all the selected decision trees due to which good generalization is an attribute of the RF classifier [26]. Random forest algorithm has the quality to generally outperform other classification algorithms in terms of accuracy. Overfitting is not an issue here. Random forest algorithm can handle large size databases and has the quality of predicting results with high accuracy by estimating the missing data by itself.

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XGBOOST: XGBoost is a type of ensemble learning model, since its discovery by scientists has been majorly used in large-scale industries and is a very popular tool to combat problem statements. This algorithm is highly flexible and has massive versatility through all the major tasks in machine learning, i.e., regression, classification, clustering, neural networks, and all. It is an open-source software which makes it easily attainable and can be used through different interfaces, softwares, and platforms. Its name stands for eXtreme gradient boosting. It now comes up with many new added features and a collection of open-source libraries, developed by the distributed machine learning community (DMLC). XGBoost algorithm has the feature of upgradation. The model was created for giving maximum possible accuracy and to have high computational power. The algorithm has proven to push the limits of computing power for boosted trees algorithms and all other kinds of algorithms. The major part of the attractive package of XGBoost algorithm lies in the capacity of scalability of the algorithm, which drives ensemble learning through parallel and distributed computing and offers very efficient memory usage. XGBoost has emerged as the most used, useful, straightforward, accurate, and robust solution in recent times (Table. 1). Table. 1 Benefits of classifiers used in analysis Logistic regression

Decision tree

Support vector machines

Naive Bayes

Random Forest

• One of the first developed, simplest, and easy to implement algorithms is the logistic regression algorithm • It can be extended to multiple classes easily • When new data occurs, this algorithm easily updates the new data unlike SVM or decision tree classification technique • When the data is presented in a linearly separable manner, the algorithm has proved to be quite efficient • It gives importance to each and every feature • It is cheaper than the other models to construct • This algorithm is faster at classifying and sorting unknown records • Easy to interpret for small-sized trees • It is good for noisy data • Decision trees do not require scaling and normalization of the data we are dealing with • The SVM algorithm can be used when we have less idea on the kind of data we are dealing with • SVM algorithm with the usage of kernel has been very effective • There is a minimal risk of overfitting • It can do relatively good by handling high-dimensional data • Naive Bayes classifier has been a better performer than other classifiers, when the assumption of independent predictors holds true • Naive Bayes has a requirement of minimum amount of training data due to which the time required to train the data is minimal • Naive Bayes algorithm is simple and not very tough to implement • It judges the significance of the features in the dataset • It has the capacity of handling large datasets with high dimensionality • Contains methods required in balancing errors in datasets in the case of imbalance classes • It has the ability to estimate the missing data and also maintains the accuracy when a large amount of data is missing

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6 Use of Feature Engineering When an analysis is being done or a machine learning model is being built, it is highly important that the selection is only of those features or predictors which are necessary for the model making [27]. Suppose we have 50 features or predictors in our dataset that does not necessarily comply that we need to have all the 50 features in the model. This is due to the fact that, not all 50 features will be having the same significance influence. The methodology is discussed below in the given diagram: The dataset has 13 independent variables, i.e., 13 most occurred symptoms around the world. But for an analysis to have more detailing and accuracy, the most prominent, maximum participation, and frequently occurring item-sets are chosen. The technique which was used in this paper is called as feature selection also known as feature engineering. Initially, the 13 features are listed as follows.

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Building the Optimal Model Using Backward Elimination Method

By using the statsmodels.api library from scikit Learn, the class and object are created and then fitted. The mode of criteria used here is P-value. A p-value is the probability under a specified statistical model that a statistical summary of the dataset. Now, we need to select a significance level to stay in the model. The SL value was chosen to be 0.05. If P-value >SL, then the predicator will be removed.

6.1.1

When all the 13 Symptoms were Present

Figure 5 shows all the symptoms as independent variables, namely from x1 to x13. The SL limit has been chosen as 0.05. All the variables which have a P-value greater than SL are now removed. The remaining predicators are: 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, and 16 as shown in Fig. 6.

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Fig. 5 Decision tree illustration

6.1.2

When all 12 Symptoms are Present

Figure 6 shows all symptoms as independent variables namely from x1 to x12. The SL limit has been chosen as 0.05. All variables which have P-value more than SL are removed. Remaining predicators are 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, and 16 as shown as Fig. 7.

Fig. 6 Working of a SVM classifier

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When all 11 Symptoms are Present

Figure 7 shows all the symptoms as independent variables, namely from x1 to x11. The SL limit has been chosen as 0.05. All the variables which have a P-value greater than SL are now removed. The remaining predicators are 5, 6, 7, 8, 10, 11, 13, 14, 15, and 16 as shown as Fig. 8.

Fig. 8 Feature engineering process

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When All 10 Symptoms are Present

Figure 8 shows all the symptoms as independent variables namely from x1 to x10. The SL limit has been chosen as 0.05. All the variables which have a P-value greater than SL are now removed. The remaining predicators are 5, 6, 7, 8, 10, 11, 13, 14, and 16. The rest of feature selection using backward elimination was done like this. The rest of the images are not shared due to space complexity. The final most important features which contribute the most to the symptom analysis are 5—Sore throat. 6—Weakness. 14—High fever. 16—Loss of sense of smell.

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7 Environmental Variables Utilization: A windows system of 64 bit and 32 GB ram was used for performing this analysis. Scikit Learn was used extensively throughout the analysis as it has many libraries, functions for the process of data sciences, data analytics, and machine learning. Scikit Learn is a free and open-source software which consists of data sciences, data analytics, and machine learning library for the language which is one of the most suitable ones to be used to solve problem statements these days, that is Python. It contains various regression, classification, and clustering algorithms in the range of unsupervised and supervised learning algorithms [28]. Simple and efficient tools are provided, and it was open source, so available directly from their platform. After performing the initial data preprocessing and statistical computation, deeper and better knowledge about the data was achieved. It was then, that the data was split into a ratio of 70:30, wherein 70% of the data is classified as the training set, that is it will be used for the training of the model according to the independent and the dependent variables, and the rest 30% was used as the test set which will be sued for testing the model that was trained earlier. Lastly, it obtained clinical reports in the form of 126 patients that are labeled into 21 classes. The process of classification was done using the machine learning algorithms with the help of the independent variables given by the 20 classes. The dataset was fitted to the models using the necessary libraries. Version: Python 3 (Programming Language used:

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Python). The algorithms used were Naive Bayes, logistic regression, decision tree, and support vector machine (SVM). Later, the analysis took place for the performances of each of the above-mentioned algorithms [29].

8 Performance Evaluation and Analysis 8.1

Precision

Precision is the ratio between the true positives and all the positives. Also known as the “positive predictive value.” Precision also gives us a measure of the relevant data points. Precision is basically a good measure to determine, when the proportion of false positive is high. Mathematically: Precision ¼

8.2

True Positive ðTPÞ Total Predictive Positive ðTP þ FPÞ

Recall

Recall is the performance metric which calculates the number of actual positives in a model. It captures this through labeling it as a positive (true positive). Applying this prerequisite, it can be known that recall needs to be the performance metric which is used to select the best performing model when a high cost is associated with false negative. Recall ¼

8.3

True Positive ( TP) Total Actual Positive ( TP + FP)

F1 Score

F1 is a function of both precision and recall in the sense that is the average of the weights of both precision and recall. F1 score is such a metric which takes both false positives and the false negatives into its measurement. We use the harmonic mean since it penalizes the extreme values. F1Score ¼ 2 

ð Recall  Precison Þ ð Recall þ Precison Þ

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9 Analysis of Machine Learning Models After Implementation of the Algorithms SUPPORT VECTOR MACHINE: From the scikit Learn platform, sklearn.svm library was obtained, and from there, class SVC is imported. Then, there was a creation of an object called classifier. As for the parameters, random state was 42, not 0 so that no similar results will come at the end. For the kernel, linear was taken, as this was the model of SVM, not kernel SVM. Next, by using the fit method, the SVM model was taken, w.r.t X_train, y_train, the training set variables. LOGISTIC REGRESSION: From the scikit Learn platform, sklearn.linear_model library was performed, and from there, class logistic regression is imported. Then, create an object called classifier. As for the parameters, random state of 42 was given, not 0 because nobody would want similar results. Rest of the parameters are taken by the default value for convenience and better performance. Next, by using the fit method, the logistic regression model was fit in, w.r.t X_train, y_train, the training set variables. NAIVE BAYES: From the scikit Learn platform, sklearn.naive_bayes library was obtained, and from there, class Gaussian NB is imported. Then, an object was created called classifier. As for parameters, a random state of 42 was given, not 0 because of similar results. Rest of the parameters are taken by the default value for convenience and better performance. Next, by using the fit method, the Gaussian NB model was fit, w.r.t X_train, y_train, the training set variables. DECISION TREE: From the scikit Learn platform, library named sklearn.tree was obtained, and from there, class decision tree classifier is imported. Then, create the object called classifier. As for the parameters, a random state of 0. Criterion was taken as entropy. Rest of the parameters are taken by the default value for convenience and better performance. Next, by using the fit method, decision tree classifier model was fit, w.r.t X_train, y_train, the training set variables. KERNEL SVM: From the scikit Learn platform, sklearn.svm library was obtained, and from there, class SVC is imported. Then, object was created and called as classifier. As for the parameters, a random state of 42 was given, not 0 because similar results was not wanted. For the kernel, it went with RBF, as this was the model of Kernel SVM, not that it discarded the linearity and substituted with a rbf kernel. Next, by using the fit method, the Kernel SVM model took place, w.r.t X_train, y_train, the training set variables.

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RANDOM FOREST: From the scikit Learn platform, we obtained sklearn.ensemble library, and from there, class random forest classifier is imported. Then, we create an object called classifier. As for the parameters, we gave random state of 0. Criterion was taken as entropy. The number of trees we use in this forest are 10. So, n_estimators = 10. Rest of the parameters are taken by the default value for convenience and better performance. Next, by using the fit method, we fit random forest classifier model, w.r.t X_train, y_train, the training set variables. XGBOOST: From the scikit Learn platform, we obtained xgboost library, and from there, class XGB classifier is imported. Then, we create an object called classifier. Rest of the parameters are taken by the default value for convenience and better performances. Next, by using the fit method, we fit XGBoost model, w.r.t X_train, y_train, the training set variables (Fig. 9). As one could see from the above models with their respective performance, SVM and Kernel SVM gave the lowest accuracy, and there was not any change after changing their kernel. As there are different symptoms as classes, the wide margin may have been hard to sustain and therefore lower performance. Naive

Fig. 9 Accuracy graph of all the models used

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Fig. 10 Final performance chart of all the models tested

Bayes gave us a much more better result because it is a simple and efficient algorithm which pertains to the fact that it is naive of us to assume the assumptions are correct. Logistic regression model was not far behind with an accuracy of 96% through the confusion matrix. Decision tree was a surprisingly good algorithm for this dataset. Random forest, which is a cluster of many decision trees gave an accuracy of around 96%. But as it is visible from the graphs above, XGBoost was by far the best algorithm to fit inside the dataset, without any overfitting and the maximum performance in terms of F1 score, recall, and precision as shown in the chat below (Fig. 10):

10

Conclusion

The increase in the numeric value of cases, of people suffering from the deadly disease of COVID-19 is alarming. The severity is so high that it has taken the form of being a pandemic. This pandemic at the moment represents a highly hazardous and perilous threat to health of all the people around the world. The solution to stop the Coronavirus pandemic to spread at the rate as it is spreading is by the evolution of techniques to recognize infected people as soon as possible. This could be a

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tough task, as the symptoms take some time to originate in a human body. However, all the increase in the numeric value of cases, of people suffering from the deadly disease of COVID-19 is alarming. The severity is so high that it has taken the form of being a pandemic. This pandemic at the moment represents a highly hazardous and perilous threat to health of all the people around the world. The solution to stop the Coronavirus pandemic to spread at the rate as it is spreading is by the evolution of techniques to recognize infected people as soon as possible. This could be a tough task, as the symptoms take some time to originate in a human body. However, all hope is not lost as we can use machine learning algorithms which is a subset of artificial intelligence (AI) that yields an encouraging approach to address this problem. It can be done at utmost rapidness and at a minimum cost. In our research, we analyzed and examined a range of machine learning algorithms and found the most serious and commonly occurring clinical COVID-19 symptoms were loss of sense of smell, breathing problem, drowsiness, dry cough, sour throat, weakness, change in appetite, diabetes, pain in chest, lung diseases, stroke or reduced immunity, symptoms progressed, kidney disease, and high blood pressure. This analysis has major advantageous implications for the medical management/ system of COVID-19 disease. If a person is sensing something wrong with his or her body, or experiencing some abnormalities, such as mild headache, fever, continuous cough, loss of smell, ., then according to this study, the person would be able to judge by himself or herself, for some period of time whether the symptom is serious or not. If the symptoms are one of the four most common symptoms as revealed by the study, then the person should definitely get himself checked and visit the hospital at utmost emergency. This will lead to timely checkup so that the quarantine period will start at the earliest, and the people near the patient would not be affected and would not suffer any consequences. This prediction can save many others from getting the horrific COVID-19. The research will also help with differentiating COVID-19 symptoms from other diseases. Such as the findings show that individuals can face many different kinds of symptoms such as headaches, muscle pains, fatigue, diarrhea, a distressed state of mind, appetite loss, difficulty in breathing, and some more. The accuracy of our algorithms was very satisfactory, the highest with the XGBoost algorithm with 98% accuracy, but it is definitely noteworthy and is worth mentioning that the other algorithms performed well with greater than 92% accuracy in all cases.

References 1. Mishra S, Tripathy HK, Mallick P, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20:4036 2. Mishra S, Mallick PK, Jena L, Chae G-S (2020) Optimization of Skewed data using sampling-based preprocessing approach. Front Public Heal 8:274 3. Jena L; Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing, pp 485–494. Springer, Singapore 77

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4. Ray C, Tripathy HK, Mishra S (2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, Hyderabad, India, 9–11 January 2019, pp. 209–219, 78 5. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms, IGI Global: Pennsylvania, PA, USA, pp 413–432 6. Joseph PC, Paul M, Lan D (2020) COVID-19 image data collection. arXiv preprint arXiv:2003.11597 7. Zhao J, Zhang Y, He X, Xie P (2020) COVID-ct-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:2003.13865 8. Wang S, Kang B, Ma J, Zeng X, Xiao X, Guo J, Cai M, Yang J, Li Y, Meng X et al (2020) A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv, 10 9. Hemdan EED, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose COVID-19 in x-ray images. arXiv preprint arXiv:2003.11055 10. Sethy PK, Behera SK (2020) Detection of coronavirus (COVID-19) based on deep features and support vector machine, 5 11. Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) COVID-caps: a capsule network-based framework for identification of COVID-19 cases from x-ray images. arXiv preprint arXiv:2004.02696 12. Hussain S, Khan A (2020) Coronavirus disease analysis using chest x-ray images and a novel deep convolutional neural network 13. Tindale L, Coombe M, Stockdale JE, Garlock E, Lau WYV, Saraswat M, Brian Lee Y-H, Zhang L, Chen D, Wallinga J et al (2020) Transmission interval estimates suggest pre-symptomatic spread of COVID-19. medRxiv 14. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M (2020) Application of the arima model on the COVID-2019 epidemic dataset. Data in brief, p 105340 15. Dey SK, Mahbubur Rahman Md, Raihan Siddiqi U, Howlader A (2020) Analyzing the epidemiological outbreak of COVID-19: a visual exploratory data analysis (eda) approach. J Med Virol 16. I.D. Apostolopoulos, T.A. Mpesiana, COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 1–6. 17. K. El Asnaoui, Y. Chawki, A. Idri, ‘‘Automated methods for detection and classification pneumonia based on X-ray images using deep learning 18. Xu X, Jiang X, Ma C, Du P, Li X, Lv S S, Yu L, Chen Y, Su J, Lang G, Li Y, Zhao H, Xu K, Ruan L, Wu W (2020) ‘Deep learning system to screen coronavirus disease 2019 pneumonia, pp 1–29 19. Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images 20. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) classification using CT Images by machine learning methods 5, pp 1–10 21. Kumar P, Kumari S (2020) Detection of coronavirus disease (COVID-19) based on deep features, p 9. https://www.Preprints.Org/Manuscript/202003.0300/V1 22. Panda B, Mishra S, Mishra BK (2016) A meta-model Implementation with Tabu search technique to determine the buying pattern of online customers. Indian J Sci Technol 9: 1 23. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing, pp 181–201. Springer, Singapore. 24. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput 1–16 25. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486

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26. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238 27. Sushruta M, Hrudaya KT, Brojo KM (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts. Int J Control Theory Appl 10:295–310 28. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing. Springer, Singapore, pp 385– 390 29. Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India, 4–5 March 2016, pp 1–3

Chapter 5

Detection of COVID-19 Using Textual Clinical Data: A Machine Learning Approach Reenu Batra, Manish Mahajan, Virendra Kumar Shrivastava, and Amit Kumar Goel

1 Introduction to Corona Virus COVID-19 Corona Viruses are a family of viruses that have conjunction with human lives for a long period. Corona Virus is mainly a newly generated Chinese virus that has the potential power to cause disease in human beings and other mammals. This virus is in relation beyond severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). This newly Corona Virus outbreak mainly originated from Wuhan city of China in December 2019. World Health Organization (W.H.O) declared Corona Virus-19 a pandemic in pursuance of the rapidly universal spread of disease. It mainly causes respiratory illness and has a strong power of inter-human (human to human) transmission [1]. Corona Virus pandemic is also known as the COVID-19 pandemic. Starting from Wuhan, Chine it is now an ongoing pandemic. Symptoms of the Corona Virus are quite similar to flu. Transmission of the Corona Virus among human beings can occur via either direct way or indirect ways. People may get infected when they come in close contact with others. The virus can attack via nose and mouth secretions. If a person gets infected with Corona Virus, symptoms will start appearing between 2 and 14 days. As per W.H.O, dry cough, fatigue, and fever may be considered as the symptoms of the mild or moderate level of infection. On the other hand shortness of breath, fatigue and fever are major symptoms of severe Corona Virus disease. Peoples who are already diabetes patients, heart patients, and asthma patients, for them it is very difficult to get recovered from the Corona Virus. If a person is Corona Virus infected, he/she got diagnosed based on their moving history and symptoms. No specific vaccine has been yet discovered against COVID-19 [2]. For R. Batra (&)
M. Mahajan
V. K. Shrivastava Department of CSE, SGT University, Gurugram, India A. K. Goel Department of CSE, Galgotia University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_5

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the treatment of this virus, a patient may be given medicines like antiviral, hydroxyl-chloroquine, antipyretic, etc. Many of the countries are participating in a race of discovering the vaccine for CoVID-19 along with India. COVID-19 has been entered in many countries. Any country’s status can be found out by their active corona case, recovered till yet, and no. of deaths because of Corona Virus (Fig. 1). As per W.H.O guidelines, every human must take precautionary measures for interruption of corona disease. Wearing masks is mandatory for all and washing hands for at least 20 s after every one or two hours. It is advised by W.H.O to make a distance of 2 feet with other human beings to avoid direct contact. It can help in a reduction in the distribution of disease from one person to another person. Various preventive measures can be taken in avoiding virus are like covering mouth and nose with help of some tissue or cotton cloth piece or avoid to make direct contact with anyone. If we study about the past, in the year 2003 a disease name SARS originated in China country and it affected 26 other countries. SARS infected 8K persons by their direct connect and symptoms of SARS were fever, cold, diarrhea, shivering, and myalgia. Corona virus infected almost 3 cr persons in the world. In the initial phase, because of less testing due to lack of medical resources, the rate of detection of infected persons was low [3]. But now as testing is going on the increase, more number of infected persons are detected day by day. Unfortunately, India is now in 2nd position in the race with other countries with maximum infected cases. Many paramedical companies are trying to make vaccines for the curing of the Corona Virus. But till yet no vaccine is introduced for curing this rapid increase of Corona Virus. Also, the government is helping by providing the facility of free testing of Corona Virus in government hospitals. Corona Virus mainly belongs to a family of acute respiratory distress syndrome (ARDS) that mainly affects on lungs by inflammation and results in respiratory failures. Shortness of breath, change of body color from the skin to blue, fatigue are major symptoms of ARDS. Many of the

Fig. 1 Countries with active cases, recovered cases, and death cases

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clinical procedures are going to be applied in the identification of the Corona Virus. Many of the Ayurveda herbs/medicines are also helpful in increasing human immunity power. On the other hand, if we talk about machine learning, it also helps in the identification of the Corona Virus by analyzing clinical textual and image data. Machine learning can also help in forecasting the nature of disease around the whole world.

2 Introduction to Machine Learning (ML) Along with clinical procedures, Machine learning is also proving to be helpful in the identification of diseases. Machine learning was first introduced in the year 1959 by a scientist named Arthur Samuel. Machine learning is a subset of artificial intelligence. Machine learning includes computer algorithms that are programmed and executed automatically to get experienced. In machine learning, a model is to build based on algorithms and that model is trained based on some sample data [4]. The sample data is also known as trained data. Machine learning also helps in making predictions by analyzing data. In machine learning, the system learns from data. Based on data studied, it identifies data patterns, classifies data, and also makes decisions without any involvement of human beings. Machine learning models can self-learn. They can easily deal with complex data with their self-learning capability. Many of learning algorithms can learn with their experience. Many of the application which mainly deals with huge and complex data. Such applications may include image recognition, medical diagnoses, financial trading and industries, speech recognition, prediction, etc. Machine learning algorithms are mainly of three types (Fig. 2). 1. Supervised learning algorithms 2. Unsupervised learning algorithms 3. Reinforcement learning algorithms. In supervised learning algorithms, the data used is mainly labeled data. Here labeled data means every data is tagged with the correct label. If our system is experienced with a supervised learning model, it can easily map new input variable X on to output variable Y based on prediction. We can better understand the supervised learning concept with an example. Suppose we have a set of data and we labeled it by two values “SPAM” and “NOT SPAM”. After labeling of data, we trained our supervised model on behalf of this labeled data. After the model gets trained, it can easily classify new e-mails on basis of prediction that which mail is “SPAM” and which mail is “NOT SPAM”. On the other hand, unsupervised learning algorithms are based on unlabeled data. In this, the system works on data without training. In unsupervised learning, data is classified by analyzing the type of data. Clustering and association are two forms of unsupervised learning algorithms. In clustering, the grouping of data is done by their behavior. In association,

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MACHINE LEARNING

SUPERVISED LEARNING (TASK- DRIVEN)

UNSUPERVISED LEARNING

REINFORCEMENT LEARNING

(DATA-DRIVEN)

(LEARN FROM ERRORS)

Fig. 2 Machine learning algorithms

rules are discovered to describe a large portion of data. The third type of learning algorithm is reinforcement learning. In the reinforcement learning algorithm, learning of the system is done by interaction with the environment. In this learning algorithm, there is an agent that receives an award for correct functionality. In this learning algorithm, there is no human intervention [4]. It mainly trains the system on basis of reward and penalty (when does not perform the correct operation).

3 Machine Learning and Disease Detection Pattern recognition, prediction, and explanation tasks can be done with help of machine learning models and natural language processing (NLP) models. NLP is mainly used in text analysis. In the case of text mining classification is done. To mine the unstructured data supervised and unsupervised learning algorithms can be used. Fraud detection and spam detection are some of the applications of text classification. In disease diagnosis, machine learning plays an important role. In diabetes diagnosis, ensemble learning algorithms perform with an accuracy of 98.6%. Machine learning can be used to predict and diagnose COVID-19 disease. In this way, machine learning can help to save many lives together. There is a massive amount of data under COVID-19. A model can be trained on behalf of this huge data using a machine learning algorithm. Based on the clinical textual reports and radiography images diagnosis of COVID-19 can be done [5]. When we use machine learning algorithms for diagnosis, it is to be performed accurately and without human intervention. The main advantage of diagnosis using ML is that it is much time-saving and cost-effective. In diagnosis, the machine learning model can be trained using X-Ray and computed tomography (CT) scan. In recent researches, a deep conventional

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neural network named COVID-NET has been developed for the diagnosis of COVID-19. After the identification of the Corona Virus in a patient, it can easily find out how severe a patient is infected and how much attention a patient needed. As the level of infection of disease in different patients are different. Some patients may have a mild infection of the Corona Virus while others may have a moderate or high level of infection. This can be better identified by finding out the mortality risk of disease by using a prognostic prediction algorithm [5]. In recent researches, a machine learning model has been developed for predicting a patient with Corona Virus disease. This model gains an accuracy rate of 80%. Data of 53 different patients have been collected in the training of the model. By classification of text and clinical reports diagnosis of Corona disease can be done easily without human involvement.

4 How ML Can Be Used in Disease Detection Machine learning algorithms and ensemble learning algorithms can be used in disease detection. These algorithms work upon data and then a model based on these algorithms can be trained using past data. This can be helpful in the classification and prediction of disease in human beings. This task can be done by collecting all the patients’ data in a repository. Further, a data set is required to train the machine learning model [6]. The data set mainly consists of labeled data that mainly helps to the training of the model. Some data values from the data set are mainly used as test data. After labeling the data values, the data processing task is to be performed that mainly includes the normalization of all data values (Fig. 3). Feature engineering is the next step in the implementation of the Machine Learning (ML) model. Feature engineering mainly includes selecting some features which are required to process the data. At last machine learning algorithm can be applied for the classification of reports [7]. We can discuss all the steps involved in disease detection one by one. A series of steps are to be followed in disease detection (Fig. 3).

4.1

Data Collection

At first, there is a process of data collection. For this, there is a data repository Git-Hub from where anyone can access data of hospitals. We can start working by collecting data from the repository [7]. This data includes various parameters like patient id, sex of the patient, age and location of the patient, admitted in ICU, oxygen requirement, the modality of risk, and other clinical/text reports.

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Patient Data

Data Collection

Relevant Dataset

Data Processing

Feature Engineering

Both (SARS, COVID) ARDS

Machine Learning

SARS

COVID

Fig. 3 Machine learning process in disease detection

4.2

Data-Set Used

In the next step, clinical reports having text data can be collected. Text data may include various attributes. Based on attributes, the length of each clinical report can be found and all clinical reports are written in common English language [8, 9]. A curve that mainly describes how we can identify report length and how the length of the report varies (Fig. 4). All of the clinical reports of different lengths can

Fig. 4 Length of clinical textual report

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Fig. 5 Classification of reports into different classes according to length

be labeled and categorized into four different classes name COVID, SARS, ARDS, and COVID and ARDS Both (Figs. 4 and 5).

4.3

Data Preprocessing

Preprocessing is the next step towards the diagnosis of disease. In pre-processing, refinement of clinical textual data is done by removing/deleting redundant and unnecessary attributes [10]. By doing preprocessing a better accuracy can be achieved in diagnosis.

4.4

Feature Selection

After prepossessing of data our next step is to select the relevant features from the collected data by using an appropriate feature selection technique [11]. All selected features have some corresponding value and all the valued features can be considered as input to the machine learning algorithm.

5 Machine Learning Algorithms Various machine learning algorithms can be used in the classification task. These algorithms can be used to classify collected textual data into different categories. There are mainly four classes of viruses named COVID, ARDS, SARS, and

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COVID and ARDS both [12]. For performing this classification various supervised machine learning can be used. These algorithms may include Random Forest (RF), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Decision Tree (DT), and Bagging.

6 Traditional Machine Learning Algorithms Many traditional machine learning algorithms can be used in the diagnosis of the virus.

6.1

Logistic Regression

This approach performs the prediction based on class membership value. As every feature in the textual report has some value. These values can be worked as input to the model. As we have mainly four classes 0, 1, 2, and 3, i.e., C = {0, 1, 2, 3}. Based on the input value given to model class membership probability can be found out.

6.2

Multinomial Naïve Bayes

This traditional machine learning algorithm mainly uses the Bayes rule for finding out the probability of class membership [13]. As we have C = {0, 1, 2, 3} and Number of features, N = 40. Bayes rule can be used to find out the highest probability of text that is to be tested. The highest probability defines how much a particular test report belongs to a particular class.

6.3

Support Vector Machine

Support Vector Machine (SVM) is mainly used to generate a classifier. In SVM, this classifier mainly takes data points and the number of features selected. All of these selected features mainly have a value associated with themselves [14]. These values can be passed as inputs and based on these inputs, classifier categories text data.

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Decision Tree

Decision Tree (DT) is also a type of supervised learning algorithm used for classification. In DT whole input set is divided into regions and classification is done on these regions separately. The major concern in DT is to split the data based on some criteria. As at last our performance of the classification task mainly depends upon how well data is divided [15]. The task of classifying data into four categories can be done at the leaf nodes of the tree. We can calculate the performance by finding out the information gain ratio which is mainly the ratio of information gain to intrinsic information.

7 Ensemble Machine Learning Algorithms Besides traditional machine learning algorithms ensemble, machine learning algorithms can be used to identify disease [16, 17].

7.1

Bagging

Bagging is an example of an ensemble machine learning algorithm that can be used in classification resulting in high performance. In bagging algorithms at first a training set T with size n is given as input [18]. After proper sampling, another training set Ti with size m can be given. Size n training set is replaced with a size m training set. In all these iterations some of the inputs are repeated inputs. These duplicates’ inputs are to find out and removed to increase the performance value.

7.2

Ada-Boost

In this type of learning algorithm, a classifier is developed for the classification task. In this algorithm, dataset values are weighted value [19]. At first, the model is trained with datasets with equal weights. It results in the development of a weak classifier. At some of the points, weights are to be increased and decreased as per weight value to generate a correct strong classifier. In this way, a weak classifier can be transformed into a strong classifier by increasing and decreasing weight values.

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Random Forest Classifier

Random forest algorithm mainly works the same as of decision tree algorithm. In this bootstrap aggregating technique is used by taking the average of all predictions of regression trees [20]. In this way, by aggregating all the predictions values total predictions can easily find out. This input is given in form of feature values as a table [21].

7.4

Stochastic Gradient Boosting Algorithm

In this algorithm, trees ate created from training dataset values, and the correlation between trees is needed to be minimized. Data set are given in form of features value as a table as input [22]. At every pass/iteration, a subset of the dataset is drawn at random without ant replacement with full dataset values.

8 Classification of Textual Reports Using ML As discussed in earlier sections, a model can be created for the classification of reports into four categories. To train our model we collect textual reports of 212 patients. We divided our dataset into 70:30. 70% of data is used as training data and 30% of data can be used as test data. The feature selection approach is used in the classification of data into four categories. We divided our dataset into a training subset and test subset to make our model more generalize [23, 24]. Tenfold cross-validation is applied to all algorithms to avoid sample bias that may come into account while random partitioning. If we compare all classical machine learning algorithms, we can find out two classical machine learning algorithms named logistic regression, multinomial naïve Bayes gives a result with high accuracy (Table 1). With help of Table 1, we can analyze that logistic regression and multinomial naïve Bayes have an accuracy of 96.2%. Others also perform well in disease detection like decision tree has an Table 1 Comparison of traditional machine learning algorithm Algorithm

Precision

Recall

F1 score

Accuracy (%)

Logistic regression Multinomial naive Bayesian Support vector machine Decision tree

0.94 0.94 0.82 0.92

0.96 0.96 0.91 0.92

0.95 0.95 0.86 0.92

96.2 96.2 90.6 92.5

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accuracy of 92.5%. Support vector machine algorithms result in an accuracy of 90.6%. We can compare all these algorithms based on four factors named precision, recall, F1 score, and accuracy. Logistic regression and multinomial naïve Bayes performs with the same precision i.e. 0.94%, same recall value i.e. 0.96%, and same F1 score value of 0.95%. On the other hand, we can also analyze traditional and

Table 2 Comparison of traditional and ensemble machine learning algorithm Algorithm

Precision

Recall

F1 score

Accuracy (%)

Logistic regression Multinomial naive Bayesian Support vector machine Decision tree Bagging Adaboost Random forest Stochastic gradient boosting

0.94 0.94 0.82 0.92 0.92 0.85 0.93 0.93

0.96 0.96 0.91 0.92 0.92 0.91 0.94 0.94

0.95 0.95 0.86 0.92 0.92 0.88 0.93 0.93

96.2 96.2 90.6 92.5 92.5 90.6 94.3 94.3

Fig. 6 Comparative analysis of traditional and ensemble machine learning algorithms

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ensemble machine learning algorithms together (Table 2). From Table 2 we can easily analyze that the traditional machine learning algorithms perform better than ensemble machine learning algorithms [25]. Figure 6 illustrates each machine learning algorithm either transitional learning algorithm or ensemble machine learning algorithm can be collectively defined by four parameters named precision, recall, F1 score, and accuracy [26]. As it is a challenging task to identify the Corona Virus in all patients. But up to some extent, this model can be beneficial in the detection of the virus in the patients.

9 Conclusion At present, the whole world is under the attack of COVID-19. Various scientists are in trying of producing a vaccine for it. With help of machine learning algorithms, we can easily classify our disease into four categorize. We can also compare various traditional machine learning algorithms and ensemble machine learning algorithms. These algorithms can be compared on basis of factors like precision, recall, F1-score, and accuracy. We analyze that our logistic regression and multinomial naïve Bayes performed with an accuracy of 96.2% which is better than other machine learning algorithms. Also, the other values like precision, recall, and F1 score is also high in the case of these two algorithms. We performed this classification based on the feature selection approach. For a trained model we selected a set of 40 features. On the other hand, random forest stochastic gradient boosting, bagging also showed better results in the classification task.

