Artificial Intelligence Techniques in Human Resource Management (21st Century Business Management) [1 ed.] 9781774911686, 9781774911693, 9781003328346, 2023002612, 177491168X

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Series Page
About the Book Series Editor
About the Editors
Table of Contents
Contributors
Abbreviations
Reviewers
Preface
1. A Brief Introduction to Human Resource Management
Section I: Empirical Analysis and Modeling
2. An Empirical Study on Adoption of Artificial Intelligence in Human Resource Management
3. The Confluence of Smart Computing and Traditional Businesses to Foster Productivity, Profitability, and Prosperity
4. Application of Artificial Intelligence in the Recruitment Process of HRM
Section II: Industry and Organization Modeling
5. Applications of the Internet of Robotic Things in Industry 4.0 based on Several Aspects
6. Fabric Defect Detection and Fault Identification of a Fabric Video for the Purpose of ERP in Industry 4.0: An Image Processing Technique
7. A RF-Based Social Distance Smart Band System in Organization
Section III: Resource Optimization and Modeling
8. Manpower Optimization System in College: A Linear Programming Approach
9. Homogenization in the Technique of Artificial Intelligence in Human Resource Management
10. Conflicting Strategy Management Technique for Companies: An Intelligent Optimization Technique
Index

Citation preview

Artificial Intelligence

Techniques in

Human Resource Management

21st Century Business Management

Artificial Intelligence

Techniques in

Human Resource Management

Edited by Soumi Ghosh, PhD

Soumi Majumder

Santosh Kumar Das, PhD

First edition published 2024 Apple Academic Press Inc. 1265 Goldenrod Circle, NE, Palm Bay, FL 32905 USA 760 Laurentian Drive, Unit 19, Burlington, ON L7N 0A4, CANADA

CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 USA 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN UK

© 2024 by Apple Academic Press, Inc. Apple Academic Press exclusively co-publishes with CRC Press, an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the authors, editors, and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors, editors, and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged, please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library and Archives Canada Cataloguing in Publication Title: Artificial intelligence techniques in human resource management / edited by Soumi Ghosh, PhD [and two others]. Names: Ghosh, Soumi, editor. Description: First edition. | Series statement: 21st century business management | Includes bibliographical references and index. Identifiers: Canadiana (print) 20230156355 | Canadiana (ebook) 20230156398 | ISBN 9781774911686 (hardcover) | ISBN 9781774911693 (softcover) | ISBN 9781003328346 (ebook) Subjects: LCSH: Personnel management—Data processing. | LCSH: Artificial intelligence. Classification: LCC HF5549.5.D37 A78 2023 | DDC 658.300285—dc23 Library of Congress Cataloging‑in‑Publication Data Names: Ghosh, Soumi, editor. Title: Artificial intelligence techniques in human resource management / edited by Soumi Ghosh, Soumi Majumder, Santosh Kumar Das. Description: First edition. | Palm Bay, FL : Apple Academic Press, [2023] | Series: 21st century business management | Includes bibliographical references and index. | Summary: "This new volume presents a range of techniques aimed to enhance the operation of human resource management by applying state-of-the-art artificial intelligence technology. With illustrative case studies, the volume uses as examples from several types of problems and includes their possible solutions using advanced AI technology. The volume explores the confluence of smart computing and traditional businesses to foster productivity, profitability, and prosperity and goes on to apply AI techniques in the recruitment process, with enterprise resource planning (ERP) management software, for manpower optimization systems in colleges, for creating uniformity in HRM across organizations, for creating conflicting strategy management techniques, and more. One pandemic-related chapter discusses the use of radio frequency (RF) based technology for monitoring social distance. Artificial Intelligence Techniques in Human Resource Management will help HRM professionals keep abreast of today's smart technological solutions and will provide valuable information for academicians and researchers on the diverse application of AI in business and management"-- Provided by publisher. Identifiers: LCCN 2023002612 (print) | LCCN 2023002613 (ebook) | ISBN 9781774911686 (hbk) | ISBN 9781774911693 (pbk) | ISBN 9781003328346 (ebk) Subjects: LCSH: Personnel management--Technological innovations. | Strategic planning. | Artificial intelligence. Classification: LCC HF5549 .A9555 2023 (print) | LCC HF5549 (ebook) | DDC 658.300285/63--dc23/eng/20230306 LC record available at https://lccn.loc.gov/2023002612 LC ebook record available at https://lccn.loc.gov/2023002613 ISBN: 978-1-77491-168-6 (hbk) ISBN: 978-1-77491-169-3 (pbk) ISBN: 978-1-00332-834-6 (ebk)

ABOUT THE 21ST CENTURY BUSINESS MANAGEMENT BOOK SERIES Series Editor:

Arvind K. Birdie, PhD

Associate Professor, School of Humanities, Social Sciences and Education, G. D Goenka University, Sohna, Gurgaon, India Email: [email protected] CURRENT BOOKS IN THE SERIES Employees and Employers in Service Organizations: Emerging Challenges and Opportunities Editor: Arvind K. Birdie, PhD The Future of Organizations: Workplace Issues and Practices Editor: Arvind K. Birdie, PhD Cross‑Cultural Exposure and Connections: Intercultural Learning for Global Citizenship Editor: Arvind K. Birdie, PhD Artificial Intelligence Techniques in Human Resource Management Editors: Soumi Ghosh, PhD, Soumi Majumder, and Santosh Kumar Das, PhD Reshaping the Business World Post‑COVID‑19 Editors: Arvind K. Birdie, PhD, and Ruchi Joshi, PhD Responsible Management Practice for Sustainability Editors: S. Vasantha, PhD, A. Menaga, Renuka Pushpanjalee Herath, PhD, and Nithya Ramachandran, PhD Other topics/volumes are planned on these topics: 1. 2. 3.

Globalization and Emerging Leadership Positive Psychology and Today’s Organizations Changing Consumer Behavior and Organizations

vi

About the 21st Century Business Management Book Series

4. 5. 6. 7. 8. 9. 10. 11. 12.

The Impact of Technological Advancement on Organizations Emerging Employer and Employee Relations Designing Future Organizations and Emerging Sectors Aging in South Asia and the Impact on Emerging Businesses Issues in Intercultural Management The Role of Spirituality in Management Increasing Workforce Diversity in Organizations Creating Innovation in Organizations Purchasing Power and Happiness in Customers

ABOUT THE BOOK SERIES EDITOR

Arvind K. Birdie, PhD, has been with various organizations in different roles and has more than eighteen years of experience in higher education. Presently she is an Associate Professor in the School of Humanities, Social Sciences and Education at G. D. Goenka University, Sohna, Gurgaon, India. She was formerly Assistant Director of International Affairs at Amity University, Gurgaon. As an avid reader, she has expertise in teaching various interdisciplinary subjects with equal ease. In addition to academic teaching and training, Dr. Birdie organizes management development programs for corporate and academicians. She is a regular presenter at international and national conferences, and she has published papers in refereed journals. Her areas of interest include leadership, work-life balance, virtual work, and positive psychology. Dr. Birdie has been honored with the Prof. Mrs. Manju Thakur Memorial Award 2016 for Innovative Contributions in Research/Test Construction/ Book Publication for her book Organizational Behavior and Virtual Work: Concepts and Analytical Approaches, presented at the 52nd National and 21st International Conference of the Indian Academy of Applied Psychology. She is also editor-in-chief of the book series 21st Business Management, published by Apple Academic Press.

ABOUT THE EDITORS

Soumi Ghosh, PhD Soumi Ghosh, PhD, is currently working as Assistant Professor in the Department of Information Technology in Maharaja Agrasen Institute of Technology (MAIT), affiliated to GGSIPU, Delhi, India. She worked as an IT faculty for for years at IPD College, Delhi, India. She has about two and a half years of teaching experience as Assistant Professor at several colleges (IMS Engineering College and Inderprastha Engineering College, both affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India). Her research areas include data analysis, software engineering, computational intelligence, data science, soft computing, machine learning, pattern recognition, data mining, and fuzzy logic. Her research papers (more than 25) have been published in various international conferences and journals (SCOPUS/SCI-indexed). She is also working as a member of the editorial review boards pf various reputed international conferences (Springer, IEEE, Elsevier) and Scopus-indexed journals, such as IEEE Access, IGI Global (International Journal of Ambient Computing and Intelligence, International Journal of Healthcare Information Systems and Informatics, International Journal of Rough Sets and Data Analysis), International Journal of Knowledge-Based and Intelligent Engineering Systems, and many others. She is also a member of the International Association of Engineers and the International Society for Technology in Education. Her h-index is 9 with more than 993 citations. She is actively involved and has a keen interest in research activities. She has presented her research papers at various organizations in India and has received best paper awards and patents grant for her work. Dr. Ghosh received her PhD degree in Computer Science and Engineering from Amity University Uttar Pradesh, India. She received a Gold Medal for her MTech (CSE) from Amity University Uttar Pradesh in 2013. She completed BTech (CSE) in 2009 from Dr. APJ Abdul Kalam Technical University (AKTU), Lucknow, Uttar Pradesh, India.

x

About the Editors

Soumi Majumder Soumi Majumder completed her Postgraduate Diploma in Management with a specialization in Human Resource Management from the All India Management Association, Ministry of Human Resource Development, Government of India, in 2012. She holds a Diploma in Labour Laws with Administrative Laws (DLLAL) and a master’s in Business Administration (HRM) from Annamalai University, under Tamilnadu Government, in 2013 and 2020, respectively. Currently, she is a PhD student in the Department of Business Administration at Vidyasagar University, Midnapore, West Bengal, India. She is Associate Researcher at the Universidad Internacional de La Rioja, Logroño, La Rioja, Spain. She is also working as Assistant Professor in the Business Administration Department at the Future Institute of Engineering and Management, Kolkata, India. She is associated with the Department of Management Science, Sister Nivedita University, Kolkata, India, Dept. of Business Administration, Netaji Subhash Engineering College, Kolkata, India. Previously, she was associated with several other institutes, including theTechno India College of Technology, NSHM College of Management and Technology, Dinabandhu Andrews Institute of Technology and Management, J D Birla Institute of Science and Commerce, West Bengal State Labor Institute, Siliguri, etc. She has six years of experience in academia and two years of industrial experience. She has published two authored books and more than 20 research papers in national and international conferences and journals in quality work-life, decent work-life, work-life balance, stress management, employee engagement, job satisfaction, leadership, training, and learning, etc. She holds two copyrights from the Government of India for her research work. Furthermore, she is a member of the All India Management Association, Association for Computing Machinery, Society of Digital Information and Wireless Communications, National Institute of Personnel Management, etc.

About the Editors

xi

Santosh Kumar Das, PhD Santosh Kumar Das, PhD, is Assistant Professor in the Department of Computer Science and Engineering at Sarala Birla University, Ranchi, India. Prior to that, he was Assistant Professor at the School of Computer Science and Engineering at the National Institute of Science and Technology (Autonomous), Institute Park, Odisha, India, for three years. He has more than eight years of teaching experience to date. He has authored/edited of five books with Springer in the series Lecture Notes in Networks and Systems, Tracts in Nature-Inspired Computing, and Studies in Computational Intelligence. He has contributed more than 50 research papers. His research interests mainly focus on ad-hoc and sensor networks, artificial intelligence, soft computing, and mathematical modeling. His h-index is 16 with more than 840 citations to date. Dr. Das received his PhD degree in Computer Science and Engineering from the Indian Institute of Technology (ISM), Dhanbad, India, and completed his MTech degree in Computer Science and Engineering at Maulana Abul Kalam Azad University of Technology (erstwhile WBUT), West Bengal, India. https://scholar.google.com/citations?user=AkQx5KoAAAAJ&hl=th Amazon: https://www.amazon.com/~/e/B09PZ8M8MV

CONTENTS

Contributors............................................................................................................ xv

Abbreviations ........................................................................................................ xvii

Reviewers ............................................................................................................... xix

Preface ................................................................................................................... xxi

1.

A Brief Introduction to Human Resource Management .............................1

Soumi Majumder, Soumi Ghosh, and Zdzislaw Polkowski

SECTION I: EMPIRICAL ANALYSIS AND MODELING.............................27

2.

An Empirical Study on Adoption of Artificial Intelligence in Human Resource Management....................................................................29

Soumi Ghosh, Soumi Majumder, and Sheng-Lung Peng

3.

The Confluence of Smart Computing and Traditional Businesses to Foster Productivity, Profitability, and Prosperity..................................87

Sumit Gupta and Sourav Biswas

4.

Application of Artificial Intelligence in the Recruitment Process of HRM........................................................................................... 113

Ramesh Chandra Goswami, Hiren Joshi, and Sunil Gautam

SECTION II: INDUSTRY AND ORGANIZATION MODELING ...............125

5.

Applications of the Internet of Robotic Things in

Industry 4.0 based on Several Aspects ......................................................127

Garima Jain, Ankush Jain, and Divya Mishra

6.

Fabric Defect Detection and Fault Identification of a Fabric Video for the Purpose of ERP in Industry 4.0:

An Image Processing Technique ................................................................153

Manali Sarkar and Sraddha Roy Choudhury

7.

A RF‑Based Social Distance Smart Band System in Organization ........173

Ranjit Kumar Behera, Mohit Misra, and Amrut Patro

xiv

Contents

SECTION III: RESOURCE OPTIMIZATION AND MODELING..............195

8.

Manpower Optimization System in College:

A Linear Programming Approach.............................................................197

Santosh Kumar Das, Bikram Mahapatra, D. Hema Kumar,

Manoj Kumar Mandal, and Joydev Ghosh

9.

Homogenization in the Technique of Artificial Intelligence in Human Resource Management.................................................................. 211

Sunil Gautam, Kaushal Singh, and Mrudul Bhatt

10. Conflicting Strategy Management Technique for Companies: An Intelligent Optimization Technique.....................................................239

Santosh Kumar Das, Kimmi Kumari, Sagarika Daripa,

Amit Kumar Singh, and Aditya Sharma

Index .....................................................................................................................253

CONTRIBUTORS

Ranjit Kumar Behera

Department of Computer Science, National Institute of Science and Technology, Odisha, India

Mrudul Bhatt

Department of Computer Science and Engineering, Institute of Advanced Research, Gujarat, India

Sourav Biswas

Department of Computer Science and Engineering, University Institute of Technology, The University of Burdwan, West Bengal, India

Sraddha Roy Choudhury

Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India

Sagarika Daripa

National Institute of Science and Technology (Autonomous), Odisha, India

Santosh Kumar Das

Department of Computer Science and Engineering, Sarala Birla University, Jharkhand, India

Sunil Gautam

Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India

Joydev Ghosh

National Research Tomsk Polytechnic University (TPU), Russia;

Soumi Ghosh

Department of Information Technology in Maharaja Agrasen Institute of Technology (MAIT), affiliated to GGSIPU, Delhi, India

Ramesh Chandra Goswami

Department of Computer Science, Indus University, Gujarat, India

Sumit Gupta

Department of Computer Science and Engineering, University Institute of Technology, The University of Burdwan, Golapbag (North), West Bengal, India

Ankush Jain

Department of Computer Science and Engineering, Bennett University, Greater Noida, India

Garima Jain

Department of Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, India

Hiren Joshi

Department of Computer Science, Gujarat University Ahmedabad, Gujarat, India

D. Hema Kumar

National Institute of Science and Technology (Autonomous), Odisha, India

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Contributors

Kimmi Kumari

National Institute of Science and Technology (Autonomous), Odisha, India

Bikram Mahapatra

National Institute of Science and Technology (Autonomous), Odisha, India

Soumi Majumder

Assistant professor, Business Administration Department, Future Institute of Engineering and Management, Kolkata, India

Manoj Kumar Mandal

Jharkhand Rai University, Ranchi, Jharkhand, India

Divya Mishra

Department of Computer Science and Engineering, Subharti Institute of Technology and Engineering, Meerut, India

Mohit Misra

Department of Computer Science, National Institute of Science and Technology, Odisha, India

Amrut Patro

Department of Computer Science, National Institute of Science and Technology, Odisha, India

Sheng‑Lung Peng

Department of Creative Technologies; Product Design, National Taipei University of Business, Taiwan

Zdzislaw Polkowski

Department of Humanities and Social Sciences, The Karkonosze State University of Applied Sciences in Jelenia Poland

Manali Sarkar

School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, India

Aditya Sharma

Institute of Nanoengineering and Microsystems, National Tsing Hua University, No. 101, Taiwan

Amit Kumar Singh

SRM University AP, Guntur District, Andhra Pradesh India;

Kaushal Singh

Department of Computer Science and Engineering, Institute of Advanced Research, Gujarat, India

ABBREVIATIONS

ACMS AI AIDC AME ANN AP AR ASK ATS BASK CRC CRS CSS DSS DTNs ESS FCS HDE HRM I2C IIoT IoRT IoT IT ITS IVR LDE ML MSE NLP OLED OT PLL PSNR

adaptive cognitive manufacturing system artificial intelligence automatic identification and data capture all most equal artificial neural network access points augmented reality amplitude shift keying applicant tracking system binary amplitude shift keying cyclic redundancy check cognitive radio system cooperative spectrum sensing decision support system delay tolerant networks worker self-administration frame check sequence highly defective frames human resource management inter-integrated circuit industrial Internet of things Internet of robotic things Internet of things information technology intelligent transportation systems intuitive voice reaction less defective frames machine learning mean square error normal language handling organic light emitting diode operational technology phase lock loop peak scale-to-noise ratio

xviii

RFID RSSI RTC SAS SDGs SHRM STT TTF TTS VR WANET WFH

Abbreviations

radio frequency identification received signal strength indicator real-time clock software as service sustainable development goals strategic human resource management discourse to message fit methodology text to discourse virtual reality wireless ad hoc network work from home

REVIEWERS

Arun Prasad Burnwal

GGSESTC, Bokaro, Jharkhand

Santosh Kumar Das

Sarala Birla University, Ranchi, Jharkhand

Praphula Kumar Jain

IIT(ISM), Dhanbad, Jharkhand

Abhishek Kumar

Swami Vivekananda SubhartiUniversity, Meerut

Ranjit Kumar

IIT(ISM), Dhanbad, Jharkhand

Manoj Kumar Mandal

Jharkhand Rai University, Ranchi

Nabajyoti Mazumdar

Central Institute of Technology, Kokrajhar, Assam

Divya Mishra

Subharti Institute of Technology and Engineering, Meerut

Vishwas Mishra

Swami Vivekanand Subharti University, Meerut, U.P.

Anand Mohan

NHSM- Department of Computer Science & Management Studies, Business School, Durgapur

Soumen Nayak

SOA University, Bhubaneswar

Amitesh Kumar Pandit

Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh

Jitesh Pradhan

IIT(ISM), Dhanbad, Jharkhand

Amalendu Rana

GITAM Deemed to be University, Hyderabad

Amit Kumar Singh

Indian Institute of Technology (ISM), Dhanbad

PREFACE

In recent years, application of human resource management tools has increased manifold, mainly in organizational management function areas. These business functions are largely related to the private sector organizations’ operations that include management policies and other variants used to provide required services to customers. While providing adequate information and services to the customers, business houses face various issues and problems carrying out desired management activities. The reason behind such problems is an exponential growth in business management activities along with changing patterns of choices and strategies that conflict with each other based on the requirements of the society. There is a need for modeling these types of problems through the application of intelligent and more effective techniques, which are described in this book. The Objective of This Book The main objective of this book is to enhance the operation of human resource management by using artificial intelligence (AI). This book discusses several types of problems faced by researchers and the solutions to the problems. The nature of the problems is basically nonlinear, which makes it difficult to understand and solve directly. Intelligent techniques need to be applied, which can produce optimal and feasible solutions that will help the society. This book will be valuable to academicians, recruiters, and researchers as it is based on the points of view of both business and management. Organization of the book This book begins with a basic introduction to human resource management and contains ten chapters that are organized into three sections. These are as follows: Part 1 provides an empirical analysis and modeling of several operations based on management points of view. Part 2 discusses organization and industry modeling operations. Part 3 describes resource optimization based on modeling information.

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Preface

Section 1: Empirical Analysis and Modeling (Chapters 2–4) This section analyzes and discusses several empirical modeling techniques of human resource management systems. Chapter 2 Chapter 2 provides a comprehensive analysis of the use of AI and its impact on human resource management processes. It illustrates technological advancement in the landscape of information technology. Chapter 3 This chapter analyzes a few case studies that are based on algorithm using workflows and graphs. It helps to showcase analysis that facilitates smart computing technologies. It rewards bringing a revolution to the overall business workflow and to scale up revenue generation based on newer techniques and technologies. Chapter 4 This chapter discusses AI techniques applied in the recruitment processes. Artificial intelligence uses automated techniques in the recruitment processes and removes time-consuming human resource activities. These techniques help in hiring the right candidates, which allows recruiters time to pay attention to more serious issues that require human intervention. Artificial intelligence gives powered solutions that will automate hiring and improve recruitment efficiently. Section 2: Industry and Organization Modeling (Chapters 5–7) This section focuses on several operations and management processes based on organization analysis and modeling. This section encourages modeling and designing industry applications based on AI techniques. Chapter 5 This chapter discusses application modeling of Industry 4.0 based on several aspects. It discusses designing models that results in industry-based applications. It discusses uses the Internet of Robotic Things and its variations. The work is based on information analysis and modeling of new techniques. The work contains analysis based on AI along with Cloud computing and Internet of Things.