References 1. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, Hu Y, Tao ZW, Tian JH, Pei YY, Yuan ML, Zhang YL, Dai FH, Liu Y, Wang QM, Zheng JJ, Xu L, Holmes EC, Zhang YZ (2020) A new coronavirus associated with human respiratory disease in china. Nature 44(59):265–269 2. Medscape Medical News (2020) The WHO declares public health emergency for novel coronavirus. https://www.medscape.com/viewarticle/924596 3. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223):507– 551 4. Khanday AMUD, Amin A, Manzoor I, Bashir R (2018) Face recognition techniques: a critical review. STM J 5(2):24–30 5. Kumar A, Dabas V, Hooda P (2018) Text classification algorithms for mining unstructured data: a SWOT analysis. Int J Inf Technol. https://doi.org/10.1007/s41870-017-0072-1

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6. Verma P, Khanday AMUD, Rabani ST, Mir MH, Jamwal S (2019) Twitter sentiment analysis on Indian government project using R. Int J Recent Tech Eng 8(3):8338–8341 7. Chakraborti S, Choudhary A, Singh A et al (2018) A machine learning-based method to detect epilepsy. Int J Inf Technol 10:257–263. https://doi.org/10.1007/s41870-018-0088-1 8. Sarwar A, Ali M, Manhas J et al (2018) Diagnosis of diabetes type-II using hybrid machine learning-based ensemble model. Int J Inf Technol. https://doi.org/10.1007/s41870-018-0270-5 9. Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M (2020) Mapping the landscape of artificial intelligence applications against COVID-19 69:807–845 10. Wang L, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images 11. Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, Li S, Zhang M, Guo Y, Xiao Y, Tang X, Cao H, Tan X, Huang N, Amd A, Luo BJ, Cao Z, Xu H, Yuan Y (2020) Prediction of criticality in patients with severe covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. medRxiv 12. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T, Wu Z, He G, Huang Y (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Compu Mater Contin 63(1):537–551 13. Description of Logistic Regression Algorithm. https://machinelearningmastery.com/logisticregression-for-machine-learning/. Accessed 15 May 2019 14. Description of Multinomial Naıve Bayes Algorithm. https://www3pillarglobal.com/insights/ document-classification-using-multinomial-naive-Bayes-classifier. Accessed 15 May 2019 15. Khanday AMUD, Khan QR, Rabani ST. SVMBPI: support vector machine-based propaganda identification. SN Appl Sci 16. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14):4036 17. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 18. Description of Boosting Algorithm. https://towardsdatascience.com/boosting. Accessed 10 July 2019 19. Description of Adaboost Algorithm. https://towardsdatascience.com/boosting-algorithmAda Boost-b673719ee60c. Accessed 10 July 2019 20. Katuwal R, Suganthan PN (2018) Enhancing multi-class classification of random forest using random vector functional neural network and oblique decision surfaces. Arxiv: 1802.01240v1 21. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378. https://doi.org/10.1016/S0167-9473(01)00065-2Int.j.inf.technol.123 22. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 23. Mallick PK, Mishra S, Chae G-S (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput 1–16 24. Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10:19549. https:// doi.org/10.1038/s41598-020-76550-z 25. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486 26. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10(3):244–256

Chapter 6

Application of Machine Learning Algorithms for Effective Determination of COVID-19 Clusters Vazeerudeen Abdul Hameed, Selvakumar Samuel, and Kesava Pillai Rajadorai

1 Introduction Coronavirus disease (COVID-19) that is caused by the coronavirus is the cause of the worldwide pandemic since its inception in the last quarter of 2019. The disease causes acute respiratory illness which has been lethal and the cause of death of 1.78 million people worldwide [1]. The spread of the virus and its impact went beyond the control of all the nations in the world due to the ease at which the virus could spread and that the patients with viral infections in general cannot be cured but only be given general supportive care to ease the symptoms. According to the specialists, any virus has a parasitic cycle in a human body, and hence, any medication to kill the virus will have a negative impact on the human body due to which anti-viral drugs are often not prescribed [2, 3]. Another reason for non-availability of drugs for viral infections in general is the numerous varieties of viruses and their mutations that exist. These numerous viruses and their mutations are extremely different from one another which means a drug needs to be discovered and manufactured for each virus that exists [2]. This is extremely expensive for drug manufacturers which demotivates the productions of anti-viral drugs. Hence, viral infections have most often been treated with supportive care that can help a patient to cope with the symptoms of the disease but not to cure the disease. Vaccines are considered as viable options where a person could be protected from being infected. However, coronavirus is a newly discovered virus, and hence, a lot of medical research is V. A. Hameed (&)
S. Samuel
K. P. Rajadorai Asia Pacific University of Technology, Innovation, Kuala Lumpur, Malaysia e-mail: [email protected] S. Samuel e-mail: [email protected] K. P. Rajadorai e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_6

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involved in identifying the most effective vaccination to prevent the spread of the virus. Meanwhile, the cause of spread of the virus has been identified as to be in close contact with infected people. The coronavirus could be easily passed from an infected person from his sneeze or saliva or any bodily fluids [3]. Hence, social distancing was put in place to minimize the possibility of contracting the virus among people. Social gatherings and events were curbed with the aim of distancing the people so as to reduce the spread of the virus. Several waves of coronavirus spread were experienced by all the countries around the world. Complete lockdowns with severe restrictions were imposed in many regions. Although the aim of the lockdown was to control the spread of the virus and save the lives of the people, the economy of the countries had a severe backlash [4]. Several industries such as aviation, auto parts and equipment, oils and gas and many had negative repercussion from the COVID-19 lockdown. “Mandatory closures and the gradual re-opening process, decline in workforce due to morbidity, mortality and avoidance behavior, increased demand for health care, decreased demand for public transportation and leisure activities, potential resilience through telework, increased demand for communication services and increased pent-up demand” have been the major repercussion of COVID-19 as explained in [4]. Eventually, every country has been hit by a heavy decline in its GDP. Social distancing could help prevent the spread of the disease. Infected individuals could be isolated until full recovery so that they do not transmit the infection to others. Whereas it has been very expensive and inconvenient to conduct COVID-19 tests on people due to the inherent nature of the tests. There have been at least three types of test to date [5]. Molecular test which is also known as the ribonucleic acid (RNA) or polymerase chain reaction (PCR) test involves the collection of mucus from the nose or throat using a specialized swab. The sample is then tested in a laboratory which may take few minutes if the laboratory is located on-site or few days if the laboratory is located at a different location. The second common method is called the antigen test for which sample is collected in a similar method as molecular test. However, this method is much faster and cheaper compared to the molecular test. Although this method is highly accurate, an active infection might go unnoticed in this test. In such cases, a molecular test may have to be conducted. The third method is called the antibody test or serology or blood test. The test is usually recommended after fourteen days of suspected exposure to the virus. Hence, based on the characteristics of the test methods, an appropriate choice is made. Research is in progress to determine more cost effective and rapid medical tests to identify people infected with COVID-19 which could be conducted at home. The following section presents an understanding of the COVID-19 infection. Knowledge on the characteristics of the infection is important to make a wise selection of the machine learning algorithms to address the crisis.

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2 Characteristics of COVID-19 Infection COVID-19 disease being one of the challenging diseases that mankind had ever seen requires in-depth study about the virus which requires time and resources. With the knowledge gained so far from the infections that the world has encountered, following are some of the observations about the characteristics of the infection. The infection has been broadly classified into symptomatic and asymptomatic infection as studied in [6].

2.1

Asymptomatic Infection

Some patients were identified to be infected with the coronavirus but showed no symptoms. Such patients are said to be asymptomatic [7]. Asymptomatic patients pose severe threat as they act as carriers of the virus and help the spread of it. It is generally close to impossible to identify such patients unless tests were conducted upon them. Hence social distancing is very crucial to curb the spread of the virus.

2.2

Symptomatic Infection

COVID-19 patients develop symptoms within seven to fourteen days of contracting the virus. This time lapse calls for people to isolate themselves if they doubt of contracting the virus well ahead so that they could prevent the spread of the virus before strong symptoms of infection become evident. The most common symptoms are said to be high body temperature, persistent cough and/or tiredness. Less common symptoms may include body pain, sore throat, and loss of taste or smell. Symptoms suggesting lethal infection include severe shortness of breath with chest pain and loss of speech and movement. Patients at lethal stages of infection are easy to identify but difficult to ensure their speedy recovery. On the contrary, people with less serious symptoms are difficult to identify and to prevent them from transmitting the virus to other healthy individuals. Hence, its evident that while people may exhibit symptoms while some others may not. The following section presents a discussion on the machine learning algorithms which could be effectively deployed to address the COVID-19 pandemic.

3 Machine Learning Machine learning includes a set of all algorithms that have the ability to improve automatically through experience and enable automated decision making based on the gained experience. Machine learning in other words is a means of mimicking

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MACHINE LEARNING FOR COVID-19 Decision Tree

K-Means Clustering

Linear Regression

Mean Shift

Logistic Regression

Neural Networks

Fig. 1 Machine learning algorithms

the decision-making capacity of a human brain. Several machine learning algorithms have been proposed to date, and they have been used to solve numerous problems. Support vector machines (SVM), linear regression, logistic regression, k-means clustering and many more machine learning algorithms have been deployed in resolving several problems. The machine learning algorithms are broadly classified into supervised and unsupervised learning. A wide array of supervised and/ or unsupervised machine learning algorithms is suitable for determining COVID-19 clusters. Figure 1 shows a presentation of the different types of such machine learning algorithms. The nature of the problem to be solved allows the selection of one or more types of machine learning algorithms to solve it. The following section presents the working of a decision tree classifier, logistic regression and clustering. These are a selected subset of machine learning methods for decision making about the COVID-19 infection among individuals. Clustering is applied to determine potentially dangerous groups of infected individuals that could cause an outburst of viral spread.

3.1

Decision Tree Classifier

A decision tree classifier, as the name implies, builds a tree structure. Works accomplished in the research contributed in [8–13] are valuable sources of knowledge about the working of decision trees. A sample representation of a decision tree is shown in Fig. 2. The tree always starts with a root node and ends with several leaf nodes depending on the nature of the data used to build the decision tree. The decision tree algorithm adopts a greedy search strategy to create

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Level -1

Level -2

Level -3

Level -4

Root Node Leaf Node Fig. 2 Decision tree

branches based on the information gain calculated at each level of a decision tree. Certain available sample data is used to construct a decision tree. The constructed decision tree is then then deployed for decision making [14, 15]. It is important to understand that all decisions hence made are dependent on the sample data on the basis of which the tree was constructed. The construction of a decision tree involves the determination of information gain for the given training data. The information gain is calculated from the entropy of information as measured using the formula in Eq. (1). EðxÞ ¼

n X

pi ðxÞ log2 ðpi ðxÞÞ

ð1Þ

i¼1

The decision tree algorithm involves the measure of three important values in its creation. The entropy of the outcome is measured namely E(O). The entropy of the outcome is measured while given the attribute Ai that may have contributed to the outcome, namely E(Ai, O). The final information gain is calculated according to Eq. (2). GðO; Ai Þ ¼ EðOÞ  EðO; Ai Þ

ð2Þ

The diagram in Fig. 3 presents the steps involved in creating a decision tree. Although entropy and information gain are the two most important measures used to create an ideal decision tree, several other parameters are also included to optimize the process.

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Measure the entropy of the outcome namely E(O) and the entropies E(Ai,O) for all the attributes that contribute to the outcome and are not currently found in the decision tree.

Determine the Gain G(O,Ai)

Select the attribute Ai that contributes to the maximum information gain.

Create branches in the decision tree for the selected attribute Ai.

Measure the entropy of the outcome E(O) for each new branch.

E(O)=0 for a branch

Stop expanding the branch

Fig. 3 Flow of steps to create a decision tree

Ideally, the process of adding branches to a decision tree should stop when the entropy of the branch becomes zero. This has been shown in Fig. 3. However, in real-world scenarios, the training data may not allow for the entropy of a branch be become an absolute zero. It may not be a practically viable choice to allow a decision tree to grow to greater depths. Hence, a threshold of entropy is set. The branching is stopped once the set threshold of entropy is reached. The selection of the threshold must be done wisely. A large value of threshold might enable faster development of shallow decision trees. However, such trees may cause serious errors in decision making. Similarly, very small threshold value

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may result in the creation of a deep decision tree. This results in greater computational time to yield the results. Therefore, a wise choice of threshold for the entropy is important. Several other criteria such as maximum depth of the tree, minimum number of samples required to create a new branch and the number of attributes to be considered for a branch could also be taken into account in optimizing the creation of a decision tree. A decision tree classifier could be used to gain a probabilistic estimate of contracting the virus for an individual. It must be noted that the results of the decision tree classifier are a probabilistic estimate. Therefore, the person for whom the classifier is applied may or may not actually contract the virus. Table 1 presents some of the vital symptoms that are used to determine the possibility of contracting the coronavirus. Each of the parameters namely dry cough, high fever, sore throat and difficulty in breathing was measured on a scale of zero to twenty. The final column namely infected with COVID-19 explains the status of infection of the patient [16]. The dataset is relatively new and had only fifty records. Thirty records were used to build the decision tree classifier. Twenty records were used to test the prediction capability of the classifier. Table 2 presents the attributes of the decision tree. The tree is shallow in depth with only two levels and has an accuracy of 75%. This is clearly attributed to the very small sample size of the data. The data must be populated sufficiently to obtain better reliable results. It might sound cumbersome to collect the information for these attributes for everyone. Unlike x-rays or blood tests, these attributes could be collected through wearable devices and/or mobile phones fitted with sensors. Wearable devices have emerged as vital-sign monitoring systems that enable the reduction of health-care cost [17]. It has been clearly suggested in [18] that prevention of diseases and

Table 1 Symptoms of coronavirus [16] Dry cough

High fever

Sore throat

Difficulty in breathing

Infected with COVID-19

0 15 4 4 0 6 16 17 0 16 0 7 8

2 15 5 7 0 0 17 17 18 15 8 8 0

3 20 0 9 1 6 18 0 0 18 0 10 6

0 16 0 10 0 0 16 0 18 20 0 0 10

No Yes No No No No Yes Yes Yes Yes No No No

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Table 2 Decision tree attributes

Depth of tree

2

Number of leaves Accuracy score Confusion matrix

3 75% Yes 7 3

Yes No

No 2 8

enhancement of quality of life is evidently achieved through the use of wearable devices [19, 20]. Researchers at the University of California, San Francisco, have evidently proved that apple watches could determine the abnormality in the heart rhythm with up to 97% of accuracy [21]. Machine learning algorithms involving deep neural networks had been deployed in the construction of the watches. The system had been trained with data collected from over 9,750 users. 139 million heart rate and step count measurements were used for the training. It must be understood that a huge amount of training data has been used for building the system which ensures a reliable degree of accuracy. Real-time data collection is hence feasible. More attributes could be included in Table 1 should they be found to contribute to the decision making. The following section elaborates on the use of logistic regression as a machine learning approach to understand the impact of the symptoms on a patient infected with COVID-19.

3.2

Logistic Regression

Logistic regression is yet another machine learning algorithm that could be deployed in scenarios where a binary decision such as a yes/no, true/false kind of decision needs to be made. Work accomplished in [22] is a vital source of in-depth knowledge on the working of logistic regression. The logistic regression algorithm is built upon the definition of the sigmoid function as shown in Eq. 3. f ðxÞ ¼

1 ð1 þ eðax þ bÞ Þ

ð3Þ

Sigmoid is an “S” shaped curve that tends to a minimum value of zero and a maximum value of one. Hence, this function is best suited for binary outcomes as explained above. Figure 4 shows a diagrammatic view of the sigmoid function f(x) for a = 1 and b = −5. Although sigmoid function is continuous, it is discretized at the point x for ax + b = 0. For the function plotted in Fig. 4, f(x) = 1 for all values of x > = 5 and f(x) = 0 for all x < 5.

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Fig. 4 Sigmoid function

Logistic regression aims at finding a point or a line or a plane that could categorize the data into two groups. The number of input attributes determines the characteristic function that divides the data. A single input attribute contributes to a point that defines the characteristic function. Two input attributes contribute to a line that defines the characteristic function. Similarly, three input attributes contribute to a plane that defines the characteristic function. The data presented in Table 1 could be subjected to logistic regression. Dry cough and high fever are selected as input attributes and the infected with COVID-19 attribute is the output attribute. The output attribute is a binary decision namely yes/no. Since there are two input attributes, it is expected that a straight line could divide the samples into the two output classes. Figure 5 shows the plot of the input attributes. Upon logistic regression the straight line, Eq. 4 is obtained and plotted on the graph. 0:4669d þ 0:5599h  7:9317 ¼ 0 d ¼ Dry Cough

h ¼ High Fever

ð4Þ

It can be clearly seen that a huge number of blue samples (infected with COVID-19 = No) lie below the line, whereas a vast majority of the red samples (infected with COVID-19 = Yes) lie above the line. Hence, given the values of the measure for dry cough and high fever for an individual, the equation can help to predict the status of COVID-19 infection for the individual. However, it can also be seen from the graph that false positives and false negatives are possible. A few data points can be seen to be located on the wrong side of the straight line. This is an inherent characteristic of a machine learning approach. A perfect classification of real-time data may not be always possible. False positives and false negatives may be produced by this approach. With sufficient training, the probability of such false positives and false negatives can be largely diminished.

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Fig. 5 Logistic regression over two attributes

The logistic regression algorithm was extended to three input attributes, namely dry cough, high fever and sore throat, and the infected with COVID-19 attribute is the output attribute. This resulted in the formation of a plane that divided the data points into two categories. Equation 5 shows the representation of the plane. The three-dimensional plot of the data points and the plane that divides the data points is shown in Fig. 6. 0:4679d þ 0:6240h þ 0:1658s  9:9246 ¼ 0 d ¼ Dry Cough h ¼ High Fever s ¼ Sore Throat

Fig. 6 Logistic regression over three attributes

ð5Þ

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It is clearly visible from Fig. 6 that a large number of blue samples (infected with COVID-19 = No) and a large number of the red samples (infected with COVID-19 = Yes) are separated by the plane (shown in green). Hence, given the values of the measure for dry cough, high fever and sore throat for an individual, the equation of the plane can help to predict the status of COVID-19 infection for the individual. Decision tree classifier and logistic regression have been understood to be effective methods of obtaining a probabilistic estimate of infection among individuals. The following section presents clustering as a viable machine learning algorithm to determine the COVID-19 clusters.

3.3

Clustering

Clustering is the process of grouping a set of all data points that tend to have a similar characteristic. Upon clustering several groups of clusters of points could be formed. Each cluster explains that the points in the group are closer to each other and share a common characteristic feature. Clustering may be broadly divided into two types namely hard clustering and soft clustering. Hard clustering is the well-known approach of clustering where a data point strictly belongs to one and only one cluster. On the contrary, soft clustering is a fuzzy approach. A data point can be associated with several clusters with different degrees of strength. Hence, a data point may be strongly associated with one cluster while being weakly associated with another cluster at the same time. Depending on the needs of solving a problem, either of the clustering approaches may be chosen. There are numerous clustering algorithms that have been proposed by various researchers [23–28]. Some of the most used clustering algorithms are k-means clustering, k-nearest neighbors clustering and agglomerative clustering. The different methods of clustering proposed in research have a recursion of steps with a well-defined rationale and may result in the formation of different types and numbers of clusters for the same given dataset. Density-based spatial clustering of applications with noise (DBSCAN) is an important clustering algorithm. Work done in [29–31] elaborates a good example of the application of the algorithm to identify galaxy clusters. The algorithm enables the clustering of the data points based on their closeness and the volume of the data points. This is a vital characteristic due to which the technique can be used for the partitioning a COVID-19 cluster. The working of the DBSCAN algorithm requires the initial knowledge of three vital parameters namely radius, minimum points and core points. Through the execution of the algorithm, added information such as border points and noise/ outlier points is generated. Figure 7 illustrates the types of points identified by the DBSCAN algorithm. In Fig. 7, e, represents the radius of a cluster. This factor defines the relative closeness

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Noise Point Border Point

Core Point

Fig. 7 DBSCAN algorithm

of the points. All the points within the radius e, of a given core point have the potential to form a cluster. However, the algorithm also considers minimum points as yet another deciding factor to form a cluster. Although a certain number of points may be at e distance from the core point, a cluster may be formed only if the number of points in that cluster is greater than or equal to the minimum points factor. In Fig. 7, for a minimum points = 3, there is one core point that satisfies the minimum points criterion and hence forms a cluster as shown in blue. A noise point is a core point that has not enough minimum points to form a cluster. This is shown as a black point in Fig. 7. There could be some points that do not satisfy the minimum points criteria but may be located in the neighborhood of another core point. Such core points are known as border points. In Fig. 7, a border point shown in red shares a neighborhood with a core point. The working of the DBSCAN algorithm could be adopted for effective determination of the COVID-19 clusters. To demonstrate the application of the DBSCAN algorithm, a random sample data was generated. The sample data represents the geographical location of people in a region. The location of people is identified by latitude and longitude on the maps. In accordance with the DBSCAN algorithm, the core points could be the vital COVID-19 patients in a region. These points act as seed information to execute the algorithm. The minimum points factor of the DBSCAN algorithm could be translated to the density of population. This density is required to define a region to be of a potential outburst of COVID-19 infection. The distance e, could be set to define the distance between the individuals used to define a cluster. Figure 8 presents the plot of the geographical distribution of people. For a value of e = 0.5 and minimum points = 5, the points in Fig. 8 were clustered as shown in Fig. 9. The black points are the outliers which have no affiliation to any clusters of infection. These outliers are still COVID-19 infection points, but they may not pose a potential outbreak if precautions are taken to treat the infected individuals.

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Fig. 8 Sample plot of geographical distribution of people

Fig. 9 DBSCAN clustering over geographical distribution of people

There are three clusters of potential outbreaks that need to be contained as shown in red, green and blue. DBSCAN algorithm identifies border points. However, the border points were connected to one of the clusters to which they were associated. Such a decision is essential in this problem because a border point is still a potential cause of spread of virus. Lockdowns and/ or standard operating procedures for the clusters could be formulated for each of the clusters to prevent the outbreak. Hence, DBSCAN clustering could be deployed to determine clusters of infections. Like DBSCAN, there are numerous clustering algorithms which could be used to handle this problem. Understanding the working principle behind those methods and translating them for the problem of discussion is vital for effective results.

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4 Conclusion The chapter presents discussions on machine learning algorithms that could be deployed to handle the COVID-19 pandemic. Machine learning algorithms such as decision tree and logistic regression could be used to obtain a probabilistic estimate of infection in people. These algorithms need sufficiently large dataset for training, so that reliable results could be obtained. Wearable devices could be used to enable faster collection of real-time data among individuals. Clustering algorithm is an effective technique to identify potential clusters of infections. The density of the cluster could be used to determine the nature of the containment measures that could be adopted to control the spread of the virus. DBSCAN is one of the several clustering algorithms that could be implemented to determine the COVID-19 clusters. Several machine learning algorithms have been proposed in literature with their effective implementation to solve numerous problems. A wise choice of such algorithms would be of great help to contain the spread of the coronavirus.

References 1. Coronavirus Update (Live): 82,481,466 Cases and 1,800,180 Deaths from COVID-19 Virus Pandemic-Worldometer. https://www.worldometers.info/coronavirus/ 2. Why the coronavirus and most other viruses have no cure. https://www.medicalxpress.com/ news/2020-03-coronavirus-viruses.html 3. Coronavirus Disease 2019 (COVID-19) Saxena SK (ed) Epidemiology, pathogenesis, diagnosis, and therapeutics 4. Walmsley T, Rose A, Wei D (2020) The impacts of the coronavirus on the economy of the United States. Economics of Disasters and Climate Change 5. The 3 Types of COVID-19 Tests, and What Each One Tells You. https://www.health.com/ condition/infectious-diseases/coronavirus/covid-19-test-types 6. Buitrago-Garcia D, Egli-Gany D, Counotte M, Hossmann S, Imeri H, Ipekci A, Salanti G, Low N (2020) Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: a living systematic review and meta-analysis. PLoS Med 17: e1003346 7. Pollock A, Lancaster J (2020) Asymptomatic transmission of COVID-19. BMJ:m4851 8. Datsenko N (2018) Application classification and regression trees for forecasting time series of financial assets. Sci Notes:195–206 9. Dialsingh I (2013) Applied categorical and count data analysis. J Appl Stat 41:915–915 10. Luo G (2013) Extension clustering-based extreme learning machine neural network. J Comput Appl 33:1942–1945 11. Duca Iliescu D (2020) the impact of artificial intelligence on the chess world. JMIR Serious Games 8:e24049 12. Quinlan J (1986) Induction of decision trees. Mach Learn 1:81–106 13. LS Evans LS, CR Johnson CR (2018) Machine learning programs predict saguaro cactus death. J Comput Sci Syst Biol:12 14. Mishra S, Tripathy HK, Mallick P, Bhoi AK, Barsocchi P (2020) EAGA-MLP—An enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20:4036 15. Mishra S, Mallick PK, Jena L, Chae G-S (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Heal 8:274

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16. Srivastava P COVID-19 Symptoms Dataset. https://www.kaggle.com/prakharsrivastava01/ covid19-symptoms-dataset 17. Kaur P, Singh Saini H, Kaur B (2018) Wearable sensors for monitoring vital signs of patients. Int J Eng Technol 7:62 18. Clover J (2019) Study Confirms Apple Watch Can Detect Abnormal Heart Rhythm with 97% Accuracy. https://www.macrumors.com/2018/03/21/apple-watch-abnormal-heart-rhythm/ 19. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494. 77 20. Ray C, Tripathy HK, Mishra S (2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the International Conference on Intelligent Computing and Communication, Hyderabad, India, 9–11 Jan 2019, pp 209–219. 78 21. Alotaibi F (2019) Classifying text-based emotions using logistic regression. VAWKUM Trans Comput Sci 31–37 22. Naeem A, Rehman M, Anjum M, Asif M (2019) Development of an efficient hierarchical clustering analysis using an agglomerative clustering algorithm. Curr Sci 117:1045 23. Zhang T, Ramakrishnan R, Livny M (1997) Data mining and knowledge. Discovery 1:141–182 24. Lu W (2019) Improved K-means clustering algorithm for Big Data Mining under Hadoop Parallel Framework. J Grid Comput 18:239–250 25. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms. IGI Global, Pennsylvania, PA, USA, pp 413–432 26. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201. 82 27. Mallick PK, Mishra S, Chae G-S (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput:1–16 28. Ren Y, Kamath U, Domeniconi C, Zhang G (2014) Boosted mean shift clustering. In: Machine learning and knowledge discovery in databases, pp 646–661 29. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing. Springer, Singapore, pp 385–390. 92 30. Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India, 4–5 Mar 2016, pp 1–3 31. Zhang M (2019) Use density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify galaxy cluster members. In: IOP conference series: earth and environmental science, 252, 042033

Chapter 7

A Deep Learning Application for Prediction of COVID-19 Aradhana Behura

1 Introduction Presently the COVID pandemic postures not just a danger to life of human over the earth, with mounting quantities of fatalities, however it has additionally unprecedentedly affected our day-day-today lives and the worldwide economy. The battle against this disease is probably the best test looked by human race [1, 2]. The entire extent of AI strategies (Xie and Zhang 2019) has pulled in noteworthy consideration just as a huge scheme of time and exertion among researchers looking for answers for the issues presented by COVID-2019 [3–5]. For instance, AI and deep learning [6–8] have been effectively applied to the prediction of disease COVID utilizing the dataset and this procedure has developed a significant procedure in battle in contradiction of the disease. • New algorithm for detection, feature extraction and the diagnosis of the coronavirus (COVID-19). • Deep learning used for survival rate forecast, and the exploration of features affecting the rate of survival. • Severity classification and assessment of this disease (COVID-19). The coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. In this emergency problem like corona, the clinical business (Gupta and Mishra 2020) is looking for new advancements to control and monitor the spread of COVID-19 (coronavirus) like pandemic [9]. Deep learning is an innovation which can effectively track the spread of this infection, distinguishes the risk of the patients, furthermore, is helpful in controlling this disease progressively. The Fig. 1 describes to predict corona disease.

A. Behura (&) Veer Surendra Sai University of Technology, Burla, Sambalpur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_7

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Fig. 1 Methodology to identify the COVID-19

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7 A Deep Learning Application for Prediction of COVID-19

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I Projection of cases and mortality This innovation can forecast and track the idea of the infection from the accessible information, media stages, medical platform and social media about the dangers of the disease and its reasonable spread (Wan et al. 2019). Additional, this can forecast the quantity of death and positive cases in any district [10–12]. Deep learning can help to distinguish the most affected areas of the individuals and nations then take quantifies appropriately (Pirouz et al. 2020). II Prevention of the sickness With the assistance of real-time information investigation, deep learning can give updated data which is useful in the corona disease prediction as well as prevention. This can be utilized to forecast the probable sites of contamination, the convergence of the infection, requirement for beds and medicinal services experts during this emergency. Deep Learning is useful for the maladies avoidance and future infection, with the help of past information over information prevalent at various time. It distinguishes attributes, purposes behind the spread of contamination and various causes (Ting et al. 2020). In future, this will end up being a significant innovation to battle against different scourges and pandemics [13–16]. This can give a preventive measure and battle against numerous different maladies. In future, deep learning will assume an essential role in giving progressively prescient and preventive healthcare maladies avoidance [17].

2 Proposed Model Autoencoders, which is an efficient deep learning technique accomplish dimensionality reduction of data, by lay off non-significant information and the features of the autoencoder is utilized as labels. This procedure is used to predict the outliers from the datasets. Notification that outliers are observations that “stand out” from the norm of a dataset. If the system trains with a particular dataset, the outliers will provide more reconstruction miscalculation, thus outliers from the dataset will be easy to predict by taking the help of sparse autoencoder network [18]. The analysis of data pre-processing, classification procedure of disease prediction includes these stages, and the whole procedure is appeared in Fig. 2.

2.1

Pre-processing

Noise indicates to incorrect and missing qualities in an accident dataset. The information preprocess was required to process the variable determination of information for training also, every perfect estimation of the factors. The factors or variables of information were chosen according to every factor characteristics and

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Fig. 2 Adaptive method to handle the dataset

excluded variable in the total information. The perfect worth process was supplanted with overlooked or introductory qualities by considering each estimation of the variables. Moreover, the arrangement was important to coordinate the information.

2.1.1

Normalization of Datasets

The normalization of the dataset challenges to provide all characteristics an equivalent weight. Here, v is the individual value from the dataset, and then max value; min value are denoted as minimum and maximum values from the dataset. vj ¼

v  minA ðnew maxA  new minA Þ þ newm minA maxA minA

ð1Þ

For Individual data: Min max ¼

2.2

ðv  min valueÞ ðnew max new inÞ þ new min ð2Þ ðmax value  min valueÞ

Handling of Imbalance Dataset

Sometimes, the information of training contained the information imbalance. To overcome this issue, an over-sampling activity was prepared to fix the information. Past research proposed a strategy for over-sampling the minority class, which is brought synthetic minority over-sampling method (SMOTE). It accomplishes its objective by making manufactured models utilizing real-time information. It makes synthetic models utilizing each models k nearest neighbour models. According to

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Eq. 1, the synthetic information sample is a point along the line segment linking with xi then the randomly selected xbi 2 Rxi . xnew ¼ xi þ ð xbl  xi Þ  d

ð3Þ

Here xbi is an element in the Rxi : xbi  xmin. The feature of notation xnew is the summation of the features of xi then the result which can be achieved by multiplying the feature vector modification among xi and xbi with the help of random value ðdÞ between 0 to 1 ðd 2 [0,1]). The SMOTE-ENN (Synthetic Minority over-sampling Technique—edited nearest neighbour) algorithm uses SMOTE procedure to oversample the dataset and ENN is used to clean the unwanted overlapping among the classes that helps to clean samples which differ from other samples in the datasets. Handling of information mining has some usually experienced issues like high dimensionality, noise and imbalance of information. High dimensionality alludes to the huge abundance of attributes. Better execution can be accomplished if futile and repetitive characteristics are expelled. Imbalance of Information is another serious issue experienced in preparing datasets for information mining [19]. A dataset is imbalanced if the instances of the positive class, additionally called the class of importance, are outnumbered by instances of the negative class. This can bring about high false negatives, primarily disturbing the minority class, which is the most significant class. The essential methodology for taking care of class imbalance is the technique of sampling [20–23]. This technique changes the dataset to a progressively adjusted one by including or evacuating occasions until a required class proportion is achieved. Figure 2 demonstrates towards the addition of test dataset into the train dataset which is known as adaptive method to improve the accuracy ratio.

2.3 2.3.1

Analysis of Accident Dataset and Feature Extraction XGBoost

Set an existing accident dataset with n number of samples, then xi is known as independent variables, and each variable from the dataset has m number of features thus xm 2 Rm. There are dependent corresponding well-known variables yi 2 R. The ensemble tree forecasts the dependent variable z by taking the help of independent variables and the k additive function: Z¼

k X k¼1

fk ðxi Þ;

fk 2 F

ð4Þ

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The notation fk introduces as independent structure of the tree with the scores of leaf then F is denoted as the space of the trees. The aim is to help in minimization of Eq. 2. X X #ð;Þ ¼ lðz; yi Þ þ Xðfk Þ ð5Þ i

k

Here l represents the loss function, where Ω is used for penalizing the complexity of the model: Xðf Þ ¼ yT þ

1 kkxk2 2

ð6Þ

In Eq. (3), T is denoted as the number of leaves in the tree and xi is described as the score of ith leaf. Then solve Eqs. 1–3, and the optimal standards for xj* and the parallel results are described here: P xj

¼ P

iIj

@ 1lðyi;zt1Þ

ð7Þ

z

2 iIj @ 1lðy z

i;zt1Þ þ k

P

#

t

2 iIj @ 1lðyi;zt1Þ 1 XT z P ðqÞ ¼  þ cT 2 j¼1 2 iIj @ 1lðy t1Þ þ k

ð8Þ

i;z

z

So it is challenging to evaluate this significance for all conceivable structures of the tree, as an alternative, the resulting procedure is used: 2P

2 iIL @ 1lðyi;zt1Þ

1 z #split ¼ 4P 2 2 @ 1lðy iIL z

i;zt1Þ þ k

P þP

iIR

@ 21lðy

2

z

i;zt1Þ

z

i;zt1Þ þ k

2 iIR @ 1lðy

P P

iI

@ 21lðy z

i;zt1Þ

2 iI @ 1lðy z

3 5c

ð9Þ

i;zt1Þ þ k

where I ¼ IL [ IR

ð10Þ

Here, IL is denoted as an instance of the left nodes next the split then IR indicates about the instances of the right nodes in the tree after the split. Even two variables take the similar occurrence in a scheme, both data can be preserved, which is mainly needed here so we carry out an important analysis of feature through the help of SHAP technique.

7 A Deep Learning Application for Prediction of COVID-19

2.3.2

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Sparse Autoencoder

This is an unsupervised learning (do not necessitate labelled inputs value to enable process of learning) procedure used for the data coding and helps in reduction of dimension and information retrieval by training the datasets to avoid missing value or noise [24–27]. To minimize the loss as well as dissimilarity among the output and input, autoencoder can provide efficient result. The decoder and encoder, which can be represented as the transitions W and U, such that: F = Feature space U: X ! F

ð11Þ

W: F ! X

ð12Þ

W; U ¼ arg minkX  ðWo UÞX k2

ð13Þ

W;/

Given one hidden layer, the encoder stage of an autoencoder takes the input X 2 Rd = X and maps it to h ¼ rðWx þ bÞ

ð14Þ

Here h, r, b and w are denoted as element wise data, activation function, bias and weight. The bias and weights are iteratively updated during the time of data training over and done with backpropagation. The phase of decoder maps h to reconstruct the X| of the equal shape such as X: X j ¼ rj ðW j h þ bj Þ:

ð15Þ

To minimize the loss, autoencoder can be used and X is averaged usually over some of the input training dataset.  2  2    LðX; X j Þ ¼ X  X j  ¼ X  rj W j ðrðWX þ bÞÞ þ bj 

ð16Þ

Sparse Autoencoder (SAE) network deals with many hidden neurons than the input layers but a small number of layers are permitted to be dynamic at once [28– 30]. The notation h is the code layer involve with a sparsity penalty X (h). LðX; X j Þ þ XðhÞ

ð17Þ

Figure 3 describes the data balancing techniques, model selection and validated model for distribution of dataset [31]. By using SMOTE and data augmentation technique, imbalance dataset can be balanced then autoencoder had selected from the model zoo to reduce dimension of the dataset and it not only helps to optimize the loss but also provides better experimental result which is shown in Fig. 4. The

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Fig. 3 Smote-ENN algorithm

Kullback–Leibler (KL) divergence concept is used to activate sparsity by using the terms of penalty. m  X  bJ ¼ 1 Let P hj ðXi Þ m i¼1

ð18Þ

The notation j is the hidden neuron, m is the example of training and hj (Xi) introduces about which input importance the activation is function of. Pbj ¼ P

ð19Þ

Here P is known as scarcity parameter or activation function and leading the hidden neuron value to be zero. S X j¼1

KLðPjj Pbj Þ ¼

S X j¼1

"

P 1P P log þ ð1  PÞ log b Pj 1  Pbj

# ð20Þ

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Fig. 4 Flow chart of the model

Here j is the summing over the S hidden layer and KLðPjj Pbj Þ is the KL— divergence between a random Bernoulli variable with mean P and a Bernoulli random variable with mean of Pbj . The L1 and L2 regularization method is used to accomplish scarcity, scaled by k parameter. The loss function L1 would become LðX; X j Þ þ k

X

j hi j

ð21Þ

i

This model prevents the system from input layer reconstruction by using many neurons and helps in dimension reduction as well as information retrieval.

3 Performance and Result Analysis For safety designation and specially, identification of dangerous zones in network by ranking the sites by their accident rates, the model is also very helpful. Figs. 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and [17] describe the performance of the dataset and the highlighted countries are taken into consideration which helps to compare the results of the dataset and we can able to know the spread rate of corona disease in worldwide [32–34] (Tables 1 and 2).

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Fig. 5 Mortality rate of the virus

Fig. 6 worldwide death/confirmed cases over time

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7 A Deep Learning Application for Prediction of COVID-19

Fig. 7 worldwide death/confirmed cases over time according to log scale

Fig. 8 worldwide mortality rate over time

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Fig. 9 worldwide growth factor over time

From Table 3, we conclude that autoencoder performs better results as compared to SVM (Support Vector Machine) and linear regression. The error rate, Specificity, Prevalence, Sensitivity and average mean square error values are shown in Table 4.