Preface

xxiii

Chapter 6 Chapter 6 is based on a fabric detection system for Industry 4.0. This fabric detection system assists in managing fabric video information to manage fault identification. It provides help to manage image-processing information based on a mean square error and histogram analysis. It enhances the result based on some metrics by using machine-learning techniques. Chapter 7 This chapter is based on a radio-frequency-based low-cost smart band application that facilitates maintaining proper social distancing. This smart band also helps in managing user information that uses GPS tracking to maintain an Internet connection. It further benefits optimizing several metrics based on cost-effective solutions. It facilitates optimizing inexpensive information systems to manage small organizations. Section 3: Resource Optimization and Modeling (Chapters 8–10) This section discusses the benefit of modeling problems based on resource optimization for dealing with several types of issues. Each of the issues is modeled by AI technique that optimizes management. Chapter 8 This chapter discusses designing an efficient technique to optimize workforce of a technical college. The key element of this chapter is linear programming with fuzzy logic that optimizes the model and produces optimal solutions. The combination of both techniques facilitates optimizing several parameters based on efficient management tools. It shows the benefits of mapping the student-to-teacher ratio analysis based on various factors and information. Chapter 9 This chapter is based on human resource management that uses AI to manage information. This information is based on a homogenization analysis; the management process uses the information of human resources and optimizes it by an artificial neural network. Several strategies are offered that help to map the investigation processes. Chapter 10 This chapter is based on strategic management processes of a company to increase the productivity of a system. The proposed method in the chapter

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Preface

helps in several applications, and its usage is based on service management. This method formulates an intelligent algorithm that maps several strategies of the company. It is formulated by the use of fuzzy logic and its related linguistic fuzzy variable to map several analyses. Finally, it produces optimal solutions based on several factors of the company. Soumi Ghosh Department of Information Technology Maharaja Agrasen Institute of Technology (MAIT), Affiliated to GGSIPU Delhi, India Soumi Majumder Department of Business Administration Vidyasagar University Midnapore, West Bengal, India Santosh Kumar Das Department of Computer Science and Engineering Sarala Birla University, Ranchi Jharkhand, India

CHAPTER 1

A BRIEF INTRODUCTION TO HUMAN RESOURCE MANAGEMENT SOUMI MAJUMDER1, SOUMI GHOSH2, and ZDZISLAW POLKOWSKI3 Business Administration Department, Future Institute of Engineering and Management, Kolkata, India

1

Department of Information Technology, Maharaja Agrasen Institute of Technology (MAIT), Delhi, India

2

Department of Humanities and Social Sciences, The Karkonosze State University of Applied Sciences in Jelenia Góra, Poland

3

ABSTRACT One of the most critical assets in the area of business management is workforce, and like other resources in business, managing and retaining human resource is a challenging factor nowadays. Every organization has some purposes to serve in the society. Employees working in an organization are social beings and cannot always work like a machine. Therefore the employers have the responsibility of taking care of these human assets. They are knowledgeable, skilled, and talented, and by utilizing their efforts, organizations can accomplish their goals. Employee engagement results in organizational commitment. The more they are engaged, the more the productivity in business. To keep in mind the high significance of human resource management, we have focused on the different aspects of workforce

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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Artificial Intelligence Techniques in Human Resource Management

management in this volume. This volume also discusses the journey from personnel management to human resource management. 1.1 INTRODUCTION TO HUMAN RESOURCE MANAGEMENT There are three key resources, namely, physical resource, financial resource, and workforce resource that exist in any organization. Physical resources include material, equipments, tools, and machineries. Financial resources are cash, debt, credit, while manpower resources are the employees or people of the organization. We all know that people are critical assets and play a vital role in a business organization. In a business strategy, marketable products and services, efficient processes, and technologies are important components. These depend on the ability of the workforce in the workplace to execute planning, processes, programs, and strategies.1 Without the effort of people, businesses cannot succeed. Business organizations can be more profitable and can create brand loyalty when they manage people effectively. The HR functions of an organization play a prime role in the growth of an organization and in successful business strategies. The nature of an organization is to be people-centric and sending direction to the workforce starting from the recruitment stage to the retirement stage.2 Human resource management (HRM) provides best services to people and helps them to achieve objectives through team synergy. According to Michael Beer, as proposed in the Harvard model, HRM is a strategic approach to the recruitment, growth and development, as well as the well-being of the workforce. It consists of all kinds of management decisions and actions that create a relationship between the organization and its employees.3 Sometimes, the application of HRM rules is in the areas of smart application and industrial applications purposes.4,5 All managerial decisions that are related to the people within an organization are dealt with by the HRM. It focuses on making efficient human resources decisions in business and enhances their performance at its apex level to achieve the objectives of a business organization. The HRM in the workplace ensures effective and seamless application of policies, procedures, and programs in the business. It maintains the balance between employee needs and level of satisfaction with the organization’s capabilities and profitability to accomplish the objectives. The HRM system is a tool used to foster individual development and to make optimum level utilization of human resources processes with a compilation of government mandates. Most of the time, larger organizations

A Brief Introduction to Human Resource Management

3

have an HRM department and their objective is to formulate company goals compatible with the employee goals. A company can attain goals with the help of these human resources and their efforts. In Section 1.2, the evolution of the HRM is discussed. Section 1.3 covers the importance of the HRM department in an organization. In Section 1.4, HRM functions are discussed. In Section 1.5, various advantages of HRM are reported. Section 1.6 is on strategic human resource management (SHRM). Section 1.7 covers more details on SHRM and in Section 1.8, few examples on SHRM are discussed. Finally, in Section 1.9, conclusions are reported. 1.2 EVOLUTION OF HUMAN RESOURCE MANAGEMENT The term “human resource management” is comes from personnel management. Personnel management is an old method by which business organizations used to manage their employees. One needs to know the history of centuries of research by different psychologists and management scientists on human behavior and their action–reaction on a particular situation to know the evolutionary story of personnel management.6 It also behaves as nature-inspired optimization purpose for solving some problems of real-life applications.7 Among all these researchers, a very famous psychologist Elton Mayo from Australia made different experiments on human behavior in 1924. He strongly believed that to increase the productivity of businesses, an employer must emphasize human relations. It has a huge influence on the productivity of the workers. He also emphasized improving the quality of productivity, where work–life balance is also required. He is named as the father of HRM. If we go back to the roots of evolution on personnel management, we come to know about Robert Owen, the creator and originator for introducing reformation for workers. He created some principles in the context of personnel management. His principles were based on 8 h of daily work, 8 h of rest time, and 8 h of sleep. Robert Owen emphasized the importance of better working conditions at the workplace.8 He identified the area that there is a relationship between good working conditions with productivity and employee's efficiency. The stated application is also used in the area of medical information system.9 After implementing better working conditions and work–life in the workplace, he observed there is a significant change in the increase of productivity as well as efficiency of workers increased. During that period, he introduced and implemented various social and

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Artificial Intelligence Techniques in Human Resource Management

welfare practices for the workers. His workers became happy, satisfied, motivated, and did work in a better way. Therefore, in his journey, he has been referred to as the father of personnel management.10 According to an article on HRM, it is said that the evolution of the personnel management department to the HRM department started at the National Cash Register Co in the early 1900s. The company leader John. H. Patterson introduced and organized a personnel department to manage grievances, discharge, health and safety, and training for supervisors on new laws and practices after facing several strikes and lockouts. The main purpose of HRM is to prepare the job and manage the job holders. In an organization, one needs to be identified to perform a job better. A notification should be given when a right person for a particular job is identified. This notification contains job description, which is the duties and responsibilities, and also job specifications which include physical, mental, and academic qualifications of the candidates.11,12 It is also used in the area of wireless network, because without the application of wireless network, we cannot do anything in the areas of HRM.13 To verify the authenticity of the invited candidates and pick the right person, they must be tested by using suitable methods of selection. As per the notification, selected candidates need to perform their duties and responsibilities and for that proper training is required. Next, their performances should be assessed to know whether employees are performing up to the standard level that has been set by the management or there are some deficiencies. In return for their performances, they must be rewarded and paid for their job. The responsibility of the HR manager is to provide safety to the employees in the organization. The safety officer generates some instructions on safety measures and monitors that these are actively followed. To keep the employees happy, satisfied, and motivated, health and welfare measures are important. These have a direct impact on the employees’ productivity. The employees and management should maintain a cordial, healthy relationship to avoid all sorts of conflicts that can have negative consequences on the overall functions of the organization.14 In this segment, we discuss another important topic which is the implementation of labor laws that helps to govern all the above-mentioned activities for a job. Ignoring the employment laws will cost the business organization its image and brand. It is believed that for an organization, HRM is like a guardian angel to sail it smoothly. According to Alan Price, a famous author, there are 10 important Cs of HRM, namely, cost-effectiveness, communication, competitive, credibility, coherence, competence, competitive advantage, creativity,

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change, and commitment. These Cs are important for a workable and effective business environment. 1.3 IMPORTANCE OF HRM IN AN ORGANIZATION The HRM is a very crucial practice in any organization. To maintain the traditional role of enabling employment-related laws, compliance, and strategic development, human resource department has an impactful effect on this practice. An important asset of the organization is the people who have a deeper understanding of the range of responsibilities in the organization. The responsibility of the HR department is to provide all types of support regarding the physical, psychological, and social betterment of all the staff. The following sections discuss some critical areas where the HR department helps the employees:15 1.3.1 OVERSEE EMPLOYEE RECRUITMENT Employee recruitment is one of the key areas where the HR department helps the organizational employees a lot. This process consists of job postings, offering jobs, structuring the salary, and other benefits through an exact package, screening applicants, scheduling interviews for the candidates, and so on. The HR team takes initiative in this case to filter the right candidate for the right job at a right time. They use different methods, such as assessment tests, personality tests, interviews, observation to select the appropriate candidates.16 In this relation, the HR department should have relevant experience to identify the best suitable personnel and take them for the job in the organization. 1.3.2 INDUCTING NEW EMPLOYEES The HR team is also responsible for onboarding the new joiners. The HR department provides the corporate guidelines of the methods of working and a brief description of organizational structure. To become familiar with the organization, the new staff get induction training. By this, they become aware of their duties and roles along with the broader objective of the organization. In this way, the HR department helps the managers of other departments by

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saving their time for the induction process. This also helps the employees to be clear about what is expected from the new joiners. 1.3.3 FOCUSING ON THE LEVEL OF EMPLOYEE SATISFACTION In any organization, HR department is also responsible for employee satisfaction. After onboarding, the employees get the training for enhancement of their skills, knowledge, and competencies at work.17 A good interpersonal relationship between subordinates and superiors in the workplace increases the morale and satisfaction level of employees. If the staff is happy and satisfied, they become productive. The levels of the outcome for the organization are enhanced by satisfied staff. Their effort can be considered in qualitative and quantitative ways. The grievances of employees can give a negative effect on productivity. Therefore, the grievance should be eliminated from the system by giving proper solution to the problems that are related to employees. The HR department is highly involved to reduce and eliminate employees’ grievances through the process of counseling. This level of employee satisfaction is also measured by intelligent decision-making system based on analysis of nature-inspired application and optimization.18,19 1.3.4 ENSURING FAIR AND JUSTIFIED EMPLOYMENT PRACTICES Unfair employment practices are not healthy signs for any organization. The treatment should be justified and fair to all the employees who are engaged in the establishment.20 All the legislations related to employment should be followed, maintained, and revised by the HR department. The specialist should know how to identify workplace issues timely and solve them. If these issues go unnoticed, it will give rise to conflicts in the workplace. Harassment, discrimination, victimization, and so on must be abolished from the system. 1.4 THE FUNCTIONS OF HUMAN RESOURCE MANAGEMENT To understand HRM and its all parameters, one must consider its various functions in the organization. HRM has three broad categories as per its

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functions, namely, (i) managerial and (ii) operative, and (iii) advisory. Managerial functions include planning, organizing, directing, and controlling. On the other hand, operative functions are recruitment and selection, job analysis and design, training and development, performance management, compensation management, policies, employee welfare, HRMS (Fig. 1.1). Advisory functions include top management advice and departmental head advice.

FIGURE 1.1

Functions of HRM.

1.4.1 MANAGERIAL FUNCTIONS 1.4.1.1 PLANNING Under HRM, the planning function ensures the best fit between employees and jobs when it comes to either avoiding workforce shortages or surpluses for an organization.21 There are four key stages under the human resource planning processes, such as (i) analysis of present human resource supply, (ii) forecast the demand of human resources, (iii) balance the projection with human resource demand and supply, and (iv) alignment of these three steps with the goal of the organization.

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1.4.1.2

Artificial Intelligence Techniques in Human Resource Management

ORGANIZING

The HRM involves function pay attention to organizational structure to achieve the organizational objectives effectively. The structure is denoted by an organizational chart that establishes the graphical representation of the chain of command within the organizational hierarchy. Each of the information of organization is nature-optimized that helps to solve some issue of the real-life applications.22 1.4.1.3 DIRECTING Directing emphasizes different actionable activities at different levels by the human resources through proper direction. It helps to accomplish the objectives of the business enterprise. Directing includes continuous motivation and command to tap the maximum potentialities of employees. It is a prime factor for the enhancement of employees’ performance. 1.4.1.4 CONTROLLING After planning, organizing, and directing, controlling is added under the functions of HRM. Employees’ performance needs to be evaluated after completing the above stages.23 It should be verified and compared with organizational objectives. If any deviation exists between the standard and actual, then controlling measures are implemented. 1.4.2 OPERATIONAL FUNCTIONS 1.4.2.1 RECRUITMENT AND SELECTION Recruitment is the process of searching prospective candidates from a pool and selecting the right candidate for the right job is known as selection. As per research, it has been said that attracting and retaining talent is a prime challenge for every organization. Recruitment and selection define the requirements of the positions of the job holder in a business organization, by means of advertisements for the position and selecting the right candidate for the right job. This is one of the major objectives of management in any organization. Even it can be said that the success of any business depends

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on the quality and efficacy of its employees. Recruitment of employees with appropriate skills and knowledge is a way to add value to a business. Therefore, employees should be selected very carefully. Managing them effectively may lead to higher retention.24 1.4.2.2 JOB ANALYSIS AND DESIGN The functions of the HRM include specifying the nature of the job, description of the job, and workforce requirements for the job. These functions are important to combine tasks, roles, and duties into a single work unit to achieve the objectives of the organization. Job analysis has two factors: job description and job specification. Job description provides entire description about the job such as job title, job location, job duties and responsibilities, job worth, machineries, and equipments that are associated with the job, terms, and conditions of the job. On the other hand, job specification deals with the physical and mental skills required for the job. Physical skills, such as height, weight, eyesight, and energy are considered and mental skills include analytical ability, forecasting, decision-making skill, problem-solving ability, intelligence level, talent, and so on. 1.4.2.3 PERFORMANCE MANAGEMENT Performance management is another important operative function of the human resource management. It is a process of checking and analyzing employees’ performance. It depicts the difference between the actual and the standard performance. Employees are rated on the basis of their performance and it is managed by the organization effectively.25 There are traditional and modern techniques of performance appraisal systems. Traditional methods include confidential report, ranking method, checklist method, rating method, and paired comparison whereas modern methods consist of BARS (Behavioral Anchor Rating Scale, MBO (Management by Objectives), 360-degree performance appraisal, and so on. 1.4.2.4 LEARNING AND DEVELOPMENT To perform a job effectively, employees need skills, knowledge, and capabilities. This HRM function allows employees to acquire new skills.

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For taking a higher level of responsibilities, learning and development is very much required. This helps to grow talent as well as human capital. It is a process by which employees can meet the present work goals and making the potentiality for future jobs. Learning and development programs increase the skills and knowledge of employees.26 It is a subset of the HR that improves individual and group-level performance. It forms a talent management strategy that aligns with the business goals. Talent management and development strategy must identify the skill gaps among the group members. After identifying, it should implement a suitable training program to fill up the gaps. Effective training shows the employees how to apply new tools and techniques to their job profile within their organization. 1.4.2.5 COMPENSATION MANAGEMENT The HRM determines the pays for different kinds of jobs and other compensations, such as incentives, bonuses, and benefits associated with job responsibilities. Direct compensation and indirect compensation are included under compensation management. Direct compensation contains a salary that must be paid to the employees along with the other benefits.27 Money comes under direct compensation. It includes pay received in the form of wages, salaries, and commissions, bonuses that are provided at a regular and consistent interval. Basic salary, house rent allowances, provident funds, conveyance, and so on are the different components of direct compensation. On the other hand, indirect compensation consists of some social security programs, including health insurance, retirement benefits, childcare benefits, paid leave, pay for a vacation, and different welfare facilities. There are a few other factors that help to generate confidence in the employees and provide the motivation to perform better at their job. A better compensation system boosts the employees’ career opportunities and makes them hard workers. 1.4.2.6 POLICIES Another HRM function is to draft, revise, publish, and implement the various policies of the organization. These policies are related to fair and equitable treatment for all levels of employees. In the major areas of business, a policy is a guideline for formulating actions.28 It is one kind of statement that is accepted for decision-making criteria. These policies of the organization are leading with the accomplishment of several benefits. All the necessary

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guidelines and decisions of top-level management are formed with the help of these policies. Human resource policies cannot be considered in isolation. It is an integral part of policy structure. There exists one organic unity in the framing of policies. This promotes the unity of action and helps to prevent unnecessary duplication. If a major policy formulation is weak in structure, it gives weakness to the effectiveness of other policies. 1.4.2.7 EMPLOYEE WELFARE Employee welfare consists of different services, benefits, facilities, and betterments that keep the employees happy and satisfied in the workplace. These are necessary for the well-being of the employees. All the physical, economical, and social benefits are included. It helps to maintain employee commitment toward the organization. People become productive and the retention level is high.29 Employee welfare says about the social and intellectual improvement for the employees and brings comfort to them. It is something that is evaluated over and above the wages. The facilities that are provided by progressive entrepreneurs are voluntary provisions or statutory provisions. The main goal of employee welfare is to improve the life of the working class. In the areas of employees’ personality, it brings holistic development. The welfare of employees is the intention of the employer, employee, and also society as a whole; therefore three parties are involved in this process. It helps people to perform their work in a safe, healthy, and favorable environment. 1.4.2.8 HUMAN RESOURCE MANAGEMENT SYSTEM The human resource management system (HRMS) involves all the information related to recording, maintaining, and retrieving of the employees. Working hours, earning history, employment history, and so on are covered by HRMS. At present, HR work has been so much complex. There are different management information systems on the basis of recruitment, selection, training, evaluations, compensation, and performance criteria that need to be stored and recorded in a proper format. HRMS makes it easy in the workplace. To handle HR information efficiently, human resource professionals depend on human resource management software. The HRMS is a combination of systems and processes that connect HRM and information technology with the help of an HR software.30 The HRMS can work on

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recruiting and hiring candidates, payroll management system, approval of leave, tracing attendance, succession planning, career development progression, performance evaluation, and finally the entire maintenance of employee information in the organization. 1.4.3 ADVISORY FUNCTIONS 1.4.3.1 TOP MANAGEMENT ADVICE One of the most prime functions of HRM is to provide advice to the top management regarding policy making and procedures. It also suggests appraisal systems for the workforce. This function is focused for qualitative human relations, employee morale, and employee’s loyalty. 1.4.3.2 DEPARTMENTAL HEAD ADVICE Under this department head advice function, HRM deals with the policy making on the job description, recruitment, selection, job design, and performance appraisals. This advice is communicated with the different departmental heads. 1.5 ADVANTAGES OF HRM 1.5.1 STRATEGIC MANAGEMENT The HRM helps to enhance the company’s bottom-line by utilizing positive outcomes. Strategic management leads toward organizational success. Leaders take part in the process of corporate decision-making with their expertise in HRM. 1.5.2 MISSION, VISION, VALUES, AND GOALS The HRM strategically serves the organization’s mission, vision, values, and lastly objectives. Mission and vision statements are striking parameters for the organization. This enables employees to see how they are a fit for the organization and able to define their roles.

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1.5.3 BRANDING INTERNALLY It is an important responsibility of the HRM to create a positive brand value for the business organization for both the internal stakeholders and employees. Employer brand value and company culture help the companies in raising the numbers of qualified candidates, diverse candidates, employee referrals’ and attracting the right people at the job.31 1.5.4 AWARENESS CREATION FOR EMPLOYEES All kinds of functioning, governance, and workplace information are provided to employees through awareness programs of HRM. If employees are well known about the facts and issues of the workplace they can perform accordingly. It is also clear by informing them that what is expected from them at their work. 1.5.5 TALENT MANAGEMENT Human capital is a crucial asset of the organization. The firm can be competitive by utilizing this human capital. Talent is such a wealth of manpower that can create huge revenue for the business house. The HRM is committed to talent management dimensions that are associated with recruitment and selection, onboarding, training, performance management, management development program, succession planning, career development, and other aspects.32 1.5.6 CONFLICT MANAGEMENT Conflict is part of a robust and healthy organization. Everyone needs to create and develop healthy working relationships for quantitative and qualitative productivity. Sometimes, this conflicting management is also used in variant and image analysis that is used in nature-inspired optimization.33,34 Contributions of employees are considered in this scenario. The HRM helps people to know about the individual personality in the workplace. As people of different backgrounds’ work together, there is a high chance of making conflict regarding any issues of work. These conflicts should be handled and managed properly by HRM initiatives.