4 Conclusion Deep learning is a forthcoming and important method to know infections rate due to virus corona and gives assistance to monitor the situation of the contaminated patients [21]. This procedure can remarkably brush up the disease prediction method by taking the help of upgrade algorithm. Autoencoder technology is used to identify where coronavirus will affect in future by collecting and analysing all previous data. By analysing this data, We know about the mortality rate of coronavirus over the time, death cases and growth factor over the time, countries deal with fatalities, daily new confirmed cases by country as confirmed cases for top thirty countries till June 2020. This protocol is not only useful in health monitoring but also used for disease prediction. This is a useful technique to facilitate the testing on this coronavirus by utilizing the dataset which is available in UCI repository for the research work.

7 A Deep Learning Application for Prediction of COVID-19

Fig. 10 Confirmed case for top thirty country

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Fig. 10 (continued)

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7 A Deep Learning Application for Prediction of COVID-19

Fig. 10 (continued)

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142

Fig. 11 Daily new confirmed cases by country

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Fig. 12 Growth factor by country

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144

Fig. 12 (continued)

Fig. 13 Daily new confirmed cases in Asia

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Fig. 14 Growth factor by country in Asia

Table 1 Performance parameters Number

Parameters

Description

Search space

Selected/ optimal value

1

Iteration size

[0, 1000]

500

2

Batch size Optimization algorithm

4

Learning rate

[4, 8, 16, 32, 64] SGD, Adadelta, RMS Prop, Adagrad {0.0001, 0.001, 0.01,0.1.0.5}

32

3

Number of weight updates performed on the network Number of training examples used in one iteration It is used to update the network weights

5

Momentum

6

7

Number of neuron in the hidden layer Weight decay

8

L2

9

Drop out

Controls the size of weight as well as bias changes in the learning of the training algorithm Method to prevent the model from converging to a local minimum

It controls the annealing of learning rate in the network Regularization Method Helps in randomly drop neurons from the neural network during training

SGD

0.001

0.9

0.9

[16, 32, 64, 128, 256, 512]

32

0.0001, 0.001, 0.01, 0.1 0.0001, 0.001, 0.01, 0.1 0.1, 0.2, 0.3, 0.4, 0.5

0.0001 0.01 0.2

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Table 2 Confusion matrix Prediction

True

False

Actual True False

TP FP

FN TN

Table 3 Experimental result analysis Sampling Classification Positive samples Negative samples Accuracy True positive rate

First experiment

Second experiment

Third experiment

Under SVM 145 (7.8%) 1702 (92.2%) 73.63%

Over Linear regression 1557 (3.9%) 38,443 (96%) 84.77% 39.4%

Proposed Autoencoder 1421 (0.27%) 522.710 (99.73%) 99 40.83%

Table 4 Evaluation of dataset All (%)

Training data

Testing data

Specificity Prevalence Sensitivity Average mean square error Error rate

99.46 59.11 96.24 0.2244 2.44

99.26 52.67 94.08 0.0067 0.67

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25. Stebbing J, Phelan A, Griffin I, Tucker C, Oechsle O, Smith D, Richardson P (2020) COVID-19: combining antiviral and anti-inflammatory treatments. Lancet Infect Dis 26. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg 27. Ting DS, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 27:1e3 28. Wan KH, Huang SS, Young A, Lam DS. Precautionary measures needed for ophthalmologists during pandemic of the coronavirus disease 2019 (COVID-19). Acta Ophthalmol 29. Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, Walters J (2019) Improving K-means clustering with enhanced firefly algorithms. Appl Soft Comput 105763. https://doi.org/10. 1016/j.asoc.2019.105763 30. Zhu Q, Zhang F, Liu S, Wu Y, Wang L (2019) A hybrid VMD-BiGRU model for rubber futures time series forecasting. Appl Soft Comput 105739. https://doi.org/10.1016/j.asoc. 2019.105739 31. Chaudhury P, Mishra S, Tripathy HK, Kishore B (2016) Enhancing the capabilities of student result prediction system. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies, Uidapur, India, 4–5 March 2016, pp 1–6 32. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing, vol 92. Springer, Singapore, pp 385–390 33. Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India, 4–5 Mar 2016, pp 1–3 34. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10 (3):244–256

Chapter 8

Application of Big Data in Analysis and Management of Coronavirus (COVID-19) Deepa Benny and Komaldeep Virdi

1 Introduction In this time, the rise in the numbers of Corona affected patients all over the world has inspired people to come together to find a solution to the disease [1]. Scientists and researchers are applying intricate technologies such as Analytics of Big Data, synthetic intelligence, ML and Deep Learning, to cater to the urgent need of data with regards to the virus and the characteristic traits of the disease. First, data is collected on a global scale and surveyed to be used to estimate the fatality rate. This data includes data from the medical database, tourism database, and the surveillance infrastructure to name a few [2]. With immense amounts of data collected globally, it makes the collection and analysis of the solution related data considerably hard. At this point, when time is precious, people cannot afford to lose time in compiling the data for studying, Big Data plays a vital role by providing and analyzing these datasets and identifying patterns and behaviors that can be of help regarding the detection and treatment/cure of COVID-19. The first step in handling the spread of a disease is to measure the severity of the disease. Here the continuous inflow of sorted data in real-time data is precious. The data collected is then put to use in the determination of methods to contain the disease and to help the infected patients. Various technologies are employed to spread awareness among the general people with regards to the DOs and DONTs in such situations. The next step involves the detection of the patients who are infected with the disease already [3]. The earlier they are detected, the lesser is the risk of spread to the other people. Using the methods of identifying the patients—namely the molecular assays and immunoassays, and application of PCR methods on the tests have helped in the testing of asymptomatic patients, thus immediately reducing the risks to the uninfected people around the infected person [4]. After detection, we D. Benny
K. Virdi (&) School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_8

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come to the step where the infected patients are treated for the disease and the spread of the disease is contained utilizing the implementation of curfews to minimize human to human contact.

2 Objective of the Paper The objectives of this paper are to explore the various techniques to identify the traits of coronavirus. We emphasize the role that Big data plays in the analysis of various factors contributing to the spread of the disease and on how Big Data can be effectively used to study the disease to find its characteristic traits and eventually the cure to the disease.

3 Motivation In this time of crisis when we battle against a disease, we know little about it, it is astonishing how much help data can provide in the identification, containment, and treatment of the disease. With so many healthcare personnel who bravely stand in the frontline, any possible help that one can offer is infinitely precious. SARS-CoV and MERS-CoV have quite a few similarities to the novel coronavirus in nature and came too suddenly for us to be prepared to face it. However, we have the advantage of technology and data which has helped us determine its similarities to the previous pandemics we already experienced and hence helped us in determining methods to deal with it appropriately. Reference [5] By merit of the newest advancements and achievements within the fields of techniques of computation and knowledge and technologies requiring communication (ICT), large data, and Artificial Intelligence (AI), it has become relatively convenient to handle the large unrivalled amounts of knowledge obtained from various sources which include data from supervising the public health, trend nowcasting/forecasting, real-time monitoring of the virus outbreak, briefing the situation regularly and acquiring updated data from private organizations and government institutions [6]. A great example of the utility of massive data is its implementation by Taiwan for the identification of the disease. Taiwan mobilized and found out particular case identification approaches, the containment, and allocation of resources to guard the health of the public. Taiwan sorted through its national insurance database and integrated it with its immigration and customs database to begin the formation of massive analytical data; thus generating real-time alerts for a clinical visit supported travel history and clinical symptoms for assistance in identification of cases. This, in turn, helped them in containing the spread by a substantial proportion before the right outbreak of the disease. With the immense amount of help that Big Data has played in the supply of real-time data required for the studies, it is only right that we know about all the

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applications that Big Data can be put into so that the full potential of Big Data can be realized.

4 Role of Big Data in Handling COVID-19 Crisis In these desperate times when a multitude of people is coming in contact with the life-threatening disease worldwide, time is of utmost importance and data is our most important asset. Big data is a field that involves finding paths and methods leading to research, extracting information in a systematic manner from, or otherwise, affect complex or over large data sets to be evaluated by traditional data-processing software application [7]. The information and patterns obtained in these data sets are often used to analyze the formation, spread, and thus help find methods to contain the spread of the disease and also find a correct cure to an equivalent.

5 Sources of Big Data Reference [8] The data in Big Data is collected from three base sources— Transactional data, Social data, and Machine data.

5.1

Social Data

Social data is collected from sources like the liking of online material, tweeting and retweeting messages, commenting, video uploadings, and general media that is uploaded and shared via the varied online platforms. This information provides precious intel with regards to the sentiment and the behaviour of the consumer and maybe influences the marketing analytics enormously. Social data can be gathered from the general public server, where one can implement tools like Google Trends to extend the quantity of massive data. The information is analyzed, the conclusion is received about the pandemic situation and public reaction. The government and therefore the health personnel can then take necessary action considering the analysis obtained. Techniques requiring computation enable us to monitor real-time spread of the Covid-19 virus, for instance, the interactive dashboard designed at John Hopkins University, USA. Moreover, data collected from social networks and other related non-conventional data streams enables one to rebuild the first epidemiological story of the virus outbreak, thus efficiently helping the scientists in understanding the origins of the disease.

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Machine Data

Machine data includes the information formulated by the industrial equipment, machinery which implement the use of sensors, and user behaviour tracking weblogs. This category of acquired data grows exponentially as the amount of data collected via the Internet Of Things grows ever more ubiquitous and pans around the world every instance of time [11]. Medical devices for sensing activity, road surveillance, satellites, games, and the fast-evolving IOT will provide high speed, value, volume, and data variety very shortly. The data collected can be used to estimate the essential viewpoint of the general public as well as keep track of the real-time changes in the health of different areas and the effect it has on the mental health of the general public.

5.3

Transactional Data

Transactional data includes data generated and picked up via each and every transaction that happens both offline and online. The sources of those data include pre-recorded messages, payment orders, records regarding storage, receipts for all the deliveries, etc. albeit all data collected are characterized as transactional data yet, unstructured data is nearly meaningless. Most organizations find it difficult to know the information that they generate and the way to efficiently utilize it. The information sets collected at a worldwide rate are often traveled by multiple algorithms and maybe used to determine the direction of spread also because of the extent of spread. They will even want to evaluate the medical facilities available during a region hence getting a rough estimate of the medical supplies and help required therein region.

6 Examples of Successful Applications of Big Data in the Domain of Handling COVID-19 Crisis Technology is vital in the fight against coronavirus and future pandemics. Additionally, the advantage of possession of facility to support modeling efforts and predict the flow of the pandemic, and concepts such as big data, machine learning, to name a few can quickly and effectively analyze and explain data to assist humans on the frontlines to hunt out the sole preparation and response to the present and future pandemics. A short-term application of Big Data is that it can effectively be used to monitor the outbreak of disease in real-time. Unlike all the previous pandemic outbreaks, coronavirus is unparalleled, such that datasets providing open-access containing

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updated accounts of new infections broken down according to the countries are made available everywhere. Adding this to the information acquired regarding the mass movement showcases the ideal dataset to bring together mathematical modeling and Artificial Intelligence.

6.1

Blue Dot

Blue Dot is an excellent example of an AI-enhanced surveillance system startup. It was one of the first to get notified about the epidemic outbreak, way before its resurgence in its edition one report. The epicentre of Wuhan in China, well before the authorities in that place got to it and other international agencies and institutes was also analyzed by it [5]. BlueDot was established to track and categorize the risks from infectious diseases by developing the world’s first global early warning system. This goal requires a combination of having expertise in the med field and the public health in general. With the valuable assistance from Toronto Innovation Acceleration Partners (TIAP) organization, BlueDot is an advanced approach of utilizing a software as a means of being of service to the people that is designed to identify, pin-point, and make assumptions on the spread of these diseases. Data is gathered for around 150 diseases and casualties all over the world every 15 minutes, 24 hours a day. The data collected includes certified information from institutions like the Center for Disease Control or the World Health Organization. The system is also flexible working with less structured information. A bulk of BlueDot’s ability to predict comes from the data collected by it. This data includes data from absolute places other than the official health care sources which include the worldwide traveling of more than four billion travelers on commercial flights each year; population data of all the living creatures on the planet; satellites sharing all the climate changes; and local information from journalists and medical workers, pouring through 100,000 online articles each day in all 65 languages. BlueDot’s specialists manually sort the information and develop a taxonomy so that relevant keywords are examined efficiently, followed by applying ML and tongue processing to coach the software. Therefore, hardly a couple of cases are left for data scientists to research. BlueDot regularly notifies all the clients supplying a quick synopsis of anomalous disease outbreaks that the Artificial Intelligence engine found out and the potential risks these outbreaks may pose. As far as COVID-19 is concerned, the system found and marked articles scripted in Chinese which confirmed 27 pneumonia cases in association with a food place that had sea delicacies and living animals in Wuhan. Additionally, BlueDot correctly pin-pointed the cities that were properly in contact with Wuhan through ways like global airline ticketing data to get to the core of this spreading infection. The international destinations that BlueDot pin-pointed, were all the heavily trafficked places from Wuhan. These places include Bangkok, Hong Kong, Tokyo, Taipei,

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Phuket, Seoul, and Singapore. In the end, it was brought forth that 11 of the cities at the top of the list were the initial places to see COVID-19 cases. COVID-19 was not BlueDot’s first hit. The engine successfully predicted the ‘Zika virus’ which spread to Florida in the year 2016, six months prior to it taking place. The software was also implemented to specify that the 2014 Ebola outbreak would exit West of Africa. BlueDot’s self-evaluating infectious disease surveillance platform is a significantly important early warning system that can provide a timely-critical heads-up to health professionals everywhere and potentially save thousands of lives.

6.2

Geographic Information Systems (GIS)

References [9, 10] Geographic Information Systems (GIS) and technologies concerning big data have played a significant role in plenty of aspects of understanding the origins of this disease. This includes the rapid increase of multi-source big data, proper structuring of epidemic information, spatial pin-pointing of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, organizing and managing the supply and demand of the required material resources, and social-emotional guidance and panic elimination. The GIS provides concrete spatial information support for making decisions, formulation of the required measures, and accessing the effectiveness of COVID-19 prevention and control. An excellent example of the appliance of GIS in these times is the WHO Dashboard for COVID-19. The WHO looks over international health, fighting communicable diseases by being watchful, prepared, responsive, and applying GIS tech to the present work. On 26 January 2020, the WHO revealed its ArcGIS Operations Dashboard for COVID-19, which also identifies and structures coronavirus cases, therefore, the death count by country, and China with information panels about the map and its data resources. The WHO dashboard includes a plague curve upfront, date-wise structuring of cases. Putting the epi curve visualization above the cumulative cases graph also shares useful information about outbreak progression, decreases fear, thus spreading awareness among people concerning the severity of the infection or death rate within the said region.

7 Analysis of the Severity in Outbreak of Covid-19 in a Region In the event of infectious disease, specially Sars-CoV-2 it is very important to have an estimate of the severity which is the measure of its ability to exterminate people. Fatality rates help us acknowledge and understand the severity of any health issue,

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identify at-risk masses, and roughly estimate the quality of healthcare facilities available. Figure 1, portrays the global rise in confirmed infected cases and the death rate until the 9th of October 2020. Two measures are adopted to evaluate the count of individuals down with the virus with fatal outcomes—infection fatality ratio (IFR), which evaluates this death-rate concerning all infected individuals, and Case Fatality Ratio (CFR), which evaluates the proportion of identified confirmed death cases. For a precise calculation of IFR, comprehensive knowledge of the number of patients infected by the disease, and virus death cases must be collected and interpreted. The estimation of the fatality rates is mostly not accurate due to a significant proportion of patients being left out of the radar either because of them being asymptomatic or are suffering from mild symptoms and so remain unable to approach medical wards. There is a huge part of the masses who are very prone to be neglected and are unable to utilize the benefits of healthcare or testing being provided. Detection of such cases may be neglected or under-served or completely erased during an epidemic, when testing capacity may be limited and restricted to people with severe cases and priority risk groups which consider the front line healthcare workers, elderly people, and people with comorbidities. Cases can also be diagnosed incorrectly and confirmed with other diseases with similar clinical presentation, such as influenza due to shortage of knowledge about the disease. Inconsistencies in mortality among people and countries play an important role of significant indicators of the relative fatality risk. They lead strategy decisions regarding insufficient medical resource allocation during this pandemic. Figure 2, gives a graphical representation of the rising confirmed casualties in the different regions.

Fig. 1 The global pandemic situation on 9th of October 2020 [2]

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Fig. 2 A graph of confirmed casualties in countries across the world [1]

7.1

Infection Fatality Rate (IFR)

It gives a rough estimate of the true severity of disease by calculating the ratio of the death count caused due to the disease to the total mass of people infected by the disease. This disease [11]. This ratio gives us a basic idea of the severity of the disease in a particular area, which then helps in preparation of the required supplies and medical help accordingly. It is calculated per the following formula: Infection fatality ratio ðIFR, in %Þ No of deaths due to the disease  100% ¼ No of persons infected by the disease

7.2

Case Fatality Rate (CFR)

It is also known as case fatality risk, and is a measure of the severity among the detected cases, which is given by the ratio of individuals diagnosed with a disease who die from that disease. It can be calculated per the formula: Case Fatality Ratio ðCFR, in %Þ No of the deaths caused by the disease  100% ¼ No of confirmed cases of the disease CFRs that are reliable enough to assess and evaluate the deadliness of an outbreak and any implemented public health measures respectively are obtained at the end of an outbreak after the resolution and recovery of almost all cases.

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Reference [12] However, this calculation may be considered invalid in the case of a currently ongoing epidemic, because two assumptions have to be taken well into consideration. Assumption 1: There exists a consistency in the likelihood of detecting cases and deaths throughout the outbreak In the early stages of an outbreak, more attention is focused on symptomatic patients who seek care. The milder and asymptomatic cases are less likely to be detected, leading to an overestimation of CFR. This overestimation may decrease with an increase in testing and active initiative towards case finding. A way to account for this is the removal of all those cases from the analysis that occurred before the establishment of robust surveillance, using methods such as left censoring. Assumption 2: All confirmed cases are already resolved During an ongoing epidemic, a significant portion of the active cases detected may subsequently die, which causes underestimation of CFR estimated before their death. This effect is stressed upon in fast-growing epidemics for instance, during the exponential growth phase of COVID-19. CFR calculated using the aforementioned formulae during an ongoing epidemic will provide a conditional estimate of CFR which has a possibility of being influenced by lags in reporting of dates for cases and deaths. This will cause a wide alteration in CFR estimates throughout the epidemic, which will later tend toward a stable, final estimate of CFR as active cases will be resolved appropriately. A simple solution to mitigating the bias caused because of delays to case resolution during an ongoing disease outbreak is to limit the analysis and observations to resolved cases by following the given formula to calculate the predictability of the severity of the disease. Case Fatality Ratio ðCFR, in %Þ No of deaths from the disease  100% ¼ No of the deaths from the disease þ No of recovered from the disease

However, there is no guarantee of the elimination of all considerations related to delayed reporting in this method. Even the differences in the amount of time taken for cases to be resolved can affect this estimate. If the death rate is faster than the rate of recovery, CFR can be overestimated. Similarly, in a reversed situation, it may be underestimated. Therefore, more elaborate approaches should be used, those which make use of statistical techniques to predict outcomes among active cases based on the probabilities of past outcomes, which includes the modified Kaplan-Meier survival analysis [13, 14]. Two significant disadvantages of these methods are that firstly, they generally require individual-level data that is relatively harder to access in real-time in comparison with the aggregate case and death counts; and secondly, they are very

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complex and time-consuming, generally requiring the application of highly advanced methods. How severe a COVID-19 outbreak in a region is can depend on age, sex, and many other underlying co-factors on a wide-scale [15–17]. These risk factors should also be carefully considered. Evidence proves that other factors, like ethnicity, can also be included as independent risk factors [18]. Due to the underlying differences between various risk groups, any design will remain unsuccessful to judge the measure of population fatality. Irregular distribution taking place between or within the population are highly influenced by this underlying bias. Thus, care must be taken to evaluate risk-group-specific approximations of fatality risk, for the analysis and description of truth patterns of a fatality that is occurring during a population. Reference [18] Potential bias in the detection of cases and deaths must also be taken into consideration [19, 20]. These biases may keep fluctuating throughout an outbreak: • Originally, the detected cases are inclined to be more critical or fatal. • Only those patients seek help at medical facilities and are verified by a laboratory test who has a visible illness. • Underestimation of CFR is caused because there are delays in death reports. • Many cases of COVID-19 go undetected or are reported late within a community because the illness may be incorrectly identified. • An overestimation of CFR may occur if more deaths are reported than recoveries. The opposite situation results in underestimation. It is unpromising that each death will be discerned and properly allocated, albeit detection of deaths could also prove to be subject to less bias than case detection.

8 Real-Time Scenario Considering the Outbreak Reference [21] People have yet to unlock the full potential of Big Data in the medical field. Sure they have come to terms with the effectiveness of it in the other departments but not many have tried to experiment with it regarding the current virus outbreak or the medical department in general. Given below is a way in which Big Data can influence and simplify our lives if we decide to think outside the box and let Big Data take the reins here. The novel coronavirus, also now known as COVID-19 has spread globally after it’s the first case was reported on December 8, 2019, in Wuhan, China. The symptoms of patients suffering from COVID-19 were studied and papers were published recently [22]. We referred to a study which estimated how frequently the asymptomatic carriers of the COVID-19 virus belong to the age group of 18–29 years in order to predict patients of which age are more likely to contract the disease.

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Reference [23] The patients affected by the COVID-19 virus can be classified into symptomatic patients and asymptomatic patients. Symptomatic patients showed the symptoms of contracting the disease, including symptoms like loss of the senses of taste and smell, fever, and others. Asymptomatic patients include patients who did not show symptoms that were common for all symptomatic patients. Patients who turn out to be asymptomatic carriers generally do not appear among the people within the age groups of 18– 29 years. They include people who were in close proximity and contact with infected family members. A large percentile of such patients had mild or ordinary 2019 novel coronavirus disease during isolation and hospitalization. With availability and access to authentic data more research can be made in order to gain progress in the finding of a cure to the disease. The availability of access to big data helps in the collection of data, monitoring of variable parameters and thus helps in proper research on this topic. A study of the epidemiological and clinical results of 55 asymptomatic carriers was conducted who were laboratory confirmed to be positive for SARS-CoV-2. Their test was conducted through nucleic acid testing of pharyngeal swab samples. Per the latest recommendations, COVID-19 cases are categorized into four types of cases: • Mild: Cases of this category include mild symptoms with no pneumonia seen at chest computed tomography. • Ordinary: Cases under this category include symptoms such as fever and other respiratory symptoms with pneumonia observed at imaging. • Severe: The symptoms of this category of cases include respiratory distress, hypoxia (oxygen saturation :(;(:( are smiley-negative (Fig. 5).

Fig. 5 Block diagram for sentiment analysis

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Sentiment Identification

Sentiment score is based upon the nature of the words present in the Tweet. Sentiment score = no of positive words-no of negative words Sentiment score > 0 implies positive tweet Sentiment score < 0 implies negative tweet Sentiment score = 0 implies neural tweet

3.3.7

Sentiment Analysis of COVID-19 Tweets Using Supervised Machine Learning Approaches

Sentiment analysis is carried out by feeding labeled dataset to machine learning models. These datasets are used to train model in order to get significant output. Overall two datasets are used training sets and test set. Thereafter various classifiers are used to detect sentiment in Twitter. The performance of Twitter sentiment classifiers is principally depended upon the number of training data and the features sets are extracted [26]. The most popular classifiers are SVM and NBC. NBC has proved to have the highest efficiency than any other classifiers. The following block diagram will give a precise idea about the entire process (Fig. 6).

Fig. 6 Block diagram for Twitter sentiment analysis using supervised machine approaches

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Sentiment Analysis of COVID-19 Tweets Using Ensemble Approaches

Ensemble approaches combines a no. of classifiers to provide more accurate result. Basically it learns from the errors of base classifiers and improves accuracy in the final classifier. The effectiveness of creating ensemble learners, for sentiment analysis purpose, has been investigated in various research publications [27, 28]. The main motive was to create a powerful classifier by mixing various feature set and different classification algorithms. However, in some test cases, NBC classifier has proved to have a slightly better accuracy than ensemble learners.

3.3.9

Sentiment Analysis of COVID-19 Tweets Using Lexicon-Based Approaches

Lexicon-based approach is one of the main approaches of sentiment analysis. It calculates the sentiment from the semantic orientation of word or phrases present in a text [29, 30]. We need a set of predefined positive or negative words along with this technique. Then positive or negative sentiment value is assigned to each of the words in the dictionary and there are other approaches to create dictionaries which includes manual and automatic approaches as well. Generally, a piece of text or a document is represented as a bag of words in lexicon-based approach. Sentiment values are assigned to word or phrases in the text comparing with that of in dictionary [31–34]. In this representation of the message. We either take the sum or average of the total score is taken for predicting the sentiment of the tweet. Along with sentiment value of a word, the local context, e.g., intensification or negation in taken into consideration.

4 Result and Analysis After putting our model results together in the context of pandemic (we used the growth rate of confirmed case to reflect the spread of the disease) we gathered some interesting findings. First, we will start with a small-scale finding. We collected 200 tweets of 14/07/ 2020 And the results are as following: Positive tweets percentage: 27.36842105263158% Negative tweets percentage: 13.68421052631579% Neutral tweets percentage: 58.94736842105263% \

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These are top five positive tweets

These are top five negative tweets

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Fig. 7 Word-cloud of tweets related to COVID-19

We have generated a few word clouds to represent the most used word in the tweets related to COVID-19 (Fig. 7). Overall Sentiment Change from January to April According to the graph shown above, average subjectivity increased from 0.33 to 0.35 and as days passed tweets tend to become more positive (polarity changes from 0.4 to 0.6). But with rapid increase in number of confirmed cases, it is quite abnormal to experience such growth. For the reason we opted for multi-dimensional feature extraction. For this purpose, Google’s pre-trained BERT model has been used. Overall 5 sentiment types have been identified (Fig. 8). Sentiment Level At the very beginning of January, analytical tweets were high in number but eventually it came down. With the reporting of more and more confirmed cases, fear among the tweets was high during January. But people became more assertive toward February. The arousal of mixed emotion from January indicates increase of awareness among people. However, from February different sentiment diverges. Probably because people were overloaded with information (Fig. 9). The most density of sentiment can be observed between February and March. In April though it decreased but it maintained a higher level than the beginning. Next we will see a study of subjectivity and polarity of COVID-19-related tweets

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Fig. 8 Overall sentiment analysis during the time period Jan 25, 2020, to Apr 24, 2020

Fig. 9 Sentiment level of tweets related to COVID-19 from January to April

between Feb 2020 and July 2020. For this purpose, 1,66,656 tweets have been used (Fig. 10). From Fig. 11, we find maximum concentration of tweets in polarity region of −0.25–0.5. The following graphical representation of polarity density and polarity distribution will provide a better outlook toward the situation. It is evident from Figs. 12 and 13 that a large number of tweets were found to be neutral in this period of time. During one of the most spread weeks the analysis of tweets as follows (Fig. 14).

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Fig. 10 Sentiment density from January to April

Fig. 11 A scattered plot for subjectivity and polarity of COVID-19 related tweets between Feb’20 to July’20

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Fig. 12 Polarity distribution of COVID-19 related tweets from Feb’20 to July’20

As we go on the later part of pandemic, the following graph shows the change positive and negative tweet over time (Fig. 15). At this stage, positive tweets overtook the negative one. Though the margin is low in some situation shows that people are recovering from the fear and sad situation that was there at the beginning. A weekly study of COVID-19 related tweets in the later part of pandemic situation is given below (Fig. 16). In this period of the tweets are tend to be positive than neutral or negative. The increasing rate of recovery and commencement of regular life may be a reason behind this trend.

5 Conclusion and Future Work The objective of the research work is to analyze the sentiments and emotions of the people during the pandemic COVID-19. After studying the overall trend, it was noted that people were more optimistic over the time while analyzing the word clouds of different countries, it was concluded that people are tweeting words like Pandemic, Death, Quarantine, Hope, Stay Safe, Government, Political, Fight and Masks with different emotions. All tweets were collected in English language which also became a barrier for the study. Digging into the multi-dimensional sentiment analysis, it was observed that “Assertive” went up and Fearful went down. The sentiment density indicates how much public is loaded with emotions. The details behind the sentiment are more significant as it presents more insight to the situation. All the data used here are from 09-4-2020 to 15-04-2020 which is considered as one

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Fig. 13 Polarity density of COVID-19 related tweets from Feb’20 to July’20

of the most spreaded weeks of COVID-19. All this data were collected by Twitter API and tweepy library of python. #coronavirus and #COVID-19 are the two keywords used for determining polarity and subjectivity. Besides, hybrid and ensemble Twitter sentiment analysis techniques were explored. After completing

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Fig. 14 Comparative study of COVID-19 related tweets from Apr 9th to Apr 15th

Fig. 15 Comparison of numbers of positive and negative tweets from July’20 to August’20

Fig. 16 Comparative study of polarity and subjectivity between 15th August’20 to 22th August’20

the research it was found that machine learning techniques like the SVM and MNB produced the greatest precision, especially when multiple features were included. Support vector machine of supervised learning model uses classification algorithm as standard learning strategies but at times lexicon-based algorithms are also useful and it requires less efforts as well. An accuracy of approximately 80% was achieved by machine learning algorithms, such as the naive Bayes, maximum entropy, and SVM when n-gram and bi-gram model were utilized. Along with this, hybrid and

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ensemble Twitter sentiment analysis techniques were also used and it performed better than supervised machine learning techniques, as they were ready to achieve a classification accuracy of roughly 85%. Some of the interesting areas for future study will include performance of sentiment analysis algorithms in cases where multiple features are considered. By combining various features improved the performance in most cases. This study can be used to analyze the changing emotions and sentiments of people. It is expected that as the spread of this pandemic will increase, the sentiments and emotions in the tweets may change.

References 1. Rajput NK, Bhavya AG, Vipin KR (2020) Word frequency and sentiment analysis of twitter messages during coronavirus pandemic. arXiv preprint arXiv:2004.03925 2. Zhou J et al (2020) Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia. arXiv preprint arXiv:2006.12185 3. Tyagi P, Tripathi RC (2019) A review towards the sentiment analysis techniques for the analysis of twitter data 4. Rajput NK, Grover BA, Rathi VKJ (2020) Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic 5. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20 (14):4036 6. Sharma R, Nigam S, Jain R (2014) Opinion mining of movie reviews at document level. arXiv preprint arXiv:1408.3829 7. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307 8. Harb A, Plantie M, Dray G, Roche M, Trousset F, Poncelet P (2008) Web opinion mining: how to extract opinions from blogs? In: Proceedings of the 5th international conference on soft computing as transdisciplinary science and technology. ACM, pp 211–217 9. Tripathy A, Rath SK (2017) Classification of sentiment of reviews using supervised machine learning techniques. Int J Rough Sets Data Anal (IJRSDA) 4(1):56–74 10. Saleh MR, Martin-Valdivia MT, Montejo-Raez A, Urena-Lopez L (2011) Experiments with SVM to classify opinions in different domains. Expert Syst Appl 38(12):14799–14804 11. Kaur C, Sharma A (2020) Twitter sentiment analysis on coronavirus using Textblob. EasyChair, 2516–2314 12. Prabhakar Kaila D, Prasad DA (2020) Informational flow on twitter Corona virus outbreak topic modelling approach, vol 11, no 3 13. Alhajji M, Al Khalifah A, Aljubran M, Alkhalifah M (2020) Sentiment analysis of tweets in Saudi Arabia regarding governmental preventive measures to contain COVID-19 14. Pastor CK (2020) Sentiment analysis of Filipinos and effects of extreme community quarantine due to Coronavirus (COVID-19) pandemic 15. Jordan SE, Hovet SE, Fung IC, Liang H, Fu KW, Tse ZTH (2018) Using twitter for public health surveillance from monitoring and prediction to public response. Data. 4. 6. 10.3390/ data4010006 16. Gonzalo GA, Henriquez PA, Mascareno A (2020) Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Gener Comput Syst 106:92–104. ISSN: 0167-739X

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17. Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG (2020) Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci. https://doi.org/10.1177/0956797620939054 18. Rajput NK, Grover BA, Rathi VK (2020).Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic. arXiv preprint arXiv:2004.03925 19. Li S et al (2020) The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. Int J Environ Res Public Health 17.6:2032 20. Jang H et al (2020) Exploratory analysis of covid-19 related tweets in North America to inform public health institutes. arXiv preprint arXiv:2007.02452 21. Barkur C, Vibha GB (2020) Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: evidence from India. Asian J Psychiatry 51:102089 22. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 23. Smeureanu CB (2012) Applying supervised opinion mining techniques on online user reviews. Inform Econ 16(2):81–92 24. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 25. Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol 1, pp 61–67 26. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms. IGI Global, Pennsylvania, PA, USA, pp 413–432 27. Akshi Kumar TMS, Kumar A, Sebastian TM (2012) Sentiment analysis on twitter. IJCSI Int J Comput Sci 9(4):372–378 28. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of seventh international conference on language resources evaluation, pp 1320– 1326 29. Ray C, Tripathy HK, Mishra S (2019) Assessment of Autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, Hyderabad, India, 9–11 Jan, pp 209–219 30. Mishra S, Dash A, Jena L. Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201 31. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10 (3):244–256 32. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 33. Mishra M, Mishra S, Mishra BK, Choudhury P (2017) Analysis of power aware protocols and standards for critical E-health applications. In: Internet of things and big data technologies for next generation healthcare. Springer, Cham, pp 281–305 34. Mishra S, Mishra BK, Tripathy HK, Dutta A (2020) Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering. Academic Press, pp 1–23

Chapter 10

IoT for COVID-19: A Descriptive Viewpoint Srishty Singh Chandrayan, Shubham Suman, and Tahira Mazumder

1 Introduction The Internet of Things (IoT) comprises of a composite matrix of technological devices which can very often interchange the data over the Internet [1]. It has revamp the real-world devices into smart virtual devices. The objective of IoT is to integrate everything in the physical world under a reciprocal arrangement, assisting the end-users in not only commanding the devices around them but also keeping them updated regarding the state of the things [2]. IoT devices observe the outside surroundings and send the obtained data to the Internet cloud without the necessity of interaction between humans or human-to-machine. IoT has become a crucial part in this modern age of communication where millions of objects are interconnected through IoT which is growing in a very rapid manner [3]. IoT has the capability of playing a major role in different aspects of life, such as healthcare systems [4], autonomous vehicles [5], home and industrial automation [6], smart transportation [7], and intelligent grids [8]. Sensors acquire information of the means of transporting the data to the pertinent organization [9]. The root concept behind IoT is the realization of several interactive devices with each other flawlessly. Thus, this comprise of better utilization of the available resources, deduction of cost, and minimization of the manual interaction. As the 2019 coronavirus disease (COVID-19) continues to spread worldwide, it is unavoidable to discuss the potential of IoT during this pandemic. Figure 1 shows some basic advantages that IoT provides during pandemic times.

S. S. Chandrayan
S. Suman (&) School of Electronics Engineering, Kalinga Institute of Industrial Technology, Deemed To Be University, Bhubaneswar, India e-mail: [email protected] T. Mazumder Ajay Kumar Garg Engineering College, Ghaziabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_10

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Fig. 1 Basic benefits of IoT in COVID-19 diagnosis

As of June 21, 2020, the number of confirmed COVID-19 cases has exceeded 8.5 million [10] with 3.7% mortality rate. Researchers of various domains continue to investigate and generate various solutions which could help to fight the coronavirus. IoT helps various countries to minimize the effect of COVID-19. IoT has a broad range of applications that could be effective to ensure that all the safety guidelines are followed and proper precautions are taken. IoT has a scalable network which has the capability to deal with large amount of received data from the sensors used by number of applications to fight against COVID-19 [11, 12]. Moreover, the reliable IoT networks reduce the delivery time of vital information which can help in providing timely response during the global pandemic of COVID-19. The role of IoT was never needed to the extent to which it is required now because of coronavirus outbreak. Table 1 further layouts the detail usage of IoT during COVID-19 period.