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1.5.7 MAINTAINING A CONDUCIVE WORK ENVIRONMENT Human resource management also takes care of a conducive work environment. A favorable work environment can be generated by the application of both hygiene factors and motivators. As per Herzberg, hygiene factors include company policies, salary and wage administration, work environment, good interpersonal relationships, welfare facilities, and so on.35 On the other hand, motivators are the intrinsic factors of employees, including career advancement opportunities, growth, job enlargements, appreciation and recognition at work, and challenging responsibilities. 1.5.8 ORGANIZATIONAL CULTURE The HRM has an impact on organizational culture. Culture includes values, ethics, norms, believes, images, and many more. The culture of the workplace is always changing with changes in the demographics, industry forces, and different forms of the workplace, and other components. Organizational culture has been molded by the HRM and in the opposite case, HRM has been molded by the culture as well, therefore, they are internally connected so much. 1.6

STRATEGIC HUMAN RESOURCE MANAGEMENT

The broad aspect of HRM is strategic human resource management (SHRM). SHRM emphasizes on business organizations to manage the people. The term is a combination of business strategy and HRM.36 As per Storey (1995), HRM is one of the distinctive and comprehensive approaches toward employment management. It seeks to achieve the competitive advantages of business with the help of a highly committed and eligible workforce. This is an integrated part of personnel, structural, and cultural techniques. These techniques consist of so many activities, such as personnel techniques which include hiring, promoting, rewarding, and recognizing employees. Structural techniques include the design of the organization. Cultural techniques involve forming, maintaining, and retaining high-performance teamwork, and work culture. The objective is very simple and that is to accomplish a competitive advantage. Competitive advantage is something that helps to make differences between two firms in the market and helps to create a positive image. Strategic HRM is aligning the functions of HRM with the mission and vision

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statement of the organization. It is the smart way to handle people. There are five stages of SHRM that have a significant impact on the HRM field. 1.6.1 CREATING HUMAN RESOURCE STRATEGIC PLAN THAT FOLLOWS THE BUSINESS The first step is to make a strategic HR plan that has a strong strategic impact. HRM strategies should follow the business strategy in a broader sense. HRM has many more functions to play in the organization. These functions can be done fast and in an efficient way by the strategically implementation of the action plans. The business has long-term and short-term planning strategies.37 HRM aligns with business strategy and generates the concept of strategic HRM. The capabilities of the people are looked after by the SHRM. It focuses on the areas of skill, knowledge, capacity, potentiality, and talent management for employees. Business strategies can be met by the productive capacity of human resources. If the business strategy of a firm is cost leadership that means it has a minimization of cost for the product. Based on this, strategic policies can be generated very easily. 1.6.2 ALIGNING HR ACTIVITIES Once the strategic HRM is formulated by the higher level of management, the implementation task is started. All types of human resource activities must be aligned with SHRM. The functions, such as recruitment, selection, performance management system, compensation and benefits management, organizational design, and discipline procedure need to be aligned with business goals. For example, it can be said in a performance appraisal system when the qualities of the people are evaluated, and based on the standard quality of manpower, the criteria can be created for hiring the people. The deserving qualities also help to develop the workforce in a training and development session. In this way, the business strategy can be achieved by aligning the activities-hiring, learning and development, performance measurement, and reward system. The attractive HR practices are the methodology to retain people in the workplace for the long run. This includes SHRM as well. Employee engagement, job design, and job analysis also become easier by the smart application of SHRM. Another strategy of HRM is innovation. The touch of innovation makes the HR practices very striking. It is associated with teamwork, career development, performance appraisal,

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training, and learning and development indices. This is where HRM is truly strategic. 1.6.3 MORE EMPHASIS ON DATA The HR policies are playing a vital role in the business organization. A deep emphasis on data is the key element to measure the impact of HR policies. Business success can be evaluated on the basis of key performance indicators or KPIs. This is one kind of metric that has been used by organizations to align with the organizational strategy. The KPIs can be more dynamic and comprehensive when there is a high emphasis on the data collection process through SHRM. Finance and marketing both have a large set of KPIs like HRM. An example of a financial KPI is the stock price and marketing KPI is the product price. The plentiful data and information of a business house can be recorded and stored easily by the implementation of effective SHRM. 1.7 MORE DETAILS OF SHRM The retention level of quality staff can be increased by using the concept of SHRM. An employee wants to remain in the organization and can make a high engagement for the long run when the person feels valued and recognized. SHRM has seven important steps. The steps are discussed below (Fig. 1.2): i) ii) iii) iv) v) vi) vii)

Identify and develop business objectives. Evaluate capacity of HR. Analyze HR capacity with business goals. Estimate the future requirement of HR. Determine the useful tools for completing the job. Apply HRM strategies. Apply corrective actions and follow-up.

1.7.1 IDENTIFY AND DEVELOP A BUSINESS OBJECTIVE The success of SHRM is based on its association with the organizational goals. Business needs to understand about its aims, mission, and vision. For growth of relevant HR personnel, the company should formulate short-term

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and long-term plans. The clear communication about the goals of business can make the HR personnel efficient regarding formulating different resource management strategy.

FIGURE 1.2

Steps of SHRM.

1.7.2 EVALUATION OF CAPACITY OF HR The present HR capabilities of an organization should be clearly known by the management. It helps to fulfill the business goals along with a vivid understanding of contribution made by employees. For every employee, they need to add a skill inventory to develop strategy on personnel in the organization. This skill inventory theory will help the organization to discover the expert views of employees in different work areas.38 This also identifies the performance gap of the personnel and provides training to

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that area. In present day, the traditional method of training is not so much used and there is a gentle touch of strategy on the methods of training and development. The capacity of employees needs to be evaluated. The difference or gap between actual and standard should be omitted from the process of SHRM system. 1.7.3 ANALYZE HR CAPACITY WITH BUSINESS GOALS A proper assessment on the capacity of HR helps to recognize the barriers of the firm and it helps to implement a plan of action that deals with threats. Strategic human resource planning is focused on the concept of talent management. This gives a clear vision about the capability and potential level of the internal people and HRM analyzes their skills. To serve the organization in a better way, the senior leadership comes into a positive action to identify the ways to equip employees. 1.7.4 ESTIMATION OF FUTURE REQUIREMENT OF HR After analyzing the skill requirement of the existing employees and its relation with objectives, there is a step that is estimation or evaluation of future requirement of human resources. When it comes to strategic human resource planning, forecasting is a very critical issue to measure the requirement of HR in future.39 The forecasting should be done on the basis of demand and supply. Demand forecasting is a type of prediction in relation to the required number of employees and their associated skills that will match the company’s future needs. On the other hand, supply is the currently available skills that help the company to accomplish its strategic objectives. The forecasting method determines the following: • To accept new roles and responsibilities that make secure the company’s future. • To undertake the responsibilities of new jobs or work determine the requirement of skills by current employees. • To check about the utilization of employees’ expertise. • To evaluate the ability of current HR personnel and their practices of having the power to meet with company’s growth.

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1.7.5 DETERMINING THE USEFUL TOOLS FOR COMPLETING THE JOB HR teams should know about the correct usage of new tools, equipments, and machineries to do the work effectively and efficiently. For this reason, they must follow proper liaising methods with other departments of the organization. In this scenario, an example can be given of a joint audit on hardware and software that can be undertaken with the IT department. The gap that is identified in tools will be more helpful to facilitate organized workforce correctly. After the identification of the tool gap, it should be rectified. In this case, the example on workforce management software can be taken. This software works on managing various HR functions like scheduling, entitlement of holidays, managing sick leaves, and so on. 1.7.6 APPLICATION OF HRM STRATEGIES After analyzing and forecasting the HR requirement of a company, the process of expanding and developing the skill of workforce should be started. The implementation of SHRM becomes very easy by the following guidelines: I. Begin with recruitment This stage says about the searching of prospective candidates by the HR professionals. Once the skilled workforce is identified, the strategic HR panning processes get easier. II. Set up a selection process In this stage, interviews and other criteria of selection takes place. Different interview questions are asked and tests are conducted to assess the candidates whether they are suitable to carry out the roles or not. III. Start hiring applicants After assessment and evaluation, selection of applicants should be done. Companies provide job offers only to the appropriate candidates. IV. Structuring the onboarding and training methods Employee onboarding is a key factor to check and increase the retention level of employees. A comprehensive onboarding and training package

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must be implemented to ensure that. After the onboarding of employees, another important component is to engage them in the workplace. Employee engagement leads to organizational commitment. More they are engaged, the more they are committed. On the other hand, training and development helps to increase the skills and knowledge of employees for executing present job.40,41 1.7.7 CORRECTIVE ACTIONS AND FOLLOW-UP To carry out a SHRM review, company’s HR should take some corrective actions. This review of management will track the progress of the activities and is able to identify the areas that need improvement. The review actions related to changes are based on the achievement of the company’s goals. If there is a lack of implementation of strategic plan, it creates problem to meet the objectives and corrective actions must be taken, a little follow-up is also involved in this process.41 1.8 FEW EXAMPLES ON SHRM 1.8.1 GOOGLE Some of the SHRM practices have been listed to focus on some wellknown organizations worldwide. Google is a renowned organization known for its innovative practices. This is the main strength of the company.42 When it comes to facilitating their staffs, they are concerned about the employees’ development. They are aware about this fact that to achieve the business goals, the strategy should be based on people satisfaction. Moreover, this makes them great in front of the world. The company offers a lot of facilities and amazing pay and perks to their employees. This welfare facility consists of a 7-acre sports complex, roller hockey rings, multiple wellness centers, horseshoe pits, and financially affordable massages for all. The company retains the top talent by providing right incentives and opportunity for development.43 A wellknown phrase is used in the business environment that HR has the power to make or break a company. It means that senior management of the company are associated tightly to fair HR practices. This does not mean that only employees are protected by HR as company makes investment on them, but it also ensures that all the employees of the company are

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happy and feel comfortable. In this way, Google employees are the most productive employees around the world. 1.8.2 CISCO Another famous organization is Cisco. The discussion on SHRM also can be made effectively under this juncture. To serve better, Cisco has developed its own HR technology for business needs. It helps to guide the strategy of business. More than 70,000 employees are associated with the company. The talent cloud of this company is based on internal critical resource management or CRM. The company provides some benefits in relation to the career development opportunity and tools to make them effective. This helps managers to make the right team who can effectively complete the particular project. The employees have ample opportunity to learn by working in the projects. It helps them to develop themselves and meet their goals. Senior managers have the responsibility to access the real-time intelligence about team performance. How the team members are producing the results and executing all sorts of priorities and engagement level at work are recorded by the implementation of strategy.44 Cisco has earned the name to retain top talent in the organization for a long run and ranked as number one under Fortunes’ 100 Best Companies to Work For. 1.8.3

NISSAN

Under the field of SHRM, discussion can also be made of Nissan, Tokyo, Japan as it is also one of the top companies that have effective HR practices. Nissan uses a philosophy that is known as Kaizen.45 This means to always keep improving in the area of employee performance. This approach is applicable when recruitment starts and new candidates are encouraged to maintain improvement in all the sphere of their work by this philosophy. In this way they can stand out. At Nissan, leaders have the full authority to hire people and build their team as they want; this is also a good practice of SHRM. As leaders build their own teams, they assign best personnel for the desired work. This generates high level of employee engagement that leads to organizational commitment.46 The result achieved is higher productivity both in terms of quality and quantity. The company has mixed its manufacturing process based on

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the Japanese method with the production capacity of the British. This is named as Kaizen. It allows workers to make continuous improvement in their work procedure. Employees are recruited on the basis of their best skills, knowledge, and talent. After joining the company, they are encouraged by the management for the enhancement of their potentiality. Senior managers motivate them to make innovation in their practices.47–49 The employees’ salary structure is very much appropriate and encouraging in this company. There is no such story of hidden salaries. This approach has made a great success for the company Nissan. The SHRM philosophy always gives priority to the needs of the employees and aligns them with the goals of the company. 1.9

CONCLUSIONS

The function of the HRM is to link the goals of organization as it serves to improve productivity. A successful management on manpower requires correct administration of health and safety along with the attractive culture of organization. A firm can structure all its business processes effectively when every component of the HRM becomes effective. Organization culture denotes the values, ethics, belief, norms, and vision of the company. These are adopted by the firm over time. In business premises, the role of the human resource system is influenced by the culture of business. The business world is changing day by day and market trends, economical upliftment, technological advancement, taste and preference of external customers all are changing over the time. As well as the expectation of internal employees are also changing. To retain the employees on the long run, the company must adapt and modify the management plan. Workforce is the prime resource of the organization. Their efforts are highly appreciated and countable to accomplish the objectives of the business. Business functions are productive when people contribute a lot by using their skills, knowledge, talent, intelligence, and competencies. Employee engagement index gives the organization apex level of productivity. The firm is becoming happy in not only making the profit but also it focuses on profit maximization and business development areas. Like other resources, human resource has a great significance in the field of business management and makes the organization successful in terms of revenue, reputation, and recognition of the firm.

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KEYWORDS • • • • • •

human resource strategy function personnel management

REFERENCES 1. Boxall, P.; Purcell, J. S trategy and Human Resource Management; Macmillan International Higher Education, 2011. 2. Rao, P.; Teegen, H. Human Resource Management, 2009. 3. Noe, R. A.; Hollenbeck, J. R.; Gerhart, B. A.; Wright, P. M. Fundamentals of Human Resource Management, 2007. 4. Das, S. K.; Das, S. P.; Dey, N.; Hassanien, A. E., (Eds. Machine Learning Algorithms for Industrial Applications; Springer, 2021. 5. Das, S. K.; Dao, T. P.; Perumal, T. Eds. Nature-Inspired Computing for Smart Application Design; Springer Nature, 2021. 6. Storey, J. Human Resource Management; Edward Elgar Publishing Limited, 2016. 7. Chakraborty, S.; Samanta, S.; Biswas, D.; Dey, N.; Chaudhuri, S. S. Particle Swarm Optimization Based Parameter Optimization Technique in Medical Information Hiding. In 2013 IEEE International Conference on Computational Intelligence and Computing Research; IEEE, 2013; pp 1–6. 8. Bernardin, H. J.; Russell, J. E. Human Resource Management; Tata McGraw-Hill, 2006; p 736. 9. Lan, K.; Fong, S.; Liu, L. S.; Wong, R. K.; Dey, N.; Millham, R. C.; Wong, K. K. A Clustering Based Variable Sub-Window Approach Using Particle Swarm Optimisation for Biomedical Sensor Data Monitoring. Enterprise Inform. Syst. 2021, 15 (1), 15–35. 10. Armstrong, M. A Handbook of Human Resource Management Practice; Kogan Page Publishers, 2006. 11. Stewart, G. L.; Brown, K. G. Human Resource Management; John Wiley & Sons, 2019. 12. Wood, S. Human Resource Management and Performance. Int. J. Manage. Rev. 1999, 1 (4), 367–413. 13. Das, S. K.; Samanta, S.; Dey, N.; Patel, B. S.; Hassanien, A. E., Eds. Architectural Wireless Networks Solutions and Security Issues; Springer, 2021. 14. Guest, D. E. Human Resource Management and Performance: A Review and Research Agenda. Int. J. Human Resour. Manage. 1997, 8 (3), 263–276. 15. Guest, D. E. Human Resource Management: When Research Confronts Theory. Int. J. Human Resour. Manage. 2001, 12 (7), 1092–1106. 16. DeCenzo, D. A.; Robbins, S. P.; Verhulst, S. L. Fundamentals of Human Resource Management; John Wiley & Sons, 2016.

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17. Armstrong, M. Human Resource Management Practice, 10th ed.; Kogan Page, 2006. 18. Das, S. K.; Samanta, S.; Dey, N.; Kumar, R., Eds. Design Frameworks for Wireless Networks; Springer Singapore, 2020. 19. Das, H.; Rout, J. K.; Moharana, S. C.; Dey, N., Eds. Applied Intelligent Decision Making in Machine Learning; CRC Press, 2020. 20. Pinnington, A.; Edwards, T. Introduction to Human Resource Management; Oxford University Press Catalogue, 2000. 21. Pucik, V.; Evans, P.; Björkman, I. The Global Challenge: International Human Resource Management, 2017. 22. Dey, N., Ed. Applications of Flower Pollination Algorithm and Its Variants; Springer, 2021. 23. John, R.; Taylor, B. Human Resource Management, 2016. 24. Bratton, J.; Gold, J. Human Resource Management: Theory and Practice; Palgrave, 2017. 25. Lengnick-Hall, M. L.; Moritz, S. The Impact of e-HR on the Human Resource Management Function. J. Labor Res. 2003, 24 (3), 365–379. 26. Dessler, G. Fundamentals of Human Resource Management; Pearson, 2013. 27. Harzing, A. W.; Pinnington, A., Eds. International Human Resource Management; Sage, 2010. 28. Tichy, N. M. Strategic Human Resource Management, 1981. 29. Stone, D. L.; Deadrick, D. L.; Lukaszewski, K. M.; Johnson, R. The Influence of Technology on the Future of Human Resource Management. Human Resour. Manage. Rev. 2015, 25 (2), 216–231. 30. Majumder, S.; Mondal, A. Are Chatbots Really Useful for Human Resource Management? Int. J. Speech Technol. 2021, 1–9. 31. Becker, B.; Gerhart, B. The Impact of Human Resource Management on Organizational Performance: Progress and Prospects. Acad. Manage. J. 1996, 39 (4), 779–801. 32. Wilton, N. An Introduction to Human Resource Management; Sage, 2016. 33. De, D.; Mukherjee, A.; Das, S. K.; Dey, N., Eds. Nature Inspired Computing for Wireless Sensor Networks; Springer, 2020. 34. Dey, N.; Rajinikanth, V., Eds. Applications of Bat Algorithm and Its Variants; Springer, 2021. 35. Stredwick, J. An Introduction to Human Resource Management; Routledge, 2005. 36. Mondy, R. W.; Noe, R. M.; Premeaux, S. R. Human Resource Management, 2005. 37. Turnbull, P. B. P.; Blyton, P.; Turnbull, P. J. Reassessing Human Resource Management; Sage, 1992. 38. McGovern, P.; Gratton, L.; Hope-Hailey, V.; Stiles, P.; Truss, C. Human Resource Management on the Line? Human Resour. Manage. J. 1997, 7 (4), 12–29. 39. Stone, D. L.; Deadrick, D. L. Challenges and Opportunities Affecting the Future of Human Resource Management. Human Resour. Manage. Rev. 2015, 25 (2), 139–145. 40. Mellahi, K.; Budhwar, P. S. Introduction: Islam and Human Resource Management; Personnel Review, 2010. 41. Jackson, S. E.; Schuler, R. S. Understanding Human Resource Management in the Context of Organizations and Their Environments. Annu. Rev. Psychol. 1995, 46 (1), 237–264. 42. Korczynski, M. Human Resource Management in the Service Sector; Palgrave Macmillan: Basingstoke, 2002.

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43. Guest, D. E. Human Resource Management the Workers' Verdict. H uman Resour. Manage. J. 1999, 9 (3), 5–25. 44. Majumder, S.; Biswas, D. Fundamentals, Present and Future Perspectives of Quality of Work Life. In Proceeding of First Doctoral Symposium on Natural Computing Research: DSNCR 2020; Vol. 169; Springer Nature, Feb 2021; p 271. 45. Brester, C.; Tregaskis, O.; Hegewisch, A.; Mayne, L. Comparative Research in Human Resource Management: A Review and an Example. Int. J. Human Resour. Manage. 1996, 7 (3), 585–604. 46. Itika, J. S. Fundamentals of Human Resource Management; African Studies Centre, 2011. 47. Wright, P. M.; McMahan, G. C. Theoretical Perspectives for Strategic Human Resource Management. J. Manage. 1992, 18 (2), 295–320. 48. Ivancevich, J. M. Human Resource Management; McGraw-Hill: New York, 2007. 49. Dyer, L.; Holder, G. W. Toward a Strategic Perspective of Human Resource Management, 1987. 50. Scullion, H. International Human Resource Management. In Human Resource Management: A Critical Text, 2001.

SECTION I

EMPIRICAL ANALYSIS AND MODELING

CHAPTER 2

AN EMPIRICAL STUDY ON ADOPTION OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT SOUMI GHOSH1, SOUMI MAJUMDER2, and SHENG-LUNG PENG3 Department of Information Technology, Maharaja Agrasen Institute of Technology (MAIT), Delhi, India 1

Business Administration Department, Future Institute of Engineering and Management, Kolkata, India

2

Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan

3

ABSTRACT The term “artificial intelligence (AI)” refers to a technology that is applied to perform tasks that need the involvement of a certain level of intelligence to be accomplished. The phenomenon of AI also refers to the performance of some specific work based on the application of trained pattern of technology coupled with human endeavors. The phenomenon of AI has been widely studied in the context of several areas or fields. This paper is meant for a comprehensive analysis of the use of AI technique and its impact on performing human resource management (HRM) processes. AI is a new technique that originated from technological advancement in the landscape of information technology (IT).