2 Monitoring of COVID-19 Monitoring of Corona pandemic is a crucial issue [13]. Effective monitoring and tracking of patients and accessories determine the level of success in forthcoming days. Basically, three kinds of monitoring kits are discussed in this section.

2.1

Personal Proximity Monitoring

Nowadays, companies are getting back to work with their employees. But they have to be very careful regarding the safety of their employees. This kit provides a personal tracking device and a private network to the employee. This device gives a warning when the proximity gets disturbed.

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Table 1 Important usage of IoT during the COVID-19 pandemic Usage

Details

Hospitals associated through Internet

Hospitals require a proper network of IoT specially during this time of the pandemic Combined matrix will permit the workers as well as the patients of the hospital to react in much rapid and efficient manner Patients can take advantage of available offers without bias Choosing of any specific method for the treatment purpose assists to operate on any concerned case People living in remote locations can avail services Several legit software can be placed into smartphones, that could take the recognition process further in a very smooth manner An effective tracking of patients eventually gives a leverage to the supplier so as to take charge of the case more efficiently As gadgets are interlinked, then the present-time data sharing is done, and case is taken care of in a much more precise manner Smart gadgets are used to treatment purposes Standard of management can be obtained by producing innovations at the grass root level itself At time of diagnosis, IoT is connected to allmedical equipment which further conveys the present-time data during the diagnosis Based on available data, some statistical means could be used for the prediction of the circumstances in the upcoming future

During emergency, informing appropriate worker of hospital Transparency in the treatment Automatic procedure for treatment M-health confabulation Cordless medical web to detect the patients affected by the virus Tracking the patients via smart means Present-time data at time of the pandemic Fast screening Identification of the inventing solutions Connection of allmedical equipment via World Wide Web Accuracy in the prophesy of the virus

2.2

My Space Monitoring

This is most beneficial for the elderly people who want to be close to their family as there are dedicated panic buttons, sensors for tracking their movements as well as the doors of washrooms, etc. This can be used at homes, hospitals and restrooms. This grants an indirect access for the necessary actions to take place.

2.3

Quarantine Monitoring

Sometimes people who are supposed to be quarantine do not follow the rules strictly and tend to go out and expose the risk of spreading among the people. So in that case an alert would be sent for the prevention.

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3 Use of IoT in Face Mask Detection Wearing face mask is very important to fight with COVID-19 crisis. Every nation has made it a point for its citizens to wear face mask on mandatory basis. Figure 2 shows a sample snap of use of face mask in public view. For detecting a person who is not wearing a mask we use an AI-based platform. In order for this to work an App has to be installed which is further connected to either the existing or the new camera based on IP mask detection. The user can upload images as well as the contact number to get notified by the App if that particular person is not wearing a mask. If in any case an unrecognizable face is detected by the camera then an alert would be sent to the administrator (Fig. 3). Detecting masked faces is really challenging but with the help of the AI technology we can achieve that. With the help of this recent technology we can detect any masked face by detecting its facial features. This includes various attributes like: Shape of the face—Every person has a different face structure like round, oval, heart shaped, etc. Orientation of the face—This includes various profiles of a person say, for example, left, right, front, etc. Eye recognition—In this case retina of the eye is scanned and center of eye is marked. Type of mask—This includes rectangular, round, etc. Visibility—In this case eye, nose, chin and mouth are scanned and its location is saved.

Fig. 2 Face mask demonstration in public

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Fig. 3 A simple mobile app workflow in face mask detection

Certain features of this App are really helpful and unique. Some important features of the app are discussed below. Sending alert automatically: Reference [14] If a person is not wearing mask then an alert would be sent to him as well as to the authorities. This would help to reduce the spread of the coronavirus. Recognition of multiple cameras: Multiple cameras can be enabled within a short span of time to detect the person who is not wearing any mask. New hardware need not to be installed: No new hardware is to be installed as it can work on the existing RTPS cameras. Almost all the airports and hospitals have IP cameras which are RTPS enabled. Customization: Customization of face masks can also be done on the basis of the requirements. Accessibility: Movements can be easily accessed and controlled with the help of the App. Detection: Faces which are partially visible (by mask, hair, glasses, etc.) can be easily detected. The model was implemented in a real-time scenario near a shopping center on random time periods. In the first implementation as shown in Fig. 4, 21,123 masked faces were detected. The face size analysis was performed. It was observed that 8986 people were detected with face size of {64, 128} while the least face size was observed with only 132 face count in case of {0, 32}. Similarly in another application, as many as 16,292 masked faces were counted to figure out face orientation analysis. 9560 faces were detected to be the highest face count with front orientation. It is highlighted in Fig. 5. Later a degree of occlusion analysis was undertaken with a random face count of 18,448 people. 13,453 masked faces belonged to medium category while the least count of 1657 faces were categorized into right occlusion degree. Figure 6 illustrates the scenario. This App would be really beneficial to reduce the spread of the coronavirus as it would detect the person who is not wearing any mask with the help of number of

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Fig. 4 Face size analysis with implemented model

Fig. 5 Face orientation analysis with implemented model

the existing cameras and would notify that person and the authorities. Some common applications of usage of face mask are highlighted below.

3.1

Airport

This can be used in the airport to detect the travelers who are not wearing any mask. The image of the traveler would be captured at the camera at the entrance. If the person is not wearing any mask it would be notified to the airport authorities for a quick action and if that person is one of the staff members of the airport then an alert would be sent to his phone also.

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Fig. 6 Occlusion degree analysis with implemented model

3.2

Hospital

This can be used in the hospitals to detect whether a person is not wearing a mask. As it is compulsory for the staff members to wear their masks during their shift at the hospital, if a staff member is not wearing any mask then a notification would be sent to both the authorities as well as to that particular staff member. Moreover, the patients who are quarantined at the hospital must wear masks and if they are not then authorities would be notified [15].

3.3

Office

This can be used in the offices to detect whether a person is not wearing a mask. As the offices which are working during the COVID-19 period have to follow some safety measures and take precautions for their staff, it is mandatory for the staffs members to wear masks and maintain social distance. If a person does not wear any mask then an alert would be sent to that particular person as well as to the authorities. One can download one’s report at the end of the day also [16].

4 COVID-19 Diagnosis Using IoT-Based Smart Glasses The smart glass has two different types of cameras. One is the Optical camera which captures the image and the other one is the thermal camera which captures the temperature of the person who is in front of it [17]. The thermal camera detects the

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body temperature of a person by comparing the person’s body temperature with the external environment which is scanned by the camera. If the heat body is unreal by the thermal camera, it then generates high intensity levels of the infrared spectra. The face is detected by using Cascade Classification algorithmic program. The GPS module determines the position coordinates when tagging it and stores it with alternative detected face and temperature, so that the information can be accessed by an alternative officer via his phone. The officer can get the information of the suspected person’s face and body temperature so as to determine that person who is indicating as infected of COVID-19 through the smart glasses. Technically it is operated on the Android Studios by using JAVA as a programming language. Its SDK is designed within the system. For detecting the face, the Viola-Jones quick face detection methodology is used, this technique uses the input pictures as an integral image illustration. AdaBoost classifiers are used to verify a real face from doable candidates supported the submitted options [18]. This face detection category can yield information concerning all the faces found in associate input image frame.

No Begin

Operation of Smart Glass

Scan by using Thermal Camera

Body Temp High

Yes Track GPS Location

Scan face by optical camera

Sending alert to health administrator

Send alert to smart glass

View the image of the suspected person

Finish

View history on phone

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According to this research, we cannot only scan and process the body temperature of an individual person at once but also can do this with a large number of people who are in the visible range of the smart glass and their thermal image can be accessed by the health administrator. This is useful for us because fever is a common symptom of a person who is infected by the coronavirus and by using this smart glass one can be fully aware of the suspected person in a public place and can keep a distance with that suspected person. Moreover, the GPS location of the suspected person along with its image and body temperature can be accessed by the authorities so that they can the necessary measures (Figs. 7, 8). As the number of people who are affected by the coronavirus are increasing day by day worldwide. This innovation will help to detect the suspected person in a very quick manner so that the necessary steps can be taken by the health officer and the people who are around by the suspected person can be aware and maintain a social distance so that the virus does not infect them hence minimizing the spread of the coronavirus in mass.

5 IoT-Based Drones Drones are simple aircraft which are driven via remote. Application of IoT in drones is known as Internet of Drone Things (IoDT) [19, 20]. This can perform various tasks such as searching, monitoring and delivering. By the means of smartphones we can control the drones. Types of IoT-based drones are: • Thermal imaging drone: It converts infrared heat radiations into visible image that depicts temperature difference observed by the thermal camera. Thermography is used for safety purpose like to find a missing person, it is also used by the fire fighters to control the fire and it is also used by the agricultural workers to find out more efficient manner to cultivate and harvest. One can use various thermal cameras with thermal resolution: 336*256/640*512: 1. 2. 3. 4. 5. 6. 7.

FLIP Duo Pro R FLIP Vue Pro R FLIR Vue Pro Djl Zenmuse XT Djl Zenmuse XT2 Yuneec CGOET Yuneec E10T

• Disinfectant drone: It can be used to sanitize a particular region. By spraying the disinfectant the chances of the transmission of the novel coronavirus decreases. One can use this to fumigate indoor as well as some outdoor areas. These drones also emit the

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Fig. 7 Global wearable technological market by the product type

Fig. 8 IoT-based smart glass detecting face and capturing temperature

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UV waves which is responsible to kill germs including the virus. This process can be completed within 10 min. • Medical drone: It can be used to deliver medical utilities in a relatively shorter period of time. This is also used for telementoring in the remote area. By the means of medical drone, one can transport laboratory samples, vaccines and emergency equipment from one place to another. During this period of COVID-19, this can be used to provide the necessities to the patients who are Corona positive as they remain quarantined. • Surveillance drone: It is used to capture static image or video of people in particular area. It can also give live video feedback. It is mainly used by the government and the military officers in order to collect information. Fixed-wing and Multi-rotor drones can be used for the surveillance purpose as they can fly at lower altitudes. The UAV hovers in place when connected to the power source on the ground by the means of tethering cable, transfers images and videos to the ground operators. • Multipurpose drone: It performs multiple tasks simultaneously, consuming less power thus incurring low cost. On the drone, a wireless module is connected to a drone control board which is used to receive the data. Temperature, pressure and humidity sensor in also installed on the drone to monitor the surroundings, and by the means of IoT implementation, it can visible on the LCD screen installed on the remote control unit. Some of the drones are also installed with the metal detectors (Figs. 9, 10, 11, 12, 13). During this time of the pandemic, tracking the infected patients has become very important. By using IoT-based drones we can speed up the process of finding the infected people. This reduces the human interactions. This technology can also be used for disinfecting various places and can also be used to deliver medicines.

Fig. 9 Thermal drone

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Fig. 10 Disinfectant drone

Fig. 11 Multipurpose drone

Fig. 12 Medical drone

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Fig. 13 Surveillance drone

Benefits of drones in handling COVID-19 scenario are as follows. • This can cover the hidden areas which were under the CCTV surveillance. • This can be used for medicine delivery and sanitization. • Medical sensors which are connected to the drone can examine the condition of the patient more precisely. • There isn’t much noise produced by the drone while transmission. So by this means one or more drone can move inside the hospital as per as the instructions given. • This tries to ignore the collision through either Radar or LiDAR system. If we use n number drones then this is very efficient to use. By the means of the avoidance strategy, we can pre-plan the movement of the drones.

6 Other Use Cases of IoT By the means of IoT technology, one can monitor the people who are quarantined during this pandemic. It is very such necessary to have a proper system for monitoring. Tracing of the people who are affected by this pandemic can be done by the Internet-based systems. Tracing can be done by using virtual management system where the data and location of the person can be traced effectively. IoT gives very accurate and precise results. By using IoT the medical staffs have to comparatively work lesser than the usual. By the means of IoT, one can get diagnosed properly in a very efficient manner and can also minimize the expenses (Fig. 14). The procedures involved are: • Occupation of the health workers from native areas: Patients who are affected come from some remote areas. By the means of IoT their symptoms can be checked and location can also be traced.

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Fig. 14 Benefits of using IoT to fight against COVID-19

• Rudimentary protocol: Health workers can monitor the patients via online/ virtual means such as video conferencing. Health of the patients can be traced by IoT controlled devices. By this means social distancing is enabled and one needs to go to the hospital only in the case of emergency. • Data documentation and its management: By the means of IoT necessary measures can be taken and can be managed efficiently without any sort of ignorance. • Preparation of the results: On the basis of the data and the documents the outcome can be analyzed post-COVID-19 (Fig. 15).

7 Conclusion IoT gives a wide range of summed-up matrix for health care in order to battle with the virus during the pandemic. All the healthcare gadgets are linked over the World Wide Web, at the time of any crucial circumstances, it by default delivers a message to the concerned worker at the hospital. The positive cases can be taken care of properly in these included locations with properly linked gadgets. It tackles the cases in a very smart manner to give good service to the infected person. Here a simple yet effective face mask detection model was used and applied to determine masked faces, face size orientation and occlusion degree. It is successfully able to detect the face count frequency with precision. At the medical sector, it is very useful to continue a proper management of the present-time data. By the means of statistics, IoT becomes useful to foreseen the future of the disease.

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Fig. 15 System involved in the application of IoT to fight against COVID-19

References 1. Srinivasan CR, Rajesh B, Saikalyan P, Premsagar K, Yadav ES (2019) A review on the different types of internet of things (IoT). J Adv Res Dyn Control Syst 11(1):154–158 2. Panarello A, Tapas N, Merlino G, Longo F, Puliafifito A (2018) Blockchain and IoT integration: a systematic survey. Sensors 18(8):2575 3. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 4. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20 (14):4036 5. Al-Garadi MA, Mohamed A, Al-Ali A, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor 6. Durga Saranya K, Krishnamurthy R, Srinivas KNH, Sarveswara Rao TDNSS, Amiri IS (2019) Iot-based health monitoring system using beaglebone black with optical sensor. J Optical Commun 1(ahead-ofprint)

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7. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10 (3):244–256 8. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 9. Minovski D, Åhlund C, Mitra K (2020) Modeling quality of iot experience in autonomous vehicles. IEEE Internet Things J 7(5):3833–3849 10. Aheleroff S, Xu X, Lu Y, Aristizabal M, Velásquez JP, Joa B, Valencia Y (2020) IoT-enabled smart appliances under industry 4.0: a case study. Adv Eng Inf 43:101043 11. Mishra S, Mishra BK, Tripathy HK (2020) Significance of biologically inspired optimization techniques in real-time applications. In: Robotic systems: concepts, methodologies, tools, and applications. IGI Global, pp 224–248 12. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494 13. Kamal M, Srivastava G, Tariq M (2020) Blockchain based lightweight and secured v2v communication in internet of vehicles. IEEE Trans Intell Trans Syst 1–8 14. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 15. Yin XC, Liu ZG, Nkenyereye L, Ndibanje B (2019) Toward an applied cyber security solution in iot-based smart grids: an intrusion detection system approach. Sensors 19 (22):4952 16. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486 17. Sushruta M, Hrudaya KT, Brojo KM (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts. Int J Control Theory Appl 10:295–310 18. Kamal M et al (2018) Light-weight security and data provenance for multi-hop internet of things. IEEE Access 6:34439–34448 19. Ministry National Health Services. Covid-19 global. http://covid.gov.pk/stats/global/. Accessed on 23/05/2020 20. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201

Chapter 11

Smart Technology Application for COVID-19 Detection, Control, Prediction and Analysis Aradhana Behura, Shibani Sahu, and Manas Ranjan Kabat

1 Introduction To fight against the severe pandemic of 2020, coronavirus, the immediate tools and measures which help in speeding up the treatment of the infected patient are being made every day. A variety of companies and also individuals have come up with their technology and inventions to help diagnose the coronavirus patients. Certain digital tools such as telehealth, cloud computing, robots and AI-based chatbots have played an active part during this outbreak [1, 2]. Since China has reported the virus, many countries have started to depend on technology, artificial intelligence, data science in order to track and fight against the coronavirus. Several technologies such as 3D printing, virtual reality, augmented reality, prosthetics and even robots have been helping us during this time of crisis [3]. There is no denial in the fact that even though there has been a great fall in the industry due to this pandemic but these industries will eventually grow their business based on successful plan to fight this crisis (Table 1 and Fig. 1). The current situation is now demanding a different perspective in order to fight the novel coronavirus. Learning from the current situation in the field of health care, preparations are being made in order to face the crisis right now and also to face the aftermath [4]. Medical officers all around the globe are now moving on the technologies and creating their interest in telemedicine in order to provide care and relief to the affected patients [5–8] (Figs. 2 and 3). They are also using geological locations to follow traces of the affected person in an area. Frontline healthcare workers have been working hard every day since the coronavirus outbreak took place.

A. Behura (&)
S. Sahu
M. R. Kabat Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_11

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Table 1 Statistical analysis and prediction of infection rate due to coronavirus or COVID-19 in worldwide As of July 10, 6:10 GMT

Total infections

Active infections

Recoveries

Deaths

Total (World wide) USA Brazil India Russia Peru Chile Spain UK Mexico Iran

12,394,656 3,219,999 1,759,103 795,605 707,301 316,448 306,216 300,136 287,621 282,283 250,458

4,612,017 1,657,749 504,253 277,925 215,142 97,332 24,612 N/A N/A 76,527 25,977

7,225,129 1,426,428 1,185,596 496,048 481,316 207,802 274,922 N/A N/A 172,230 212,176

557,510 135,822 69,254 21,632 10,843 11,314 6682 28,401 44,602 33,526 12,305

They get calls from the patients who inform the medics about the potential symptoms that they are experiencing. Hence, most of the healthcare centres are now using the self-triaging mobile applications. This application helps to assist the medical and healthcare officials to keep a check on the symptoms before it becomes serious. Since technology has been playing an important role in the healthcare domain, therefore this report contains a list of such technology developed to fight the coronavirus across the globe and here are a few of them. The first case of coronavirus was reported on 10th December, 2019, in the Wuhan City, China. Considering it the symptoms of common flu, the patient had normal consultation from the doctor and went on roaming in the market. After few days, people living close to the market showed similar symptoms and it was recorded that this virus was spreading rapidly through contact [6]. This virus rapidly started spreading not just in China but in the whole world. The first case of COVID-19 in India was reported on 30 January 2020 who had returned from China. After this, the virus has been spreading like a forest fire. As of 6 July 2020, the Ministry of Health and Family Welfare has confirmed a total of 697,413 cases of the COVID-19 patients out of which 424,432 have recovered and 19,693 have been deceased. Since then the technology industries have been trying to come up with some innovative solutions to tackle the problems of COVID-19 especially in healthcare domain. Modern technologies involved machine learning, natural language processing and computer vision applications to come up with solutions [9]. Diagnosis can be done using radiology images which was used to extract radiological features; early detection of COVID-19 cases using different CNN models can be tested by increasing the number of images, COVID-Net; a deep CNN design can be used for detection of COVID-19 cases from CT images and X-rays; COVID-19 detection neural network (COVNet) detects COVID-19 and distinguishes it from community acquired pneumonia and other lung diseases;

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Fig. 1 General procedure of AI and non-AI-based applications that help general physicians to identify the COVID-19 symptoms

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Fig. 2 Life cycle of coronavirus

Fig. 3 Animal origins of human coronaviruses

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three-dimensional deep learning model can be used for early detection of the COVID-19 cases (Fig. 4). Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19. Artificial intelligence can be used to track illness will spread of COVID-19 with time and place [10]. Ongoing discoveries recommended that COVID-19 has respiratory patterns which are different from seasonal influenza and regular cold, eminently that they display tachypnea (fast breathing). Integrated AI-based drug discovery pipeline to generate novel drug compounds [11]. Coronavirus tests are short in supply and costly; however, all emergency clinics have X-rays (or CT) machines. With the assistance of deep learning, radiologists can diagnose COVID-19 using X-ray pictures. COVID-Net an artificial intelligence application is created to analyse COVID-19 symptoms in chest X-rays utilizing information of different lung conditions and COVID-19 from patients [12–14]. Tracking the spread of COVID-19 can be significant information for general public health authorities to design, plan and deal with the pandemic. A technical note is for producing connectors to breathing devices. Block chain and artificial intelligence technology for novel coronavirus disease-19 self-testing (Fig. 5). The scientific name for coronavirus is Orthocoronavirinae or Coronavirinae. Coronaviruses belong to the family of Coronaviridae, order Nidovirales and realm

Fig. 4 Corona-affected disease

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Fig. 5 Protein structure prediction of coronavirus [15]

Riboviria. They are divided into alphacoronaviruses and betacoronaviruses which infect mammals—and gammacoronaviruses and deltacoronaviruses, which primarily infect birds. • Genus: Alphacoronavirus; type species: Alphacoronavirus 1 (TGEV) – Species: Alphacoronavirus 1, Human coronavirus 229E, Human coronavirus NL63, Miniopterus bat coronavirus 1, Miniopterus bat coronavirus HKU8, Porcine epidemic diarrhea virus, Rhinolophus bat coronavirus HKU2, Scotophilus bat coronavirus 512 • Genus Betacoronavirus; type species: Murine coronavirus (MHV) – Species: Betacoronavirus 1 (Bovine Coronavirus, Human coronavirus OC43), Hedgehog coronavirus 1, Human coronavirus HKU1, Middle East respiratory syndrome-related coronavirus, Murine coronavirus, Pipistrellus bat coronavirus HKU5, Rousettus bat coronavirus HKU9, Severe acute respiratory syndrome-related coronavirus (SARS-CoV, SARS-CoV-2), Tylonycteris bat coronavirus HKU4 • Genus Gammacoronavirus; type species: Avian coronavirus (IBV) – Species: Avian coronavirus, Beluga whale coronavirus SW1 • Genus Deltacoronavirus; type species: Bulbul coronavirus HKU11 – Species: Bulbul coronavirus HKU11, Porcine coronavirus HKU15.

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Though national and worldwide associations have utilized social media platforms to communicate with the general population, but apparently this is leading to a crisis in which populaces can become overwhelmed with information, and the propagation of misinformation is progressively common. WHO’s reaction to fighting this infodemic is using its Information Network for Epidemics (EPI-WIN) stage for imparting data to key partners. A few drones are utilized to trail people, not applying facemasks, while others are utilized to broadcast information to bigger crowds and furthermore to sanitize open spaces. Small Multicopter. Applications of 3D printing technology to address COVID-19-related supply shortages. Violet, the robot that can kill the COVID-19 virus (24 April 2020). Biofourmis’ AI-fuelled remote monitoring tech provides insights in fight against COVID-19 (13 March 2020).

2 Some Technologies Used During COVID-19 2.1 2.1.1

Wearable Devices Oura Rings

Oura rings are designed to track metrics such as the respiratory rate, heart rate and other such activities. It generally keeps track of the customer’s sleep cycle and informs them if there is any problem in their sleep cycles. Nowadays, it is been efficiently used for keeping a track on the patients trying to recover from novel coronavirus. These rings are a great help in the field of health care because they help in keeping records of the patient’s condition even when a doctor is not available [9]. It helps in monitoring the patient’s brain waves, oxygen level, heart rate and other factors. It keeps a track of the activity of the user with help of in-built accelerometer and gyroscope. Apart from these two devices, it also contains sensors for tracking the heart rate, respiratory rate as well as body temperate, which helps in keeping a check on the coronavirus. These also have a longer battery life making it efficient to use as it will consume less energy. Since 2020 has been facing the outbreak of coronavirus, just like any other tech companies Oura’s CEO, Harpreet Singh Rai had worked on this ring with other healthcare institutions on medical studies to help improve the product to detect the primary signs of coronavirus. The study which was aimed at finding the way to detect coronavirus was announced in March 2020. They were able to detect the virus by measuring the temperature and heart rate of the person wearing it. Oura has given away almost 2000 rings to the medical centres across California, New York and Massachusetts. The company is inviting all Oura users, and those who have signed up for this device in a study to measure the body temperature

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changes and report any symptoms through the diagnosis. These data are being examined in order to draw conclusions about the COVID-19. This is going to help the medical community to learn more about what happens after the corona patient has recovered. The researchers need more time and data to analyse it so that they can understand whether the rings can be used to detect the illness or not. Since coronavirus has been a new virus causing great havoc today, it is necessary to do a detailed research about it and also get information such as whether it can have long-term effects or not. Still, since this virus has been spreading for only few months, therefore the study on it is also quite small, and hence, it is a bit difficult to understand whether the survivors experience any long-term effects or not. They are not trying to figure out not just the symptoms of the coronavirus but their research will in detecting the long-term effect of the illness, if any. The efforts being given by Oura are an excellent example of how the tech companies are putting their efforts and also the resources for preventing the virus. Oura is not just a wearable device but they are trying to detect the illness too when someone is wearing it. According to CEO, the main priorities of the company are to focus on ways to help the customers understand when they are getting sick. This may involve the features of the ring being refined but will help in further health studies. The Oura is now used in the study at ULCA to detect viruses, as their main aim is to identify and understand the patterns from the physiological data and construct an algorithm in order to predict the onset, progression and recovery of any further cases of COVID-19.

2.1.2

WHOOP Bands

The WHOOP application and device coherently monitors the respiratory rate of the person under its observation. In its recent update, it makes this feature pretty effortless and members are easily able to monitor and interpret their respiratory rates. And this feature of a well-established technology might be the most significant metric to be concerned about the recent pandemic of COVID-19. Sleep analytics of the web-based application by WHOOP was upgraded which included the resting respiratory rate monitoring. They are the first wrist-worn wearable to validate the accuracy of the respiratory rate which was also shown to be within one breath per minute of gold-standard truth, in the Journal of Clinical Sleep Medicine. During sleep, the heart rate of an average human decreases as he breathes out and subsequently increases when he breathes in. This phenomenon is biologically described as respiratory sinus arrhythmia. The WHOOP algorithm makes use of this phenomenon and reports the median respiratory rate while the person experiences REM sleep. Even though previously WHOOP has been recording the respiratory data due to factors like, its similarities with other more relevant data like heart rate variability (HRV) or resting heart rate (RHR) make it questionable to put it onto records since

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it would just add to the clutter of a stable user friendly interface. There was also little evidence to how the users would react to it so they did not readily put it up front the eyes of the users. But flipping to the other side of the coin it was observed that even though respiratory rate changes with different stages of sleep, it is a fairly steady graph to study which commons out in most healthy people. Respiratory rate also co-relates directly with the rise and fall of RHR which in turn provides reliable data about the functioning of the heart. With enough data and fine-tuned algorithms, WHOOP was able to do a minute-by-minute record of the respiratory rate of the user. The recorded version is the median respiratory rate. The statistics are then compared with a reference data of the average user and analysed by the application, since this should remain stable throughout and any deviations usually mean something is off. This piece of knowledge was genuinely useful to even the healthcare inspectors for the COVID-19 situation. Surveillance during sleep facilitates in trouble-free and undiluted statistics of median respiratory rate, whereas the other datasets like the RHR and HRV tend to fluctuate more randomly as opposed to the stationary respiratory rate. This high “signal-to-noise ratio” of the respiratory rate helps in determining its values as more reliable and easy to interpret and trust. For example, a person exposed to the SARS-CoV-2 virus (COVID-19 virus) was put under observation with the permission of the patient. The patient being a WHOOP member conveniently had his respiratory rates before his exposure to the virus recorded. It was clearly observed that before the date his symptoms showed his little to no deviation in his respiratory rates, but a day before his symptoms started showing physically, his respiratory rates started to go unusually higher than their normal trend. This shows that even before the physical symptoms start showing up, the WHOOP device can alert the user with his unusual respiratory rate resulting in the prompt preliminary health regulatory steps. It can be argued that this deviation also occurs due to other factors as in territorial changes or altitude changes. Since this kind of drastic deviation was also observed for a WHOOP member, (data and information collected with the necessary and entailed permission and grants by the subject) who had boarded an overnight flight and had fallen asleep. His statistics resolved back to normalcy once his flight landed, and he had a good night sleep over ground levels. But it is also worth the notice that the change caused was due to the drastic change in altitude which minimized the oxygen content in the air. That kind of environmental change cannot happen overnight while a person sleeps over his bed at home and for such deviations to happen under standard homely conditions is highly unlikely unless fuelled by some other factors (in this case COVID-19 infection). This was also brought into attention by the WHOOP members self-reporting COVID-19 infections which came out of a physiological response to the disease and therefore potentially worthy of attention. From what we know about the COVID-19 disease is that, its caused by the SARS-CoV-2 virus and is completely new to scientific pathology. It affects the lower-respiratory tract almost like its flu-virus counterparts and bronchitis. Unlike

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common cold, tonsillitis and sinus infections which are upper-respiratory tract infections bearing symptoms which are above the larynx region (runny nose, sore throat, headache, etc.), lower tract infections tend to centre their symptoms more towards the lungs and wind pipe, resulting in coughing, fatigue and shortness of breath. The virus enters through the mouth, nose or eyes searching for a host cell ending up in the respiratory tract after finding its suitable host cell. It tricks the host cell to function as per its own benefits and starts to replicate inside, and after the host body is full, burst out the cell body and searches for a new host along with its replicas. During the initial hours (usually ranging from 1 to 12.5 days depending upon the pre-existing health structure of the patient), the symptoms do not show as the body compensates for the dead cells. But with the infection spreading the regeneration rate of the body is soon overcome by the damage done and the affected organ function starts to degrade. These result in the physical anomalies that we call “symptoms”. Now from a viewpoint of a common working man, respiratory rate is subject to change throughout the day due to wide range of activities carried out by him. But this does not pose true during resting during night time. At night, the exchange of oxygen and carbon dioxide in our body is fairly constant and so it makes more sense to carry out observations over the same time period. But this does not hold true towards infections. The damage done is consistent, and the symptoms will also affect the stable statistics of a night sleep. Surely these statistical deviations can also be due to common allergies and flus, but most common flus do not affect respiratory rate and other lower tract infections have a fairly different set of symptoms and progression period. So, if a person has flu related symptoms that are mostly spontaneous; a first-hand observation of WHOOP showing noticeable highs in stationary respiratory rate will definitely compel him to take an earlier diagnosis which would further facilitate in controlling the current situation the pandemic has brought upon us.

3 Remote Patient Monitoring Devices The remote patient monitoring devices have created a great impact towards fighting against coronavirus. The remote patient monitoring device was generally using for congestive heart failure patients, and it helped the patients by providing them with adjustments on time for their medications [16–20]. Due to COVID-19, the number of positive cases is increasing every day, more and more patients are being admitted and they require ventilators. Due to the shortage of these ventilators, the number of deaths has been increasing. Several other problems include transmission of the virus easily, shortage of hospital beds, ventilators and personal protective equipment. Hence, these monitoring devices are being used which are providing certain benefits such as

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• It reduces the risk of transmitting the virus to other patients and any other healthcare workers. • The person having the illness can get immediate hospital care whenever needed. • It helps in obtaining the qualitative measures of the patient’s health in order to keep a check on the disease and also if there is any sort of progress. (According to the WHO, the onset of severe pneumonia in a person in his adolescence and/or adults is when SpO2 is less than or equal to 93%.) • When a patient is in quarantine, it helps to ease the patient and also helps in avoiding the anxiety of the patient’s family members and friends. • The potential bed shortages in hospitals are greatly relieved, and patients get recover without having to be admitted to the hospitals. They help in flattening the curve so that the systems used in health care are not overwhelmed. Due to the increase of the COVID-19 cases every day, the hospital is managing them by using these remote patient monitoring tools which is a much better option by keeping the risks of the patients as low as possible by staying at home and feeing up other medical equipment. The remote patient monitoring, also known as RPM, is a digital technology which tracks the patient’s vital signals even when the patient is at home instead of the hospital. Companies are trying to use these devices to monitor and treat COVID-19 symptoms. RPM tools have helped to monitor the coronavirus itself by providing some geographical data during this pandemic spread. These locations depict the potential places where any clusters of COVID-19 infection have occurred.

3.1

Remote Devices for Patient Use

Since the RPM technology has been widely used from the hospitals to the world wide, BioTelemetry Inc. has created a device known as mobile cardiac outpatient telemetry or MCOT. It has been contracted by health systems in various ways to use its abilities for the hospitals and triage centres. IT has been to carry any amount of significant arrhythmia or irregularity in heartbeat. With the help of mobile technology, the data can be sent to the company’s monitoring centre as well as the medics attending the patient. The MCOT is providing an additional benefit during this pandemic period. This device can be used by the doctors to observe the effects of hydroxychloroquine and azithromycin on the heart of a patient. The above-mentioned drugs are being used in treating the coronavirus on being combined together but they have the side effects of prolonged QT interval of the heart which causes the heart to relax for too long, as said by Dr. Wayne Derkac, senior vice president of medical affairs for BioTelemetry.

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This device helps the doctors to monitor the patient’s effects constantly while keeping other medical and healthcare workers safe. In the middle of this pandemic crisis, connected health and RPM are gaining more importance because they ensure that the patients are monitored properly without having to come in contact with them, hence preventing any spread of the virus through contact. Since there is shortage of beds in hospitals, hence they prevent the patients with less severe cases to stay at home and be monitored.

3.2

Babyscripts

Babyscripts is a virtual platform for maternity care which allows the user to deliver a model of new ways for parental and postpartum care. By using a mobile application and a certain remote monitoring tool, Babyscripts has helped in reducing the current in-person visit schedules from 12–14 to 4–6 for pregnant patients. It enables the provider to perform a transition care outside the clinic with the help of continuous finger prints taken digitally. With the help of digital point-of-care system, the maternal care gaps, Ob/GYN shortage and the rising rate of maternal mortality are taken care of by Babyscripts.

3.3

Biofourmis

Biofourmis is a remote patient monitoring device, and the analytics technology is based on artificial intelligence. It provides a personalized care based on all the active and passive information collected from the patient’s wearable device, taking into consideration as much as 20 different physiological parameters with the help of a mobile application. Primarily, Biofourmis focuses on areas such as heart failure and other cardio-related disorders, pain and oncology. Its technology is now used for monitoring quarantined and admitted patients in hospitals who are suspected of or are confirmed of COVID-19. Biofourmis is a wearable band wore on arms usually and is usually advised to be worn 24/7. It records the wearer’s temperature, blood oxygen level and many other physiological signals so that they can detect any sign of the virus, whenever the patient reports any symptoms to the healthcare official through the application on his mobile. Till now, Biofourmis has been used for three main sectors: (1) health care, (2) payers and (3) pharmaceutical companies. The clinician at the healthcare centre uses these Biofourmis for three to six months while conducting home observations just after the patient has been discharged. It uses AI and machine learning and combines the data from the wearable device with the patient’s medical history in order to create a unique profile which is updated from time to time based on the observations made.

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The AI actually runs on an algorithm continuously on the profile of the patient to predict the symptoms if any. This AI-based algorithm is a software-based therapeutic invention which informs the doctors what to do when such a case occurs (COVID-19). This is done so as to prepare beforehand in order to prevent any sort of emergency [21–23]. These algorithms are personalized in a different for each patient so that the patient is provided with the right dosage and at the right time.

4 Ventilators 4.1

3D Printing Ventilators

The first 3D printed ventilator was developed in Spain and was also approved by medical experts. Due to the pandemic on spread, there has been shortage of ventilators to support the hospitals and intensive care units across the globe. This device works as an emergency device that helps the patients to breathe for a short period of time. Since it is being used effectively, hence its production is being increasing nowadays. Initiations are being taken to produce more 3D printed equipment such as 3D printed masks, goggles and even shields. Although they are not that efficient, durable or safe, still they are helpful at the time of need. It was developed at the Leitat Technology Centre. During this COVID-19, there has been the shortage of ventilators, and hence, this 3D ventilator has helped in “ventilator splitting”. Just as Sung Hoon Kang, assistant professor at John Hopkins University, said, “There is an emphasis right now on using engineering to develop open-source solutions to many aspects of COVID-19 crisis, but especially for ventilator design and production”. There is still a controversy going on about the machine because it can only be used for a shorter interval of time, a transition of 1:1 patient–ventilator should be done as soon as additional ventilators are available. One of the main concerns of the medical professionals is that connecting several patients to a single ventilator could spread the germs or even cause cross-contamination. The Prisma Health has agreed to use this 3D printing ventilators which can allow a single ventilator to be used for four patients at once. Greenville has developed the design of the device with the materials available for medical use and produced at a minimal cost. The company is working with the COVID-19 teams who can use this prototype whenever an emergency occurs and the outcomes can be used to see if the prototype meets the guidelines of FDA. The VESper device is a “Y”-shaped splitting tube which is used for filtering the bacteria and viruses in the ventilator tubing and is also easy to produce. These are resistant against impact as other patients are connected to the same machine.

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Ventilators for Critical Care

With the shortage of ventilators everywhere due to COVID-19, the patient suffering from cancer, diabetes, dialysis, pregnant women and also those suffering from cardiac ailments need proper care too. According to Dr. Vardhan, only 0.33% patients were on ventilators, 1.5% patients are on oxygen support and 2.34% patients are in ICU. Hence, many have come up with different ideas for developing ventilators that can be used for the patients in the hospitals. Some of the examples are: (1) Nocca Robotics: Nocca Robotics was a start-up incubated by IIT, Kanpur. They have developed a ventilator which is designed for handling coronavirus patients and will cost less than the imported ventilator. According to Bandyopadhyaya, “IIT Kanpur and Nocca Robotics have signed an agreement with defence public sector company Bharat Dynamics for manufacturing the ventilators on a not-for-profit basis for India initially which is expected to be available for about Rs. 1.5 lakh per unit”. The company has kept its top priority to be safety and security of the healthcare workers while using these ventilators for treating coronavirus patients. The co-founder of Nocca Robotics, Nikhil Kurele says that whenever a virus-infected person on a ventilator exhales, the air is filled with a lot of virus. This virus which was exhaled out may get filled in the ICU, and this might pose a threat to the workers there. The ventilators made by Nocca have in-built ultraviolet filter chambers which kills virus. These ventilators can be used for a dual purpose, one for handling patients in places like trains and the other in ICU, etc. The ventilators are designed to work for four hours, and they are also portable. They had to go through UL and TUV testing which is a critical medical equipment. Hence, the Prime Minister of India has called for a help from Nocca Robotics in other to design and develop a high-end yet affordable ventilator which can provide necessary life support for critical cases of COVID-19. The ventilator is designed in such a way that apart from providing support to the critical patients, it also keeps in mind the safety of the healthcare workers. (2) Aerobiosys ventilator: Another such ventilator has been developed by IIT Hyderabad, which had the same benefits of being low cost, portable and for emergency use. It was named “Jeevan Lite”, and it also provides safety and protection to the healthcare workers. It uses Internet of things and can be operated using a mobile application. Jeevan Lite has been designed to work on battery and can be used anywhere without having to worry about the power consumption. During COVID-19, the senior citizens and elderly patients are getting affected and require ventilators for life support. Aerobiosys has come forward for providing the healthcare professionals personal protection by giving them the IoT-enabled monitoring to allow their analysis and diagnosis without getting neat to their patients. This will enable them to prevent the spread of the germ and viruses.

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According to a journal, Deccan Herald, “Jeevan Lite can perform both invasive as well as non-invasive ventilation across a comprehensive set of modes and settings”. This ventilators use lithium-ion batteries for like 5 h without any intervention and can be used for paediatric as well as geriatric patients. The demands of the COVID-19 are well met by this technology and are aiming to be produced and developed in large number with the assistance from the government and well as other partners from the industry. They are offering remote monitoring and wireless connectivity. (3) AgVa Ventilators: These are one of the most economical ventilators available till now. According the AgVa Healthcare, the ventilators are designed with the following features: i. It has various modes such as VC-CMV, VC-SIMV, ACV, PRVC, PC-CMV, PC-SIMV, PSV, CPAP and BPAP. ii. It does not require compressed medical air as it can even run on the room air. iii. It has the potential to deliver 100% FiO2 on being connected to a source of oxygen. iv. Detection of apnoea can be done with a guaranteed volume and backup ventilation v. A connection can be established with a standard 22 mm ventilator consisting of proximal flow sensor. vi. It contains standard ventilator alarms with user configurable limit alarms. (4) Jeevan Ventilator: To help India cope up with the COVID-19 outbreak, a low-cost ventilator was developed by the Indian Railways. The Kapurthala Rail Coach Factory (RCF) has come up with this low-cost ventilators which can be used in the isolation wards or even in the quarantine facilities made by the Indian Railways in the form of train coaches across the country. It fulfils all the parameters of a ventilator such as control on breathing rate, expiratory ratio and tidal volume. It is a great step taken by the Indian Railways to cope up with the shortage of medical tools and equipment and fight against the coronavirus and use the ventilators to save thousands of lives. This ventilator is very important because it helps to pump the air and oxygen into the lungs. This equipment is now waiting to get certified by the Indian Council of Medical Research (ICMR) so that their production in larger numbers can be started. After getting the approval, its production might increase to 100 units per day. The ventilator without a compressor would cost around Rs. 10,000 but even after adding the necessary equipment, the entire comes to not more than Rs. 30,000 which is quite economic. These ventilators are being made from the scratch from the rail factory, while the body of the ventilators is created from the compartments of the train. The compressed air container is fitted with a microprocessor-based controller. There is a valve in the system which helps in regulating the patient’s breathing pattern, be it young or old.

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5 Role of Robots for Fighting COVID 5.1

Violet, the Robot

Violet is a robot which is designed to kill COVID-19 virus. Trinity’s Robotics and Innovation Lab create robots which help the ageing individuals in care houses. Conor McGinn, a roboticist and professor at the Trinity College, Dublin, along with his colleagues work for creating such robots. When the outbreak of coronavirus was first reported in Wuhan, China, in the month of July 2019, the team members started working on their robot Stevie, whether it will be able to treat the infection or not. After doing some research and getting information from the microbiology department of their college and Knollwood Military Retirement Community in Washington, D. C. A director, he concluded that ultraviolet lights could be used as disinfectant. Hence, a robot with the ultraviolet light feature was built that could create light strong enough to kill the harmful virus keeping in mind that it was safe to use by the residents as well as the medical staffs. Ultraviolet light of wavelength between 200 and 280 nm was used as the light source. This wavelength is also known as UV-C light. A great feature of this light is that it can change the shape of the DNA and can cut the genetic material acting as molecular scissors. Since viruses have a simple molecular structure help, they can be eliminated easily. UV-C light can actually cause sunburns and results in cell getting mutated which may lead to cancer in humans. Therefore, these robots can be used safely only when no one is around and the room is empty. The robot has sensor built in it so that it can navigate on its own and can halt when a person is detected. The light source automatically stops if a movement is detected by its sensors. Violet is portable and is compact enough so that it can be used to operate in crowded places and can even clean waiting areas, bathroom, corners of public places, etc. It consists of a protective shield surrounding the back of the light and the sensors so that people do not have to leave the room when it is disinfecting the area. This robot consumes less time to disinfect any area, so rather than taking normal 30–60 by using a radiographer with disinfectant wipes, Violet only takes about 15 min to do so.

5.2

Nuanced Care Robots

The Nuance Robot or commonly known as Sayabot is the first multi-tasking humanoid robot made in India. Asimov Robotics has launched this robot which behaves as well as looks just like a human. It has been designed to perform various tasks such as retail, healthcare, education, hospitality as well as banking.

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This robot has been programmed using AI, and also, a wide range of sensors are used for collecting information of the environment so that it can work along with humans effortlessly. Humanoid robots such as Sayabot can help to raise awareness among the citizens and also automates the processes of sanitization. Another such robot is the KARMI-Bot, which can map an empty ward in the hospital and then perform functions such as providing medicines on time and dispensing food based on the patient’s requirements. It can also perform remote monitoring, video conferencing with the doctors and auto self-disinfect when needed.

5.3

Nurse Robots

Serving food and medicines to the coronavirus infected patients in the hospitals are done by nurse robots nowadays. This is done to ensure minimal contact of the patients and doctors/nurses with each other, so that the virus does not spread to everyone. In India, Chennai a hospital has bought some robots to help the doctors to provide some food and medicines to the patients. In Italy, a robot nurse named Tommy is helping the doctors to stay safe by ensuring that they do not come in direct contact with the patients during the coronavirus pandemic. This robot is one of the helping robots which are assisting flesh and blood doctors and nurses in Italy. This robot is has wheels to move anywhere it wants. These robots are left beside the patients so that they can be taken care of, and the doctors/nurses can attend other patients who are in a more serious condition. These robots have also helped in limiting the number of protective masks as well as staffs. It is necessary that the patients are told about the functionality of the robot and how it works. It is efficient enough because they get charged very quickly.

5.4

Chatbots

The Chatbots ensure that the social distancing is maintained during the pandemic. They are making a great difference in government and healthcare organization by providing information to the healthcare officials about the current COVID-19 patients. Advance technologies such as natural language processing are used in AI which are programmed into these chatbots so that they can provide essential information to the vast population and are available 24/7. Examples are Siri, Google Home, Alexa, etc.

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6 Using AI to Detect Coronavirus 6.1

BlueDot AI Technology

BlueDot is a start-up company in Toronto which uses a platform built based on artificial intelligence, machine learning and Big data. They use them to locate and predict any diseases which can be infectious in nature and how they can be spread around the globe. They were the first to alert the government as well as the private sector officials about the cluster of coronavirus occurring around Wuhan, China, which was quite unusual as a phenomenon. After this detection, within nine days the World Health Organization sent the news of the outbreak of coronavirus along with 4000 deaths. Dr. Kamar Khan from the University of Toronto had studied the science behind spreading of infectious diseases across the globe and therefore had worked to create a technology in order to predict, locate and mitigate the outbreaks. The BlueDot gathers information of around 150 diseases along with their syndromes from around the world within 15 min and keeps updating them regularly within 24 h a day. They also retrieve information from WHO or Centre for Disease Control. The BlueDot’s prediction is based on the data it receives from sources outside such as climate data from satellites, movement of travellers on flights, information from local healthcare centres and even the journalists. The data is then manually classified by the experts, so that they can create a taxonomy. On doing so, the keywords could be scanned and then be used to train the machine with the help of machine learning and natural language processing. Regular alerts can be sent to the government, healthcare centres, business and public clients by BlueDot. These alerts are in the form of brief synopses that their AI engine has found out about the outbreak of this weird disease. They even predict whether this disease can pose any threats to the community or not. The system had found out that the cases of pneumonia in China were reported as 27 which were related to a market which used to sell seafood and live animals. BlueDot sent the alert alarms and helped in identifying the cities which were connected to Wuhan and that too correctly. They used all information sources ranging from data from global airline ticketing to minor travelling data of a person to predict whether is affected by coronavirus through travelling or not. BlueDot predicted the international destinations which would have the highest number of travellers from Wuhan were Hong Kong, Taipei, Phuket, Bangkok, Tokyo, Seoul and Singapore. And there prediction turned into a true event because these cities were the first places to have COVID-19 patients.

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6.2

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Using Artificial Intelligence(AI), CT Scan and X-Ray

The use of AI over the preceding few decades has become more of a norm than a gimmick that it was considered in its early years. Due to increased data volumes, advanced algorithms and improvements in computing capabilities and storage, AI has advanced into scientific innovation that can possible pave way towards future advancements in scientific studies [6]. But still the reliability of an intelligence system made by man itself, to solve human level issues might be received with criticism. Therefore, its employment in critical sectors like health care, business and other complicated core sectors had been fairy minimal. However, with the onset of the global pandemic of COVID-19, scientists and doctors were forced to their knees with the tremendous amount of pressure that came over this highly contagious virus proliferating over almost the entire world. The escalation of the situation was such a sudden hit that leaders across the globe had to take on unconventional decisions that would have usually taken at least a couple of years of testing and implementation. One such step was taken by the Chinese technology giant Alibaba. They have started implementing an AI system that successfully diagnoses the COVID-19. Alibaba is a conglomeration of various business departments such as software, gaming, online marketing and health care. All of these vastly diverse sectors are technologically guided by the company’s world-class AI department. China had the first reported case of COVID-19 infection, and Wuhan was the first city to face the outbreak. It is obvious to say that China as a country had more time dealing with the crisis quantitatively. Being one of the worst-affected cases (not “the” worst-affected) China too was to take steps that were previously unaccounted for. A report states that, the AI system detects the virus using CT scans and the process and outcome is as far as 96% accurate against viral pneumonia cases, a common symptom of COVID-19. Moreover, it takes average personnel about 15 min to completely diagnose the images and confirm the infection, but this AI takes about 20 s only to make a determination over the same. It is claimed to have been trained over 5000 positive cases of the infection and has yielded good results over testing in various COVID-19 hospitals. In fact, this system is under use by a good number of healthcare facilities. According to other sources, Alibaba isn’t the only tech company to have thought and taken these steps but even other competitors have started to take initiatives and joined in the cause. Ping An, another Chinese company focused over healthcare sector recently revealed that they have too developed an AI-based system that too determine the infection status based upon image reading. According to the co-president of the company, more than 1500 healthcare firms have benefitted from their technology and have been carrying out diagnosis using the same. About 5000 people have already been diagnosed by the AI system free of cost. This system too can generate unerring analysis within 15 s with pinpointed accuracy.

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Coming out of China’s secretive research and enforcements, other countries have also openly embraced the idea of implementing AI into healthcare sectors to maximize efficiency and minimizing the load over the healthcare personnel. UK has also taken a pioneering position in this segment of research. Cranfield University, UK, has seen its computer and machine vision students proposing a project which uses AI to analyse chest X-rays images, and detecting minute details usually missed by the naked eye. This AI tech not only can pick up anomalies that lines up to pneumonia, a COVID-19 symptom, but also determine if the particular case is due to the SARS-CoV-2. Conventional machine learning algorithms and deep learning frameworks were put under use for development of this AI program. The system responded with pronounced accuracy, which could be further improved with finer algorithms encoded via future updates. The team is now hoping to work with computer engineering professionals to take the next step by putting in advanced algorithms and training the AI to work better with computer tomography (CT) scans to increase its productivity. With proper fine tuning and an interface to work with this system could significantly contribute to the cause of containing the global crisis of COVID-19, as said by Dr. Zeeshan Rana, lecture at Cranfield, and the person leading this pilot project. King’s College London, Massachusetts General Hospital, and health tech firm Zoe have put an AI-based diagnostic under trials, trying to correlate symptoms and results of traditional COVID-19 tests to enhance the AI predictions. Benevolent-AI, UK, put up its platform to distinguish a marketed drug—Eli Lilly’s rheumatoid arthritis therapy Olumiant (baricitinib)—which can be a potential treatment for the SARS-CoV-2 virus. The drug was committed to trials by the US National Institute for Allergies and Infectious Diseases (NIAID) in February. Exscientia, an Oxford-based company took up the initiative to first ever trial an AI-discovered drug over a human subject. The company is now sifting through 15,000 drugs along with Scripps research institute, USA, trying to isolate the ones that possibly show any trace of reactivity towards the SARS-CoV-2 virus. Cambridge-based Healx used its AI platform to analyse 4000 pre-existing drugs that can be repurposed to work against the global pandemic. The point is, even though humanity has come out of worse situations before, the COVID-19 pandemic hits the very core lifestyle of people that has been established since decades. Therefore, we are looking at such a pressure misbalance in our society, where one sector is heavily burdened and another can only sit back and watch. But what we have today against the old times is tremendous advancements in technology. So, it is only sensible to implement it to our advantage and relieving the pressure off of our healthcare sectors. Implementations of such ground breaking tech would definitely provide a breath of freshness if not complete salvation.

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7 Contactless Hand Sanitizers Defence Research and Development Organisation (DRDO) has developed a high-pressure mist-based automatic hand sanitizer dispenser which is contactless. It has been designed in such a way that it can meet the requirements of the medical health care as well as public. It can be used at offices, colleges, factories, public places and even homes. This machine can create great pressure mists so that the COVID-19 patients as well as those in their quarantine places can at least use it without having to touch it. This would ensure that since there was no contact with the dispenser; hence, no virus can be present there and since in order to use it, you do not have to touch it, so the virus won’t be transmitted through any contact from the surfaces. The sanitizer releases its mist as long as the person using it has kept his/her hands below the nozzle. Unlike any other dispenser, this machine has a timer fixed in it and will release only a fixed amount of sanitizer. The machine is designed to produce droplets at high pressure which can enter the pores of the skin and can even enter the areas under the nails. It can clean minute areas, saves time, and also reduces the waste as quoted by one of the doctors who demonstrated this machine. They are even cost-efficient.

8 Aarogya Setu Application The Government of India has launched a new application to fight against the coronavirus in India, named as Aarogya Setu. This application can inform the user if they have ever encountered a corona-infected person or have come across them unknowingly. The application was developed by the NIC eGov mobile applications and the central government and is designed to work on iOS as well as Android. It actually uses the data provided by the user to track the coronavirus. It generates a social graph based on the Bluetooth and the location of the user’s device and then tracks the person who was tested positive for COVID-19. One essential feature that should be noted about this application is that this application is based on the location of the user as well as the data of the user to work properly; hence, it requires more and more data and from different locations. Google Maps also work in a similar fashion, by tracking any traffic jams if any based on the user’s location. Some additional features include self-assessment test and a list of helpline numbers all across the country, so that if a person needs help of any kind he can dial these numbers and ask for help. The government has made it mandatory for each one of the employees of India to download the application, be it from a government organization or even the public sector.

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9 Proposed Methodology A. Questionnaire regarding Public Opinion As the global pandemic continues to pursue its horror, contact tracing, testing and surveillance have been considered to be the three important parameters while considering the public health. So, considering the ongoing situation of the country, the proposed methodology is about the analysis being on how the use technology or digital clinic is helping the patients as well as other people using them. A set of questions were prepared and the information collected from a few participants were collected and presented for this report. The questionnaire consists of certain questions such as, if the patient is satisfied with the technology being used for the current care: 1. The percentage of people satisfied and non-satisfied with the use of technology for the current health care. 2. The people were also asked whether they find the traditional OPD services efficient or to use medical techs. 3. Is it efficient to use digital clinic procedures as a saving in terms of both time and money significantly? 4. Are the facts and information being available to the public in the form of applications sufficient enough to create awareness among the people worldwide? 5. The weekly production of the number of ventilators being produced? 6. The market size of global ventilators in before and after the pandemic. 7. Impact on the global tech industry. 8. Are wearable devices such as Oura, WHOOP bands or Fitbit helpful in coronavirus symptoms? B. Statistical data analysis Other such research and statistics includes: Companies which are in the process of developing vaccine, disruption on the global pharmaceutical market, level of concern on the possible drug supply chain due to this pandemic, number of COVID-19 tests being carried out on a daily basis worldwide, opinion on the development of a treatment/vaccine for COVID-19 and rate of COVID-19 testing in our country as of July 2020. A statistics of the total number of COVID-19 cases in the leading states of Maharashtra, Delhi, Gujarat and Karnataka has also been observed along with my own state, Odisha recently by June 2020.

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Discussion and Future Work

The pandemic has been going on for a longer period now, and hence, it was not possible to visit in person, so I have used this questionnaire. Using the questionnaire as a tool for analysis, the following results have been obtained: 1. Using technology has always been efficient to do any sort of task, but when it comes to using it for the current care during the COVID-19 situation, it has been observed that people are divided into two categories of satisfied and dissatisfied. Taking into consideration a total of 223 responses, the results were obtained as 20 out of 223 were satisfied with the use of technology for their health care and the rest 203 were unsatisfied. So calculating the percentage of both the cases, about 8.23% people are satisfied by using the technology for their treatment and care, while the rest 91.77% of the people are not satisfied with it. When they were asked about the reason for not being satisfied, more than half of the responders complained about the problem they had to face as they did not know how to use them. This is one of the basic reasons for their dissatisfaction. While introducing technologies to make life simpler is good, it is also necessary that the users are taught how to use them properly. 2. The second question focused on the opinion of the people, whether they think using technology for diagnosis and care is better than the traditional OPD. It was reported that 187/223 said that they were satisfied, which meant that they think that using technology is better than the traditional OPD. The rest 36 participants preferred traditional OPD. Due to the pandemic going on, the virus can easily spread through contact; hence, the satisfied participants prefer to use technology that prevents any contact with other person. If we calculate the percentage of the satisfied participants as well as not satisfied ones, we get about 83.9% satisfied and the rest 16.1% being the dissatisfied ones. Maintaining a safe distance from each other and preventing any sort of contact with the infected ones are very necessary during this pandemic situation. Technology provides us with such efficiency and takes cares of contactless care and diagnosis. 3. The third question was about whether the participants thought that using technology for health care is a significant saving in terms of both time and money. About 92% agreed that it did save their money and time making it convenient to get all the details and information that they wanted about the coronavirus. According to them, they find it easier to get all the information about the virus, precautions and facts and services like consultations from their medic by using these technologies. It saves both their time by not having to visit their doctors in person as well as money as it is a cost-effective method. The rest 8% who did not agree at this point usually prefer going to their doctor in person and getting a proper check-up, some of which are not satisfied that an application can also provide the necessary information relating to COVID-19.

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4. The results from the fourth question suggested that about 71.2% of the participants were satisfied with the information being available through the applications about COVID-19. The results indicated that the highest responses of satisfactory were from medical students and healthcare professionals, while the least were from the non-medical staff. Due to the use of the applications, people were able to understand the use of masks/respirators as well as gained valuable information regarding hand hygiene. About 45.5% are aware of the use of masks/respirators at this time of crisis and a 52.5% of the responders were aware of the hand hygiene due to the use of these applications. 5. The fifth question was asked whether the weekly production of the ventilators is sufficient enough for the infected patients or not, about 69% were concerned about it and said that they are not being produced in a larger quantity seeing the current situation worldwide. The rest 31% are ignorant about this fact and think that there might be sufficient ventilators for all. This however is not the case; hence, the ventilator industries are trying to come up with innovative ideas to create ventilators with the use of more resources and technologies so as to be sufficient enough for everyone. In 2019, the production/week was 700 which increased in March 2020 due to the pandemic to 2000, and by the end of April, it was raised to 7000. 6. The market size of the global ventilators was found to be 464.9 (US million dollars) in 2018 before the pandemic, and after the pandemic, it was raised to 524.7 (US million dollars). 7. This question was based on the impact on the global tech industry, and according to the responses, the global ventilators market size, after the occurrence of pandemic has been recorded with an impact of about −12.4% has been affected on the spending related to the devices and that on soft wares to be-1.9%. Thus, it has indeed affected the global IT industries. 8. This question was mostly for those who use wearable devices like Fitbit, Oura rings or WHOOP bands, whether the wearers think that these devices were able to predict the early symptoms of COVID-19 or not. About 90% of the wearers agreed that these devices indeed helped to detect the early signs of COVID while the rest 10% used it for the fitness purpose and hence disagreed.

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Conclusion

In addition to the above questionnaire, some other research has also been done. According to a research done on companies which are in the process of developing vaccine about 46% people agree on the fact that the vaccine companies may soon come up with one and the 16% disagree on it. It has also been recorded that the disruption on the pharmaceutical markets due to COVID-19 may have fallen to −3.6 (US million dollars) by end of 2020 and will keep decreasing further in the

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coming year due to this pandemic situation. The number of coronavirus cases across India has been recorded as confirmed cases to be 16,492, recovered cases to be 321,781, active cases to be 210,706 and deceased as 549,035. Now if we consider the number of cases by states, Maharashtra has about 164,626 confirmed cases, recovered cases are 86,575 and deceased cases to be 7429. Delhi has 83,077 confirmed cases, 52,607 recovered and 2623 cases of deceased cases. Gujarat has 31,320 confirmed cases, 2280 recovered cases and 1808 deceased. Karnataka has 13,190 confirmed cases, 7505 recovered and 207 deceased cases. Odisha has 9070 confirmed cases and 36 deceased cases. Looking at these numbers, it is very necessary that people start to understand that technology can really help in time of these needs. In fact it is actually creating a great impact during this time of pandemic. This analysis thus tells how much of an impact technology has especially on the healthcare domain.

References 1. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. https://doi.org/10.1148/radiol.2020200642 2. Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TM, Pan I, Shi LB, Wang DC, Mei J, Jiang XL (2020) Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology. https://doi.org/10.1148/radiol.2020200823 3. Haleem A, Vaishya R, Javaid M, Khan IH (2019) Artificial Intelligence (AI) applications in orthopaedics: an innovative technology to embrace. J Clin Orthop Trauma. https://doi.org/10. 1016/j.jcot.2019.06.012 4. Haleem A, Javaid M, Vaishya (2020) Effects of COVID 19 pandemic in daily life. Curr Med Res Pract. https://doi.org/10.1016/j.cmrp.2020.03.011 5. Hu Z, Ge Q, Jin L, Xiong M (2020 Feb 17) Artificial intelligence forecasting of COVID-19 in China. Arxiv preprint arXiv:2002.07112 6. Luo H, Tang QL, Shang YX, Liang SB, Yang M, Robinson N, Liu JP (2020) Can Chinese medicine be used for prevention of coronavirus disease 2019 (COVID-19)? A review of historical classics, research evidence and current prevention programs. Chin J Integr Med. https://doi.org/10.1007/s11655-020-3192-6 7. Biswas K, Sen P (2020 Mar 6) Space-time dependence of coronavirus (COVID-19) outbreak. Arxiv preprint arXiv:2003.03149 8. Chen S, Yang J, Yang W, Wang C (2020) COVID-19 control in China during mass population movements at New Year. Lancet. https://doi.org/10.1016/s0140-6736(20)30421-9 9. Wang L, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. http://arxiv.org/abs/2003.09871 10. Xu X, Jiang X, Ma C (2020) Deep learning system to screen coronavirus disease 2019 pneumonia, pp 1–29. http://arxiv.org/abs/2002.09334 11. Wang Y, Hu M, Li Q, Zhang X-P, Zhai G, Yao N (2020) Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. http://arxiv.org/abs/2002.05534 12. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20 (14):4036

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13. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 14. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 15. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. http:// arxiv.org/abs/1511.05644 (November) 16. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 17. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494, 77 18. Ray C, Tripathy HK, Mishra S (9–11 January 2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, Hyderabad, India, pp 209–219, 78 19. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms. IGI Global, Pennsylvania, PA, USA, pp 413–432 20. Mishra S, Dash A, Jena L (2021) Use of deep learning for disease detection and diagnosis. In: Bio-inspired neurocomputing. Springer, Singapore, pp 181–201 21. Chaudhury P, Mishra S, Tripathy HK, Kishore B (2016) Enhancing the capabilities of student result prediction system. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies, Uidapur, India, 4–5 March 2016, pp 1–6 22. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing. Springer, Singapore, pp 385– 390, 92 23. Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India, 4–5 March 2016, pp 1–3

Chapter 12

Impact of Artificial Intelligence and Internet of Things in Effective Handling of Coronavirus Crisis Karan Jaju and Hiren Thakkar

1 AI in the Time of Coronavirus Artificial intelligence (AI) techniques integrated with real-time data collection, wireless set-ups and performance of end-user devices are now in high demand. Presently, it is in great demand to use AI to track and predict major natural pandemics [1]. The coronavirus 2019 pandemic (COVID-19), from Wuhan, has impacted a devastating effect on the community worldwide and has placed a heavy burden on high-quality health systems around the world. Today, when the whole world is struggling to survive, artificial intelligence comes into play with everyday new ways coming up to help us fight the pandemic. There are AI-powered predictive systems, advanced warning systems, AI driven diagnosis, robots, drones and more [2]. Smart AI-powered platforms help us keep track of the spread of the disease and monitor it. With such platforms, it creates data structure that makes it easy to track the spread of disease, keep updated information, get immediate help and reports, and much more. BlueDot is one such platform developed to trace, detect and understand the spread of contagious diseases [3]. It achieves this by using machine learning based advanced algorithms that analyze data from number of sources, including a health organization, population and veterinary fitness reports. The Indian government is using the Arogya Setu app (Fig. 1) to help boost us the fight against COVID-19 pandemic. The application keeps you updated with day to day number of infected active cases, recovered cases and death cases. It also

K. Jaju (&) School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India H. Thakkar Department of Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_12

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Fig. 1 Arogya Setu App

gives you tips about how to keep going healthy and stay protected from the deadly virus around [4]. The platform uses artificial intelligence to map the other users nearby and lets you know the frequency of cases and their distance from the user. Improved warning systems can be implemented using big data analysis and artificial intelligence to get one updated about the spreading of the disease. In South Korea, a government-run analysis program contains details of citizens, immigrants, hospitals, governmental agencies and financial services to map out the areas visited by those with the disease so that it can warn people about AIDS groups. The government has also deployed AI-based directive management for regulated supply and distribution of masks and other medical supplies. AI programs are making medical systems and diagnostics advanced in various ways, including prediction of viral formation, reading computerized tomography data, prediction of protein formation in the evolution of therapeutic drugs [5]. Baidu

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artificial intelligence scientists have implemented this algorithm for the prediction of a secondary sequence of COVID-19 ribonucleic acid sequences, with reduction of the analysis duration from fifty-five minutes to twenty-seven seconds, which is 120 times faster. Alibaba's research center, Damo Academy, claims their artificial intelligence driven smart diagnostic program can trace coronavirus cases predicting with an accuracy of 96%. The program can differentiate between coronavirus and pneumonia computerized tomography, with scanning speed within 20 s. The AI technology is being implemented in security cameras to monitor whether people are wearing masks or not. Security cameras can also be used to detect people with high body temperatures using thermal recognition [6]. Robots at this point of time can act as a boon to the mankind. Robots can be used to disinfect people and surroundings, safe delivery of things, remote diagnosis of patients and almost wherever we need them.

2 AI Applications for COVID-19 2.1

Early Detection and Diagnosis of the Infection

AI can analyze an unusual sign (Fig. 2) and “red flags” rapidly and warn patients along with health authorities. It provides a fast and inexpensive decision-making process [7]. It helps to develop a new system for management and diagnosis of active cases, using advanced algorithms. AI helps diagnose infected individuals

Fig. 2 Irregular symptom detection by detecting people with high temperature

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taking help of medical imaging technologies such as CT scan, MRI of human organs.

2.2

Monitoring of Treatment

Artificial Intelligence can create a clever platform for automated monitoring and predicting the transmission of virus. Neural network can be created for excluding visual characteristics of the disease, assist in the proper monitoring and treatment of infected people [8].

2.3

Contact Tracing of the Individuals

AI can help examine the degree of infection with the virus that identify clusters and “tropical areas” and can effectively track individuals and monitor them.

2.4

Projection of Cases and Mortality

These technologies can trace and predict virus type from obtainable dataset, and social media, regarding the risk of infection and its proliferation. In addition, it can predict the statistic of infected cases and deaths in the provided area [9]. High-risk regions, people and countries can be identified using artificial intelligence and appropriate action can be taken by the countries.

2.5

Drug and Vaccine Development

Artificial intelligence can be used for drug research by analysis of the information available in COVID-19. This helps in the drug development and its delivery. The technology is being used to speed the drug testing process in real time, where taking routine testing consumes a lot of time and therefore helping speed up the process significantly, which will not be effectively applicable manually [10].

2.6

Reducing the Burden on Health Workers

With the rapid increase in the number of positive or infected patients amidst the pandemic, health workers have a much larger work to do [11]. AI application here

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helps in reducing burden on health department employees. It aids in prior identification and provides early medications through virtual and scientific methods of conclusion-making.

2.7

Prevention from the Disease

Data can be provided taking the help of real-time information analysis using artificial intelligence which will be effective for prevention from the disease. Possible areas of infection, infection, bed need and need for health workers can be predicted during this critical time. AI assists in the prevention of viruses and future diseases, using past information taught on common data at multiple duration [12]. In future, it will act as a prime technology to combat other pandemics and epidemics. A measure for protection and combating strategies can be obtained through this technology.

2.8

Medical Claim Processing

The performance of tax-paid health care systems but also the diversity of business and administration as it tends to increase the number of patients. Ant Financial helps boost the claim processing along with reduction of in person communication between patients and staff members using a block-chain platform.

2.9

Delivery of Medical Supplies Using Drones

Drone equips us with being safest and very effective way to reach remote regions and urgent need locations with the need of treatment during any outbreaks. Medical supplies are being delivered by the Terra Drone using its illegal vehicles and for the classification of low-risk substances [13]. Drones are also used to guard public spaces, to track disregard for authority and to think critically.

2.9.1

Robots Sterilize, Deliver Food and Supplies and Perform Other Tasks

Robots are not infected, so they are deployed to complete many tasks such as cleaning and sterilizing and bringing food and medicine to reduce the number of human contact. UVD robots from Blue Ocean Robotics use ultraviolet light to independently kill germs and germs.

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Protection Using Modern Fabric

Start-up company of Israel, Sonovia, provides the medical world with face mask formulated by their special manufactured fabric containing antipathogen and antiseptic fabric based on metallic nanoparticles.

3 AI-Enabled Rapid Diagnosis of Patients with COVID-19 Coronavirus diagnosis, routinely used test, which is a SARS-CoV-2 specific reverse transcriptase polymerase reaction [14]. The RT-PCR test takes forty-eight hours for successful completion, and there is already a shortage with the supply for required kits, emphasizing the emergency need for other procedures and effective detection of patients infected. Suspected individuals with the virus can be diagnosed through CT which may impact as necessary device-cum-procedure. Early diagnosis of the infection is important for individual care associated with prior medications, along with public health perspective for ensuring required isolation along with preventive steps [15]. Chest radiology is not as effective for diagnosing covidal pneumonia as compared to computerized chest tomography (Fig. 3) and cases emerge with detection prior to syndromeomatology using CT. Preliminary studies have identified common imaging patterns in chest CT.

Fig. 3 Detection of disease using AI in CT scan and chest X-rays

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Pathological results with abnormalities in cell patterns and lymphocyte counts along with other parameters like age, gender, fever, etc. [16] have been with research scientists. With use of computerized tomography of patients having COVID-19 and those who are not infected, using neural networks and image processing, with the help of AI, within seconds, it can be accurately predicted with more than 90–95% accuracy whether a person is infected by coronavirus or not. Then to confirm, these tests can be followed by other tests. Meanwhile, the medication and quarantine for the patients can be started.

4 Coronavirus Drones and Robots The last decade has seen technological change in the automotive and automotive sectors. With the great invisibility of the average person, this problem has pushed technology out of its niche into the public sector as never before. Several hospitals in China have begun adopting independent robots for their many daily activities, such as delivering food and medicine to patients, and disinfecting the hospital. In Singapore, doctors use telemedicine robots (Fig. 4) to remotely communicate with patients from as far away as possible. These robots take the form of electric carts that carry cameras, video monitors and health scanners of service delivery. With people living at home, the need for online shopping and home delivery has increased dramatically. This puts delivery staff in a difficult position, as any end-to-end customer contact, puts both parties at risk. Taking the form of small electric vans, JD.com delivery robots travel safely through the streets of Wuhan and form the final phase of package delivery (i.e., the phase where the package is shipped from a local location to a client address). Able to train around complex road conditions day or night, these robots are reported to make the most of the company's medical delivery (Fig. 5). With the closure came new laws that needed to be enforced, and police organizations around the world were turning to remote surveillance tools [17]. These devices usually take the form of commercial drones that carry loudspeakers or other communication equipment. Using them, the police are able to monitor remotely (Fig. 6) city streets and public places, identify areas of lawlessness and engage them directly. Not only do these devices allow the police to maintain their physical distance while performing their patrols, but their speed allows the police to control much larger areas than they could reach the ground [18]. When the outbreak is over, the world will return to normal, with robots and independent devices from different workplaces at increasing prices. Homework will be available to most people, and some will find aspects of their work taken up by machines, whether they are remotely monitored by the staff themselves or controlled by artificial intelligence. New business opportunities will emerge to meet

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Fig. 4 Telemedicine robots being used in hospitals

Fig. 5 Drones delivering medical supplies

these changing needs, where new jobs will be created in the process. The evolution of automation seemed to be on the horizon for a while, but this problem suddenly brought it all to an end and showed how important technology is in saving lives.