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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At present, most business organizations in the country and abroad have implemented this technique exclusively for enhancing efficiencies in the performance of employees and achieving organizational goals in different functional areas concerning HRM tasks. The aim of the book was to study and examine the relationship and interaction between AI and HRM functions. In this process, an exhaustive review of literature has been made along with an analysis of various angles, pros, and cons of implementation of this modern technique in HRM functions vis-à-vis traditional methods used. The concluding part and scope of future work are discussed elaborately pointing out and emphasizing the advantages as well as various challenges faced in the process of using and integrating this newer technique into HRM activities. 2.1

INTRODUCTION

The phenomenon of artificial intelligence (AI) conforms to the use of technology to perform a task that requires a certain level of intelligence to accomplish it. It also refers to the performance based on trained technology that is as good as human performance. Furthermore, AI has also been defined as “an ideal intelligent” machine that acts as a flexible unit to perceive its environment and performs to maximize the possibilities of success of fixed goals. It is about the intelligence demonstrated by machines in contrast to natural intelligence of humans. The salient components of AI such as quick or high-speed computation capacity, usage of advanced algorithms, and processing a large volume of integrated, quality data differentiate it from other ordinary types of software systems. In fact, since the very beginning, technology has become one of the major influential factors in the field of industry and it has its first application or initiation way back in 1956. In the recent years, many developments and innovations have taken place in the field of information technology (IT) and AI is one of the most important innovations introduced so far. Practically, digital technologies such as machine learning (ML) and AI have entered into the realms of day-to-day working in workplace and have bought about a leading transformation and breakthrough in the area of business management. They also have a profound impact on managing the workflow of employees and so to say the management of human resources. AI is rather helpful for multiple business functions that reduce the workload and pressure of the employees in their workplace. As rapid changes in business forums require swift response, organizations apply AI to enable

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them perform day-to-day functions and focus on existing performance rate and inform accordingly. Moreover, the use of AI and ML algorithms helps in providing solutions for business problems of complex nature and also in displaying intelligent behavior patterns compared to any kind of human intelligence. According to Kaplan and Haenlien,47 AI can be classified into three different categories: Analytical, human-inspired, and humanized artificial systems. Analytical AI systems have a lone characteristic that is consistent with cognitive intelligence and are useful to generate a cognitive representation of the business world. Based on the use of ML algorithms, analytical AI may help in providing information on past experiences and also in taking future decisions. Human-inspired AI has elements of both cognitive and emotional intelligence plus emotions that largely influence cases of decision-making. Humanized AI has characteristics reflecting all types of competencies such as cognitive, emotional, as well as social intelligence. It has the ability to be self-conscious and self-aware at the time of interactions with others. In the field of computer science, this new technique has been considered a new-generation technological advancement tool. 2.2 ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT The concept of AI has entered the domain of computer science for the last couple of decades but only in the recent years has this concept gained importance and is now considered a very useful and effective tool in the business or industrial field for HRM tasks. Nowadays, AI has entered the overall system of business organizations, especially the HRM area. With the application of the AI system, practically human touch has been replaced and all important functions in HRM such as screening, recruitment, onboarding, interviewing, alignment of human resource activities, and performance management have been carried out more efficiently. The use of this system practically and effectively results in considerable improvement and greater achievement in the case of efficient and desired management of human resources. The background history and general idea regarding the use of AI in the business field for HRM reveal the fact that in 1955, John McCarthy was the first person to use the term “artificial intelligence” in his digital article “The business of Artificial Intelligence.” Although this system was there for a long time, the companies at that time were engaged in figuring out proper ways

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and means to implement AI systems into their businesses.14 In 1957, Herbert Simon made some predictions that within a decade, computers will be able to beat human power in chess and other competitions. This prediction took a much longer time of around 40 years or four decades to become a reality, as pointed out by Brynjolfsson and McAfee in 2017. Nowadays, as technology has evolved and computers are powerful and capable enough to store a large volume of data, it has become possible and easier to create more advanced and developed types of algorithms and the system of AI little more than in the last few decades.88 According to Tecuci,95 although the system (AI) was prevalent for many years and has had wide applications in different areas throughout those years, it is only during the later years that this technology has been developed further and implemented in different organizational settings. There was no single predetermined definition of the concept, even though the AI system has been there for a longer period of time, that is, for several decades (as per Legg and Hutter55). In fact, for the understanding the concept of AI properly, the easiest possible way is to break or segregate the words by themselves for understanding the meaning of each of the words. Many researchers focused on defining the term AI and expressed their views that “I” or “intelligence” in AI is rather difficult to define and pinpoint, whereas “A” denotes “artificial” and the term is universally accepted and so does not require any other definitions.13 According to the Oxford Dictionary, the word “artificial”’ is defined as “something made or produced by human beings rather than occurring naturally”.71 To be specific, the term “artificial” refers to what has been made by humans for stimulating something that usually occurs naturally. The tricky part of defining the term intelligence then lies within it. The term “artificial” has been defined by some people as the creation of robots, machines, or programs that perform intelligent behavior similar to the intelligent behavior of human beings.95,48 This definition gives rise to a problem concerning measuring human intelligence and further comparing it to that of robots and the machines within it. On the other hand, Kaplan has stated in 2016 that as per his own interpretation “intelligence” can be defined as “the ability to make appropriate generalizations in a timely fashion based on limited data.” Some other informal definition of AI states that it is something that can think, plan, and have the knowledge and can adapt to the environment or retrieve information also Legg and Hutter,.55 It may have the ability to understand data and make decisions based on available data and existing situations.101 For example, AI could be a program that may chalk out how to

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play or function, how to recognize individual faces, or even compose music as well.48 AI is overdefined as a system having the ability to learn, interpret and understand on its own as a machine which is very similar to that of human beings. In fact, AI can very well be implemented in different areas and that too in different areas and in different forms. It can perform in the form of a machine, robot, computer program, or software system.95 AI application has been expanded to many technological areas such as robotics, processing of natural languages, expert systems, and automated reasoning as well.101 Further, there are some main or basic areas of implementation of AI system which include interpretation of languages; machine perception; problem solving; robotics; and games. Ved et al. and Tecuci.101,95 have duly supported the implementation of AI technique in some key areas such as knowledge acquisition, natural language, robotics, and so on. Today, this system has widely been applied in many business areas, and because its outcome or result is predictable, it is possible to use this technology in the process or procedure. The outcome can easily be predicted only when all the actions leading to the basic goal or target are properly recorded and the inputs or information collected earlier or dated back shows a pattern detected by the AI system. For example, the system of sales investigating a purchase record or history while HRM looking into the records of employment.109 In his article,112 Zielinski mentions that the most important thing is to “get intelligent in AI” and many analysts had predicted that by 2020 almost every software product and service will be associated with this AI technology. The statement is rather close to reality and truth as at present AI system is not only applied in the business field but also in the case of everyday objects and gadgets like smart phones. Moreover, Google products include AI-driven technology exclusively. In his article, Meister63 has pointed out that there should be a deep understanding by the team leader and all his team members that the AI system helps in combining the work performed by humans with automated technology. This system learns from the data fed into it, and in case the data includes any sort of error, then it will certainly miss some information. As an example, if the information pertaining to a certain demographic group is not included in the data then it would be rather impossible for the algorithm used to detect this demographic group.111,112 Blanchard11 in his article “A Smarter Way to Run a Supply Chain” showed that how an AI system is able to facilitate business organization by means of learning similar skills that are possessed

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by the experts in a certain field. In fact, if the machine is taught the skills by the professional in any particular field and the machine learns them, it can also give a similar type of advice as given by those professionals. Based on the research studies and findings, many human resource (HR) professionals have observed and accepted or agreed to the facts that the traditional recruitment certainly offers some benefits—this traditional process of recruitment has a value of human touch which implies that there is always considerable interaction and contact of some humans with the applicants for building a special and worthy connection with them. Such human interactions help in easier communication without misunderstandings. Moreover, it helps in discussing ideas not only between employees but also between recruiters and job applicants. It is so observed that “the human touch and feeling are something which can never be replaced—people are so comfortable with the things they know.” It has also been agreed upon that the traditional recruitment process is already a tested measure. For example, conducting formal interviews with the candidates has been followed for a pretty long time as a part of the recruitment process or a valuable method for selection. This implies that such practices have a long-time background and existing theories and research work underlying them as well as validation of results and outcomes too. On this basis, employers can obtain or gather a lot of information and perform the best of selection and recruitment thus becoming confident that such recruitments will give them desired benefits or best results in the future.. It was also observed that as the traditional process of recruitment has been mostly successful for the business organization, they do not feel comfortable in switching over to another process or changing the pattern. There was a choice among many of the professionals to just get human participation or touch as one of the main factors that emerged from following the traditional process of recruitment. There was some difference among the professionals on the point of how much focus or emphasis should be put on the merits of this traditional recruitment process or pattern. Although many of the professionals expressed their views in favor of traditional methods of recruitment for the benefits drawn or the advantages of the process, there was also a second thought as some of them preferred to a shift or changing the process of recruitment as the observed some demerits or disadvantages of this traditional process. In the opinion of many professionals, there are definitely some drawbacks that prevent the traditional process from being the best method of recruitment. One such view is that this traditional method is a lengthy process for recruitment and a time-consuming one, both from the point of view of

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employers and candidates. It is quoted, “Traditional recruitment process is time-consuming and old fashioned.” Some other professionals stated, “It is a very traditional way, very time consuming and people have to go through a lot of steps sometimes too complicated steps.” Another opinion is that the traditional recruitment method is time consuming, and it gives very little time to the recruiters for taking proper and best decisions. Further, it is unanimously accepted by the professionals that in this traditional process, the recruiters generally do not get much or sufficient time to communicate with the candidates during the ongoing recruitment. Another opinion is that the traditional process is not free from being false or fake as the applicants can write or mention some qualities or background in their curriculum vitae (CV) which they do not possess and it is rather difficult for recruiters to verify those things. According to some professionals, one major problem of the traditional process is in finding out new talent and the right candidate from among the applicants, as it is really difficult to differentiate who is seeking a job and who are just casually applying for it. Moreover, in this process, the CV of the candidates remain saved in the database after getting rejected from a certain job position and usually such candidates are forgotten and instead of considering the suitability of these rejected candidates for some other posts, the recruiters opt or go for other applicants for any other new post or job. Another opinion is that biases, such as race- or caste-based judgment of the recruiters is an area, where the traditional process might not work. This method is not at all free from biases as the recruiters might have personal preferences for the working experience of applicants in specific leading companies such as Google or Apple, then the recruiters will exclusively recruit those candidates who have worked there ignoring other good candidates who have work experience elsewhere or in some other good organizations. Taking into consideration the major disadvantages or demerits of the traditional method of recruitment, in the recent years, organizations have opted to implement the method of AI in the process of recruitment for better and desired HRM functions. A comparative study or review of the traditional recruitment process vis-à-vis incorporation of AI technique in the process of HRM reveals that although a large number of business organizations have implemented AI software for interview of candidates and in the overall recruitment process as a new and interesting technology, it still has a long way to go before this method could be implemented strategically. Few professionals think that it is still a beginning and learning phase. In the prescreening and preselection phase of their recruitment process, majority

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of organizations prefer and implement the AI technique as this technology functions based on the job descriptions provided by the companies and facilitates screening of the candidates or applicants seeking jobs. Some HR professionals think that screening and selection may not be conducted merely by applying certain keywords, and it needs to take into consideration language as well as different other traits used in case of submission of their resumes or CV by the application. Screening task is said to have been done for some candidates with the help of social media channels such as LinkedIn and Facebook. Many professionals stated that screening was done based on the applications sent directly by the candidates for the job and posting. Pre-screening is also conducted by some software by applying personality traits that can very well judge whether the candidates have desired qualities, that is, qualifications, experience, and skills required for a certain job or post. It has also been observed and experienced that in terms of pre-screening and selection, AI technology provides all necessary inputs or information to the organization in case of selecting the right or fittest candidate for a certain post or job. According to some HR professionals, AI is a technology that puts together, or in other words, provides a long list of suitable candidates with ranking and thus indicating the best or the best fit candidates for a specific job. This helps the company to get a fair idea or basic understanding of what is felt and what is observed by the AI software about the fitness and qualities possessed by the candidates based on the collected data and their analysis plus evaluation as to who are the best and most fit candidates for the job. This, of course, helps the organizations to select the best and most suited candidate for a certain post. Some HR professionals mention that the AI method learns from the way companies choose to pick up the most suitable candidates among many applicants. For example, if a company contains prefers to pick up candidates from the first top 10 ranked candidates, then the AI software will also learn exclusively that these are the types of candidates that the organization wants and therefore eliminates all others who might be eligible for such a job. Apart from this, some professionals also mention that they actively use AI technology for communicating with the candidates who have applied for the vacant positions whereas others have used a similar type of AI software for a different type of communications. Some companies use software that has chatbots and can provide various information, such as the main, previous experiences, and some such inputs or data, about the candidates. These were then converted into a resume or CV and an application for the job which

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would later on go under screening done by AI software. The chatbot provides the facility of continuous interaction with the candidate and the applicants can get updates and ask questions to the chatbot. The software is capable of knowing how much time has passed since the applicant has applied for the job and also verify or check how candidates prepare for the interview and whether they have raised any questions regarding the job. According to many HR professionals, AI technology is capable of providing an overview to each candidate after the completion of the interview or even after the entire recruitment process is over. It is capable of letting the candidate know what the essential qualities the candidate had and what was lacking in connection with the requirements for the job in particular. It can also provide detailed reasons why the candidate was not selected and at the same time allows that applicant to ask further questions even if they are still unsure and doubtful regarding the selection. This is simply a unique benefit or advantage of using AI technique and as such, majority of HR professional employees advocate for and agree to apply this technique not only for HRM functions but also for other routine and administrative work, as this technique helps the organizations to perform such tasks in lesser time and expenses. In this way, any worker can fix it without taking help from more experienced workers. This technique results in saving time and effort in the place of work. Blanchard11 pointed out the subject “augmented reality (AR)”; as compared to virtual reality (VR), AR goes for changing only a part of reality instead of changing the whole scenarios as is in the case of VR. He thought that the combination of VR and augmented technologies with AI systems can create huge opportunities for business organizations. 2.3 NEED FOR ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT With the rapid changes as well as the advancement of technologies globally, the corporate sector and industries prefer the creative use of AI systems mainly for their sheer effectiveness and added positive benefits to the workflow. The extensive applications of AI technology in overall industryspecific areas including the field of HRM have brought a real breakthrough in the business sector and its effective and beneficial impact on the management of HR. According to the observations and results obtained from various research work carried out by different companies/industries, the use of the AI method can immensely contribute toward the improvement in the

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following areas: care, reduction in costs, and impact on positive, efficient, and significant management of human resources in the business field in case of employment, job evaluation, performance measurement, HR planning, training of employees, and even in case of forecasting of the labor market. A large number of companies across the globe that have adopted AI techniques for proper management of human resources in their organizations have experienced and realized the limitless potential and desired outcome of the application of this system. The findings of various studies conducted by HR professionals of different companies revealed the fact that the use of AI systems in HRM has facilitated the integration of various HR functions and helped in performing those functions more effectively and systematically. Most companies have extensively used AI systems online in case of screening, selection, recruitment, and also onboarding process which is time consuming and they have achieved desired results and success because of the higher level of efficiency of this system. This method helped to perform these critical functions rapidly and faster in an automated pattern using the online platform. In 1988, it was observed by Feigenbaum et al.27,30 that applications of AI technique as an expert system have shown great promise as a tool for decision making in case of several functional areas of HRM. In short, an expert system is “a computer system which attempts to embody the knowledge and decision-making facilities of a human expert in order to carry out a task requiring human expertise” as defined by Beardon in 1989. In the opinions of Kirrane and Kirrane, Ceriello, and Lawler,16,51,53 expert systems make their way well into the functions related to the management of human resources although such systems have not been used that extensively in other fields. In the 1990s, research studies by Hannon et al.,37 revealed the fact that there were more than thirty identified applications of expert systems in the case of HRM and the number has been considerably increased very likely since then. Applications of expert systems or AI techniques have been made in various other areas of HRM such as compensation, staffing, training, benefits administration, and also in HR planning fields. Based on their research studies, Northcraft et al.,69 Besser and Frank,9 Chu,18 and many other contemporary researchers have found and argued that expert systems (AI) could be and need to be utilized more extensively and widely in the area of HRM especially for improving the quality of decision-making tasks. In 1992, another researcher Lawler53 commented that there was limited or less empirical evidence in support of this contention that the use of AI will

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improve decision making qualitatively, and as such it is less than a foregone conclusion that use of AI technology will have its desired and intended impact or effect on the performances of the users of this system. It is worth mentioning that some of the leading companies in the world such as IBM, Amazon, Google, and so on felt the need for applying AI methods for HRM functions and they have experienced much better, effective outcomes, and have come up with more innovative solutions for problems of their employees in HR-related functions. It is now a well-accepted fact that the use of the AI technique has proved its effectiveness and worth for HRM in the corporate sector throughout the world. 2.4 LITERATURE SURVEY OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT A thorough review of the work done by different researchers on AI and its application to HRM tasks or in other words, a review of literature since the inception of the AI technique reveals the fact that this concept of AI was first proposed by McCarthy in 1956 and his view refers to an idea or thinking that machine includes cybernetics automation theory and processing of information cited by McCarthy.62 During the 1990s, Kasparov made a significant achievement by the way of fighting and winning the idea of utilizing personal computers (Darkblue PCs) for illuminating the AI work and it was an advancement of AI, as mentioned later by Vinichenko et al.103 In 1992, Salin and Winston86 defined AI as a set of techniques that allows and facilitates the computer to achieve and accomplish tasks well which will otherwise necessitate the reasoning skills and intelligence of human beings Duchessi et al.22 emphasized the relationship between management and organizations from the perspective of AI technology. They stated that AI and digital technology have a considerable impact on ownership and responsibility for the task of decision making, cost reduction, enhanced service, personal shift, and downsizing on overall organizational structure and management of the workforce. In 2006, Varian Hinton expressed the view that profound learning (ML) and AI have practically entered into a phase of fast or rapid turn of events. In his opinion, the AI system covers numerous orders such as arithmetic, the board, personal computer or PC, and phonetics and has a solid breadth indeed.45

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It has been observed in the process of an exhaustive review of research work (literature) done in this particular field that the use of AI systems in case of HRM functions has been undertaken mainly during the last decade (2010–2020) or so, that is, in the recent past. In 2010, Kapoor49 examined the role of AI and its application in management of HR. He investigated the leading business intelligence vendors mainly with the idea to look into the matter of how the features of business intelligence or AI and data analytics have been incorporated into the HRM modules. In 2011, Guo35 conducted a research study on the problems and countermeasures in the area of the HRM processes of enterprises and observed that the AI method generally plans or aims at empowering the machines to perform functions such as composing PC programs, doing arithmetic, taking part in rational thinking, following languages, and also driving the vehicles in the very similar fashion or manner as done by the individuals or human beings. In 2014, Yang and Lin108 mentioned in their research findings that during the last two decades, traditional HRM has been converted into a strategic pattern of management which has given birth to strategies of HRM. Wang et al. 104 mentioned that AI is an activity that is devoted to making the machines intelligent and intelligence is practical that quality that enables an entity to function properly and appropriately along with a clear foresight in its environment. In the idea of Wang et al., the term AI in the field of computer science refers to the intelligence demonstrated by machines in contrast to the natural intelligence shown by human beings. In 2015, researcher Dirican21 identified in his study that the use of robotics and AI in the business sector may have a negative impact or effect on the overall performances of an organization such as production, performance management, sales, strategic planning, banking system, customer care relationship, training, coaching and in a matter of taxes, and so on. In 2015, Shao91 has pointed out that since 2011 different leading IT companies have procured around 140 innovative firms in the field of AI. These business organizations are trying to consolidate AI systems into different PC frameworks to make consistent encounters on the usage of AI on these frameworks. In his critical review cum research work, Sajeevanie85 stated that accountability of HRM is a decentralized function and it is an organizational activity rather than a part of the HRM department. In 2016, Buzko et al. 15 studied the role of AI in HRM and he had a view that AI is unable to identify the effectiveness of the costs of training but this system facilitates prompt analysis of data collected by human beings. After extensive research work on hurdles or problems of AI technology, he

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came to a conclusion. Nielsen,68 in his research work explained some of the relevant concepts such as big data, algorithms, and using a “Tuning test” data-fiction. He also narrated what constitutes machine intelligence or AI and how it differs from human intelligence. He also focused on human fears that arise from losing grip and control over reality and thought that there is no reason to think that the robots will do any harm or evil. In 2017, Juhari46 stated that a vital benefit of AI technology is that it helps business organizations to determine and understand the rate of performance of the employees over a period of time. It is capable of face recognition and identification of gender as well as the psycho–emotional traits of every employee which ultimately helps in better employee engagement and higher commitment too. Wright and Ulrich107 observed and mentioned that in the final stage or phase of HRM, organizations prefer to switch or move toward simple HRM from strategic management of HR due to their realization that only the management of HR can successfully bring alignment in goals of the organization to the goals of the personnel. In 2017, Jain43 expressed the view that due to the application or involvement of AI systems in HRM in the recent years, the business organizations must have desired skill set for the employees as it has been observed that most of the time the employees face difficulties in learning and adapting AI tools well and have adequate proficiency in the field of digital technologies. In 2018, Rajesh et al.78 observed and examined that through AI application, the HR department was not only able to trace the right candidates within a short period but it also helped to identify suitable, skilled candidates as required by the organization, that is, as per the needed skill sets or norms. Rathi79 is of the view that in that present day, AI system is widely applied by the business organizations since it is helpful in case of controlling or reducing favoritism to a good extent and thus helps in enhancing the degree of transparency in the place of work. The use of the AI method helps the organizations in case of analyzing job descriptions and proper selection of the resume as well. In 2018, Riebli82 pointed out that in the present day, the organizations can manage and perform data analysis with the help of computers and modern technologies, and provide real-time feedback during the training phase and change or alter the course of action, as required mainly based on progress as well as responses given by the industries. Geetha and Reddy77 in their research study on recruitment through the use of AI method found that in the recruitment process AI plays a vital, integral role and it considerably facilitates the screening of candidates, forwarding autogenerated messages to the candidates and relations of employees as well as fixing or scheduling

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interviews. Jarrahi44 emphasized the usefulness and role of AI technology and pointed out that human–AI symbiosis supports and helps organizations in cases of decision making and dealing with uncertainty. He observed that in an industry where human beings still play an important role in handling technologies have to depend a lot on humans mainly when subconscious decisions are rather essential for evaluating and facilitating the outcomes of the decisions made. Especially for the case of equivocality of decision making in an organization, humans have a vital role, he stated. In 2018, Jian42 identified and mentioned the role played by the AI method in the case of HRM. He observed that in the case of various HR functions such as recruitment process, performance appraisal process, and also cloud-based HRM process, most companies have been adopting different modern technologies such as AI systems. Bersin8 thought that AI technology holds a promising future in the field of management of HR but integrating this technique in HR will provide many challenges and the companies have to overcome those challenges to get full advantage or benefit of use of this system. According to Bersin, to get desired results, collecting and providing quality data to ensure the security cum confidentiality of documents and policies shared by the companies is needed. Although it may not be assured or taken as guaranteed that an AI system will always perform with the highest or 100% efficiency but this system certainly is capable of eliminating human errors and bias to a large extent. He stated that despite such challenges, many organizations prefer to use AI systems and go for integrating them with HR functions because the merits or advantages of the AI method shadows the challenges faced in integrating it in HR functions. In 2019, Yawalkar110 studied the role of AI systems in HRM and pointed out that the AI method plays a major, vital role in performing various HRM functions and helps an organization to handle recruitment, hiring of persons, collection and analysis of data, and so on in a much better fashion thus reducing workload in the workplace and facilitating in enhancing and enriching efficiency in the place of work. According to Saran,87 the result of a survey widely conducted by the US- and UK-based IT and AI professionals revealed that the professionals are aware and are concerned about AI bias in case of decision-making tasks, and how the AI system is used by organizations mainly for executing functions across the departments including HRM. The professionals also believed that regulation of AI will be rather helpful and serve a good deal in finding out and defining what constitutes AI bias and how it could be prevented or removed. Harkut and Kasat36 observed that although the organizations go for the application of AI techniques and

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the management of HR, still a few challenges in the form of building trust, AI–human interventions, investment, high expectations, and security of data are faced by these organizations in course of time. Finally, in the very recent past, that is, in 2020, Aspan4 found that across the globe many of the famous and multi-international companies such as Amazon, IBM, Google, and so on have realized the potential and effectiveness of AI technique and prefer wide application of these technologies for better management of human resources. By way of incorporating the AI method with the HRM functions, these organizations have come up with innovative and desired solutions for critical employee problems concerned with HRM functions.50 made a thorough study in 2020 to find out the role of AI systems in the management of HR functions and observed that the AI technique has the capacity or ability to crunch massive volumes of big data and convert them into useful and meaningful information that immensely help in better decision making toward changing or altering workforce as well as enhancing the efficiency and experiences of the employees. These organizations or business leaders are benefitted from the use of AI techniques in making proper decision for better management of HR functions and thus making overall gain and betterment in this field. 2.5 ADOPTING ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT The task of HRM in the business sector has been experiencing a major revolution due to the wide application of AI systems that has helped in solving many problems and the application slowly trickling down to performing various HR functions efficiently and on time. Those HR functions have been reshaped with the adoption and virtual assistance of this method which was once carried out entirely and exclusively by human power. Performing the major functions of the HR which have been eased and improved by the applications or adoption of AI systems include the traditional process of HRM comprising talent acquisition, recruitment process, screening, interviews, selection, training and development, performance management, and employee motivation factors. These are undoubtedly critical, time-consuming functions but the business world has got some relief by the benefits accrued from the use of AI technology in this field. According to Raub,80 AI is an emerging technology and is gaining more attention and importance every year. AI method involves intelligent and

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self-learning programs that are used in the case of software development and many other areas of IT. Although this technology is still far from having human cognitive abilities, through AI, basic functions such as learning, advanced thinking, making difficult decisions and planning, and so on can be performed. The AI-based software system is a useful and flexible solution for various departments of an organization, especially in performing management functions related to HR. This technique provides more opportunities for the automation of various processes which do not require high level of creativity and these functions can be performed by a machine-based technology like AI. Of course, this technology is very useful and helpful for persons working in business organizations, for example, it may be applied for assisting the interviewers at the time of the preliminary interview round. The application of AI technique in case of interviewing candidates or applicants has proved to be advantageous as being a software-driven (AI) interviewer there is no involvement of any emotion or emotional element while treating the candidates or interviewees’ personal, mental, or physical traits or various other external conditions, as it is or it may be possible there in case of human interviewers. It is often observed that at the time of face-to-face interviews and assessments done by human beings, interviewers sometimes tend to make unscientific or irrational decisions for selection of candidates driven by their personal likings, views, and emotions, and as a result of this, many a times the choice is somewhat biased and so the opportunity to select the most deserving, talented or experienced candidate goes missing. Hiring or selection of the most suited, excellent candidate for a particular post or job is important and valuable for the success of an organization and all the enterprises look for and strive hard to identify and select the best, considerably talented people with desired work potential. Moreover, in cases when the job applicants can contact any HR department or private recruiter, they become keen to know whether the company is fair enough in case of selection of candidates. This perception of fairness and unbiased nature of selection serves a good purpose in creating an impression in the mind of the applicants about the organization and this idea or view may highly influence an applicant whether to accept or reject the offer given. Such impression thus largely affects the chance or opportunity for a business organization in case of recruiting talented and right candidates. According to Aggarwal et al.,1 the simple economics of machine predictions and automation processes involved in AI technology has a favorable effect on recruitment because the software or AI system has the ability to analyze big data and evaluate or estimate available options quickly.

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Therefore, AI technology has been widely applied not only in the field of IT but also in the areas of marketing, manufacturing, recruitment, and so on. Owing to the effectiveness of the AI technique, many organizations have widely used this system for getting the advantage of AI solutions in the critical process of recruitment and trying to automate the entire process of recruitment and thus find out the new ways and means for hiring talented and right persons. In the recent years, AI technology has been chosen and extensively used by HR departments as this method has proved to be the most effective and important solution in the recruitment process. In the opinion of Upadhyay and Khandelwal,99 the implications for use of AI technique toward performing recruitment functions is highly beneficial for business organizations as the use of this technology effectively reduces expenses or in other words, having cost-effectiveness in addition to saving time as well as qualitative advantages for both business houses and candidates. In course of a strategy review of HRM functions made by Leong,56 the researcher observed that AI technology is highly promising as it facilitates the recruitment authorities or HR departments of the organizations to prepare complete unified profiles from massive unstructured datasets and job profiles of the candidates based on matching skill sets required for a specific job role. The foremost advantage of this technique is that it provides the scope or opportunity for automating even two-level tasks. Further, the AI system provides the HR professional with more detailed information within the shortest possible time and thus reduces operational costs considerably. The modern software (AI) for screening and recruitment has huge benefits apart from just automation of workload related to the administration or administrative functions. In addition to these, the technique of AI comprises more sophisticated and specialized software that facilitates the recruiters to observe and evaluate how candidates demonstrate their skills in practice. For example, by using AI automated screening software and by using multiple coding challenges which are generated automatically as well as by use of filters, it is possible to make technical talent search and recruitment from among test candidates. With the help of the adoption of AI technology, some crucial and critical tasks like extracting useful information from the replies or responses given by the candidates in video interviews or online interviews have become possible. As pointed out by Raviprolu81, the basic or main benefits of using AI technology in HRM functions include enhancement in the quality of hiring persons, the betterment of integration of analytics providing much time and

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cost-saving automation system making a free, fair, and unbiased pattern of decisions in the recruitment process. Although there has been an emerging trend of utilizing the AI technology among the business organizations during the last two decades or so for performing HRM functions with more ease, rapidly, and in less expenses, there were questions and second thoughts among the companies as to what extent the humans can use or apply AI technique for hiring people or human workers. Moreover, some viewed that the use of this technique is not free from risk. Although AI system definitely provides promising solutions for the organizations and recruiters in functions like source screening of resumes of applicants, improved and qualitative selection or hiring process and minimizing human biases in those functions along with the augmented intelligence widely used produce much better and more effective results for the HR executives and recruiting authorities, it is speculated that this system will get routine administrative work replaced by smart software (AI) system and gradually that will disappear. Obviously, the system looks like a technology of the future but it has certain risks that can adversely affect the quality of the screening and selection process. In this connection, it is worthy of mentioning that research studies conducted by Arntz et al.3 have emphasized possible risks associated with the wide use of automation and threats involved with the application of AI technologies in the recruitment process, mainly in the areas of collection of data related to candidates, providing fair and equal opportunities to all the candidates, providing prompt feedback to all the candidates on selection, and so on. Because of these possible risks involved with the extensive use of AI technology or AI-based software, the organizers need to think carefully before using or adopting this technique for HRM functions according to their requirements as well as specifications. 2.6 REINVENTING HUMAN RESOURCE MANAGEMENT WITH ARTIFICIAL INTELLIGENCE There are several ways, as found by HR professionals, for reinventing HR with the application of AI. Integration of AI techniques with HRM practices will make HRM functions of business organizations much better and more efficient, as this method can analyze, predict, and diagnose well for helping the HR persons or teams to make better decisions.

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Matsa and Gullamaji61 found that although AI techniques may be embedded in HRM functions such as recruitment, onboarding, training, performing analysis, retention, and so on, the majority of organizations lag behind in integrating AI systems into their HRM practices mainly due to expenses or cost involved. They mention in their reports that the implementation of the AI technique should be considered an optimistic opportunity because this system enhances lives and creates a better future provided it is understood clearly and utilized properly. Many studies have been made on this issue and different views of the researchers have been expressed. One such view is that the task of integration of AI method into the HRM function is rather practical and the venture has been welcomed by most HR practitioners. In a study made by an organization like Oracle and Future Workplace in 2019, it was reported that about 64% of HR practitioners supported and trusted AI applications as they thought that the software or robot will help the managers in giving advice. In another study, it is mentioned that compared to only 32% in 2018, currently, 50% of the worker use AI techniques at the workplace in some form or other. It is reported that the majority of the workers (about 65%) are very optimistic, excited, and thankful for getting robots as coworkers and they are in favor of using AI systems at workplace in some form or other. It is also estimated that most of the workers in India (60%) are excited about the integration of AI in HRM functions, followed by China (56%), UAE (44%), Singapore (41%), Brazil (32%), Australia and New Zealand (26%), Japan (25%), USA (20%), and France (8%). It is also stated that male workers were more optimistic and had positive views for the use of AI techniques at work (32%) compared to female workers (23%). According to Dan Schawhel, Research Director at Future Workplace revealed in 2019 that during the last 2 years, the workers in business organizations have become more optimistic and favored the adoption of AI techniques in the workplace and they found that use of this method has not only improved HRM functions but is also leading the way. It is stated that “AI is not only redefining the relation between workers and managers but also the role of a manager in an AI-driven workplace.” Whether to adopt AI or not may still be an important question for many organizations, but some business houses are already on the bandwagon. Moreover, there are some examples and observations as to how most companies are investing in AI techniques as well as cognitive computing for their HRM workflows. Another study on the subject of why AI systems will create more jobs rather than close reveal the fact that in this online system the resumes of the

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candidates are on smart digital forms. Most companies apply AI technology extensively to help candidates to transfer information from their resumes onto smart digital forms and prepare and submit their applications more efficiently. By using AI, the organizations are more or less benefitted to alleviate some rather repetitive, tedious, and monotonous types of tasks that can generally leave them with an undesired poor impression. In the opinion of HR professional Adriana Bokel Herde, by using AI system, information about the candidates can also be sent automatically through background verification and new hire forms. For instance, the AI technique can recognize relevant information from the resumes of the candidates, store it automatically, and keep it as inputs for future without asking the candidates for repeated submission. The recruiters are also not involved in repeating their tasks over and again. For their recruiting operations, as the companies mostly assign the highest priority to the work experience of the candidates, most of the organizations have invested and applied AI techniques that can help them considerably in analyzing the previous work experience and interests of a candidate and match them with the open roles that are best suited for them. Bokel Herde also pointed out that many companies are leveraging the AI method to assess candidates even before the candidates have contacted and talked to the recruiters on phone. This study also mentioned the fact that AI system can enable the HRM team to understand better the references of persons and employees given by the candidates by way of judging or looking into what sort of employees are referred to by the candidates and thus gain insight which candidate has given references of most successful and important persons. AI technique can analyze performance data from the previous referral and also recognize which candidates are having qualities that are similar to the successful employees or persons that have been recommended by them. Further, this technique provides HR departments with an opportunity to improve the skills and qualities of the candidates and also gain more experiences by way of automating repetitive, low-value tasks and providing more time to focus on more strategic, creative nature of jobs (work) that the HR departments usually require and inclined to get done. It has been agreed to by the HR professionals that instead of developing huge time and manpower for supervising every step followed in the onboarding process of new employees, it would be better and wise to get this process automated. Bokel Herde suggested that the HR teams should spend more time and pay more attention to important tasks such as gathering feedback and mentoring.

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She also added that valuable insight can be gained through the data collected during the life cycle of the employees and candidates. The organizations should capitalize on this data and information and put in efforts to improve both in employment and in retention process of the employees. Another HR professional from Archievers Company, Michael Cohen stated that the AI technique provides data-backed resources to HR professionals and also helps in gathering insights directly from the employees. This in turn facilitates them to take action and deliver the experiences of the employees to the workforce that finally helps in boosting engagement and lowering turnover. The outbreak of the COVID-19 pandemic has badly affected the business sector and globally most of the organizations have altered their working process so they are looking for a technique that can serve the purpose of connecting workers at all levels in an organization. He also stated that it is critical that the leaders listen to the views of the employees and take action accordingly. The technique of AI is a useful tool in that way of connecting the entire working people and adhering to what they say and what they require too. As per Cohen’s view, engagement or recruitment of employees is a science that plays a vital role in measuring and analyzing the sentiments of the employees regularly. AI-backed chatbots are able to empower both the employees and the HR professionals to keep the conversation regarding engagement continue and go on throughout the year. Practically, a chatbot is a tool that provides natural, human-like functioning although on communications and helps in engaging users in personalized conversations. Such conversations are then analyzed and leveraged to solve specific problems and pay attention to the demands and requirements of the employees. That is not the end, and in fact the actions needed after the conversations are very critical as it is in the case of asking for feedback. AI-backed software or (and) chatbots permit professionals to thoroughly understand the sentiments of the employees so that they can overcome the potential hazards in taking suitable actions and assure the employees that their voices and views have been considered without ignoring. This obviously enhances their confidence and efficiencies that in turn reduces turnover. Elizabeth Greene, Director of the company “ON Semiconductor” said that the use of AI systems is expected to increase tremendously in the coming years as HR departments are interested to apply this technique for learning and development (L&D) purposes. In the future, organizations are expected to create agile and adaptable types of learning programs so that they can meet

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the individual requirements of the employees. The companies will prefer to use the data and analytics extensively and more deeply so that it may have a more effective impact on business. The main aim of the L&D department is not only to teach the employees about the skills and dexterity of AI techniques but also to prepare them for new roles and jobs that will require more knowledge and skill sets, that is, more analytical strategic and critical thinking; cultural awareness added with emotional intelligence. Green also pointed out that to adapt to such types of changes, the L&D departments prefer transitioning to more agile learning models which will provide support to individual learning as well as broad-based solutions for the organization as a whole. Further, Jayson Saba, Sr. Director, Kronos, stated that HR departments can very well use the AI method to leverage transactional data on the workforce to predict the employee potentials, that is, fatigue, flight risks, and even the overall engagement process thus ultimately enabling to have more productive conversations for improving employees’ experience, performance, and retention. He added that the AI technique is useful in building smarter personalized schedules as well as in the matter of reviewing time off and shift-swap requests of the employees from time to time based on predetermined business rules. This technology also empowers the employees, especially those employees who are in the frontline and sensitive positions and must be present to have more control and maintain a better balance between life and work. AI technique helps in reducing the burdens of the HR managers in keeping important administrative requests of repetitive nature and thus allows them to have more time and scope to deal or work with the customers and training teams. According to the findings of Forrester’s technical study, presently organizations are turning toward workforce analytics and planning and in those areas, applications of AI techniques and ML are gradually becoming more apparent and wider. Saba mentioned that this technique helps and empowers HR managers to solve various critical problems and more informed and effective decision making that have a favorable impact on employees and organizational success. In the opinion of another HR professional, Emily He, the latest advancements in AI technology are reaching the mainstream quite rapidly and this has resulted in a massive shift or change in the way HR people across the world interact with the technology and their HRM teams. She also stated that the relationship between humans and machines is redefined at work and there is no unique and “one-size-fits-all” type of approach for successfully managing this change.

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Instead, companies now require to partner with their HR departments to personalize the approach for implementing AI technology at the workplace for meeting the changing expectations of their teams around the globe altogether. 2.7 IMPLEMENTING ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT Although the technique of AI has been prevalent in the arena of computer science for a couple of decades, in the recent years this technique has been accepted and extensively applied by the business organization for its effective impact and to get desired, efficient outcome in case of HRM functions. The basic HRM functions include talent acquisition, screening, recruitment and selection, training and development, performance management, and motivating employees. Nowadays, HR professionals in the corporate sector are moving toward the digital revolution by simplifying HR management widely using the technique of AI along with a few other methods such as big data analysis and cloud management. The role and impact of the use of AI systems in different HRM functional areas have been discussed elaborately. 2.7.1 TALENT ACQUISITION The task of appointing promising talents is of highest priority and it is a very important factor for the HR department in business organizations. It is largely concerned with the future requirements of the organizations and it works within a strategic framework to achieve future goals or targets of the companies using proper identification of the positions required to be filled for attaining the desired rate of growth through the recruitment of the best candidates for those positions. For proper identification of persons and then developing leaders from among the existing talent pool, the companies need to devote productive time and fully utilize the available resources effectively to develop a useful talent acquisition program. It is a time-consuming and tedious exercise but an important task for the companies which may be a very essential component for effective and long-term HRM drive or program. It has been well accepted that this task of talent acquisition involves implementation of integrated systems and strategies designed for an improved or ideal recruitment process exclusively for identifying and appointing talented,

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skilled persons, and retaining and developing skills of those professionals for current as well as future requirements of the organizations. Most employers and HR professionals spend a lot of their productive time in this process which includes attracting talented candidates, sorting out resumes, tracking and assessing, scheduling and conducting interviews, and informing the applicants regarding their position and status. This huge time-consuming and tedious task has been reduced to a great extent with the help of the implementation of AI techniques for performing this function. In the opinion of Wislow,106 talent acquisition (AI) software is capable of scanning, studying, and evaluating the qualities of the candidates and also eliminating the undeserving applicants within a short period. As such, the use of the AI method has reduced about 75% of the entire work related to this recruitment process. 2.7.2

RECRUITMENT PROCESS (SCREENING AND SELECTION)

This process involves huge work such as advertising vacant posts with all necessary details in the newspapers, and relevant websites, then sorting the resumes of applicants suitable for such posts, conducting preliminary and (or) final interviews, and finally selecting of best (most) candidates for those posts. This process is not only monotonous but also time consuming, with huge volume of functions on the head of HR personnel and recruiters. Most HR professionals throughout the country or even the globe have agreed to the fact that among the various functions related to HRM, recruitment is the most vital as well as crucial task. To perform such functions with more ease and in effective manner, organizations implement some form of AI technology in their recruitment process, that is, in the case of screening, selection, and also evaluation of skills of the candidates by use of robots, AI software, and so on. The largest and most important area of HRM is where AI technology has been widely used in the recent years in the process of recruitment. This method can facilitate the recruiters in finding out the most suitable candidates from among the possible huge number of applicants within a very short span of time as compared to the time taken by the traditional method for this task. Moreover, in 2019, Rodney et al.83 advocated the use of the AI method and invest in this technology as it will help to make this process easier and more accurate. They were of this view based on the results obtained from their research studies that reveal the fact the top recruitment priorities of most of the organizations include 72% in sourcing candidates directly, 66% in building talent pools for the future, 59%

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in investing in tools for speeding up the hiring process, 53% for establishing a strong employer brand, and 41% in succession planning. All such functions have been performed easily within much short time and costs by use of AI techniques as compared to the traditional method or process of HRM. 2.7.2.1 SCREENING This process determines whether a candidate is qualified enough and is fit for the particular post based on educational qualification, experience, and other details as furnished in the resume. Screening is the primary of recruitment and it is a very crucial one as the candidates become eligible to go for the final round of interviews; however, they need to pass or qualify in the screening to qualify the preliminary round. A failure in the primary interview stage leads to outright rejection of the candidate for selection at the first stage. While examining the practices for screening the candidates for potential success vis-à-vis potential failure, researchers12 mentioned that the screening process is very important and requires proper study and essential knowledge on the part of recruiters. This process involves a three-step process, that is, applicant’s minimum qualification or essential qualification and then other requirements for a post. Further, both types of qualifications should be related to on-the-job performance and qualifications ideally matching to the job descriptions given by the recruitment authorities. As mentioned by researchers,32 most of the time organizations face various challenges and difficulties during the phase of screening candidates due to the huge volume of applications received and then sorting out the best candidates among them. It is one of the biggest factors that lead to more consumption of time for completing this task. As such, the easy and useful solution to this problem is to apply a modest technology such as an applicant tracking system (ATS) which has effective software for talent acquisition and performance of the function of recruitment in a better manner through a process of centralizing all the resumes received for each job or post. ATS is a system that generally serves as a measure for automating the screening of resumes through keyboard matches and (or) knock-out questions. However, it is worth mentioning that the ATS system also has a limitation as this system may screen candidates as false positive based on keyboard stuffing and similarly screen candidates as false negative because they do not meet the keyboard filters although they have good and required qualifications. Over and above, the ATS can be taken as an effective system for reducing

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the volume of resumes in the screening round but the software used in this system is not at all designed to perfectly measure the qualities of the candidates for hire. This is because ATS does not have the means to identify which candidates are likely to be a fit and successful for a certain job or which candidates may be unfit and unsuccessful employees for a particular job and so the screening process may not be improved altogether. The researchers, therefore, highlighted how important and strategically meaningful is the process of screening in an overall effective recruitment function meant for fulfilling the desired goals of the organizations. The HR professionals unanimously agree to this fact that the screening process has so far been the most time consuming and tedious task concerned with recruitment cum HRM functions. The traditional method used to consume a huge time, and manpower and as such was a more expenseoriented one. With the use and help of AI-powered computers (software), organizations have been able to find out the most suitable candidates by using the data fed into it that screen the perfect and best candidate from among many applicants for a particular job or post. According to the article published on the website of Ideal in October 2019, titled “AI for recruiting: A definite guide for HR professionals” it has been observed that about 52% of recruiters have stated that finding out the right person from among a large number of candidates is no doubt the hardest part of the entire recruitment process.41 In the opinion of most employers, screening the resumes of the applicants and conducting preliminary interviews have been experienced as a crucial task and a long process altogether especially when the number of applicants and eligible candidates is huge and so the volume of hiring is rather high. Further, in the case of proper screening of candidates, with the help of AI technology, the software companies have been facilitated to conduct audio or video interviews in executing this function with more efficiency and in lesser time.40 Based on the findings of a study conducted by Min in 2017, it was stated that all those companies who have used AI technique for their activities related to recruitment were able to reduce expenses per hire by about 71% and also enhance the efficiency of the recruiter by almost 3 times.65 2.7.2.2 SELECTION The second important phase of the recruitment process is the selection process which involves hiring new employees who are suitable for the posts.