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Fig. 6 Drones used for surveillance by Streetsboro Police

5 IoT in the Time of Coronavirus Internet of things (IoT) is a highly defined system with connected computer technologies, digitally integrated devices and along with devices that are capable of information transmission along a well-connected network without the need of any human interaction regardless of any level [19]. All countries including India are in need of a low cost, effective solution for addressing the problems amidst the existing pandemic. To put it bluntly, IoT is a system of connected devices/functions that are compatible with various communication peripherals including hardware, software, network connections or any other digital/computational requirement which making it responsive for support of information opposition and compilation. Using IoT conceptualization makes access to patients useful, providing them ultimately with the necessary care helping them recover and combat the disease. IoT is a new technology that ensures that all infected people are isolated [20]. During the separation, it helps in the proper monitoring system. Patients with greater associativity of risk can be tracked remotely using online network establishment with the technology for measurement of various parameters like heart rate, glucose level and blood pressure through biometric assessments. Major key advantages of Internet of things for fighting coronavirus pandemic (Fig. 7): 1. Superior treatment: There would be a unified treatment procedure without delays.

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Fig. 7 Major key pros of Internet of things for combating coronavirus pandemic

2. Effective control: Through IoT, there would not be need to monitor different parameters of the patients manually and whenever needed required medications can be provided. 3. Enhanced diagnosis: With eventually reduced chances of mistakes and unified diagnosis and treatment procedure, the diagnosis would be enhanced. 4. Lesser expenses: The cost of PPE kits and other one-time use materials along with number of health care workers needed would be reduced. 5. Reduced chances of mistakes: The monitoring that would be done remotely through robots with the help of IoT, it would not incur human errors and the measurements would be more accurate and precise. Government of India has launched a mobile application named Arogya Setu that builds a connection bridge connecting people and essential health care services for combating and spreading awareness about the up going pandemic. A mobile application named close connection has been brought up by the people that detects the proximity of infected people with the owner’s device, so the owner can keep precautions while around.

6 IoT Applications to Fight COVID-19 Internet of things forms an intelligent network with the interconnection of devices for formation of health management systems. The system is used for warning and monitoring different types of infections for the improvement of patient safety. It digitally captures patient data and information without human contact. These data

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are also helpful in implementing appropriate procedures [21]. Various IoT applications: 1. Hospital connected with Internet: Hospitals need to be integrated with network for implementing Internet of things planning for combating the pandemic. 2. Notification system for medical Emergency: Patients and staffs should be able to provide response rapidly with increased effectiveness through this system whenever required. 3. Unequivocal treatment of the disease: Patients can get profited with benefits without preference or bias. 4. Automation of treatment procedures: Effective case management through treatment options, increasing the productiveness of the system. 5. Telehealth consultation: This in particular makes treatment available to those in need in remote areas through well-connected resources (Fig. 8). 6. Patient identification through wireless network: Screening process can be smooth-end through installation of verified mobile applications. 7. Smart tracing of infectees: Strengthening of service providers through strong patient follow-up for dealing cases vigilantly.

Fig. 8 Telehealth consultation

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8. Quick screening of coronavirus: Superior assessment through networked devices on prior detection of infectee with appropriate diagnosis. 9. Identify innovative solution: Quality guidance can be achieved with the globalization of new things. 10. Accurate virus prediction: Depending on the available data report, the use of a particular mathematical method can also help predict the situation in the future.

7 Internet of Things Powered Diagnosis and Treatment of COVID-19 IoT was originally called the technology of identifying the frequency of radio and Internet-connected devices based on an integrated communication process to achieve intelligent data management of the object. The use of IoT in medicine is called medical Internet of things, which aims to develop a large-scale data analysis and prediction model for supporting decision-based technologies such as medicine, electronics, biology and communication [22]. Medical Internet of things can be deployed for preventing and controlling of coronavirus. We can develop a triple leveled connection system with Capp basing on the medical concept and technology of Internet of things for diagnosis and treatment of COVID-19. The Internet of things nCapp medical system platform contains basic IoT functions and has a core graphics processing unit (GPU). Cloud computing systems connected to existing medical records, electronics storage, and image storage and communication can better assist in deep capturing and intelligent diagnosis. Online monitoring activities, location tracking, alarm communications and tracking systems facilitate Internet access, monitoring, management and COVID-19 treatment. System management, remote care, command management and statistical operations can increase the expansion of large-scale COVID-19 data as well as the comprehensive management and timely treating the disease. Introduction of the nCapp assisted COVID-19 diagnosis and treatment system. The IoT three-component “cloud plus terminal” nCapp (Fig. 9) can assist in the intelligent management, control and diagnosis of COVID-19. “Cloud” is a common term for cloud technology, which can be segmented by network technology, information technology, integration technology, management platform technology and application technology based on the use of cloud computing mode [23]. (1) Patient registration: Basic patient information is registered online. (2) Start the consultation: After the patient has been accepted, the page will show the item-by-item item, the patient has selected the answer button, which relays the information back to the online cloud.

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Fig. 9 nCapp assisted COVID-19 diagnosis and treatment system

(3) Smart assistant diagnosis: Diagnostic recommendations are made automatically to identify. (4) Technology assisted treatment: Dedicated treatment recommendations based on the severity of the disease. (5) Talent specialists: Provide relevant information for relevant local specialists and first aid specialists and nurses. (6) Self-control: Appropriate information on self-control is provided. (7) Map layout: Information is provided about COVID-19 cases surrounding the user's location. (8) Relevant data: Appropriate guidelines, diagnostic and therapeutic data, expert interviews, research papers and links are provided.

8 Detection and Diagnosis System Using IoT-Based Smart Helmet One of the most common symptoms of COVID-19 can be easily identified by the flu. Since the outbreak, heat tests using infrared thermometers are used in public places to monitor body temperature to identify who is infected in the crowd.

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This prevention is still lacking because it takes a lot of time to monitor body temperature in all people and most importantly close contact with the infectee can lead to the spread of the test taker or from the person in charge of the test to the test subjects. The study suggests the creation of a powerful system to detect coronavirus automatically from a hot photo with minimal human contact using a protective helmet equipped with a mounted thermal imaging system. Hot camera technology is combined with a smart helmet and combined with IoT technology to monitor the testing process to get real-time data. In addition, the proposed system is equipped with face recognition technology and can display details of a pedestrian who can take the temperatures of pedestrians. Modular system integration based on IoT and GSM communication interface is performed. The system sends a notification when it detects that the temperature is higher than normal. The GPS module determines the status of the post-tagging status and then a notification is sent to the smart phone via GSM. The officer will obtain human face and temperature data to identify the person identifying as the COVID-19 infectee (Fig. 10). The proposed protective helmet is grouped into three sections. The first part of the program includes a machine input source that contains a hot camera, a virtual camera and a mobile phone application. Processor development was the second phase of system development. The smart helmet had two different types of cameras, which allowed for a more detailed collection of facial recognition and thermal statistics. Ocular and thermal cameras that provide information on temperatures where different focus are available. A thermographic camera, sometimes named for hot photo (Fig. 11), hot imaging, or infrared cameras, a tool that is being used by infrared radiation for creating similar images with the ones of the traditional cameras which are using light rays for production of images. This module looks at image classification based on thermal statistics and colorful pictures taken with hot and light cameras. A hot camera is used to detect hot bodies and to detect by accepting higher thermal variations in comparison with various objects scanned within a region. On detection of higher thermal statistics, this leads to production of increased levels of infrared spectras. The algorithms of conventional computer vision like the Viola–Jones and deformable part methods and related extensions have really advertised great performance in the context of restricted conditions. In addition, to detect the history of visited place made by a person suspected as infectee, the Google Location History (GLH) can give the system the detail of places that have been visited by the infectee. GLH itself is Google service that saves where user go with every mobile device. The user experience can be improved by using any Google App or services which adopt Google Location History and Reporting. Like most Google services and features, the entire history and management is completely connected with the Google account of the individual.

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Fig. 10 Work flow of smart helmet

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Fig. 11 Thermal image that detect variety of temperatures

References 1. Elavarasan RM, Pugazhendhi R (2020) Restructured society and environment: a review on potential technological strategies to control the COVID-19 pandemic. Sci Total Environ 725:138858 2. Bullock J, Luccioni A, Hoffmann Pham K, Lam CSN, Luengo-Oroz M (2020) Mapping the landscape of Artificial Intelligence applications against COVID-19. arXiv:2003.11336 3. Alwashmi MF (2020) The use of digital health in the detection and management of COVID-19. Int J Environ Res Public Health 17:2906 4. Sushruta M, Hrudaya KT, Brojo KM (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts. Int J Control Theory Appl 10:295–310 5. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486 6. Mallick PK, Mishra S, Chae G-S (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput:1–16 7. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng:1 8. Öztürk Ş, Özkaya U, Barstuğan M (2020) Classification of coronavirus (COVID-19) from X-ray and CT images using shrunken features. Int J Imaging Syst Technol 9. Beck BR, Shin B, Choi Y, Park S, Kang K (2020) Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 18:784–790 10. Ray C, Tripathy HK, Mishra S (2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, Hyderabad, India, 9–11 Jan 2019, pp 209–219

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11. Sahoo S, Mishra S, Mishra BKK, Mishra M (2018) Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of research on modeling, analysis, and application of nature-inspired metaheuristic algorithms. IGI Global, Pennsylvania, PA, USA, pp 413–432 12. Ndiaye BM, Tendeng L, Seck D (2020) Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting. arXiv:2004.01574v1 13. Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis JT, Vespignani A, Santillana M (2020) A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv:2004.04019v1 14. Venkateswaran J, Damani O (2020) Effectiveness of testing, tracing, social distancing and hygiene in tackling Covid-19 in India: a system dynamics model. arXiv:2004.08859 15. Panda B, Mishra S, Mishra BK (2016) A meta-model implementation with tabu search technique to determine the buying pattern of online customers. Indian J Sci Technol 9:1 16. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14): 4036 17. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 18. Bai L, Yang D, Wang X, Tong L, Zhu X, Zhong N, Bai C, Powell CA, Chen R, Zhou J et al (2020) Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19). Clin eHealth 3:7–15 19. Yang T, Gentile M, Shen C-F, Cheng C (2020) Combining point-of-care diagnostics and internet of medical things (IoMT) to combat the COVID-19 pandemic. Diagnostics 10:224 20. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 21. Chu LC, Anandkumar A, Shin HC, Fishman EK (2020) The potential dangers of artificial intelligence for radiology and radiologists. J Am Coll Radiol 17:1309–1311 22. Kellermann R, Biehle T, Fischer L (2020) Drones for parcel and passenger transportation: a literature review. Transp Res Interdiscip Perspect:4 23. Montag C, Becker B, Gan C (2018) The multipurpose application WeChat: a review on recent research. Front Psychol 9:2247

Chapter 13

Fight Against COVID-19 Pandemic Using Chat-Bots Akshay Pratap Singh, Komaldeep Virdi, and Aadarsh Choudhary

1 Introduction In seventeenth century began the unfulfilling quest of computers, many scientists and philosophers firmly believed that all kinds of logic and reasoning could be done by machines which would be designed by us humans only, and they would go as far as claiming that they would even surpass us in the ways of reasoning and logic building. From time to time, we have been seeing the humungous differences between theory and practical aspects of all kinds of sciences, to visualize is one thing but to implement, it is a whole different mountain to conquer. Back when all these claims were being made, the most advanced machine that physically came into existence was the adding machine created by Blaise Pascal in 1642, given in Fig. 1, which could only add and subtract. After Alan Turing built the world’s very first computer in attempt to help save lives and after he “broke the code”, people started to believe more in computers, and gradually they started acknowledging the usefulness of computers. Acceptance was clearly visible, but AI or chat-bots per se were not making headlines as of yet. The scenario was as such that in Tesler’s theorem there was a quip which defined AI as “AI is whatever that has not been done yet”. Which played in the favour of developers who were working on AI and chat-bots, they were completely concealed from the limelight and the pressure of work had no part to play in their lives. In the mid-1960s, US Department of Defence heavily funded for the research of AI, the researchers spoke in high regards for AI and were very confident, and they claimed that within 20 years the industry of AI will blossom and will flourish for a long time. They clearly oversaw it, and they must have missed to look upon some A. P. Singh (&)
K. Virdi School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, India A. Choudhary Xformics Inc., Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_13

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Fig. 1 Adder machine

of the factors because only a few years later came the “AI winter”. In this period of time, AI was criticized on large scale, and the consequences were so huge that the USA as well as the UK retracted the funds that were offered for the AI research. In early 1980s, The Expert Systems came as the saviour for AI. It was widely sold and had a sky-crashing demand, the industry for which people only spoke ill, the industry which wasn’t even making as much as a cobbler does, within a span of five years fashioned itself into a Billion Dollar business.

2 Literature Survey The team from Carnegie Mellon University and Google brings forth a new technique to training a task-oriented dialogue system. To be specific, they suggest a hybrid imitation and reinforcement learning method, where the agent is trained for the first time in a supervised manner from dialogue corpora after which, it continuously powers up its performance by learning and experiencing from users that demonstrate the correct action to take when the agent performs negatively and give positive versus negative feedback at the end of a dialogue. The experiments show that imitation learning combined with the reinforcement learning based on the user feedback significantly improves the agent’s performance [1].

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In context with Reference [2], M. Chandhana Surabhi expresses how natural language processing is a method where the computer can act more human, thereby reducing the difference between human being and the machine. We believe that modern natural language processing techniques can make possible the use of natural language to express programming ideas, thus drastically increasing the accessibility of programming to non-expert users. We believe that in the near future, people will be able to not only talk, but also code in their native language [2]. This system can be of great use to people in conducting daily check-ups, while within the safety of their homes, as proposed by Rohit Mathew, it makes people conscious of their health status and stimulates people to take proper measures to stay healthy. According to this research, such a system is still protested against and people are less aware of it. Executing this proposed framework can help people stay away from the time-consuming method of visiting hospitals by using this free of cost application, wherever they are [3]. The team from the University of Washington has come up with a social bot called sounding board. This is a chat-bot with which you can have an informative, coherent and interesting conversation on sports, politics, entertainment, technology and other popular topics and events. The success comes from its ability to deliver something interesting to the user thanks to the rich content collection as well as the ability to show interest in the conversation partner by acknowledging user’s reactions and requests [4]. Facebook AI research team brings forth a very large-scale persona-based dialogue data set created from Reddit conversations. The corpus includes 5M personas spanning more than 700M conversations. The researchers argue that adding some personalized backstory to the model improves the performance of the chit-chat dialogue systems. The experiments confirm that models trained to align answers with both the persona of their author and the context achieve the state-of-the-art results on retrieving the right response among 1M candidates [5]. A lot of treatments are made available for people for all kinds of diseases. In this paper, Divya Madhu elaborates on how you would need a supercomputer for a human brain to keep track of all the diseases and their respective symptoms. This is where the AI bots get to handle the situation, suggest proper medicines and keep track of the medicine composition so as to prevent an overdose [6]. Rashmi Dharwadkar expresses that the medical chat-bot functioning depends on NLP that helps users to accept their problem about the health. The user can enquire about any personal query related to health care through the chat-bot without actually physically being available to the hospital. The system’s major concern behind coming up with this Web-based platform is analysing customer’s sentiments [7]. This paper by S. J. du Preez presents the design and development of an intelligent voice recognition chat-bot. It discusses the procedure in great detail. It also views the idea of an artificial brain, which replies aligned to the desired character and also learns by keeping the data archive so that it can be used in future [8]. Divya S expresses that all that is needed for a AI bot in the healthcare department is to figure out the disease and give basic details about the disease and its

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medicine, before consulting a doctor. It is all about coming to terms with the pandemic and trying to make the jobs of the people easier [9]. Amiya Kumar Tripathy comes forth with the take on the lack of convenient and inaccurate tech availability to people and that also it is not mobile; in order to put tick on that demand list, the idea of having a chat-bot on mobile for healthcare applications has been tossed by integrating various types of applications [10]. Facebook AI research team suggests that providing a personality to the agent will make chit-chat dialogues much more entertaining, consistent and engaging. Saizheng Zhang states PERSONA-CHAT appeared to be a very reliable source of training data for conversation starters, when the speakers do not know each other and focus on asking and answering questions [11]. F. M. Barcala, in this paper, considers a set of natural language processing processes that can be used to survey huge amounts of text chats, focusing on the modernized tokenizer which sums for a number of complex linguistic phenomena, as well as for pre-tagging tasks such as proper noun recognition [12]. Dr. John Woods discusses the different techniques of making a chat-bot and has compared certain techniques with the given environment of application, and it has been concluded that a lot is yet to be dug and that there to no specific way to design a chat-bot [13]. Chat-bots could assist in sharing timely information quick, if effectively and conveniently designed and deployed, bringing forth desired health assisting behaviours and reducing the psychological damage caused by fear and isolation. Despite chat-bots being of so much help, the risk of maximizing wrong information and the absence of previous effectiveness research are causes for concern. Immediate collaborations between healthcare workers, companies, academics and governments may assist in future pandemic preparedness efforts [14]. To overcome the coronavirus pandemic, governments nationwide have set up phone hotlines to prescreen potential cases. These hotlines have struggled with way too much traffic, resulting in wait times of hours or, even, an inability to get a hold of health authorities. Symptoma is a symptom-to-disease digital health assistant that can identify the difference between more than 20,000 diseases with an accuracy of more than 90% [15]. Collaborating with Verily and Google, machine learning and natural language processing were combined to put together and launch an interactive tool to help patients get solutions to their questions and an array of symptoms related to coronavirus. The end product is publicly available worldwide through the Google Contact Center AI initiative [16]. Telemedicine can be used by the medical field to get in touch with their patients during the recent coronavirus outbreak, while attempting to bring COVID-19 transmission among patients and clinicians, to a minimum. During this outbreak, telemedicine has the potential to assist by allowing patients to receive supportive care without having to physically visit a hospital by using a conversational AI-based application for their treatment [17]. The screening of medical team workers for COVID-19 symptoms and exposures prior to every clinical shift is important for preventing the spread of coronavirus. To

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make the screening process simple and efficient, University of California, San Francisco Health designed and implemented a digital chat-bot-based workflow [18]. Plenty of Web-based COVID-19 symptom analysers and chat-bots have been introduced, but previous tests have suggested that their conclusions are highly variable. Till this date, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner [19]. Designing and implementing a chat-bot in a media news field involves firstly, creating a fully functional and robust chat-bot in a situation of crisis to make it easier for us and secondly, the population’s take on a news chat-bot as an alternative path to access existing stats during a crisis situation [20]. The chat-bot’s ability to get job done more efficiently than human agents and the primary factor driving perceptions of ability is the user’s trust in the hotline provider. People have yet to completely accept the practicality of chat-bots [21]. Telehealth uptake also requires a significant change in management effort and the redesign of existing models of care. Implementing telehealth proactively rather than reactively is more likely to generate greater benefits in the long term and help with the everyday (and emergency) challenges in health care [22]. Screening processes can be made easier with the involvement of chat-bots. A completely robust, decision-making and implementation framework for implementing COVID-19 screening chat-bots at paediatric healthcare facilities can make controlling the situation much easier and efficient [23].

3 Diving into Chat-Bots With AI finally taking its rightful stand on the podium, the advancement in technology escalated exponentially. An extremely apt example of innovation in the present era is a chat-bot. Artificial intelligence software- a bot, is programmed in such a way that it can accomplish a series of chores on its own merit, without the involvement of human beings. From marking events on a calendar, to making reservations at restaurants, these bots are capable of adapting and being useful in every situation [24, 25]. Being most up-to-date model, it was designed to persuasively simulate the way a human would behave as a conversational partner. These conversations are done through textual or auditory methods. Chat-bot systems typically require continuous tuning and testing. They need to undergo the industry standard Turing test. This test was proposed by Alan Turing in the 1950s, which showcased the ability of a computer programme to impersonate a human in a real-time written conversation. This Turing test became a great source of inspiration for Joseph Weizenbaum, and he came up with his ELIZA project which was published in 1966. ELIZA became the first prototype of a chat-bot, and its method of operation involved the recognition of small phrases or keywords and then responding with programmed and prepared replies so as to get the conversation going in a meaningful direction. Figure 2 shows a real-time interaction with the first prototype of a chat-bot.

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Fig. 2 A real-time conversation with ELIZA

These bots are abundantly available in messaging apps, mobile applications, websites and telephones and are effective and versatile. While a couple of bot applications use broad word-order processes, sophisticated AI and natural language processors, others simply scan for general keywords and generate replies using common short phrases obtained from any database, minimizing the need for human intervention, providing refined customer experience, amplifying productivity and so much more. ‘Chat-bots will fundamentally revolutionize how computing is experienced by everyone’ said Satya Nadella, CEO of Microsoft. So, this does not come as a surprise that businesses in countless fields are harnessing the abilities and power of these bots to provide prompt answers to questions, resolve complaints and respond to queries.

4 What Makes These Chat-Bots so Unique? Now, chat-bots are comparable to human beings in many ways—they have their own personalities, they acquire information from their past experiences, and the most fascinating of all, they can hold natural human-like conversations with people, be it a visitor or a customer. These aspects of chat-bots have led to this huge influx

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of these bots into our system. Some examples of chat-bots making our lives easier are— • These bots automate conversations that would instead require an employee to handle. More visitors at the website would leave a huge heap of inbound messages from these customers. Instead of getting a human to do this taxing task of catching up on these loads of messages, a bot would accomplish this task more efficiently and in less time. • Chat-bots themselves figure out the appropriate questions to ask so as to gather the right information required to filter out unqualified prospects. • Customers may expect an early response within a few hours of posting their query. Manually attending to all these queries would take up a lot of time. Instead, chat-bots can do this in a jiffy, hence enhancing the customer experience. In terms of efficiency, people seem to have gained more interest towards these chat-bots rather than the actual application, which is represented in Fig. 3. Ability of a chat-bot to carry out all these tasks can be majorly classified into two main core activities— • It analyses the request of the user with precision, to understand the user’s intent and extract relevant data. • After the intent of the user is correctly identified, it provides the user with an appropriate response. Figure 4 provides a flow chart for the two prominent steps every chat-bot follows in order to get its job done.

Fig. 3 Some of the factors where chat-bots surpassed apps in case of efficiency

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Fig. 4 Structured tasks of chat-bots

A well-built bot is capable of handling situations on its own merit— • If existing conversation data is available, the bot uses it to understand the type of general questions people ask. • Goes through a “training” period, where it will analyse those questions and come up with relevant answers. • Uses ML and NLP to learn the context and continually get better at responding to those questions in future. The usage of chat-bots has immensely accelerated around 2016 with Facebook. Right after that, Google came up with Google Assistant, and ever since then, chat-bots became a common sight in apps, on websites, social media, etc. Figure 5

Fig. 5 People started taking more interest in talking to chat-bots than using actual application

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represents graphically how people got more invested in interacting with AI bots rather than paying attention to the actual apps on which they were being run. Now, bots can differ from each other on the basis of how they approach the set of questions given to them— • Rule-based chat-bot—These bots have a set of rules to follow. They follow specific paths designed by the bot developer using a decision tree. Hence, these are not very effective at answering questions. • AI chat-bot—These bots go through self-training. They train themselves based upon the previous interactions with the end user. These get a little complex with AI which run on analytical platforms, integration with APIs, etc. Depending on the purpose for creating a chat-bot, its functionality can be classified into four basic categories: • Virtual assistant: Getting a chat-bot to assist customers instead of human agents allows a company to provide enhanced customer service. • Idea generation: AI bots have the ability to learn. This allows them to adapt to the ever-changing statistics of the data and improve their performance as more and more data pours in. • Automation of manual processes: AI is a rapidly automating routine and a mechanical cognitive process which leaves a lot of room for innovation. The use of intelligent algorithms can now automate the process of collecting data from enough reliable resources and then performing an analysis on that data to determine the most profitable path. • Analysis of unstructured data: Consider that almost 80% of the digital data is not structured. Organizing previous data and keeping track of the continuous influx of new data lead to a better understanding of the user.

5 Applications of Chat-Bots Evolution is inevitable, and all we can do is adapt. With time, ones who do not change themselves and the ones on whom the idea of survival is lost are forgotten by the world as if they never existed. Companies like Blackberry, Pan Am Airlines, Compaq, Yahoo and Xerox did not change with time and were soon out of the picture; some ran out of business, while the others are just struggling to make it work somehow. Visionaries who are well aware of the world that they live in, know very well that chat-bots are the future and throughout the globe today we can see that industries in all walks of life are utilizing the immense power of chat-bots. That way they are able to use their resources very efficiently. Chat-bots are time savers and are making it easy for the customers. Now they do not have to go through long website scrolls and read huge paragraphs; instead, the chat-bot inquires and delivers the essentials. Now they do not have to pop their eyes

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out by browsing through thousands of products online, they can simply command the chat-bot, and it will take care of the rest. News can be pretty boring to read. However, chat-bots just know the trick to tell it an interesting manner. Chatbots can also be efficiently used for getting an appointment of a doctor or to get a prescription or to know the perfect substitute of a drug. As a matter of fact, people sometimes find talking to customer service over a call irritating and time consuming, and the process is so tedious that the customer has to pay attention to every sound that has been enunciated. Sometimes due to network issue, the voice breaks, the information gets lost and several other issues make this method of communication highly inefficient. On the other hand, the chat-bots present all the information to you, all at once, and make it a fruitful experience. Figure 6 shows the percentage of people who were assisted by these chat-bots before buying anything online. Chat-bots have been introduced in so many industries, not only the mainstream ones but in the industries like online trade, health care, telecommunication, banking, financial portals, insurance and vehicle trade. These industries are flooded with chat-bots, and nowadays, worldwide different governments are also investing hugely in chat-bots.

5.1

Mass Media

Chat-bots are capable of making conversations with user to provide a personal touch, and this is the very feature of the chat-bot which makes it unique and different from all other forms of technology. This is the secret ingredient behind the huge success of chat-bots in messaging applications. The pot really began to stir in 2016, when Facebook announced their agendas regarding chat-bots. They developed an open platform for all Facebook users around the globe to flourish their own chat-bots and enhance their skills along with that, the idea was so well received that within six months of announcement there were around 30,000 active chat-bots employed on Facebook, by September 2017 the count rose up to 100,000, and by the end of 2018 the numbers got tripled to make it a mega army of 300,000. Many

Fig. 6 Use of AI bots for online purchases

19% 14% 67%

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big business hounds have shown their deep interest in chat-bots since then and have made quite an impactful journey. The heavy sides of Mass Media business CNN, the Guardian and Wall Street Journal, interact with their users on Facebook messenger and present news to them in a very compact and subtle manner. There are certain chat-bots that help the female users of Facebook to keep track of their menstrual cycle and remind them when the time is fair. Figure 7 gives a graphical representation of the fields where these bots started being of help for the industries.

5.2

Chat-Bots in Online Retail

There were days when a buyer had to browse through several products on online sites to finally land on their desired good as the bracket is so vast, but since chat-bots took over the online retail, everyday the bracket has gone wider and wider yet smaller for the user. The user just has to make demands, and the chat-bot does the rest; they filter, they sort, and most importantly they suggest and pick out. H&M pulled out the rabbit from hat when they managed to build a chat-bot that could make an outfit for a user when provided with the details of cloth. Lenskart did the same thing with their 3-D working model, and one can easily find how they would look after putting the lens on. People are so habitual of texting and talking that they prefer to book Uber cabs using Facebook messenger rather than using the Uber app, and the sole reason for this is the interaction that they experience.

Fig. 7 Chat-bot implementation by industries

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Chat-Bots Under Government

Chat-bots have been welcomed in politics as well, developer Nick Gerritsen of Touchtech, New Zealand, developed SAM, and it is facilitated to inform citizens with news, thoughts, opinions and weather report. It discusses with citizens the subjects of development, education and festivals and records their feedback. In our own country, India, Maharashtra State Government launched Aaple Sarkar platform.

5.4

Chat-Bots for Mental Health

In today’s world, people have started to come around the fact that mental health is a genuine affair, but at the same time life is walking with a rapid pace and companionship is a luxury. Once, the creator of ELIZA walked into his office to find that his assistant was in a deep emotional conversation with ELIZA and that was enough for the Russian company Endurance to develop companion’s Chat-bot, which has its mastery with senior citizens dealing with Alzheimer’s disease. Not only mental health, but chat-bots are fighting tremendously at all healthcare fronts.

5.5

Chat-Bots in Other Healthcare Facilities

No matter how wealthy a person is, their general tendency is to waste neither their money nor their time to visit a hospital or a clinic for every minor inconvenience that they face; rather, all of us prefer to ask the Internet about the symptoms we are facing. Internet is full of information, and sometimes it gets puzzling to distil the correct information from the false ones. A study which was published in Journal of Pediatrics confirmed that 56% of the websites provide with erroneous information on health care [26]. With time, chat-bots were developed to provide trustworthy and brand new information straightaway coming from the highly qualified physicians, Babylon Health, set in motion in the year 2014, and by 2018 they successfully developed robo-doctor, it listens to what the patient has to say and accordingly advises them; if the matter is beyond its skills and reasoning, it connects the patient to a physician via video call and provides prescription. In times like this, when the whole world is terrified to step out of their havens, bots like this are providing alternatives to keep ourselves healthy. Ada Health companion is another option and a perfect alternative of robo-doctor. ‘Your.MD’ is also a diagnosing assistant which runs on messaging applications like Facebook Messenger, telegram, etc. [27]. As mentioned earlier, there are certain chat-bots that can provide people with absolute replacements of the drug they ask for; SafedrugBot provides users with the

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details about the ingredients used to fabricate some specific drug, and it also provides with the proper substitute of a given drug. Surveys have approved that more than 76% of older patients tend to forget, some even avoid to take the prescribed medication on time and in some cases not at all. Chat-bots have played a vital role in reminding and convincing them to follow the instructions imparted by their physicians. Florence is the chat-bot having expertise in this arena, it keeps a track on the user’s weight, and for the female users it also keeps an eye on their menstrual cycle and reminds them when required. It sends reminders when the medication prescribed in prescription is about to exhaust, it keeps the users posted with fresh daily health tips.

6 Pros, Cons and Scope of Chat-Bots Humans are capable of serving only a limited number of customers at once, but that is not the case for chat-bots. They can manage all the customer queries at once!

6.1

Advantages of Chat-Bots

• Cost efficient—Chat-bots reduce the work load of users during online conversations with the clients. This is a huge advantage for companies which receive heavy traffic in their query inbox every day. This reduces labour cost, consumes less time and enhances customer experience. • Bots are present 24/7, 365 days a year—Chat-bots are capable of handling queries at any time of the day, unlike humans. Thus, the customer does not have to wait long to get his/her query answered. • Self-learning and updating—For a new employee, they need continuous training and attention to figure out the ropes at work. Rather for bots, once installed, they are able to learn from every interaction and update independently [28]. • Managing client traffic—As previously said that there are no such limitations as “too-many-clients” for bots, they very efficiently manage the customers such that no one is left unattended. This is one of the main advantages of having chat-bots work for us.

6.2

Disadvantages of Chat-Bots

• Installing chat-bots is pricey—There is no denying that chat-bots save a lot of labour by being present 24/7 and ensuring to answer all the queries on time, but

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every bot needs to be programmed differently according to the system surrounding it will be working in. This increases the cost of installation. Moreover, updating these programs has additional costs. Interface complexity—Chat-bots are complex codes, and sometimes they require a lot to time to figure out what exactly a customer needs. Thus, they end up providing unfiltered responses which can sometimes annoy the client or customer. Time and cost factor—The aim for installing these bots is to be time efficient and provide enhanced customer service. However, time is required for self-learning and updating and because there is limited availability of data, these bots tend to get slower and costlier. Hence, there are times when these bots instead of responding to several clients at once end up getting confused and not respond to anyone at all. Processing faults—Sometimes due to lack of data, if there is a new query which does not relate to any of the previously asked queries, the bots will fail to understand it. If it so happens that they do understand it, they still would need twice or thrice the messages from the customer to get there. This in no means enhances the experience of the customer, thus failing its purpose. Loss of memory—Bots are incapable of memorizing the entire conversation they previously had, which leads to the user typing the same thing again and again. This is cumbersome for the client. Therefore, careful designing of the chat-bot is necessary. Indecisive—Chat-bots can get irritating to talk to when they are unable to make any decisions. This happens when the bots are not well optimized to work in that particular system environment.

We know that the potential of these bots is incredible and they are very advanced in their ability to help out in the real world. Though, that does not erase the fact that there is still a lot to be done.

6.3

Scope of Chat-Bots

Claiming to be human-like interactive and at the top of the AI game, why are chat-bots still failing to deliver their potential? This brings forth the restrictive nature of these bots due to which it was estimated that 40% of first-generation virtual assistant applications launched in 2018 will be abandoned by 2020 [29, 30]. Though the future looks up, as the new advancements in this field are making developments to optimize these bots and eliminate the limitations given below: • Inadequate training data—It is a common misconception that these bots single-handedly carry out all the operations without the supervision of humans. This is not true. What comes naturally to us while conversing needs to be

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learned by a machine. So, this machine learning process requires human to acquire, organize and provide proper training data for the bot to work properly. It was found out in a recent study that 81% of respondents said that training an AI with data was much more difficult than they expected. • Conversational hazards—There are instances when the bot is not able to figure out what the customer really needs. As humans, it is easier for us to understand the meaning behind the sentences even when they are twisted. To understand them, the machine needs to learn. Moreover, pure machine learning systems do not have a consistent personality. This is because the data fed to the machine is fragmented and acquired from many sources. This leads to the bot being confused over the queries asked. • Protecting data—Data is the core of AI. Without the influx of data, no machine can be trained. Data is used to improve how the system works and to personalize the interactions. With regulations on protecting data, there is only less of it left for the machines. It is likely that these regulations will increase throughout many countries in future. • Appealing to people globally—Organizations need to support customers from all over the world. This leads to the bot knowing all the languages so as to converse fluently with everyone. This is easier said than done. It requires a great deal of effort. For every new language, it needs to be completely rebuilt for it to operate properly. That is neither easy nor light on your pockets. These are some limitations faced by the chat-bots which are being worked upon by the data scientists and the AI experts.

7 Case Study As COVID-19 is a contagious virus, the doctors around the globe are putting their lives on edge to make this world a better place. There are certain cases that need assistance of doctors, but not all the patients need doctors, who might have moderate symptoms or in some cases might not even be affected by the virus; should these kinds of individuals be provided with doctors instead of the ones who actually require? At times like these, hard work pays off but smart work pays off a thousand folds, and developers devised a separate methodology to deal with this, to filter the needier patient from the false alarms. They programmed chat-bots to screen the affected before actually letting him visit the doctors and other medical staff. As chat-bots are widely welcome to many platforms, they can be channelized to mails, Web, social media and many more certain online platforms, and they are the most apt prospect to screen patients, teach them and provide them with course of action to be made after the process of screening. The Centers for Disease Control and Prevention (CDC) clearly defined the ways to get the most out of the situation by setting stepwise parameters for the chat-bots.