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In this phase, generally, the organizations undergo a preliminary and then a final round of selection of qualified candidates already screened from among the possible applicants. Finally they select the right and best candidate for a certain job or post mainly through conducting interviews. The final selection of the candidate is done by the organizations after the initial phase of selection and thereby establishing a pool of possible suitable candidates. Thereafter, the right and the best-suited candidate is finally selected for the job.67,93 In the opinion of Newell,67 mostly the companion tries to select the right person for a job by commonly using the traditional methods such as interviewing the listed candidates for such post or job. It was pointed out by Elearn26 in his study that gradually the companies switched over more and more to nontraditional methods as means of creating more faith and reliability in case of selection. In the idea of Stoilkovska et al.,93 a very important thing that recruiters should consider at the time of selection is that everyone in the established pool of candidates must have an equal opportunity for getting selected for a specific job. Trevino et al.97 pointed out that during the selection process a crucial point that the selectors should keep in mind is that it is highly important to select and recruit those candidates who are found fit for the organizational culture, which is characterized in terms of shared norms, values, and belief. Ethical culture should also be considered which results from coaction of formal system (e.g., training efforts, codes of ethics, as well as informal system such as peer and leadership behavior patterns; norms related to ethics) intending to promote ethical behavior and attitudes of the employees and thus maximizing their fitness for the organization so that they can contribute their best for building and developing the organizational culture and devote themselves to this. In other words, the culture of the organizations and their employees bilaterally influences the values of each other to a good extent. In this context, Onnekikami and Okpala, 201670 mentioned that the basis for selecting the right candidate is not only necessary but knowledge, skills, and abilities possessed by the candidate along with fitness for the cultural and ethical structure within the company. In their research studies, Millmore et al.64 mentioned that the selection process for recruitment function should be considered as a strategic HRM task and it must portray separate identifies such as strategic integration, long-term organizational focus, and also a method for properly judging the strategic demands for a suitable selection and recruitment design and planning. It was stated that strategic integration is a key feature in HR strategic

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management functions. In the case of strategic planning, the matching of resources for a developing and constantly changing work environment is a very important factor. Further, Sparrow et al. emphasized the importance of the selection process in the recruitment function of an organization and stated that this process aligns with the performances of the employees with corporate strategies. In their views, strategic integration does the job of alignment of business strategies with the business environment that is constantly changing. In 2013, Vernon and Brewster102 observed that organizations having experience in strategic HRM often advocate for and encourage strategic approaches more and more. HRM plays a vital role in the formation of strategic integration as per the opinion of Millmore (2003). Selection and recruitment tasks in the overall HRM function play a very significant role in the case of an organization, as it helps the alignment of the workforce or employees with the objectives of the business organization. In 2010, Chanda et al. 17 suggested that organizations should adopt and need to choose a consistent selection process comprising the tasks such as the collection of applications, sorting and screening, arranging interviews, video assessments, as well as taking formal tests to recruit the candidates best suited for the job. Siddique, 92 made a job analysis along with HRM practices in strategic pattern and found that a well-designed and structured human resource implementation plan minimizes employee turnover considerably and so significantly contributes to financial performances. It is worth mentioning that many researchers such as Anderson et al., Breaugh and Starke, Durcker in Chanda et al.17and Drucker in Siddique92 expressed rather similar views and opinions that accurate HRM planning is the main and most important feature for converting business strategy into specific policies and practices for HR management tasks particularly in the case of selection and recruitment functions. Further, they commonly pointed out that there is a scenario competition among the business organizations to have strong HR as well as innovative management practices altogether. It is a fact that those organizations which implement and integrate their HRM system with their business objectives and strategies will achieve greater success in the long term, and aligning of strategies of selection and recruitment process with the objectives of the business house is vital. This can be achieved through an efficient and dedicated HR team along with wellthought-out plans and policies. The researchers also agree with the fact or idea that the main objective of HRM planning should be to get the right number of persons with the right

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knowledge, skills, and abilities in the right job role at the right point in time in a cost-effective or saving manner. In 2019, Hogg39 categorically mentioned that the selectors or (and) recruiters should give the highest priority to the potential of the candidates over their work experiences. Because of the constantly changing nature of the work environment, the hiring methods need to be changed accordingly along with building diverse and innovative business practices. This will leave sufficient room for the implementation of improved AI techniques and at the same time give more stress on the importance of skilled HR workers who are capable to identify potential even better than any machine. 2.7.3 TRAINING AND DEVELOPMENT Apart from screening and selection, training and development and performance analysis are two very essential as well as crucial tasks in the recruitment process involving the HRM. In the rapidly changing scenario of technology in the present days, it is essential that employees must be conversant with the pattern of technological changes and must assign the highest priority to learning and improving their professional skills. Corporate training may be considered as an area of the entire HRM process where everyone is, and also need to be, treated equally and given equal opportunity for entering into a new position, and receiving similar, proper, and adequate training facilities depending upon one’s position. Any decision of a company for changing policies should be informed or circulated to all its employees. In addition to arranging and imparting proper in-service training for newly recruited employees regarding the nature of jobs assigned to them, the goals of an organization, ways to enhance skills to raise productivity, ways to make them aware of the technology applied for undertaking efficient HRM functions, and rapid technological changes taking place in the domestic and global scenario, the employees especially those who are already employed or engaged in the job but underperforming must be given or imparted required training to boost their knowledge, skill, and performance too. All the employees must be given equal chance and scope, and provided with required facilities to undergo such periodical or regular facilities to know more, and perform better by enhanced knowledge and skills needed for the job. It is rather vital on the part of HR professionals and trainers to understand that the employees might have different ideas and tactics for learning things

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to see how much workers may learn most effectively and increase their skills and which particular areas require improvement for better performance and thus achieving desired competence. In particular, these are the crucial and critical areas where the application and implementation of advanced AI technology may facilitate the companies to a great extent. So say, in the recent years, the use of AI systems in HRM tasks seems to have made its mark through improved performance of training and development functions of the organizations by dint of focusing on providing adequate and proper training facilities to the candidates for improving their knowledge, skills, behavior pattern as well as cultural fitness for the company. It is worthwhile to mention that most of the organizations have agreed to this point that implementation of AI technologies in their HRM functions has not only facilitated them to improve the content, course, schedule, way of delivering and imparting various training to their employees but also considerably helped in motivating and thus increasing the overall engagement and participation of the workers in different training programs conducted. Moreover, compared to the traditional process, AI-based training programs have proved to be more effective and efficient in case of enhancement of learning and skills of the employees which has increased their performance levels. This is mainly because AI technique can successfully plan, organize, conduct, and coordinate different training and development programs for all types of workers or employees. Compared to the huge complex and tedious traditional methods for imparting training to employees, the application of AI technique or AI software has brought the facilities of online courses classes, digital classrooms, video conferencing, virtual meetings, seminars, and ML processes which considerably reduced the load and burdens of HR managers and trainers. This AI system has, of course, solved many common problems of the HR professionals in case of arranging and imparting training to the employees as the method has empowered the trainers to spend lesser time required as well as relevant for developing the skills of the employees through an easier way of gaining knowledge and skills. With the use of this technique, HR managers can plan digital training programs, and provide more opportunities for both trainers and trainees based on the skill-gap assessments done by machines. Nowadays, computerized information systems help workers to follow a certain career path and pattern in a much easy and sure way with fewer expenses through electronic access. With the help of computers, information available in the database regarding the skills and competencies of each

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worker, it is possible to know their base and background, status, or positions held and the role played within the organizations along with developing individual skills and performance.. This has reduced the huge burden of the HR managers and trainers along with the reduction in costs and the time spent for knowing the required details for every employee. Some self-assessment informatics applications are also there which help and assist individual workers in formulating and also judging their careers well. As mentioned by Schermerhorn and Osborn,90 a wide range of computer programs are available in the market domain such as “Career Planning Centre.” Parys is considered an expert in the HRM field and Miram is taken as an expert in the area of guiding the workers in their movement on a career path. The use of AI technique helps to ensure that each employee gets a very personalized experience and it also identifies the position and level of the employees on the basis of results obtained from regular assessments conducted at the end of every phase of the training program. Another key factor of using AI techniques in the training and development program of an organization is that it makes career tracking of the employees possible and easier by way of collecting and combining the data on each employee from the employer based on the appraisals and feedback forms provided by the employee himself. By analyzing the available data, the AI method then helps in creating career paths and goals that may be achievable but of course, challenging a task for the employee indeed. AI-supported and AI-based training and development programs have also helped the employees during the training session itself and provided them with real-time data and information that adequately helped them make real-time suggestions also. Moreover, the use of AI techniques linked with an online internal learning system helps recruits of a company and gives opportunities to know the pros and cons of the operations of the machines that they are to handle and operate properly after the training session. By creating interactive training modules and plans, this technique helps the fresh recruits to know and be aware of the safety measures and other essential technical aspects. Finally, it is often observed that the employers, and trainers have earlier faced and experienced complex problems in recruiting as well as arranging and making the newly recruited employees participate and undergo useful training programs and sessions conducted by the company, but with the application of AI-technique-based training and development programs, the employees have been able to access them anytime from anywhere. This system has considerably helped the HR professionals in delivering and imparting proper and high-quality training to the employees at low costs or budgets and with much better means too.

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Different researchers have made comprehensive studies and expressed their views in respect of the use of AI technology on HRM functions with special reference to training and development fields. In 2018, for instance, Bhatia10 mentioned that this technique helps in making proper and timely decisions and by using employee data, the software can predict accordingly and direct the concerned authorities on the areas the employees need help and guidance in. The AI algorithms can help in preparing and scheduling learning programs by customizing as well as suitable for different employees having different working backgrounds, cultures, different personalities and educational qualifications and experiences and different interests and behavior patterns. Similarly, there may be quite different types of training and learning experiences that may require to be modified to suit a specific employee or worker. This is useful due to the reason that all types of training programs may not be suitable for every worker since some employees may require focus on certain areas much more than others and vice versa. As such customized training for different employees is quite logical, beneficial, and both time and cost saving , and the HR managers will not be required to conduct multiple training programs wasting time, money, and workforce that can very well be utilized in other work and projects. In another research work, “Future of AI in Corporate Training Development’ conducted by Pribanic,75 it is pointed out that those companies who use the AI technique achieve more success and better results compared to their competitors who do not apply this technique. Duindam23 studied the role of AI technique in the evolution of the L&D process of HRM functions and said this technique can identify areas that require improvement and learning metrics that may be linked and applied for achieving desired outcomes. He also stated that on the basis of results, the HR manager can understand the needs of employees for better performance and the type of actions the management should take for more successful operations. According to the reports of some research work done in 2018, many leading companies like IBM and Docebo have preferred and planned for using AI techniques and automate the L&D process entirely. These companies have created desired and important platform for this technique. Some researchers state that the organizations should focus on VR and AR since they are useful tools for companies to implement in training and development programs. The combined application of AI techniques with both VR and AR tools will be more beneficial for the business houses as it will be possible to include new areas into the training program and the employees

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may get more knowledge and experience through training in a congenial and risk-free environment. In 2019,63 Meister pointed out that some reputed companies such as Verizon, Mater Card, and Walmart have implemented AI methods with VR and AR technologies in their training programs meant for store managers to gain ideas for better management and security aspects. In 2018, Lawson54 mentioned that organizations like Verizon used these technologies for training the newly appointed employees and educating them on newer ways of working, and to enhance their communication skills and customer service skills so that they can deal in a better way and communicate better with the virtual customers. In this process, the employees gain more experience so in companies like Walmart use these technologies for testing, finding, and evaluating the potentials and skills of those employees who may be promoted to higher posts and assigned greater responsibilities. This testing process helps the employees to learn more about controlling their temperament and behavior that are required to deal with customers. Based on the studies by Tuchscherer,98 many leading companies in the USA are now applying these technologies with more emphasis on VR tools. A similar opinion was made by STRIVR,94 that this technique is used by many companies in a wide range of training for listing prospective customers as well as collaborating with other major companies such as BMW, Pepsi, Jetblue, Verizon, Mater Card, and others. Moreover, in 2018, it was stated by Alonso2 that AI systems can be used in the form of chatbots in corporate training programs as they have been successfully applied for other HRM functions like recruitment process and in case connecting the job seeker with the employees or recruiters. He talked about the “Rise of Machines,” that is, how machines or AI software can be utilized inside organizational functions connecting the employees with the company goals and providing them with the opportunities to interact with the chatbots and enquire about matters related to work and the company. The machines, on the contrary, can also gather different questions from the employees and the inputs or information can be used for developing and improving the quality of teaching materials and training basics. Chatbots or machines serve a good deal of purpose as they are asked by the employees within much less time and as such the burdens of the trainers are reduced to a great extent along with saving of costs and time. The employees are also benefitted very much, as they can get proper answers to their queries within a short time period or even instantly.

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Further, Eubanks28 studied how machines or chatbots can directly gather valuable information and feedback from the employees on the job and management-related aspects that affect and influence the workers considerably. By way of analyzing and understanding such views, the system can educate the managers about the feelings and ideas of employees about job situations and management activities. It can help the managers to understand the prevailing situations well and also to take a better and more effective approach to train the concerned employees. Marr59 shared his ideas based on the amazing ways which a reputed company like Unilever has applied AI techniques for recruiting and training its huge number of employees at work by using chatbots in the corporate training process. The chatbots also called “Unabots” were built on the framework of Microsoft’s bots that were capable of answering different questions asked by the employees as well as interacting with the workers too. The machine was able to answer different types of day-to-day questions related to various subjects or matters like availability of transport (buses), bus schedules, parking facilities, and even regarding annual salary fixed and paid by the company for different posts. It is true that the use of AI techniques has made the training and development functions of the organizations easier and –cost saving, and much simpler a task compared to the traditional methods that were followed earlier in performing these tasks. 2.7.4

PERFORMANCE AND MANAGEMENT

The process of performance appraisals or in other words analyzing the performances of the employees is an ultimate major and important task of the HRM professionals of the organizations. Evaluation of the performance of all the employees irrespective of their positions, that is, from the top ranks to the lowest positions is valuable and crucial indeed, as it reflects the prevailing working scenario along with the progress parameter of the organization very clearly. This analysis, if done properly, can highlight the overall present position of the company and the possibility and probability of achieving the set and desired targets of the organization. This task of evaluating the performance standards of the workers as well as managers of a company undertaken by following the traditional or orthodox methods are often observed to be time consuming, lengthy process and the outcome is less than desired and expected because the managers entrusted with such a job were found unable to collect proper and all the

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required information or inputs related to a huge number of persons engaged in different jobs in the company within the stipulated time span and schedule. Mostly it was observed that the performance reviews or analyses were incomplete and (or) incorrect due to a lack of adequate information about all types of employees as well as due to negligence, unwillingness, and less time and energy on the part of the managers to process and analyze huge volume of information about a considerably large number of employees engaged in various types of work in different departments of the big organizations. According to Pawar,72 it was commonly observed that while evaluating the performance of workers as well as executives of any company, the concerned professionals have often made minimum efforts and used insufficient and little information about the performances of the large number of workers engaged in the company. As a result, some important contributions of the employees have either been left out of the count or not even properly collected which made the performance reviews incorrect as well as inaccurate. This results in demotivating the employees and badly affects their productivity. Pawar,72 therefore, advocated that with the application of AI technology, it is possible and easier to collect information from multiple sources and that can truly reflect a much better picture of the performances of the workers. By use of this technique or AI software, it is possible and easier to collect, analyze the data and information, and make the assessments, that is, performance appraisals in real time and so it is easier to react and make possible interactions with the workers as it is done in the training process. This view is seconded and supported by many other researchers like Duindam23 and leading companies like IBM. Pawar 72 thought that when the performance analysis is done with the help of machines, then the personal views and feelings of the managers about their employees may be ignored or left out, and as such, the possible and common human biases related to gender, race, religion, place, and others may be eliminated from the review process. However, it was pointed out by Marr58 how the performance review results are ignored by some managers if they do not match well with the personal ideas and feelings of those managers about any worker. He stated that machine is unable to make fair assessment and judgment as it lacks human emotions and sentiments. Zielinski111,112 mentioned that AI technology should be used in organizations to spot or identify workers having high potential and talent and who may be suitable for higher assignments. Such workers must be helped by way of suggesting appropriate learning processes and high-level management training. AI system can suggest actions based on performance analysis

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or reviews. The system can provide suggestions for more learning, larger compensation, promotion to the higher post, and (or) imparting management training to such workers with more potential and skill. He expressed that some workers may be there who are fit and suitable for management training and there may also be some workers who have the required potential for other jobs. The rotation of the employees from one department to another and one type of job to other may help the workers to gain valuable knowledge and experiences that may make them fit for varied work and valuable workers for the company. It is important to note that although the use of AI techniques may help the company to evaluate the performances of the employees more easily and effectively, the workers must be interested to keep or maintain a professional relationship with their managers. The machine simply suggests action patterns based on the reviews made, but the managers have to decide on whether the employee deserves promotion or upliftment. Further, the system or machine does not consider or judge the employees' personality and willingness to learn more and the workers require to show such qualities and eagerness to their superiors so that they may be aware of better qualities and potentials of those employees and use their views or reflect their ideas at the time of making decisions for promotion of the employees or so. Such a view was expressed by Fisher31 while making a study for a suitable algorithm that may decide on promotion and pay hikes. 2.8 BENEFITS OF ARTIFICIAL INTELLIGENCE IN RECRUITMENT PROCESS Despite some conflicts on the subject of the usage of AI in HRM functions, the majority of HR professionals have agreed to the fact that this technique plays a significant role in performing various HRM functions that benefitted not only the professionals but also the employees and the organizations as a whole. It has been accepted unanimously by the HR professionals that the use of this technology has benefitted the recruiters by way of reducing huge workloads, and routine plus administrative tasks required to be performed during the process of requirement. This has resulted in saving both time and expenses. The result of a survey conducted by LinkedIn Talent solutions in 2018 revealed that nearly 80% of the recruiters think that the AI technique has a vital role and very significant effort in performing HRM functions and particularly in the case of recruitment. It was found in that survey that

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50% of the HR professionals viewed the application of AI techniques in the recruitment process has been proved to be beneficial in performing the tasks related to sourcing, screening, and nurturing the candidates, as by the use of this technique those functions could be performed in a much better way spending lesser time and money compared to the traditional methods. Further, studies and surveys conducted by different researchers, namely, Faliagka et al.29; LinkedIn Talent Solutions,57; Upadhyay and Khandelwal,99; RES Forum,34; commonly found that the use of AI technique has benefitted a lot in terms of saving time and expenses and made performing recruitment tasks easier and more efficient. It was also believed that the application of AI or e-recruitment systems has made the hiring or recruiting process considerably faster and easier without compromising on quality factors, that is, without deterioration in quality in the process altogether. In the ideas of Upadhyay and Khandelwal,99 it is because the AI technique is quite capable of collecting and processing rapidly and easily compared to humans and this system can also attain and recognize these data better. According to Benfield,7 since the use of the AI technique or recruitment system saves both time and money, it helps and enables the HR managers to have more resources and assets for implementing in the strategic recruiting process. While studying the role of AI systems in HR functions, it was pointed out by Scherer89 that by proper applications of AI techniques, the HR proper applications will certainly be helped and it is able to get better ideas as to how the programs can be used more effectively and in much better ways without any human bias and at the same time it will help them to identify and ensure choosing the best fitted and most talented candidates for recruitment. In the opinions of Zielinski111,112 and Ryan 84, the technique of AI can even analyze the honesty and emotional intelligence of the candidates just by the way of analyzing video interviews and is also capable of assessing the personality traits of the candidates through their online and social media presence and appearance. Although in most of the studies during 2015–2019, the researchers highly supported and found the use of AI technique very beneficial for recruitment functions, Tolan96 expressed a different view and reminded that the AI technique will not replace the recruiters but simply assist in the process of recruitment and despite being very useful, the AI technique does not perform as per the expectations of many HR professionals. This may be because even when robots are taking over functions at the workplace but still many recruiters perceive and consider AI as an advanced technique. Tolan has mentioned and emphasized the type and nature of work

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that the HR managers have to handle in the recruitment process involves nurturing human relationships and the use of AI techniques will not lead to any change in that process at all. Almost all the researchers agree to this that actual applications of AI techniques in the recruitment process of an organization involve social media platforms or websites which is considered ideal and suitable for interactions and efficient communications between the recruiters and the candidates or applicants. This view was totally supported by Edwards, Martin,25,60,and Kunes.52 Moreover, the view of Banks et al.5 was that an active social media presence can act like marketing yourself on the part of both the applicants and recruiting organizations. Martin60 considered that it would be very useful for the candidates to have a particular website that is fully dedicated to the recruitment process. Many researchers have this view that the use of AI techniques in the process of recruitment will surely help and facilitate the recruiters especially in the tasks of evaluation, ranking, as well as processing qualifications of the applicants with more ease and correctly and it will be possible for the employers to recruit potential candidates directly by holding interviews. The majority of the recruiters expressed this view that apart from performing the tasks related to the recruitment process in lesser time and less costs with the help of using AI software, this technique or machine will provide an equal chance to all the candidates to get selected to the job due to very little or no human bias. Another opinion was that the use of the AI method will make it possible to find or identify both the silent but potential candidates as well as the most talented candidates simultaneously. It is stated by an HR professional that the use of an AI system to gain talent would make it possible to have a better insight into talent than the competitors, and hence it will help to increase the competitiveness of the company. Some HR professionals thought that even though the application of AI techniques will replace some tasks conducted by human beings in HRM functions, it is very important to maintain the human touch in the recruitment process as HRM functions are related to and deal with the people. It is important to mention that some professionals raised the issue and importance of the real need as well as the benefits of using AI technology in the recruitment process of the company. Therefore, it is crucial on the part of the companies to consider the actual effectiveness of the use of this technique for the organizations and also to ponder or think and observe more carefully as to how to apply the AI technique in the recruitment process without affecting the effectiveness of the organizations.

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2.9 BENEFITS OF ARTIFICIAL INTELLIGENCE IN RECRUITMENT FROM THE APPLICANT’S AND HR PERSPECTIVE It is a fact that although most HR professionals and researchers have accepted the vital role played by AI technology in the recruitment process of business organizations, not much has been told about the benefits of this technique, especially from the perspective of individual applicants. Such a view was expressed by Van Esch et al.100. In his study, Barber6 emphasized the individual and organizational perspectives and commented that it is rather critical to view the transformation in the process of recruitment from the point of view of the applicants, in particular rather than understanding wholly the organizational recruitment process as a concept. Practically, the workers are like a generation who got accustomed to doing things online by use of advanced technology, and the HR managers and recruiters need to be aware of this fact to be able to appoint the most qualified, talented, and best applicants. Kunes52 studied the advantages of AI technology in recruitment functions. It is important to mention that during the recent years, the HRM area has experienced technological advancement very well because the overall functioning of the organizations in the HRM field has experienced the impact of the Internet as online or e-recruitment had an enormous impact on HRM tasks in especially in terms of largely automating the recruitment process apart from other functions such as evaluation of performances of the employees and rewarding them accordingly. Dhamija20 pointed this out in his empirical study on e-recruitment subjects. He added that due to the advantages of online recruitment, organizations have turned toward using this technique widely because the tasks of finding potential candidates through the e-recruitment process are considerably easier, quicker, cheaper, and more efficient. Leong56 in his strategic review on technology and recruitment observed that the use of AI techniques enable the applicants to get real-time feedback at the time of applying for the job and it is quite valuable as the feedback is exclusively unbiased in nature due to the working pattern of AI technology. Further, Scherer89 said that the unbiased programs of AI should be assessed properly and critically only because they are self-learning programs and are prone to be learnt in prejudicial manner and patterns. Van Esch et al.100 in their studies on the role of computers in the recruitment process found that the applicants interacted with and were motivated in using technological devices due to the advantages and that has a positive

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effect on their willingness for jobs based on AI recruitment. This means that their studies have established the idea that the attitude and preference of the applicants for the AI technique-based process largely affect the recruitment procedures and process. This study observed that the main factor is the attitude of the applicants toward the organization and its hiring process that influence and affect their willingness to apply for a job rather than anxiety that arises due to less understanding about the AI technique. They could not come up with a clear-cut view of suggestions in their review as to whether the companies that use AI techniques in recruitment should remain completely transparent in their activities and fully trust the AI software-based recruitment process. Another view expressed by Tolan96 was that applications of AI techniques and social media should be effective for interactions between recruiters and applicants and adequate and proper communications between them should be really useful and important too. Hence, adding this technique to communication between HR professionals and candidates may be beneficial. It was pointed out by Upadhyay and Khandelwal99 that some tools like AI-powered interactive chatbots on social media sites and (or) websites of the companies that are able to answer the queries and questions of the candidates and provide even real-time feedback will be useful. Moreover, chatbots that can operate round the clock seem to be useful especially when communications may take place across several time zones or so to say for a long time, as a continuous process. This view was supported by Leong56, LinkedIn,57 and RES Forum.34 On these background applications of AI techniques can be estimated as very beneficial for the recruitment process from the perspective of both the recruiters and the applicants. 2.10 MAJOR CHALLENGES OF ARTIFICIAL INTELLIGENCE IN RECRUITMENT The application of the AI technique in HRM function gives rise to several challenges and the organizations using this technique for their tasks related to HRM face those major challenges in the process. (i) One important challenge relates to the improper and wrong implementation of AI techniques into the process and the lack of trust in the system. Having proper knowledge and adequate adaptability to AI technology are important for HR professionals because they need

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to know how to apply AI for HRM functions in the organization. It is argued that many professionals in HR departments are considered traditional system users and so it is very crucial that they need to pay proper attention to overall adaptability and proper use of the new technologies in the process. Unless the recruiters are well aware and conversant with the use of AI software and other tools in the technique, faulty applications may result in utter failure. Lack of skilled talent and (or) a dearth of skilled people can cause major challenges in integrating this technology with HR functions. (ii) Another challenge while implementing AI systems in HRM functions relates to the mindset of both the employers and employees. Due to a lack of proper knowledge and understanding among the recruiters about the application and operation process of the new technology, there is a slow growth of positive mindset of professionals in general and so this may be a major reason for the delay in adopting this new technology, mainly in our case. One more factor that influences having a negative mindset among professionals is the general idea and notion among the professionals that AI systems will replace HR in companies and they do not have the understanding that both machines and human resources are interdependent for achieving better outcomes and higher performances. (iii) The most important and major challenge is concerned with the case of eliminating bias of any form and prejudices. Although the system is designed to be free from all sorts of bias, this technique analyzes past data for learning and thereafter provides insights. A particular pattern may be present within the old data in the form of unconscious bias. The case of unconscious bias has been clarified in this way while analyzing the data, the AI system may find that attrition or conflict among men workers in the company are increasing in recent times, so the AI system may form a bias against men and may go for selecting only female candidates from a pool of resumes. As such, in the case of HRM functions, the AI technique may also be susceptible to error and biases like humans and so the recruiters may find it a real challenge for the process while implementing the AI technique. Moreover, it is also argued that an AI system can hardly be free from bias as in some situations a job applicant may be suitable based not only on the basis of job experiences, skills, and knowledge but also based on passion or attitude toward the job position. The AI system might not give any weightage or recognize attitude and motivation

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and will settle for qualifications and experiences. Hence, it is better if the opinion of the recruiters is given more preference. (iv) The matter of secrecy and privacy is of great concern and challenge in this system. The HR data is confidential and requires to be maintained quite securely. The matter of security of data is a huge concern in the case of amalgamating HR functions through AI techniques. (v) As it is in the case of other technologies, constant reviews followed by upgradation processes are a necessary part of AI technology which can make it a rather tedious maintenance process altogether. (vi) Furthermore, the case of integration of AI technology into the recruitment process is not widely recognized and accepted by many applicants in general and so many such candidates may miss potential job offers. (vii)Finally, due to shifting priority and emphasis toward “software as service (SAS),” the matter of availability of data is very limited and it poses a major challenge and reduces the scope of integrating HR functions technologically with full bandwidth, that is, with the AI technology successfully. 2.11 ARTIFICIAL INTELLIGENCE SOLUTIONS FOR DIFFERENT PROBLEMS It is a well-accepted fact that during the last couple of decades, both researchers and recruiters put more emphasis on finding out the advantages and real merits of AI techniques for introducing or integrating this system into the HRM functions for achieving better results and achieving their desired goals. In the process, although some major drawbacks are found or challenges of AI systems have been found and pointed out, very little effort has been made by the researchers and (or) the HR professionals to find out suitable solutions to those problems. In fact, with the rapid pace at which AI technology is evolving in the recent years, organizations require to exercise the AI technique with much more caution and rather carefully. First of all, HR professionals put adequate effort into finding out real-time accurate data as it is very important and essential for getting effective results from using this technique in HR functions. The datasets and information need to be free from bias and all-encompassing. As such, priority should be

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given and emphasis need to be put for collecting the right set of data first and then clear the output-driven and output-oriented objectives. Next the AI environment is indeed different from many other IT environments. Hence, it requires more emphasis on identifying plus scientific skills and preparing methodologies for creating suitable environments and platforms for implementing newer techniques like AI. It is truly essential to make sure and be specific about the collection of the right data sources and also cleaning and curating the same thoroughly. Another important solution lies in the aspect of clarity. Understanding and knowing about the insights to be driven out of processing and analyzing data is, of course, very important. There has to be clarity and a useful training process for knowing and understanding the correct patterns for studying and then acting accordingly. Elimination of all sorts of bias will surely serve a great purpose and be an effective solution for a major problem in the case of using AI software or AI technique. It is crucial on the part of HR professionals and recruiters to pay extra attention to the part and very task of feeding proper and effective algorithms and logic into the system. AI technology can deliver accurate and unbiased results based on the proper feeding of algorithms into the system. HR professionals must ensure the accuracy of data and they should be aware of the fact that this AI technique will perform as desired by the user but the system will not decide things on behalf of the users. Finally, HR professionals must be aware and should look into the matter of keeping a desirable integration between machine and human resources for solving problems and achieving the best result using advanced technologies in HR functions. The approach should be toward solving the technical problems and enhancing users’ cognitive experience and designing effective processes. 2.12 RELEVANCE OF INTEGRATION OF ARTIFICIAL INTELLIGENCE The main and underlying reason behind the willingness of organizations to integrate AI techniques into their recruitment process is the competitive advantages of using this method. In this context, it is clearly emphasized in the article, “Recruitment goes viral (in 2013)” that for modern business organizations, it is necessary to know and understand how to apply the latest technologies as well

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technological advancements in their general functions and recruitment operations properly and effectively. Researchers Edwards25 and Martin60 have categorically mentioned that techniques such as AI have changed the outlook of the recruiters in case of strategic recruitment because the use of this technology has enabled even smaller companies as large enterprises with the same opportunities to know and decide as to how much amount should be invested in the recruitment process. It can be stated safely that both the researchers and recruiters have realized and have agreed to the potential of utilizing AI techniques in recruitment operations. Although the AI system was taken as an invariable trend in the area of recruitment, surprisingly, very limited number of companies have opted for utilizing this technique. A valid argument for careful integration of AI technology into recruitment functions focuses on the possible generational difference between digital natives, that is, the modern younger generation who are well conversant with digital technology and digital immigrants, that is, the people who have very little knowledge about the technology. Practically, a lack of proper understanding and knowledge about how AI systems can be integrated into the process of recruitment can make the employees rather skeptical or doubtful about the new technologies and this may attribute to several negative ideas and emotions for use of AI techniques. It is observed that even today majority of the senior HR managers in organizations are in the category of “digital immigrants” whereas a large portion of the employees or workforce entering the companies belongs to the category of digital natives. This idea was reflected in the study of Dumeresque24 on the impact of modern, net generation on the business landscape. Further, he stated that the difference in ideas and contradictions between the two generations will certainly affect the way or pattern of conducting the business. This is because, the ways of processing information, analyzing, and communicating them with digital natives are considerably different from those ways in which digital immigrants are accustomed to doing or performing and this may result in disputes between the two generations. Dumeresque expressed his opinion stating that by realizing the potential of technological advancements or revolutions, the entire process and infrastructure of business must be changed and this will ensure fitness for the digital natives into the business world. Prensky74 mentioned that there is a digital language barrier between the two generations which may be led to significant problems in the case of communications related to education and the educational tools that are used. Neither Prensky nor Dumeresque talked directly about the usage of AI technology by the digital natives, however, Dumeresque did mention

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the technological revolution which entailed technologies related to the AI system. The reluctance observed among HR managers in the present time for using AI techniques in their HR functions including recruitment processes may perhaps be due to the fact that overall, they are unaccustomed or very less accustomed to using technological devices. 2.13 ETHICAL AND LEGAL IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE Business organizations need to focus their attention on the ethical and legal implications of software resulted from limitations of software usage but also concerned with deliberate choices of the companies in designing HR functional patterns. Practically, thorough analysis of some features of usage of AI technique gives rise to some valid legal and ethical questions because many times such questions are put or asked intentionally or unintentionally which can be considered illegal both from the legal and ethical point of views and may be even pose problems for the recruiters as well as regulatory authorities. HR professionals often ask questions on marital status; children; court cases and arrests, if any; country of origin; first language; debt status may be in common interviews which may be considered illegal and have no bearing on the job. This was the view of many researchers like Ging33. In the case of analyzing big data in the era of the application of newer and modern techniques like AI mostly, discrimination has been noticed in the data-driven system and in the language of law it has been termed as “proxy discrimination.” In their studies, Datta et al.19 and Prince and Schwarcz76 have expressed similar views. This term “proxy discrimination” means discrimination in favor or against a protected class or group, for example, higher group, higher income group people, or any particular race. It has also been stated that algorithms can very well detect and sometimes can even protect against indirect discriminations that result from automated analysis. In their research study, Pedreschi et al.73 found the possibility of getting an automatic Decision Support System (DSS), which is generally used for screening purposes in case of tasks, such as access to credit or mortgage, and others, that are socially sensitive in nature. The proposed use of inducting and deducting patterns for avoiding discriminating or discriminatory decisions despite the drawing of training datasets that may be biased in nature.

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On this background, the candidates need to prepare suitably for the interview process followed, and at the same time, the companies should make efforts for customizing their system and operation procedure for the selection of candidates in the future. It is true that the technologies not only have technical problems and limitations and biased datasets but also contain and provide misinformation as well as prejudices that worsen the problems. Typical and classic algorithms used in the software system invariably have favorable effects on people of the origin where the technology was created and developed. Moreover, the legal implication of some analysis and analyzing pattern followed is another important datable issue. According to many researchers like Wang and Kosinski,105 Hawkins,38 and Murphy66 image recognition technology and the use of deep neural network method are capable of identifying some features like age, sexual orientation, and others and therefore such tools have widely been developed and applied nowadays. It is worth mentioning that even big companies like Google are also considered to have biases toward male voices which commonly raise questions regarding the legal validity of most technological advancements. Often algorithms are tuned and used in the case of detecting age based on biological factors, type of skin, baldness, and so on. Under Spanish law, persons below 30 years and over 40 years of age pay less tax. Under this condition, AI technology may be taken as the cost-saving factor for persons of that age group and may be considered as discriminating against young persons, that is, between 30 and 40 years ago. This technology, therefore, is tuned in such a way that discriminates against any particular age group like senior or junior people, females, minorities, and so on, and as such the technique can reject them or leave up-to-the discretion of the employers for temporary or arbitrary nature of employment. Many researchers and even the American Society of Psychologists have criticized and spoken against such a system. Some have also argued that the publication of research studies undertaken by Wang and Kosinski105 is illegal and the use of that kind of software proposed by them is against fundamental civil rights too. 2.14

DISCUSSION AND CONCLUSION

Comprehensive study and analysis of the operational pattern, effectiveness, benefits and drawbacks of the technology of AI in the HRM functions of

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the organization have enabled us to come to the idea that this technology is altogether a new generation cum most modern technology or technological advancement tool which can facilitate the HR professionals to feel, think, plan, implement, and undertake the tasks that considerably improves human performances without facing much better and in a more effective manner than humans resulting reduction in turnover rate and increase in talent retention. Researchers, in general, proposed such an AI framework that comes under the umbrella of HRM functions. In the recent years, AI software and ML algorithms have been widely used in the field of HRM functions where AI technique plays a very vital as well as an integral role in cases of recruitment, screening selection, hiring analyzing employee performance, collecting data related to employees, providing real-time as well as accurate information. Practically in the recent years, business organizations have experienced an era of competition and competitiveness along with tremendous growth in the industrial sector. They have recognized the need along with tremendous growth in the industrial sector. They have recognized the need for enhancing the speed and performing HRM-related functions with much more effectiveness and efficiency. Therefore, most organizations are keen on adopting modern technologies like AI and ML tools, and digital technologies. Such efforts on the part of HR professionals have proved quite paying as the integration of AI techniques into HR function has helped getting better performance outcomes and achieving desired organizational goals. It has been observed that by educating companies and also the individual employees about the usage of AI techniques, it may be a transition from traditional and orthodox HRM functions to a more modern vision or pattern may be possible where AI plays a vital role. In addition to that knowing and being aware of the possible challenges or problems that may arise out of the utilization of this technique can also help the organizations to achieve greater success in HRM tasks and thus improve their business operations with the help of applying the most modern technologies. It is observed that AI technology has been used in HR functions for a couple of decades and the system had a beneficial impact invariably on the recruitment process by reducing work-hours needed and helping the recruiters to make rapid hiring decisions. The technique has also made it possible for the workers to rehearse and be prepared more in extreme situations without any adverse effect on the business house and client or customer relationships by facilitating the recruiters to conduct and impact corporate training effectively. AI system has also helped HR managers in case of evaluating

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the performance of the employees by way of monitoring the workers on different digital platforms instantly. As such, operation pattern AI-backed and powered technology must be understood better and utilized correctly and properly for harvesting the benefit of improved performance of both the employees and the organization as a whole. The crucial points that the recruiters must know and emphasize relate to the fact as well as to what extent the organization should have already integrated the AI system in the recruitment processes. Second, how the present generation workers may know and perceive the integration of the AI system into the HRM functions. It has been well observed and experienced by the companies today that AI software-backed HRM functions have helped to enhance employee throughput considerably and thus attaining overall growth of the organization. Practically, HRM is the important area that serves the entire organization and so the companies obviously need to pay extra attention and make extra efforts in this field, in particular. It is true that during the last two decades or so, the process of undertaking HRM functions has experienced radical changes, and the basic introduction of modern technology as an AI system means that changes are really needed to maximize the benefits and also to minimize or mitigate the shortfalls in business functional. 2.15 FUTURE WORK It may be safely and logically concluded by mentioning that the basic aim was to understand a study or research on AI-based software and its usefulness in performing HRM functions of business organizations. The study has revealed the fact that the technique of AI is advancing in an unsurprising sequence and pattern with mechanical applications before analytical and the latter is chiefly forerunning intuitively and going ahead of empathetic intelligence. There is least of doubt that the AI technique will effectively carry out even the intuitive and empathetic undertakings and in that process make possible human–machine blending for organizational functions in well-oriented, groundbreaking manners. It is obligatory on the part of the HR professionals of the companies to know, be aware and understand that this technique has the potential to cover and perform better in many other areas of HR functions such as

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employee retention, turnover, employee promotion matters, and also in cases of employee attendance and activities within the organization and outside too. Hence, the recruiters in the future design their business function plans in an extended and suitable format to cope with desired and more effective working and function patterns by proper utilization of this technique in those fields also. Looking into the fact that rapid technological changes and so to say technological revolutions are imminent and rather unstoppable and in the recent years, the area of HRM tasks are constantly developing becoming quite vast, so the HR professionals of the organizations should make comprehensive future work plan on this perspective and keeping pace with rapid technological advancements in the coming years. The highest priority and most important concern of every company in the future should be on more effective and efficient management of human resources realizing the fact that human resources or human capital are a valuable source for development and may be considered as the pillar of success by way increasing productivity, attracting more talented candidates and larger customers to cope up with more competitions in future days, on the other hand, companies must focus on the ever-evolving pattern of intelligence field and the emergence of new techniques, approaches and methods in this particular area. In the present-day setup, the majority of the HR professionals of the organizations are well aware of the fact that integration of AI techniques into HR practices will have a stronger impact in improving and enhancing organizational performance but at the same time they have a real fear or they are suffering from an acute belief that AI technique will replace humans and (or) have an impact of job cutting in a different sector. However, this is neither true nor a reality as modern and advanced technologies are not replacing people. The HR professional should and require changing their views and outlook and simultaneously broaden their ideas and outlook by gaining knowledge about the effect of these technologies in creating and increasing wealth and prosperity. It is observed that currently most organizations are successfully using or integrating AI techniques in their HRM functions, especially in the recruitment process but they should have the foresight and need to understand that in near future AI will be everywhere in the HRM process and widen its operational area. Moreover, most organizations today are still lagging in the case of utilizing AI techniques in their HRM functions because of the cost or expense factor associated with this process. They should look beyond,