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References [31–34] The pandemic has taken everyone by storm! Every institution is facing unique and peculiar challenges, and the paediatric ward is no exception. While children seem kind of immune to the virus, less likely to get infected and sustain minor symptoms, they are still living with their guardians/ adults. For these adults, as far as the criteria for infection are concerned, they do seem to have more exposure to the virus and hence have higher medical risk, even more so than the children they are living with. Diving deeper into this prospect, when the children need medical assistance, they are always accompanied by an adult. This puts both the parent and the child under scrutiny, screening for signs and checking for symptoms. Separating young infants or kids with medical complexity from their parents is highly impractical and unethical; instead, appropriate precautions should be implemented and duly followed. Maintaining social distancing and minimizing crowd density at paediatric wards can get challenging. Children requiring medical assistance are accompanied by at least an adult, or both the parents and siblings or even grandparents. The task of managing this huge influx of visitors is taxing. This is where chat-bots come to the rescue. These bots can come in handy-providing parents and visitors with relevant information about visitor restrictions, personalized appointments of the patients, appropriate precautions to be followed and so much more. To prevent perfectly healthy relatives or siblings from getting infected, to break the chain of virus contamination, there is a plausible way. Though, to get this idea started, there is an issue of consent and medical care access for the kids under 18 years of age. This is where the power of technology can be accessed; if the chat-bots are widely accessed, institutes can develop protocols and together work with their legal terms to get help to the ill kids while keeping the parents and relatives duly informed and out of the risk of contamination.

7.1

Stepwise Breaking of Enigma

1. They programmed the chat-bots with one and only mission at their hand, they delimited the chat-bots to screening, and education to the general public and then all other functionalities are bound to work around the mission assigned to the chat-bot in making. 2. They very honestly admitted the powers of chat-bot and concluded that after chat-bot is done having a proper conversation with the user/patient, they could only react in combinations of the three courses. Chat-bot can either provide the user with the information it has acquired over the period while doing screenings for patients and if required it may also provide the patient with phone numbers or emails or any sort of means of communication. Or when bot does not deem itself fit for the case in hand, it can connect the patient to a real-time service provider which will in turn guide the patient with the guidelines to follow. There can be some cases in which bot does not find itself capable of helping the patient

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directly, but it knows about a certain other chat-bot or a software that is fit to handle the certain case. 3. As mentioned earlier, all the functionalities should be coherent to the mission with which the chat-bot has been deployed. The questionnaire that is supposed to determine the fate of the patient should be built very carefully and with a top to bottom approach, by asking the very basic questions first like does the patient has symptoms of COVID-19, and then proceed with more questions with well-defined hierarchy of queries. They also need to keep their databases up to date in accordance with the best CDCs. Below, there is a sample of how the flow of questions look like and how chat-bot treats and processes the information provided it is provided with, Table 1.

Table 1 Flow of questions for a bot and how it collects data through conversations Criteria

Questions from questionnaire

Procedure (w.r.t the chat-bot)

Disclaimer

∙ “Are you expecting or going through some emergency?” ∙ Privacy guidelines ∙ Terms and conditions ∙ “State your whereabouts” ∙ “Mention any upcoming appointments, if any” ∙ “Which health adversities are you facing?” ∙ “List the countries you have travelled in past 8 months” ∙ “From these countries, have you been in contact with some COVID-19 patient?” ∙ “Do you have any knowledge of being infected that can be certified by any official?” ∙ “Have you been in a close entity with any COVID-19 infected?” ∙ “What medications are you currently on?” ∙ “Are you facing any of the known symptoms of COVID-19?” ∙ “Please provide a phone number or email to contact you” ∙ “Do you prefer a physical or a digital visit?” ∙ “How many times have you been already screened?”

1. Inform and ask for guidelines from the consulted team providing them with the exact information acquired from the user 1. Inform users if they are eligible for the further questionnaire

Suitability

Vulnerability

Symptoms Miscellaneous

1. Inform user with suitable information tipped by CDC 2. Include language to address children and their adult close contacts

1. Inform user with suitable information tipped by CDC 1. Questionnaire totally depends on the specific CDC in charge

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Implementations

Now, this is where the interesting part kicks in. After the chat-bot has been designed, made human-like and optimized, they are brought out to be implemented in the real world. Implementation is the make-or-break of any digital health project. This is where their potential is tested. Given below are some of the ways a chat-bot can be put to good use— Keeping tabs on the experience and workflow of the user— The chat-bot designs a storyboard or a process map and acts as a link between the med staff/provider and the patient/user. To keep the family updated, a link will be provided to them from which they will be able to access the chat-bot and the information contained in it regarding the user. The chat-bot would administer all the phases along the process map and keep everyone updated. How all of this is done is reflected in the flow chart given in Fig. 8. Exploring the architecture of the chat-bot— After the ideal user experience is optimized, the next phase is how the bots integrate into our ecosystem. Its actions are highly dependent on the decisions we make. Now, the bot can take up to two types of approaches depending upon what the user demands. • Patient-initiated approach: This kind of approach takes place outside the secure computing environment of the institution. The patient has a personal device from where he can access the website using a link. The chat-bot looks over the process map and then interacts with the patient accordingly. Other facilities like resources, education and tips on the next phases of the process map are provided accordingly. The patient uses this facility to reach out to the hospital medical team.

Fig. 8 A generic example of a chat-bot keeping tabs on the workflow of the user

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• Provider-initiated approach: This kind of approach takes place within the secure computing environment of the institution. The patient has a personal device from where he can access the website using a link. The chat-bot looks over the process map and then interacts with the patient accordingly. Unlike the patient-initiated approach, the chat-bot here collects data and integrates it with the workflow of the institution and the process map of the patient, making it all the more user-friendly. Other facilities like resources, education and tips on the next phases of the process map are provided accordingly. Furthermore, the patient can reach out to the staff for any queries or vice versa. Chat-bot testing: Chat-bot testing cannot be overlooked before launching it. This is the last opportunity we get at finding faults with the programme and fixing errors before deploying them to the patients. Overall errors are checked, be it typographical errors, broken links or incorrect phone numbers. This is also an opportunity to check if the database is properly collecting and organizing data the way it was designed. Once several rounds of testing are done, the bots are ready to be accessed by the patient.

8 Proposals 8.1

Chat-Bots in Health Care

By today, there have been 20,120,919 confirmed cases of COVID-19, including 736,766 deaths, reported to WHO. Every day, millions are being treated in hospitals globally, and patients do not have to just fight with the physical deficiencies and fatigues but also have to endeavour disease’s mental health ramifications. Patients suffer from post-intensive care syndrome (PICS), which is a collection of physical, mental and emotional symptoms that continue to persist after a patient leaves the intensive care unit (ICU). People are facing all sorts of adversities, they are having nightmares, agitated responses and PTSD, they are facing difficulty in falling asleep, they are getting emotionally unstable, they are having alterations in their appetite, they are feeling lost, they are tasting depression for the first time in their lives, they are experiencing a change of heart at interests, they are not liking what they used to before, and they spend quite a time being confused. Above all this, when they are cured and are discharged, they discover their family struggling for finances, and the state of mind of such a person is far from normal. Feeling so much at a time in such a quick succession of treatment of COVID-19 that is severe can crush a human to unimaginable depths. Now given that these patients have suffered from the virus, their immunity system has obviously been crippled; in circumstances like this, it will be very lackadaisical for the patients to release themselves out of quarantine. At the same time, mental health also cannot be ignored, people have a lot of myths piled up in their heads regarding mental health,

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and such matters cannot be left at the hands of families or the society. They need proper attention from the concerned experts. Chat-bots can prove their worth in providing people with proper solutions to their respective problems and connecting them to experts.

8.1.1

Fundamental Briefing of the Chat-Bot

The chat-bot will not be a model with scattered and incompetent information. The information it will hold will be backed up by renowned psychologists, psychiatrists, counsellors, clinical social workers and many more professionals who have expertise in mental health. Database will be maintained which will not only keep records of the registered/signed-up patients but also monitor the symptoms they face. With the help of such futuristic technology, the chat-bot will not be much distinguishable from a medical professional. It will communicate with the user, hold conversations and ask basic questions from their day-to-day life. Upon acquiring the information, it will forward the refined details to the experts, who in turn will notify the chat-bot about which professional will be most befitting for the patient. Details will then be sent to the concerned expert, who in place will instruct the user with the necessary procedures. While this goes on, the database runs in the background, collecting and storing data. The chat-bot feeds on this data and trains itself. Once an adequate number of records get stored in the database, the chat-bot is then ready to run on its own. As a result of the training it has been receiving on the data, it will start to advise on its own. It will draw similarities upon the symptoms of the patient with that of past patients in its database and will then guide the patient. Figure 9 shows the percentage of attributed the bot designers go for, while designing a health bot.

Fig. 9 Attributes taken up for a chat-bot assigned for health care

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How Would the Chat-Bot Function

All the patients who have been treated and have recovered from COVID-19 will be sent an app link via WhatsApp along with a recommendation for downloading it; if anyone is facing any sort of unease in settling after the treatment, they should sign-up. All the points discussed below are shown as a flow chart in Fig. 10.

Fig. 10 Flow chart for how the chat-bots will function in the healthcare department

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1. Chat-bot will help the user to sign up and will ask questions prepared by the professionals. 2. User will indulge into a conversation with chat-bot regarding subjects of their mental health. 3. Chat-bot will (a) send the data to the database and will look up for the data with similar kind of symptoms, and if found, (b) it will provide the user with the same advice that was previously given to a patient who was facing the same problems. 4. It will also inform them (users from past) about the discovery of a patient just like them. 5. In case of a new variety of combination of symptoms, the user’s data will be forwarded to an expert who will resolve the case and revert that back to the database. 6. For some severe cases where only routine and exercises will not make it up for them, then professionals may provide the patient with some proscription via chat-bot. 7. After every 5–7 days, chat-bot can ask for the user’s health with some flow of questions and can update his status; if there is a decline in their state of being, chat-bot can move it up in the chain.

8.1.3

Additional Features

• Support group: Over the years, support groups have been very successful in uplifting people from sufferings and pulling them out of solitude. With the help of professionals, AI can decide under which category a patient lies within and with the consent of users, chat-bot can combine all the similar kinds of patients and form a group where they can share their thoughts and how they feel, and it can be a safe and open space where one can talk. • Prescription refiller: If a patient is prescribed with some medicines, chat-bot can remind them when to purchase more and can also remind them every day to take medicine. • Routine sorcerer: For people who are failing to have a normal life, the easiest way is to stick to a routine, but most of us fail to build and stick to the timetable and thus a chat-bot can conjure one for users according to their habits, hobbies and priorities.

8.2

Volunteering and How Chat-Bots Influence It!

On 11 August, Russia announced the world’s first COVID-19 vaccine. If this vaccine is successful in catering to all the symptoms of COVID-19-affected people

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and succeeds at it, then it will not take long before these go into mass production and then distributed around the world. People have been waiting for something like this to happen, and now the future finally looks up. The wait is over. But, is this enough? Will everything go back to just the way it was before all the hell broke loose? Will people with higher risk of losing their lives get the shots first? Will the less privileged once be able to get cured on time? Or will the help not reach them at all? So many uncertainties, so many unanswered questions, at times like these how are we supposed to make the situation better? There have been 20,120,919 confirmed cases, including 736,766 deaths, globally as of 12 August 2020. The medical facilities are collapsing under pressure from COVID-19 casualties. Everyone is tired of fighting against it, and we need a break. This pandemic has taken a toll on each and every one of us. Doctors feel helpless seeing so many people at the brink of death and not being able to save them. They themselves are risking their lives along with the rest of the staff to bring stability and normalcy in the ecosystem. The medical institutions are jam-packed with patients. There is outbreak everywhere. New patients are being refused at the hospitals. Even though the worst seems to be over, with the vaccine coming into the picture, the medical frontline workers need more assistance and man power to contain and control the pandemic situation. The medical institutions are not enough to provide treatment to the patients, and the normal folks have to join in as well. Only if everyone comes together in helping to uplift this situation, it is only then we could go back to our daily lives, getting back the normalcy we first had. Achieving something on this big of a scale requires assistance. This is where we will use the power of technology to give ourselves a head start in rectifying the situation.

8.2.1

Chat-Bots: How Can They Help in Volunteering and Providing the Required Assistance?

• They can streamline the volunteering application process: To bring about a change, the institutions will have to be dependent on continuous influx of new, qualified volunteers. Instead of hiring volunteers manually, considering the loads of work already piled on the medical staff shoulders, this work can be handled by the chat-bots! We can optimize a chat-bot to— – Share volunteering information: The bot can automatically share links, images, videos and reliable educative information to the volunteers, briefing them on what they are supposed to do and what the requirements are. – Receiving volunteering applications: This chat-bot can gather relevant data like contact information, learn about the volunteer’s experience and accept their resume if needed for verification purposes. Moreover, this bot can send it automatically to the database or mail the information to the concerned authorities.

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– Scheduling interviews or shifts: A scheduling tool can be integrated into the bot that can be accessed by both the authorities and the volunteers where the volunteers can book their time slots according to the free time they have or for a preliminary screening. • These bots can automate routine tasks: Automation helps us conserve energy, time and money, so instead of manually doing all of it, get bots to do the work instead, and the first task will be to answer automated FAQs. Nothing is out of the ordinary for a messenger bot. Manually responding to the same questions each and every time is a waste of human time and effort. According to previous bot tests, a bot can easily provide answers to 80% of the queries. For this, we just need to provide the bot with previously structured answers and every time a person asks the same query, leave it on the bot to relay the necessary answers. This makes the task easier at hand. The bot can easily respond to— – – – –

What do you do? How can I get involved? I know someone who needs your services. What should I do? Do you have any job openings?

• Supercharge charity efforts and raise reasonable funds: A proper conversation is much more effective in getting people interested in a task. For fund raising, it is necessary to keep the donors interested. Financially stable people can chip into these charities, and the amount generated can be used in providing help to the financially challenged folks. These are a few ways a bot can be used to get more volunteers in donating funds— – It can share educative material regarding the cause and get more volunteers to willingly fund for it. – Share links to cause related merchandise if volunteers are willing to buy them, and all the amount that will be collected will go directly to the cause. – Keep the donors updated about new and upcoming donations considering the situation at hand. – Or link to donations pages where they can directly carry out transactions. • Get the bot to tell compelling stories: It is a very crucial way to form an emotional bond with people to win their support for the cause you are supporting. Chat-bot is perfectly helpful in telling important stories in an interactive and impactful way. Chatting back and forth with a person, telling them compelling stories and answering their questions personally are great ways of gathering volunteers for a great cause. Figure 11 shows how many volunteers risk their lives, for a good cause.

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Workers at risk by region Americas Europe and Central Asia Asia and the Pacific Arab states Africa 0.00%

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Fig. 11 Regional risks for volunteers

8.2.2

How Would the Chat-Bot Function

This is how everything will pan out. All the below-mentioned points are expressed in a flow chart in Fig. 12. 1. The bot will send the respective authorized links to the target audience. 2. The interested folks will have conversations with the bot and try to figure out whether they want to volunteer for the cause or not. 3. Once they make up their minds to volunteer, they will send in their verification documents for security purposes, through the bot. 4. All their personal information will be accepted by the bot and stored in the database. If needs be, the bot will send a copy of their personal information to the concerned authorities. 5. The bot will keep the volunteers updated on new and upcoming charities, jobs or projects. 6. The volunteers will even have the liberty to choose their own time slot, and this will be reflected in the database as well as reach the concerned authorities.

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Fig. 12 Flow chart of the influence of chat-bots in volunteering

8.3

Business Revival

Imagine a time after pandemic, what then, will we start to live again the same way we used to live? Will profiles of people restore? Will it all start to make sense again? Yes, yes and yes. We can all turn back time to the good old days, it will take a lot of effort to turn things around, but the sun will surely shine upon us again. Desperate times will come, every business would want to boom off the boxes again, every person who is capable of performing some skill will be desperately needing a job, and of course, desperate times need desperate measures to be taken. Both the sides, the endangered businesses and the citizens, will be in dire need of each other. In times like this, all that is required is a perfect place for integration of both the elements and what could be a better match-fixer than a chat-bot. Figure 13 shows the risk for business individuals after the pandemic is off our heads.

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Fig. 13 Pandemic factor resulting in the downfall of businesses

8.3.1

Functionality of the App

Chat-bot would act as an intermediate between the two sides of needs. It would only help people to get temporary jobs with manageable wages, and employers as per their desires may extend their period of occupation [35]. The chat-bot’s task is really simple, it would connect businesses with suitable wannabe employees, and a well-built decision ladder will help in determining that. STEPS: All the steps discussed below are expressed in a flow chart in Fig. 14. 1. Chat-bot will ask the user if they are looking for employees or employers. 2. Chat-bot will further ask the user about their requirements such as number of employees they would be needing, what expertise of their employees would they be requiring, the age bracket for which they would be needing their employees to be in, the hours they will be demanding from their employees and the salary they will be providing as an employer, and it will ask the employees about their educational qualifications, their experience and the post for which they are looking for. 3. A request will be registered on their names. 4. Every time a new user joins the community, it will be checked if the new user is an employee or an employer; if employee, then in the database of employers, the perfect employer will be searched in accordance with the demands of the employer whose vacancies have not been occupied yet. Else if they are an employer, vice versa will happen.

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Fig. 14 Chat-bots assisting in salvaging business downfalls

5. As soon as the perfect match is found at both the ends, the business and the wannabe employee are informed about the new discovery. 6. As per the convenience of both the ends, a virtual meeting will be set between both of them. 7. As soon as the wannabe employee gets a job, their request will be terminated. In the same way, when employers acquire sufficient number of employees assigned to them, their request will be terminated too.

9 Conclusion The greatest pandemic, COVID-19, is leading an open rebellion against the human race, and we already have lost more than eight lakh precious lives. Now we need to use every resource in a very efficient manner, and the most efficient method would

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of course be the one with least physical human contact. Chat-bots have been discussed in great depth in the chapter and will prove to be the best fighters in these social-distanced times. The scope of the chat-bots is very wide with the advancements it has made in recent years, and the range of its applications is immeasurable, as it has been discussed in the chapter. It can be of great help wherever it is required to make conversations, to convince, to remind, to inform, to spread word and to make decisions. From mass media to just asking Alexa to play some song, from online retail sites like Amazon to the restaurant selling minimal Indian cuisine, from world politics to campaigning college elections and from healthcare facilities to telling people to take their medicine, everywhere one can see chat-bots invading the troubles of people. With the possession of these many powers, chat-bot may see us through the pandemic. Keeping an eye at the ideas discussed in the chapter, we can speed up the tedious process of vaccine distribution by the help of chat-bots to convince and get volunteers ready for the distribution. It can also be a very learned companion to the patients who require to cope up with the aftermath of the COVID-19 treatment and be again wholly the soul they were. All the endangered and even extinct businesses can be sprung back into market by making the necessities of the businesses meet with the appropriate man power and skills. One can really change the things around and restore mankind to its original self by promoting the ideas presented in the chapter.

References 1. Liu B, Tur G, Hakkani-Tur D, Shah P, Heck L (2018) Dialogue learning with human teaching and feedback in end-to-end trainable task-oriented dialogue systems, 18 Apr 2018. NAACL 2. Chandhana Surabhi M (2013) Natural language processing future. In: Proceedings of 2013 international conference on optical imaging sensor and security (ICOSS), 3 July 2013. IEEE 3. Mathew RB, Varghese S, Joy SE, Alex SS (2019) Chat-bot for disease prediction and treatment recommendation using machine learning. In: Proceedings of 3rd international conference on trends in electronics and informatics (ICOEI), 25 Apr 2019. ResearchGate 4. Fang H, Cheng H, Sap M, Clark E, Holtzman A, Choi Y, Smith NA, Ostendorf M (2018) Sounding board: a user-centric and content-driven social chat-bot, 26 Apr 2018. 2017 Amazon Alexa Participant and Winner 5. Mazaré P-E, Humeau S, Raison M, Bordes A (2018) Training millions of personalized dialogue agents. EMNLP, 6 Sep 2018 6. Madhu D, Neeraj Jain CJ, Sebastain E, Shaji S, Ajayakumar A (2017) A novel approach for medical assistance using trained chat-bot. In: 2017 Proceedings of international conference on inventive communication and computational technologies (ICICCT), 11 Mar 2017. IEEE 7. Dharwadkar R, Deshpande NA (2018) “A Medical ChatBot”. International Journal of Computer Trends and Technology (IJCTT) V60(1):41–45 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group. 8. du Preez SJ, Lall M, Sinha S (2009) An intelligent web-based voice chat bot. IEEE EUROCON, 23 May 2009 9. Divya S, Indumathi V, Ishwarya S, Priyasankari M, Kalpana Devi S (2018) A self-diagnosis medical chat-bot using artificial intelligence. J Web Dev Web Des 3(1):1–7

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10. Tripathy AK, Carvalho R, Pawaskar K, Yadav S, Yadav V (2015) Mobile based healthcare management using artificial intelligence. In: Proceedings of 2015 international conference on technologies for sustainable development (ICTSD), 6 Feb 2015. IEEE 11. Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J (2018) Personalizing dialogue agents: i have a dog, do you have pets too? ArXiv, vol 5 12. Barcala FM, Vilares J, Alonso MA, Grana J, Vilares M (2002) Tokenization and proper noun recognition for information retrieval. In: Proceedings of 13th international workshop on database and expert systems applications, 6 Sept 2002 13. Abdul-Kader SA, John Woods (2015) Survey on chat-bot design techniques in speech conversation systems. Int J Adv Comput Sci Appl 6(7) 14. Miner AS, Laranjo L, Baki Kocaballi A (2020) Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Med 3(65) 15. Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Zobel M, Knapp B (2020) An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Sci Rep 10(19012) 16. Herriman M, Meer E, Rosin R, Lee V, Washington V, Volpp KG (2020) Building a chatbot to address covid-19-related concerns. Nejm Catalyst, Division of the Massachusetts Medical Society 17. Bharti U, Bajaj D, Batra H, Lalit S, Lalit S, Gangwani A (2020) Conversational artificial intelligence powered chatbot for delivering tele-health after COVID-19. IEEE. 2020/6/10 18. Judson TJ, Odisho AY, Young JJ, Bigazzi O, Steuer D, Gonzales R, Neinstein AB (2020) Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic. J Am Med Inform Assoc 27(9):1450–1455 19. Munsch N, Martin A, Gruarin S, Nateqi J, Abdarahmane I, Weingartner-Ortner R, Knapp B (2020) Diagnostic accuracy of web-based COVID-19 symptom checkers: comparison study. J Med Internet Res 22(10):e21299 20. Maniou TA, Veglis A (2020) Employing a chatbot for news dissemination during crisis: design, implementation and evaluation. MDPI Future Internet 12(7) 21. Dennis AR, Kim A, Rahimi M, Ayabakan S (2020) User reactions to COVID-19 screening chatbots from reputable providers. J Am Med Inform Assoc 27(11):1727–1731 22. Smith AC, Thomas E, Snoswell CL (2020) Telehealth for global emergencies: implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 26(5):309–313 23. Espinoza J, Crown K, Kulkarni O (2020) A guide to chatbots for COVID-19 screening at pediatric health care facilities. JMIR Publ 6(2) 24. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10(3): 244–256 25. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14): 4036 26. Goldman RD, Macpherson A (2006) Internet health information use and e-mail access by parents attending a paediatric emergency department. Emerg Med J 23(5):345–348 27. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274 28. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 29. https://www.gartner.com/smarterwithgartner/27297-2/ 30. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing, vol 77. Springer, Singapore, pp 485–494

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Chapter 14

Computer Vision in COVID-19: A Study Oisika Paul, Naveen Singh Rajput, and Chinmaya Dehury

1 Introduction Corona 2019 (COVID-19) is an infectious disease caused by acute respiratory syndrome corona virus 2 (SARS-CoV-2). Its outbreak led to a worldwide epidemic [1, 2]. This fight against the corona virus leads to effective research to find better ways to overcome all the challenges posed by the virus. In terms of computer viewing, testing never ends. Over time there are always better ways, better tools and better knowledge to take the whole idea and perfect it to another level. Computer vision is a sub-field of artificial intelligence used by computers to better understand the physical world [3, 4]. Understanding computer vision means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. Figure 1 shows the various uses of computer imaging such as image processing, which are used in computer graphics to improve visual quality, used in the field of machine learning and artificial intelligence. Computer vision is a field where learning and assessment can never stop [5]. Every day in its development there is a remarkable growth in the research and the practical application of its concept. Figure 2 gives us an idea of how computer vision is used to identify and locate objects. It is so flexible that these days almost everywhere we see it, we will

O. Paul (&)
N. S. Rajput School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India e-mail: [email protected] N. S. Rajput e-mail: [email protected] C. Dehury University of Tartu, Tartu, Estonia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_14

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Fig. 1 Components computer vision

Fig. 2 Object identification using computer vision

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certainly find its practical application. In recent years computer imaging technology has evolved into a number of functional areas such as health care, the mobile phone industry, retail, and financial services.

2 Growth of Computer Vision Frank Rosenblatt, the father of computer vision, in 1957 was the one to bring a breakthrough in computer vision with the help of his invented giant “Perceptron” machine [6]. Figure 3 shows the organisation of Rosenblatt’s perceptron model. It represents the organisation of a biological brain in which red dots represent active cell, responding to letter X. Soon more research and development of computer vision started in respectable universities around the world, which were exploring artificial intelligence. Computer vision was initially compared to an existing and well-known field of digital photography, but what distinguished the computer’s concept of digital photography was its ability to extract 3D view from images, resulting in a better understanding of full image [7]. Further advances and studies in the 1970s formed the basis for the basic building of computer vision and these concepts are still present in many algorithms to this day, for example finding the edges of objects, movement measurements, building polyhedral and non-polyhedral structures and so on. The late 1990s saw a dramatic increase in the progress of this field. There has been a lot of interaction between computer vision and computer graphics, which has

Fig. 3 Organisation of a perceptron

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Fig. 4 No of articles published on health care

led to the development of image quality, IMBR, image processing and the provision of the original light field. With all these developments in hand, computer did not get what they needed, until 2012. It achieved its first development in 2012 and since then there has been a significant increase in the development of computer vision technology. One of the most widely used methods of computer vision was convolutional neural networks (CNNs), which is often used for image analysis. Since 2012 there has been nothing but great growth and development in the computer field (Fig. 4). We know that it is not possible to include other nurses and other paramedics because there are so many tests, diagnoses and surgeries that require human technology to do but computer vision has great potential to bring about real change and development in the field. With the help of computer vision, it is easy to diagnose the patient by extracting data from the image data. It is also used to detect tissue, skin colour changes, arteriosclerosis. With it, it is possible to obtain advanced brain structure, X-rays or ultrasonic images. Computer vision has great potential for self-improvement in the medical field. Another new application is Microsoft’s Inner Eye research project that uses machine learning technology and computer technology to discover new ideas for automatic analysis and size of 3-D medical images.

3 Computer Vision for COVID-19 Handling In Fig. 5 the flow chart shows how patients are handled during the COVID-19 pandemic. The hospitals are divided into COVID +ve wards and COVID −ve wards. Depending on the COVID-19 test result the patients are admitted. Patients

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Fig. 5 Steps in handling patients during the pandemic

are divided into categories like: emergent (patients tested positive for COVID-19 and are in serious condition and need immediate attention), urgent (patients who need to take the COVID test, and action will be taken based on their result), elective (patients with other needs). Based on every individual patients condition, the next steps are taken [8]. During this pandemic, our basic concern is whether people are maintaining social distancing or not, whether all exposed surfaces are cleaned or not, whether people are wearing masks, what steps to be taken to stop the spread and how to spread awareness throughout the world and the solution to all these problems and questions somehow relate to computer vision. With the help of cameras build on the basis of AI technology, we can analyse people at different places like airport, street, market, parks, etc., and with this analytical data, we will be able to know are people maintaining safe social distancing or not and what surfaces are people touching frequently and the number of people wearing masks and all these information help in decision making by various governing body and to design better system. Some hospitals for maintaining certain distance between patients in hospital are using robots with vision sense, and also this kind of robot is efficient in maintaining least contact with any kind of object in hospitals. These vision guided techniques help in vaccine making as well for analysing data, for a logical view of certain set of information. Some major applications of computer vision in times of COVID-19 are discussed below. • Healthcare: Health care is the most important area that needs the most attention at this critical time. With all the uncertainty and lack of health personnels and

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Fig. 6 Computer vision for COVID-19

test kits, it is very important to have the equipment and effective reliance mechanisms. Below is the most important application of computer vision in this field (Fig. 6). i. Representative Work for Infected Disease Control and Prevention. See Table 1. Table 1 Diseases and their control Study

Approach

Wang et al. [9]

Masked Face Recognition based on deep learning

Pearce [10]

RepRap-class 3-D printers and open source microcontrollers, mass distributed manufacturing of ventilators Inrfrared thermograpgy: Mass-fever screening

Chiu et al. [11]

Hay [12]

Convolutional neural networks for identification of bacteria

Dataset implemented

Performance The multi-granularity masked face recognition model we developed achieves 95% accuracy 3D printing can facilitate supply chain

72,327 patients or visitors passed by the only entrance where a thermography station was in operation Light sheet microscopy image data

Over a period of one month, 105 patients or visitors were detected to have a thermographic fever detection Over 90% accuracy

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• Diagnosis: Ranging from X-rays to CT scans and MRI or Magnetic Resonance, Imaging reconstruction that we know from healthcare-related tasks people need a computer vision to examine complex patient body images to know their health status and begin diagnosis. It is possible to identify a small object with a level of accuracy. At human level, it is not possible to identify microscopic thing with that level of accuracy. ii. Representative Work for X-ray Based on COVID-19 Diagonosis See Table 2. Table 2 X-ray-based COVID-19 diagnosis Study

Model

Dataset

Performance

Gaal et al. [13]

Attention U-Net + advqualisation (CLAHE) [14]

DSC of 97.5% on the JSRT dataset

Ali Narin et al. [15]

Pre-trained ResNersarial + Contrast Limited Adaptive Histogram Eet50 model with transfer Learning

Wang et al. [16]

COVID-Net: lightweight residual projection expansion projection-extension (PEPX) design pattern

Sethy et al. [17]

Deep features from Resnet50+ SVM classification

247 images from Japanese Society of Radiological Technology (JSRT) Dataset + Shenzhen dataset contains a total of 662 chest X-rays The open source GitHub repository shared by Dr. Joseph Cohen + Chest X-Ray Images (Pneumonia) COVID dataset: 16,756 chest radiography images across 13,645 patient cases from two open access data repositories Data available in the repository of GitHub, Kaggle and Open-i as per their validated X-ray images

Apostolopoulos1 et al. [18]

Various fine-tune dmodels: VGG19, MobileNet, Inception, Inception Resnet V2, Xception

1427 X-ray images. 224 images with confirmed COVID-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included

accuracy (97% accuracy for InceptionV3 and 87% accuracy for Inception-ResNetV2) Accuracy 92.4% on COVID dataset

Resnet50 plus SVM achieved accuracy, FPR, F1 score, MCC and Kappa are 95.38%, 95.52%, 91.41% and 90.76% respectively Accuracy with Xception was the highest, 95.57, sensitivity of 0.08 and specificity of 99.99

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• Thermal Imaging: Thermal imaging is a process in which the local distribution of body temperature can be viewed with thermal cameras. Thermal scanners are used worldwide at airports, offices and all other public places to measure human body temperature and normal body temperature which is one of the major symptoms of COVID-19 (Fig. 7). • Distinguishing between diseases: With the help of medical imaging, it has become easier to differentiate between diseases with common symptoms such as COVID-19, viral fever and pneumonia. • AI-Based Systems in Hospitals: AI powered systems are helping doctors to diagnose their patients faster and soon start their respective treatments and keep on monitoring their reports (Fig. 8). In times of COVID-19, it is very important to start the treatment as soon as possible of critical patients, and with the help of these technologies, it has become easier to get reports of patients (e.g. with the help of computer vision and artificial intelligence, it has quite easy to get CT scan reports of lungs). iii. Representative Work for CT-Based COVID-19 Diagnosis See Table 3. • Vaccine development: The computer vision and machine learning technology is actually helping in the vaccine development process. The ML models are

Fig. 7 Thermal screening

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Fig. 8 Computer vision used for CT scans

helping examine protein structures and hence determine their reactions\impacts on the corona virus (Fig. 9). • Use of Chatbots: In scenarios like these, chatbots contribute a lot in reducing the workload of staff members in hospitals. Chatbots are used to spread awareness about the virus. Many hospitals have put have chatbots to help people determine their probability of been infected by the virus. They use temperature readings, location details and travel details to determine the risk of an individual of getting infected of the deadly virus. For example, Microsoft and The Centers for Disease Control and Prevention (CDC) have developed a chatbot that can determine the probability an individual has been infected by COVID-19. • Predicting Virus Spread: Machine Learning and location data help track the spread of the virus. It monitors people who are supposed to on self-quarantine and notifies the government if it is not followed. This technology also helps to identify people who have been or are in contact with a COVID infected person and soon takes action on it. It also informs us about the total numbers of cases near us and suggests us proper measures to stay safe.

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Table 3 CT scan-based COVID-19 diagnosis Study

Classification model and availability

Segmentation model

Dataset

No. of participants

Performance

Wang et al. [19]

Modified inception [20] with transfer learning

–

450 images of COVID confirmed pathogen

99 patients from jiatong university and Xi’An no. 8 hospital of Xi’An medical college

The internal validation has archived accuracy of 83% with precision of 80.5% and sensitivity of 84%. The external dataset shows accuracy of 73% with specificity of 67%

Xu et al. [21]

Combination of two CNN three dimensional classification model. (ResNet-18 network [22] + location-attention oriented model)

VNET-based segmentation model. [23]

A total of 618 CT samples were collected:19224 CT samples Fluel175 CT samples from healthy people

219 from 110 patients with COVID and 224 patients with influenza

Model accuracy 86.7%

Gozes et al. [24]

2D deep convolutional neural network architecture based on Resnet-50 [25]

U-net architecture for image segmentation [26]

–

56 patients with confirmed COVID-19 diagnosis

98.2% of sensitivity 92.2% of specificity

VB-Net neural network to segment COVID-19 infection region in ct scan

249 CT images

249 COVID-19 patients and validated using new COVID-19 patient

Dice similarity coefficient of 91.6% ± 10% between automatic and manual segmentation

2D CNN-based AI system, model name is not specified

970 CT volumes

496 patient confirmed with COVID-19

accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%

Shan [27]

Jin et al. [28]

–

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Fig. 9 Computer vision been used to develop COVID-19 vaccine

4 Industries Adapting Computer Vision During COVID-19 i. Robots Deployed in Hotels, Hospitals and Airports Robots are playing a very vital role in the hospitality industry in the COVID-19 era. Luxury hotels, for example The Waldorf Astoria Beverly and The Beverly Hilton are deploying robots in their premises to get assistance with the upgraded cleaning protocols [29]. These hotels are deploying Xenex Light Strike robots which will be using UV light to destroy germs after the normal cleaning procedure in done. Figure 10 shows a robot cleans a room at the Waldorf Astoria Beverly Hills using ultraviolet light. Many other luxury hotels like The Westin Houston Medical Center and The Crowne Plaza White Plains-Downtown in New York are also using robots to sanitise their premises (Fig. 10). They are also using the same LightStrike germ zapping robots to kills germs and these robots also take very little time to sanitise rooms. Figure 11 shows us the basic working of a robot based on Internet of medical things (IoMRT). Based on the need and the condition of the patients, these robots are programmed to take the necessary steps. They help doctors and surgeons to store information and also assist them during surgeries. IoMRT along with incorporated robots build their communication based on new communication process like Li-Fi and information technology.

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Fig. 10 A robot cleans a room at the Waldorf Astoria Beverly Hills using ultraviolet light

Fig. 11 Robotics in healthcare -computer vision with Internet of medical things

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Robots are now also used in hospitals, for example the Mayo Clinic to help the customer staff members with their workload and of course with the sanitisation process. Robots are also used to treat the patients. The first corona virus diagnosed US patients was treated by a robot (Fig. 12). Airports, for example Fig. 13, the Northern Kentucky International Airport, are also using floor scrubbing robots, dubbed Avidbots Neo in addition to the already existing maintenance and support staff for the sanitisation purposes. They use 3D sensors, cameras and AI to roam around the terminals and clean and disinfect without colliding with other objects on its way. The Pittsburgh International Airport used to use robots even before COVID-19. Their added UV functionality in their Nilfisk Liberty SC50 Autonomous Scrubber/ Dryer robots to maintain the seriousness of cleanliness as the airports are now again open to fliers. ii. Rayvision Uses Computer Vision To Detect COVID-19 Rules Violation RayVision is a computer vision-based solution, part of the RayReach technology. This start-up company is looking after all the violations that are been made like face mask violation, crowding at public places, PPE suit violation, usage of sanitisers and so on [30]. The company makes AI solutions that work with the already existing IP cameras and capture images for real-time alerts in any of the above violations. With the Unlock processes on, it is very important to monitor all situations. It is very important to check over all the violations that are been made and take proper actions on it. RayVisions’s motto is to make “every cameras out there

Fig. 12 Hospitals in China used robots to treat COVID-19 patients

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Fig. 13 A robot cleans surfaces at Cincinnati/Northern Kentucky International Airport, Ryan Ankenbauer, CVG airport

smarter” and this kind of surveillance is very much needed and helps in reducing human error and hence dictates a more effective workforce. iii. Baidu’s AI Technologies Battle With COVID-19 Baidu is on the largest multinational AI and Internet companies in the world. It is Chinese company specialised in artificial intelligence and Internet-related services and products. COVID-19 had many challenges that needed immediate action to be taken, like mass screening of people, correct and quick treatment of patients, reduce human contact and maintain social distancing. To make sure all these challenges were responded to Baidu made use of its specialised AI and associated technologies to help the people in the frontline and control further spread of the pandemic. For the screening of body temperatures of people Baidu has developed and installed AI-based temperature measurement system Fig. 14, that quickly measure and record body temperatures. Baidu’s AI infrared vision technology helps with the rapid screening of temperature in crowded places. These systems are very efficient, accurate in their work and involve zero human contact thus maintaining perfect social distancing. Many of Baidu’s technologies are now been used overseas to tackle the spread of the virus. Hence Baidu is playing a very important role in the fight against the global pandemic. To manage the labour shortages, Baidu has made its self-driven autonomous car services available to industries that are fighting against the virus from the frontline. Baidu has also partnered with other industries which are working on the same cause

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Fig. 14 Baidu’s AI-based temperature measurement system quickly and easily monitors people’s temperatures. Rapidly deployed in transport hubs, it has become an effective anti-epidemic technology

thus fulfilling its aim to provide for those in need, to provide meals for the frontline workers, to maintain cleanliness of medical vehicles and so on. It is been able to achieve all these by maintaining zero human contact and hence encouraging other industries to take steps to fight against the virus. iv. Drones Used to Spread Awareness Even after spreading awareness regarding the deadly virus, there are people and places where violations are made. To look after all these drones are used for surveillance. These drones consist of cameras and a speaker through which they can communicate with the people violating the rules [31]. Apart from surveillance, these drones are also used to broadcast important messages like the lockdown dates, spreading awareness amongst people (Figs. 15 and 16). A Global Times video on twitter shows a drone hovering over a village in Inner Mongolia, warning old lady with audio in Chinese “Yes grandmother, it’s the drone who is talking to you. You should not go out without wearing a mask. You’d better go home and don’t forget to wash your hands,” Hospitals and doctors need medical equipments as fast as possible, and with the help of the drones, deliveries are been as fast as possible. With the help of these drones food supplies are also been provided to the ones to the needy ones. Other than medicals and food supplies, these drones are also delivering groceries in some parts of USA, China and

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Fig. 15 Drones used for thermal screening of citizens

Fig. 16 Drones used for broadcasting

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Fig. 17 Drones transporting essential items

Australia, which come under the red zones as stated by their respective governments (Fig. 17).

5 Challanges by Computer Vision Computer vision is one of the most advanced emerging technologies, and with such advanced technologies, expectation of getting more and more efficient work done goes high and high and hence it is surrounded by challenges that need to be overcome. Below discussed are few of them [32–34]: • Making the systems more human like: Though computer vision has come quite far but still replicating human-like features are extremely difficult. There is nothing as great as the human visual system, getting that kind of visual system is not easy. Moreover in humans, the brain coordination with every part is unimaginable. It is difficult to make systems programmed with such great qualities all at once.

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Nowadays we see robots helping in every situation but still there will always be something that is not possible with technology, because everything has a limit, and at the end, we do need human brain to take decisions at crucial times [35–38]. • Object Identification: Tasks like identifying and detecting objects are complicated since objects in images, videos and in real life appear in various shapes and sizes. It is difficult to detect one object from multiple objects within its view. Computer vision is yet not able to differentiate between handwritten texts and typed texts due to the variety of handwriting styles. These are few skills that computer vision yet to get better at. • Privacy: It is the biggest threat that computer vision poses. Every individual’s information is stored in a cloud which a threat to human privacy.

6 Conclusion Computer vision techniques are used for a variety of purposes in automotive, retail industry and health care. Computer vision is for sure putting positive effect in healthcare industry. From predicting medical disease, leading to accurate diagnosis of various disease including COVID-19 and help to generate positive environment in this pandemic time. Computer vision will certainly continue improving the patient experience and healthcare expertise in general. Artificial intelligence is very wide field in which there is no end for development it keeps on developing with time.

References 1. Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P (2020) EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20 (14):4036 2. Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing, vol 77. Springer, Singapore, pp 485–494 3. Hui DS, Azhar E, Madani TA, Ntoumi F, Kock R, Dar O, Ippolito G, Mchugh TD, Memish ZA, Drosten C, Zumla A, Petersen E (2020) The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—the latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis 91:264–66 4. “WHO Director-General’s opening remarks at the media briefing on COVID-19”. World Health Organization (WHO) (Press release). 11 March 2020. Archived from the original on 11 March 2020. Retrieved 01 April 2020 5. Mishra S, Mallick PK, Jena L, Chae GS (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Publ Health 8:274. https://doi.org/10.3389/ fpubh.2020.00274

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6. Ray C, Tripathy HK, Mishra S (2019) Assessment of autistic disorder using machine learning approach. In: Proceedings of the international conference on intelligent computing and communication, vol 78. Hyderabad, India, 9–11 Jan 2019, pp 209–219 7. Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquit Comput. https://doi.org/10.1007/ s00779-020-01461-9 8. Bernd Jähne; Horst Haußecker (2000) Computer vision and applications, a guide for students and practitioners. Academic Press, Cambridge. ISBN 978-0-13-085198-7 9. Wang Z, Wang G, Huang B, Xiong Z, Hong Q, Wu H, Yi P, Jiang K, Wang N, Pei Y, Chen H, Masked face recognition dataset and application. arXiv preprint arXiv:2003.09093 10. Pearce JM, A review of open source ventilators for COVID-19 and future pandemics. F1000Research 11. Chiu WT, Lin PW, Chiou HY, Lee WS, Lee CN, Yang YY, Lee HM, Hsieh MS, Hu CJ, Ho YS, Deng WP. Infrared thermography to mass-screen suspected SARS patients with fever. Asia Pac J Publ Health 12. Hay EA, Parthasarathy R, Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets. PLoS Comput Biol 13. Gaal G, Maga B, Lukacs A, Attention U-net based adversarial architectures for chest X-ray lung segmentation. arXiv preprint arXiv:2003.10304 14. Reza AM, Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol 15. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) Using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 16. Wang L, Wong A, COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv preprint arXiv:2003.09871 17. Sethy PK, Behera SK, Detection of coronavirus disease (COVID-19) based on deep features 18. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 19. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B, A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv 20. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z, Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition 21. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Chen Y, Su J, Lang G, Li Y, Deep learning system to screen coronavirus disease 2019 pneumonia 22. Ayyachamy S, Alex V, Khened M, Krishnamurthi G, Medical image retrieval using Resnet-18. In: Medical imaging 2019: imaging informatics for healthcare, research, and applications (vol 10954). International Society for Optics and Photonics, p 1095410 23. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC, Automatic multi-organ segmentation on abdominal CT with dense v-networks. In: IEEE transactions on medical imaging 24. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E, Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis 25. Yamazaki M, Kasagi A, Tabuchi A, Honda T, Miwa M, Fukumoto N, Tabaru T, Ike A, Nakashima K. Yet another accelerated sgd: Resnet-50 training on imagenet in 74.7 seconds 26. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241 27. Shan+F, Gao+Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y, Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655

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28. Jin C, Chen W, Cao Y, Xu Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J, Development and evaluation of an AI system for COVID-19 diagnosis. medRxiv 29. Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137 30. Ramsey Quebin(2020) Robots play pivotal role in keeping travel safe during covid-19 era. Forbes 31. Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10 (3):244–256 32. Mukesh Sharma (2020)How drones are used to combat covid-19. Geospatial world blog 33. Mishra M, Mishra S, Mishra BK, Choudhury P (2017) Analysis of power aware protocols and standards for critical E-health applications. In: Internet of things and big data technologies for next generation healthcare. Springer, Cham, pp 281–305 34. Mishra S, Mishra BK, Tripathy HK, Dutta A (2020) Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering. Academic Press, pp 1–23 35. Chaudhury P, Mishra S, Tripathy HK, Kishore B (2016) Enhancing the capabilities of student result prediction system. In: Proceedings of the 2nd international conference on information and communication technology for competitive strategies (vol 88). Uidapur, India, 4–5 March 2016; pp 1–6 36. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486 37. Sushruta M, Hrudaya KT, Brojo KM (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts. Int J Control Theory Appl 10(295–310):91 38. Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, Udaipur India, 4–5 March 2016, pp 1–3

Chapter 15

Futuristic Intelligence-Based Treatment Methods to Handle COVID-19 Patients Sanya Raghuwanshi and Saurav Bhaumik

1 Introduction Artificial intelligence in today’s advancing times is a brand new, emerging and useful tool/technology to detect infections due to the novel coronavirus and later helps the hospital staff in monitoring the current condition and situation of the infected patients [1]. It can improve the consistency of treatment and decision-making skill set from the development of efficient algorithms and predict precisely where the virus will reoccur in the near future by a collective analysis of all the previous data [2]. Famous hospitals are using AI to test, identify and discover advanced futuristic techniques for the treatment of COVID-19. The hospitals are doing their work diligently by scanning the faces of patients to check their temperature and observing them using a fitness data tracking device, to be able to pay attention to individual aspects and cases of the patients and their potential hold of clusters and to also keep a check on the coronavirus and prevent it from growing [3, 4]. We follow all the procedures to know the set of people who are suffering from the disease, the severity of their situation, who has more chances to be severely affected by the disease. Other factors like medicines, equipment and supplies that are running out are kept under a timely check, that can be in days, weeks or even months [5, 6]. Predictive computing techniques/tools and hybrid AI techniques are also a grand success, and precision of AI-based and AI-driven algorithms is consistently gaining popularity and becoming more accurate as time passes. We will

S. Raghuwanshi (&) School of Electronics and Computer Science Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] S. Bhaumik Bachelor of Computer Application Department, Inspiria Knowledge Campus, Siliguri, West Bengal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Mishra et al. (eds.), Impact of AI and Data Science in Response to Coronavirus Pandemic, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-2786-6_15

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306 Table 1 Symptoms of COVID-19

S. Raghuwanshi and S. Bhaumik Most common symptoms of COVID-19 Fever Dry cough Fatigue Sputum generation Less common symptoms of COVID-19 Breath shortage Myalgia Sore throat Headache Chills Rare symptoms of COVID-19 Nausea Nasal congestion Diarrhea Hemoptysis Conjunctival congestion

87.9% 67.7% 38.1% 33.4% 18.6% 14.8% 13.9% 13.6% 11.4% 5.0% 4.8% 3.7% 0.9% 0.8%

also discuss methods to make use of precision medicines in AI models by the use of AI-based helpful tools for drug repurposing medicines for human diseases and the problems and changes during the this COVID-19 pandemic [7] (Table 1).

2 Background Study In this part of the study, we observe an overview of the preexisting work in the particular area. In [8], as we can see in the reference provided, it is a review about the role and study of IoT, AI, drones and blockchain, and also 5G in analyzing and handling/managing the novel COVID-19 is performed with all kinds of possible tried and tested methods and equipment. In [9], it is a review paper on the whole current situation of automata-based computerized tomography (CT) scan image processing method/approach that is attempted and observed. And along with that, the study of the techniques for modeling to be able to predict a whole pandemic (COVID-19) which also includes mathematical, logical, algorithmic and AI-based methods/approaches is shown in [10]. In [11], a review of the latest methods in managing and handling COVID-19 is shown. A study about the different uses of AI and the areas of its applications is discussed in [12]. A detailed and established study on the latest deep learning transfer techniques and machine learning-based techniques/technologies in handling the pandemic is written in [13]. In [14], a highlighted review of the processing of audio/music-based, signal, video, speech and language is seen to be performed well in detail keeping the advancements in

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mind. An overall study of machine learning (ML) and AI specifications and algorithms for handling and analyzing the COVID-19 situation is experimentally performed and written well in [15]. In [16], the restrictions, constraints, limitations and fallbacks to be able to apply AI in battling/fighting and overcoming the disease are reviewed. In [17], the uses and real-life applications of AI, big data and other such technologies in the treatment of the disease caused by COVID-19 are written. In [18, 19], a detailed and assembled study on the uses of AI in processing X-ray of the chest and its images is written, along with the applications. We see that [20] is a study on machine learning algorithms in medical image by scanning and processing that can be found out about the disease. A very specific study on AI-based processes and applications for handling the COVID-19 pandemic is noted in [21]. Here, [22], is a study about technologies based on data to monitor, model and forecast the coronavirus. [23] shows us observations and research on other natural epidemic and pandemic models. In [24], it is a review on big data and its uses and applications in handling coronavirus better. A study in [25] revolves around the current studies and the use of ML in handling and managing the coronavirus-caused

Fig. 1 Here are some graphs taken out by an analytic code of COVID-19 cases around the world on Jupyter notebook using Python

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Fig. 2 Epidemic curve of confirmed COVID-19, by date of report and WHO region

disease. A research on the use of ML-based algorithms and to be able to predict the number of cases in a particular place is shown well in [26]. Also, the uses of AI in the discovery and making of drugs and vaccines are shown in [27] (Figs. 1 and 2; Table 2).

3 Current Challenges in Control of COVID-19 Worldwide There are numerous problems for hospital staff and the government officials to proficiently and properly handle this disease that causes respiratory problems taking a horrific turn for people who are already suffering from other illnesses [35, 36]. Despite all the appropriated supplies that the government or the authorities offer, almost all the medics and hospitals in this situation are bound to suffer from problems like not enough free hospital beds, safe and sanitized masks, sanitary, hygienic liquids and sanitizers and also hospital machinery for patients with serious and hazardous cases. Most essential points to be taken into account are: • Developing the effective vaccine samples to protect individuals from COVID-19 and stop its spread.

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Table 2 Applications of artificial intelligence in CT diagnosis of COVID-19 Place of study

Authors

Application used

Sample size

Accuracy

China

Wang. et al. [28]

Transfer learning model, modified and calculation

Accuracy is: 79.3% Specific is: 0.83 Sensitive is: 0.67

Cheng et al. [29]

Deep learning-based CNN in 2D

Xu et al. [30]

Deep learning-based asset in 3D

Li et al. [31]

Neural network for detecting COVID-19 using COVNET Deep convolutional neural network is COVID-NET

The number of CT-scanned images is 1,065 (COVID-19 affected 325 and viral pneumonia affected 740) CT-scanned cases in terms of volumes are 970, coronavirus positive patients are 496, and negative cases are 1,385 CT-sampled cases are 618, where 110 patients give 219 samples 3,322 patients give out 4,356 CT scans on chest From 13,645 patients, we get 16,756 CT-scanned images in chest by radiography From 246 positive or symptomatic cases of COVID-19, we get 340 experimental specimens for treatment Use of inception— ResNetV2, InceptionV3, ResNet50

Toronto, Canada

Wang et al. [32]

Thailand, Hong Kong

Shannon [33]

RT-PCR in real-time mechanism

Global

Pamuk et al. [34]

CT-scanned images by X-ray on chest, calculate for 50–50 each normal people and coronavirus positive cases

Accuracy is: 94.98% AUC is: 97.91% Sensitive is: 94.06%, Specific is: 95.47% Accuracy is: 86.7%

Accuracy is: 95%

Accuracy by: 92.4%

Detection limit can potentially reach less than 10 copies of genome for every reaction Percentage for ResNet: 50.98% InceptionV3: 97% Inception– ResNetV2: 87%

• To make sure that hospitals do not lack any supplies and an equal amount of distribution of these supplies happens among all the countries, more focused on the ones with the most amount of serious patients, India being one of them. • More people should study and do researches on effective and proper treatment methods, to find them out and later use them to control the spread of the coronavirus, that can be done by AI screening methods. • Easy access to diagnosis via medics and in the hospitals should be provided for free to all the countries suffering in this COVID-19 pandemic, and making treatment easier and faster using AI-based techniques (Fig. 3).

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Fig. 3 Standard approach for COVID-19 treatment using AI

4 Future Directions In addition to the applications investigated in this paper, AI can also contribute to the battle of COVID-19 from the following 10 potential directions. 1. Detection of a disease without contact: By the use of images of CT scans and CXRs and their detection and analysis, we can predict and detect diseases via zero-contact automatic image acquisition techniques that can effectively avoid the risk of infection between radiologists and patients during the COVID-19 pandemic. AI can be used for patient posture positioning, standard section acquisition of CXR and CT images, and movement of camera equipment. 2. Remote video diagnosis: AI and NLP technologies can be used to develop remote video diagnosis systems and chat robot systems and provide COVID-19 disease consultation and preliminary diagnosis to the public. 3. Patient prognosis management: AI technology (such as intelligent image and video analysis) can be used to automatically monitor patient behavior during the follow-up monitoring and prognostic management process, in addition to long-term tracking and management of patients with COVID-19. 4. Biological research: In the field of biological research, AI can be used to discover protein structures and features of virus through accurate analysis of biomedical information, such as large-scale protein structures, gene sequences and viral trajectories. 5. Drug and vaccine development: AI can be used not only to discover potential drugs and vaccines but also to simulate the interaction between drugs and proteins and between vaccines and receptors, thereby predicting the potential

15

6.

7.

8.

9.

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responses to the drugs and vaccines of patients with COVID-19 with different constitutions. Identification and filtering of fake news: AI can be used to reduce and eliminate fake news and noise data on online social media platforms to provide reliable, correct and scientific information about the COVID-19 pandemic. Impact simulation and evaluation: Various simulation models can use AI to analyze the impact of different social control strategies on disease transmission. Then, they can be used to explore more effective and scientific approaches of disease prevention and social control. Patient contact tracking: By constructing social relationship networks and knowledge graphs, AI can identify and track the trajectories of people in close contact with patients with COVID-19, thereby accurately predicting and controlling the potential spread of the disease. Intelligent robots: Intelligent robots are expected to be used in applications such as disinfection and cleaning in public places, product distribution and patient care. Intelligent Internet of things: AI is expected to be combined with the Internet of things to deploy in customs, airports, railway stations, bus stations and business centers. In this case, we can quickly identify suspicious COVID-19 and patients through intelligent monitoring of the environment and personnel.

5 Futuristic AI-Based Methods of Treatment of COVID-19 Patients Novel coronavirus (COVID-19) and the respiratory disease it is causing has been creating a horrendous rendezvous around the world, full of chaos, bringing a negative effect on everyone’s lives, with numerous people dying every day in every part of the world. Numerous medicine researchers are trying to find the solution and create and analyze the drugs and medicines to treat the people who are suffering from this disease, while others are working to build an effective vaccine to protect from the virus, and almost every country is making their best efforts to find a proper vaccine [35, 37]. Scientists and science and CS researchers in some parts are still working on managing the methods to be able to detect the patients who are infected by the disease using technologies that can analyze imaging data medically like with X-ray image and computed tomography (CT) scans. Such computer-based technologies come under AI, whose application has been successful in many-a-fields. Here, we will focus on the uses and applications, also the purpose of AI-based techniques in this fight against coronavirus and the global pandemic that it has caused as futuristic treatment and screening methods [5, 38].

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Medical Image Processing with Deep Learning

As experts, researchers, radiologists and clinic doctors are constantly studying to know the symptoms of the novel COVID-19 patients and their detection using computerized tomography (CT) scans on chest, these tasks are mostly done manually and consume time specially when there are abundant cases. A total of 3 Chinese and 4 US radiologists are put to the job of differentiating the virus from another epidemic of a virus using CT scans and images generated through it from a research of 424 of such patients, 205 from the USA with non-COVID-19 pneumonia, while 219 were Chinese, tested positive for the coronavirus disease. Genuine results also show that experts, scientists, mainly radiologists and researchers are achieving large specifications (meaning the number of positive cases), in differentiating between coronavirus and the other features or reasons that causes a similar kind of viral epidemic with the use of CT scan using image data on the chest. A 3D deep learning method is the COVID-19 detection neural network (COVNET) that is the new source for the detection of coronavirus using images generated from CT scans (volumetric method). There are three kinds of CT-scanned images that also include coronavirus, community-acquired pneumonia (CAP) and different non-pneumonia cases, which are mixed together to experiment the solidity of this mixture. Similarly conjunction based Deep Learning techniques from location-attention mechanism to 3-D CNN ResNet-18 network are among the ones that are stated to help find out more coronavirus patients using pulmonary CT scan images. A certain deep learning system helps us find out the differences between the several types of CT-scanned images and their roles in the different epidemics of their kinds (e.g., influenza A, etc.). A data set that was discovered comprising CT images of coronavirus patients, the influenza A patients and healthy cases is used to judge the working of the above-stated methods. The method’s accuracy is appropriately 86% that is obtained by the data set that proves the ability to be able to help medics and doctors to screen coronavirus cases using CT scan images of the chest.

5.2

AI-Based Data Science Methods for COVID-19 Modeling

Deep learning models that are self-sufficient and self-working (that can encode one self) are coming into use to observe and display the increasing and alarming number of positive patients/cases of the coronavirus across many other countries, like China also. The enhanced self-encoder or self-built chain has 4 layers, i.e., input, first latent layer, second latent layer and output layer, as the nodes are numbered to be 8, 32, 4 and 1, respectively. Several intervention technologies and strategical closures like maintaining a distance, (social distancing), methods taken up by schools, closures and ban on traveling, and isolating a patient are the terms that are

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Table 3 Images segmented and classified by AI/ML/DL/data science-dependent techniques for the analysis of coronavirus and diseases caused Literature

Source of data

Size of data

COVID cases

AI/ML/DL methods

ACC/AUC sensitivity and specificity

Chen [39] Gozes [40] Huang [41] Jin [42]

Private

35,355

20,886

U-Net++

95.24%

100%

94.0%

Private

157

56

99.6%

98.2%

92.2%

Private

842

842

U-Net and ResNet U-Net

–

–

–

[11, 43]

2,095

970

94.98%

94.06%

95.47%

Li [44] Qi [45] Shan [46]

Private Private Private

4,356 52 549

1,325 52 549

96.0% 97.0% –

90.0% 100% –

96.0% 75.0% –

Shi [43] Shi [47] Song [48] Tang [49] Wang [50] Xu [51] Zhao [52] Zheng [53]

Private Private Private

2,685 196 1,990

1,658 196 777

DeepLab, ResNet U-Net, ResNet LR and RF V-Net and ResNet RF V-Net DRE-Net

87.9% 89.0% 94.0%

90.7% 82.2% 93.0%

83.3% 82.8% –

Private

176

176

RF

87.5%

–

–

Private

453

195

Inception

73.1%

74.0%

67.0%

Private [239]

618 470

219 275

ResNet DenseNet

86.7% 84.7%

– 76.2%

– –

Private

630

630

DeCoVNet

90.1%

90.7%

91.1%

performed and experimented through the technology. The experiments show the results to be combinations of several strategies, screening methods and treatment plans to fight, decimate and stop the spread of the coronavirus (Table 3).

5.3

AI for Text Mining and NLP

This method is apt, and we can apply it to detect and predict the coronavirus and its fast spread issues and laws, and also the way by which its development trend is recursively increasing or decreasing habitually, and that is why it has officially become a source of great use in the future for similar pandemics, their prevention and methods to control and treat the patients suffering. The review also harnesses and displays the important issues and causes like public awareness of the preventive

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affairs/policy of the government to fight the virus, play a vital role of being open and transparent about the forthcomings of the pandemic and the reports and stop the spread of the deadly virus.

5.4

AI and IoT Based on Its Concept

The essence of AI and standardization of data sharing protocols, as per the instructions to aim at a greater cause, global peace, mutual understanding and handling the health of the people in urban areas is well known and acknowledged even in the smallest village areas through the spreading period of the novel coronavirus. As people are becoming more aware about this technology through the passing days. For example, the noticed and hence added/implemented advantages and perks are noticed by use of AI and its integration with thermal cameras and other such similar technologies that are actually fixated in different cities of the world, mainly the smart cities, to be able to identify and predict the outbreak of COVID-19. Such AI-driven techniques can also be used well helping the hospital staff, managing team, government officials, etc. Also, they can be in charge of fighting the virus and helping the patients in urban and village areas through the data and information being carried forward to the metro cities for their help. This can be considered as a pact, and rules must be followed (Table 4).

5.5

Chest Imaging

In the future, it will turn out to be a big boon for scanning and treating coronavirus patients. The technique of chest imaging is not a standard technology for scanning and treatment of the disease caused by coronavirus, but it was efficient when it came to removing any other threats or cases that were caused by COVID-19 that confirmed a diagnostic help offered by other methods or simply giving out information and access to important data about a person’s medical progress in this case through different aspects. Hence, data-driven AI methods can help the authorities whenever an unnecessary or slightly necessary need to divide the resources arises, so that is the conclusion of this statement.

5.6

CT Scans

Multiple studies and cases reported that CT scans were a great help in finding how severe are the symptoms of a particular patient, for how long has the disease been active in their body and rate of recovery. Abnormal CT scans have proven to be risky that indicate a positive RT-PCR test for symptomatic as well as asymptomatic

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Table 4 Applications of modern technology during COVID-19 pandemic S. no.

Application

Description

Status in India

1

Radio-based technology and CT scan-based image diagnosis

2

Tracing a disease

3

Predicting the health and its aspects of a patient

4

Medical and bio-based activities

5

Predicting the structure of protein

∙ Usage of AI to find out the functions and Yes features of radio-based technology for correct dialysis and analysis of coronavirus ∙ Detecting coronavirus patients at an early stage using various convolutional neural networks and with an increase in images used ∙ A proper CNN that is used for detecting coronavirus patients using CT scans and X-rays is COVID-Net ∙ COVNET is used well for detecting COVID-19 and differentiating diseases like pneumonia and other lung deteriorating diseases ∙ Deep learning researches in 3D form for detecting COVID-19 in earlier stages ∙ Classifying and tracking the abnormal Yes respiration activity in a patient can help in detecting and screening people affected by coronavirus ∙ An SIR work flow model which is gauged w.r.t time dependency helps in analyzing the COVID-19 cases ∙ Neural network named GRU with two-directional activity and movement for classification of respiration tracts ∙ SEIR—susceptible, exposed, infectious, and removed or recovered model used to track and screen the oncoming of COVID-19 ∙ Supervised XGBoost classifier can be used No for a simple clinical test to find the increasing number of deaths ∙ The ML-based CT scans and radio-based models show accurate results for detecting the hospital stays of COVID-19 patients ∙ A concept named BenevolentAI helps in No predicting the power of the coronavirus to affect/damage the lungs ∙ Deep neural networks predict the behavior Yes and features of protein that is critical assessment of techniques for protein structure prediction (CASP) ∙ CNN is tested for accurate and precise analysis, detection and screening of the same ∙ For image recognition, learning framework is used to make the training of networks easier (continued)

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Table 4 (continued) S. no.

Application

Description

Status in India

6

Discovering convenient drugs

Yes

7

Social and Internet awareness

∙ AI-inspired discovery of drugs for generating drugs that are novel ∙ Unsupervised clustering, data visualization, dimensional reduction used to design and scan images, etc., and discover drugs ∙ Smartphone thermometer as precise for testing and measuring the temperature of affected patients ∙ Detection cough for audio recording of a larger population of both positive and negative cases

Yes

cases, and hence COVID symptoms can be screened and treated. CT scan functions and applications do not display an output of symptoms for most cases (hospitalized ones). So, the ultimate weapons like AI and ML technologies and methods should be used by the researchers and medics to differentiate between patients with light symptoms and others with serious/heavy symptoms, depending on medical conditions that allows them to discover data and the effects of COVID-19 and how it is impacting the lives of millions. If used to our advantage, then this technology has proved itself to be very useful and will eventually be very helpful in detection of the virus and the treatment methods involved in the process.

5.7

Early Detection, Diagnosis and Screening of the Infection

An emergency ‘red flag’ can be analyzed as severe symptoms using AI and hence alert the patient and healthcare helpers about the case. More efficient and quicker decision-making ability is granted, which also is cost-effective, and ultimately helps to test and identify coronavirus patients, using algorithms. A bunch of technologies under AI is also used for the same purpose that are medical imaging technologies like computed tomography (CT) and magnetic resonance imaging (MRI) scan of human body parts (Table 5).

5.8

Monitoring and Scanning the Treatment

A smart source can be built, using AI and to monitor and automatically predict the growth and transmission of the coronavirus. Neural networks also help in extracting and identifying the easy-to-be-seen symptoms of the disease that helps in further detecting, screening and treating the patients. The power and high capacity of

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Table 5 CXR visual representation-dependent artificial intelligence-based techniques for classifying, detection and treatment of COVID-19 disease Literature

Source of data

Size of data

COVID-19 cases

AI-based techniques used here

Accurate %

Afshar Castiglioni Ioannis

[54] Private [54]

100 610 1,427

50 324 224

95.7% 89.0% 96.78%

40 50

25 25

[54]

306

69

Maghdid Narin

[54] [54]

170 100

60 50

Sethy

[55]

50

25

Wang Zhang

[56] [57]

13,800 1531

183 100

COVID-CAPS ResNet Inception, MobileNet, VGG, Inception, Xception, InceptionResultNet SVM VGG, DenseNet, ResNet, Inception, InceptionResNet, Xception, MobileNet Alexnet, GoogleNet, ResNet CNN, AlexNet ResNet, Inception, InceptionResNet AlexNet, DenseNet, GoogleNet, Inception, ResNet, VGG, Xception, InceptionResNet, SVM COVID-Net New DL

Hassanien Hemdan

Private [54]

Loey

97.48% 80.0%

80.6% 94.0% 98.0% 95.38%

92.6% 96.0%

providing everyday, accurate information and data of the patients as well as solutions for the future still have some hope for this method.

5.9

Contact/Direct Tracing of the Individuals

AI helps in analyzing the severity of the infection and symptoms in some cases, identifying the clusters and ‘hot spots’ that can complete the contact tracing of patients and keep them in observation. Predictions about the future activities or the severity of the virus spread, or similar diseases and reappearances of the same findings can be made.

5.10

Projection of Cases and Mortality

The technique can identify, monitor, observe and treat the virus by understanding its nature from the data available, social media and other media platforms, about the high risks and its transmission or spread. It can also count the number of positive

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Table 6 AI-influenced detection and treatment techniques for diseases caused by COVID-19 Literature reference

Source of data

Technique used

Country/region

Huang [58]

WHO [36], Yang [59]

China

Hu [60] Yang [61] Fong [63] Ai [65]

The paper [38], WHO [36] Baidu [62] NHC [64] WHO [36]

GRU, MLP, LSTM, CNN Clustering and MAE LSTM and SEIR PNN and SVM FPA and ANFIS

Rizk [66]

WHO [36]

ISACL-MFNN

Giuliani [67] Ayyoubzadeh [69] Marini [72] Lai [73] Punn [76]

Italy [68] Worldometer [70], Google [71] Swiss population IATA [74], WorldPop [75] JHU CSSE [77]

EMTMGL LSTM and LR

Lampos [78]

Media Cloud [79]

EnerPol ML RNN, LSTM, PR and SVR Transfer learning

China China China China and USA Italy, Spain, USA Italy Iran Switzerland Global Global Global

patients and mortality rate in an area. AI helps in identifying, predicting, detecting and recreating the most vulnerable regions, people and countries and takes preventive and protective measures accordingly (Table 6).

5.11

Development of Drugs and Vaccines

AI is used for finding out about drugs by studying the available information on the novel coronavirus. Its use is to deliver drugs, its design, repetitive nature, effectiveness and development [36]. AI is also used to make things faster, like the creation of drugs and drug testing in real-time, where standardized testing takes a lot of time and here AI is helpful in accelerating this process apparently, which is a difficult task in the eyes of a human, but only AI can help in this case to provide the best results. Identification of efficient drugs that can treat COVID-19 patients is the key purpose that also makes it a powerful tool for a diagnostic test for the designing and development of a proper vaccine [59]. This is done at a quicker rate using AI and is also used in medical trials during the development phase of the vaccine.

5.12

Reducing the Workload of Healthcare Workers

As there is tremendous increase in the number of COVID-19 positive cases, healthcare workers and professionals have a whole lot of workload upon

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themselves. Here, AI can be proved helpful in reducing that workload. It is proven helpful for the faster screening and diagnosis and providing a solution for the best treatment more efficiently through digital approaches and technological algorithms, along with data science, also offering the best training to students and doctors on COVID-19. [38] AI can impact future cases, and patient care addresses also their other diseases, their overall mortality rate and more such problems that can reduce the workload of the hospital staff, managers, nurses, doctors, etc.

5.13

Prevention of the Disease

Using real-time data analysis and other analytic techniques, AI provides up-to-date information that can help in the prevention of coronavirus disease as well as other diseases that can severe the effects of COVID-19 or the heart rate of the patient in general. It is useful in predicting the most appropriate parts for the virus to spread. There is also a need for more hospital beds and healthcare workers at the time of the pandemic. AI is helpful for the future virus and disease prevention, also at the time of such pandemics, taking help from pre-mentored data and information. Identification of causes, traits and effects of the infection transmission has also become easier by the day because of the advanced AI technology as understood from resources [62]. In the near future, AI will undoubtedly be the most effective technology to help fight other epidemics and pandemics. It provides preventive measures and fights against similar other diseases. In the future, AI will play a vital role in providing more of such predictive and preventive health care (Table 7). Table 7 Medicine-based sources of data through images for detecting COVID-19 and the disease caused by it

Sources of data

Type of data

Zhao [64] HRCT [68] Armato [70] COVID cases [71] Medical segmentation [74] Cohen [75] Wang [77] COVIDx [79] Adrian [80] COVID-Net [81] Kermany [82] Mendeley Data [83] Kaggle [84] BSTI [85] SIRM [86] Eurorad [87] Radiopaedia [87]

Images Images Images Images Images Images Images Images Images Images Images Images Images Images Images Images Images

by by by by by by by by by by by by by by by by by

CT scan CT scan CT scan CT scan CT scan CXR CXR CXR CXR CXR CXR CXR CT scan CT scan CT scan CT scan CT scan

and and and and and

CXR CXR CXR CXR CXR

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6 Applications of AI in the War Against COVID-19 Since its beginning, AI is a proven ideal technological growth and similar ways. Proper use can help AI to prove to be an efficient tool against coronavirus, which it has proven to be and will do so in the future as well [88, 89]. Here are some real-life applications of AI that can potentially be an aid to medical staff and workers in their fight against the COVID-19 and the pandemic: • • • • • • • •

Surveillance and observation of a disease. Predicting high risks. Screening and medical diagnosis. In-depth research. Analysis and modeling of virus. Identification of host. Identifying fake news and stop its spread. Enforcing the measures to be taken during lockdown.

7 Conclusion The people in this combat that was raged against COVID-19 have observed to have experienced tremendous pain, unexpected and untold suffering and destruction to the world, but it also shows that greater heights can be achieved and we can unitedly defeat any such pandemic or virus if we work together. The intellectual, detailed and fluent nature of Artificial Intelligence research and development all across the world has proven to us, that when people from different parts of the globe join forces for a common cause, the success rate becomes higher with every step that we take with a bright future where the world is improving at an unexpected rate. The application of these outstanding AI tools and technologies reaches far beyond one virus or this pandemic in particular. As we fight coronavirus, we must also look beyond the current crisis and recognize the greater potential this technology has for the future of health care.

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