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ignore the cost factor, and have more practical as well as optimistic views and approaches in the coming days realizing the fact that implementation of AI techniques in HR functions will provide much more optimistic opportunities and the system has the potential of enhancing the possibilities of the betterment of the lives and status of the employees through more successful business operations. The professionals must know and understand that AI technology is a sort of machine capacity to impersonate human ability for solving problems, learning, critical thinking, and perceptual skills. Organizations must understand the useful impact of this technique in a better way and properly utilize this technique in the future. They should have a fair, clear idea of the fact that strong AI systems can very well foster a deeper understanding of the behavior patterns of people. The HR professions should pay more attention in the future to consolidating and comprehensively analyzing the mood and intentions of the employees on some different digital platforms so that human behavior can be simulated and validated further for a rather more useful type of employee experience. It may be highlighted that there are, of course, some common challenges that HR professionals might face or come across in its current state in the process of integrating AI but HR professionals and recruiters should make efforts in finding out means for overcoming such challenges in the future days and emphasize more on getting the most valuable benefits of using such a technique rather than keeping in mind a negative approach or avoiding tendency. Although the progress in the area of merging AI with HR functions is at present lagging in India, still there are plenty of opportunities available for catching up with the tool and it solely depends on the changing mindset of the users of the technique along with spreading more awareness about the transformation benefits of the AI technology for overall HRM functions. The matter of the merger of AI and HR is a positive affair and HR professionals should have the intention to utilize this technique for achieving better results in the functioning of the organization and being enabled to act as strong strategic partners. Finally, it is important enough to point out at present the vital and significant role of HR managers evolve in planning and driving strategic growth and it is equally important in developing technological or business leadership. In the present, existing scenario of the advent and widespread COVID-19 in the world, the HRM functions need to be redefined by the organizations positively. Truly, HRM as a function has been transformed

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from a core intrinsic profile to a mechanism that is much more technologyoriented and will be largely based on AI software and machine learning algorithms. Now the majority of companies have realized that the only and best way of survival in the present era of rapid and wide technological advancements is to keep pace with those changes and design and plan work format or pattern of functioning accordingly learning more toward implementation of advanced modern technologies. It must be taken into account by the HR managers that advanced technique like AI is capable of maintaining Folders and Bulletins related to the health conditions of the employees that may be useful for overcoming the problems and dealing with further phases of the pandemic in the future days. More extensive studies and research work on the above-stated narrated issues by the researchers in the future will serve a good deal of purpose for the fault-free and least problem-oriented application of AI-backed and AI-based techniques. If conducted successfully, these techniques will reflect ways and means to overcome possible challenges by the user in a new and more effective era of organizational development through applications of advanced technologies in the near future. KEYWORDS • • • • • •

artificial intelligence human resource management screening recruitment applications of AI HR perspective

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96. Tolan, T. Why A. I. Will Never Replace Recruiters. Healthcare Informatics; Third Quarter 2018, 44. Retrieved from: Aalto Finna Database. 97. Trevino, L.; Butterfield, K.; McCabe, D. The Ethical Context In Organizations: Influences On Employee Attitudes And Behaviors. Busi. Ethics Quart. 1998, 8 (3), 447–476. 98. Tuchscherer, R. Walmart Uses Virtual Reality to Test New Store Managers, 2019. https://eu.usatoday.com/story/tech/2019/07/08/walmart-uses-virtualreality-hire-newmanagers/1635311001/ 99. Upadhyay, A. K.; Khandelwal, K. Applying Artificial Intelligence: Implications for Recruitment. Strategic HR Rev. 2018, 17 (5), 255–258. 100. Van Esch, P.; Black, J. S.; Ferolie, J. Marketing AI Recruitment: The Next Phase in Job Application and Selection. Comput. Human Behav. 2019, 90, 215–222. 101. Ved, S.; Kaundanya, N. S.; Panda, O. P. Applications and Current Achievements in the Field of Artificial Intelligence. Imperial J. Interdisc. Res 2016, 2 (11), 932–936. 102. Vernon, G., Brewster, C. Structural Spoilers or Structural Supports? Unions and Strategic Integration of HR Functions. Int. J. Human Resour. Manage. 2013, 24 (6), 1113–1129. 103. Vinichenko, M. V.; Rybakova, M. V.; Сhulanova, O. L.; Kuznetsova, I. V.; Makushkin, S. A.; Lobacheva, A. S. Using Natural and Artificial Intelligence in the Talent Management System. Int. J. Recent Technol. Eng. 2019, 8 (3), 7417–7423. 104. Wang D. et al. A Problem Solving Oriented Intelligent Tutoring System to Improve Students’ Acquisition of Basic Computer Skills. Comput. Educ. 2015. 105. Wang, Y.; Kosinski, M. Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation from Facial Images. J. Abnorm. Psychol. Soc. Psychol. 2018, 114 (2), 246–257. 106. Wislow, E. Beworkhappy.com, 2017. https://www.faceb ook.com/beworkhappy. 107. Wright, P. M.; Ulrich, M. D. A Road Well Traveled: The Past, Present, and Future Journey of Strategic Human Resource Management. Annu. Rev. Organiz. Psychol. Organiz. Behav. 2017, 4 (1), 45–65. DOI:10.1146/annurev-orgpsych-032516–113052 108. Yang, C.; Lin, C. Does Technical or Strategic HRM Provide a Better Explanation of Organization Performance? Ibusiness 2014, 06 (02), 52–62. DOI: 10.4236/ib.2014.62007 109. Yano, K. How artificial intelligence will change HR. People and Strategy. [e-journal] 2017, 40 (3/43). http://web.b.ebscohost.com.ezproxy.metropolia.fi/ehost/pdfviewer/ pdfviewer?vid=3&sid =bcb89e30–3620–44fe-8b28–3c32bec05e6b%40pdc-vsessmgr03 110. Yawalkar, V. A Study of Artificial Intelligence and Its Role in Human Resource Management. February 2019, 6 (1) (E-ISSN 2348–1269, P- ISSN 2349–5138). 111. Zielinski, D. Recruiting Gets Smart Thanks to Artificial Intelligence. HRNews, 2017. Retrieved from: Aalto Finna Database. 112. Zielinski, D. Get Intelligent on AI: Artificial Intelligence Can Boost HR Analytics But Know What You’re Buying. HR Magazine. [e-journal] November 2017. http:// web.b.ebscohost.com.ezproxy.metropolia.fi/ehost/pdfviewer/pdfviewer?vid=11&si42 d=bcb89e30–3620–44fe-8b28–3c32bec05e6b%40pdc-v-sessmgr03 113. Thomas, J. R.; Nelson, J. K.; Silverman, S. J. Research Methods in Physical Activity. Human Kinetics, 2015.

CHAPTER 3

THE CONFLUENCE OF SMART COMPUTING AND TRADITIONAL BUSINESSES TO FOSTER PRODUCTIVITY, PROFITABILITY, AND PROSPERITY SUMIT GUPTA and SOURAV BISWAS Department of Computer Science and Engineering,University Institute of Technology, The University of Burdwan,Golapbag (North), West Bengal, India

ABSTRACT Human beings have permitted technology to slowly yet steadily step into their personal space and become a part and parcel of their lives. In this era of digitalization, popularly known as Industry 4.0, computers and Internet have become a necessity for businesses to grow and flourish. From cyber security to product manufacturing, e-commerce purchase to freight management, ticket reservation to tour planning, hotel booking to food ordering, banking transactions to bill payment, etc. Smart computing is turning out to be one of the leading and the most advanced transformational technologies for driving industries and impacting livelihoods. Systems and products built using machine learning, artificial intelligence, cloud computing, mobile computing, social computing, Internet of Things, and various other sensor-based technologies are instrumental in boosting up the revenue and

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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productivity. Through this work, few case studies have been presented in an algorithmic fashion and/or using workflows and graphs to showcase how smart computing technologies have revolutionized the overall business workflow and scaled up the revenue generation. Such latest computing frameworks not only reduce the time of processing, but with the aid of advanced technologies, they also provide the current market trends and patterns as per which the production rate of the most sought-after items can be enhanced. This sort of data analysis on voluminous data, technically called as the big data analytics, enables businesses and industries to invest properly on the type of raw materials that are marketable, and unnecessary wastage and exploitation of raw materials and natural resources can be avoided. Smart computing can save a lot of time in transaction processes as well. It enables the industry and business to adopt affordable technology easily and opens up job avenues for tech-smart people. In near future, smart computing will be the foundation for newer technologies, thereby proving effective for better decision-making, optimum strategy-building, and efficient business outcomes. 3.1 INTRODUCTION Of late, due to incorporation of smart computing and its allied technologies, there has been a lot of improvement in the field of business, education, government sectors, and other such areas.1,2 With the advancement of technologies like machine learning and artificial intelligence, smart computing not only improves flexibility and security, but also boosts up the net worth or profit in businesses and industrial applications.3–5 Presently, due to the COVID-19 outbreak, many businesses have either closed their operations due to bankruptcy or cut the manpower and other resources to anyhow survive the huge losses they have suffered due to the operational lockdown and economic inflation. It has been forecasted that smart computing can surely come to the rescue of the business industry.6,7 Through the concepts of machine learning and artificial intelligence, huge data can be handled easily and analysis could be made for the betterment of a company in the business. Data analytics allow large amount of data to be analyzed and a suitable outcome can be produced by the industries. On the basis of such data, certain sectors of the manufacturing units can be enhanced and their manufacturing speed and productivity can be scaled up in an efficient manner. The sectors where either very high revenue or very low revenue is generated can be detected easily through smart computing technologies and a strategic

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approach can be adopted to reduce the losses and maximize the profit. Smart computing is indeed an effective method to integrate and synchronize the computer hardware, software, social media, and other communication networks with digital sensors, smart devices, Internet of Things (IoT), big data analytics, computational intelligence, and intelligent systems to realize various innovative applications.8,9 Smart computing can be classified into two domains, namely, designing and building smart computing system and how smart computing can be used to design smart solutions to make human life better. Smart computing is believed to improve the business in three domains: first, improvement of transaction process, second, optimizing the assets of service-based industries where the main focus is on building assets’ base and selling them in the form of service, and last, different industries have different needs, smart computing will be a great technology to boost up the productivity of business. In fact, a study suggests that smart computing can improve the GDP of a nation.10 Smart computing is one of the highly ranked technologies for adoption plans. It is believed that smart computing can link the government and the industries which will not only boost up the revenue of the industries but also will scale up the revenue of the government too. Eventually with the increase in revenue of the government, the overall GDP of the country is believed to increase. Unlike the horizontal technologies of personal computing and network computing, smart computing will have a greater focus on vertical industries, such as hardware and software industries, aerospace, chemical, education, energy, and various other industries too. Smart computing will revolutionize the present era of business industries or can perhaps mark a new beginning for a smarter business where the main focus would primarily be on productivity, profitability, and prosperity. It will surely bring an end to the unnecessary exploitation of natural resources and raw materials and can prove to be a game changer in building a sustainable society.11 It will not only save the manpower, but also will cut down the time of a lot of operations as the processing speed will be enhanced and augmented. Smart computing will reduce the errors and mistakes made by humans as computers are capable of storing and managing large amount of data, so with the help of smart computing, it becomes easy, efficient, and faster to manage large amount of data and analyze them without committing any error. Smart computing will indeed change the business scenario in the world. Business will flourish in leaps and bounds with the introduction of smart computing. Thus, due to the use of smart technologies in various industries, such as automobiles, genetics, medicine, finance, agriculture, and education, traditional procedures can be aptly automated, processing time

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and production errors can be minimized, huge volume of data can be easily analyzed, and better and optimal planning and decisions can be made to build a sustainable society.12 The rest of the chapter is organized as follows: Section 3.2 explains some of prominent related works and few real-life applications developed in the domain of smart computing and smart technology-enabled systems. Section 3.3 discusses various challenges and issues that surface while designing the real-world smart systems for various industrial usages. In Section 3.4, the proposed work has been presented using multiple algorithms enlisting the steps required to implement the notion of smart computing in major industries, such as banking, ecommerce, railways, food ordering, and hotel booking. Section 3.5 presents an analysis using graphical plots showcasing the prospective change in the rate of productivity, profit, production, and growth of various sectors before and after the implementation of the concept of smart computing in traditional business workflows. The conclusion of this chapter has been discussed in Section 3.6 followed by the future work that needs to be taken up to create smart systems deployable in the society for achieving profitability and building a sustainable world. 3.2 RELATED WORKS The effectiveness of computing resources can be very well estimated from the fact that whether it can provide low latency and continuous production in the industries or fails to attain its set goals and targets. Smart computing is one of the best techniques through which a low latency environment can be provided. Constant upgradation of the system will be required to keep the system updated with newer techniques, processes, and cultures. To understand more about the role of smart computing technologies in various domains and industries, a lot of research papers have been studied. A few of the prominent works have been discussed in this section. The authors in their work13 have proposed a hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. There has been lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low latency requirement. They have proposed a hybrid computing framework and the intelligent resource scheduling strategy devised by them aims to fulfil the real-time requirements in smart manufacturing businesses with edge computing support. In Ref. [14], the importance of artificial intelligence in the cyber space and during the Industry 4.0 era has

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been discussed. In continuation to the work15 related to the growth of cyber technology for cyberization, it has been seen that the cyberization process encompasses the computational intelligence techniques and/or methods that are at the core of cyber world enabling processes. Recently, there has been a tremendous advancement in the fields of machine learning and artificial intelligence. The authors in Ref. [16] have discussed about smart computing drives that showcase the new era of IT growth and give an overview of the growing demand and the need of technology in the imminent future for catering to the needs of various stakeholders. It also provides an insight into the latest and modern smart computing-based technologies with the onset of Industry 5.0 revolution that are going to be adopted. In the studies of Cvar et al. and Khosravy,17,18 various applications of IoT in real world such as to safeguard natural resources, ocean underwater and energy, in transport and mobility, for smart building works, in government sectors, and for building economy and society have been discussed. This research is a successor to the work by the authors in Ginige et al.19 that propose a smart computing framework centered on user and societal empowerment to achieve the sustainable development goals. This framework basically focuses on a series of solutions that the authors have developed for the agriculture domain to address the first three United Nation’s Sustainable Development Goals (SDGs) viz., no poverty, zero hunger, and good health and well-being. Elmaraghy et al., in their work,20 have discussed about the prospect of Adaptive Cognitive Manufacturing System (ACMS) that can act as a harbinger for achieving transformations in the digital as well as the cognition space by working in tandem with four axes viz., products, technology, business strategies, and production. Chen et al., in their study,21 have explained that smart factory adopts the combination of physical technology and cyber technology and it deeply integrates the previously independent discrete systems making the involved technologies more complex and precise than they are now. Furthermore, it has also been surmised that with the application of smart computing, the complexity can be reduced significantly. It can be inferred from Ref [22] that a proper management setup and a proper user-friendly entry interface should be designed so that the information can be stored consummately, just as in the case of the passenger information system and/ or the freight information system. Multiple smart computing-based applications have also been developed and proposed in the past. For instance, Smart Street Dustbins23 and the use of IoT technology in managing and disposing waste,24 smart farming,25 AI-Chatbot-driven Advanced Digital Locker,26 ADIET Recommender System for balanced diet recommendations,27 nature

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inspired computing for Wireless Sensor Networks,28 etc. Such applications open up avenues for further exploration in the domain of emerging technologies where smart devices and/or systems can come to the rescue or aid of human beings in solving real-world challenges. 3.3 CHALLENGES AND ISSUES In the past decade, it has been observed that most of the businesses and industries did not adopt machine learning and artificial intelligence on a greater aspect, which has led to the minimization of profit and no such growth in revenue generation has occurred. Moreover, tasks, such as data manipulation and data analysis have been performed manually using more number of people, which ultimately resulted in poor resource utilization. The increased human intervention consumed a lot of time and effort, thereby reducing the revenue generation, minimizing the profit, and affecting the productivity of the business. Due to the delay in handling and analyzing big data, the output that was to be fetched also got delayed, thereby reducing the efficiency and productivity of many industries and businesses, and the outcome was not optimal. There is no denying the fact that transaction processes have always had a major effect on many businesses. The cumbersome and intricate process of banking and billing consumes a lot of time and manpower. Delay in transaction has often led to delay in providing the services or the products. There has always been a dearth of fast, efficient, and hassle-free transaction system since past few decades. Due to these reasons, the overall productivity is affected and it has reduced heavily. The COVID-19 outbreak has had a multidimensional impact across the world, and in this pandemic, “work from home (WFH)” culture has been adopted by most industries. This has kept intact the productivity of some industries and allowed them to function normally but myriad industries failed to synchronize with the WFH culture, and thus, such industries and businesses could not survive the lockdown period imposed due to the pandemic. A major factor behind this downfall and menace is the issues behind on-time transactions, payment billing, and other various services that led them lose clients and customers, and as a result, these business and industries came to an end, thereby increasing the unemployment rate in the society. Failure in adopting the best business methods for corporate houses has often led to decrease in effectivity and productivity. There are a variety of

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business domains or field, namely, accounting, marketing and sales, finance, consulting, entrepreneurship, and so on. These fields are totally different from each other and depend on the varied and context-dependent methodologies and require investment on manpower or personnel who are experts in their allied fields. Some industries have initiated the adoption of new working culture and principles from different parts of the world to ameliorate the workplace and enhance the performance of individuals as well as the organization. Although the rate of adoption of new technologies is quite low and that negatively results in a big impact on the throughput, these transformational technologies are not much trusted by many industries, thereby creating a backlog in this emerging era. In fact, the bigger challenge lies in realizing and accepting that only a handful of employees and personnel are inclined toward acquiring knowledge about the latest and emerging tools and techniques. There is a sharp reluctance among many people, especially the senior-level staff members, in up skilling themselves with the latest market trends and technologies because they have been used to the traditional setup for quite a long time and have been accustomed to the workflow and requirements. The Indian Railways needs a lot of upgradation with the introduction of new technologies. Some of the services in railways are inefficient and they lack a lot in providing faster services to their passengers. Some slow operations cause huge delay in providing services and are responsible enough for the decrease in system’s efficiency and passengers’ frustration. Sometimes passengers, who face any technical or functional problem, need to complete a lot of paperwork which consumes enormous effort and time of both passengers and railway employees. In case of hotel bookings, customers often face scam and fraud bookings followed by some inefficient services like slow transaction processes. It has come to the notice that during any religious event or during peak time, some of the places that are tourist attractions witness a lot of sour experiences. The hotel owners fail to match the demand for rooms with the supply and this may end up returning customers without accommodations. As a result, many people have to cancel their plans. Sometimes, during the booking process, due to network glitch or feeble Internet connectivity, payment transactions are slowed down leading to the cancellation of the hotel booking and the tourists or customers have to unwillingly call off their excursion plan. Unfortunately, incidents also crop up where customers are cheated by some hotels or lodges whereby they are denied of their booking and their money is not refunded. There is a lack of an efficient customer rating review system as well, and it

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has become a common sight where quite frequently, the hotel management manipulates the ratings and reviews, thereby duping the customers or tourist using fake or maligned ratings online and ultimately leading to scams. Another important challenge in the traditional workflow is that manufacturing suffers from wastage of raw materials. The prime reason behind this loss is the overuse of those raw materials for developing the items that are not required or are less demanding for the society. Also, due to this, there is sometimes a shortage of the most demanding items in the market. A lot of natural resources have been overexploited in the past and they continue to face the same fate in the present times as well. Along with natural resources, time, money, and energy are also being wasted in some insignificant places.29 3.4 PROPOSED WORK With the introduction of machine learning and artificial intelligence, smart computing has emerged in the recent scenario. Smart computing aims not only to cut down the time but also to save efforts of the manpower engaged in the task. Smart computing is believed to maximize the profits of industries and businesses to a large extent. Smart computing will not only increase the productivity but also will increase the efficiency of the industry and business as it will cut down the processing time by using ML and AI. With the application of ML and AI, a large data can be analyzed, and with the help of that data, an estimation can be made where it will find the products, services, and items that are mostly needed for the customers. This will remove the unavailability of items, products, or services, so that needy people get their desired products on time and there is no dearth of products, items, or services. Figure 3.1 demonstrates the process followed in any industry or business from getting input from the customer/client to developing and delivering products/services using the smart computing paradigm. Thus, it can be easily observed that smart computing technologies, such as ML and AI are used; the processing time to serve the customer gets reduced. The less the processing time, the more will be the acceptance of services or product orders, which will eventually maximize the net profit. Certainly, the implementation of smart computing will not freeze or reduce the vacancy because experts from ML and AI background will be required to upgrade the systems, and therefore, learning about such emerging technologies and acquiring modern skill sets to meet current demands will become the prerogative of active and alert employees. “Nothing in this world is constant,

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the only constant thing is change”—these lines are so true that no one can reject or disagree to the fact that only knowledge acquisition can pave the way for a better and prosperous future. Thus, newer job opportunities will blossom for skilled manpower and the myth of technology taking away the jobs can be busted. Smart computing aims to impart a new dimension to the expertise of an individual rather than taking away their employment. Smart computing, with the help of AI and ML, will handle, analyze, and provide an appropriate outcome. As a result, newer technologies will flourish. Figure 3.2 highlights the fact that besides opening new job opportunities, smart computing and its allied technologies will boost up many tasks, operations, and processes in corporate and other sectors.

FIGURE 3.1

Role of smart computing in business product development or service delivery.

FIGURE 3.2

Potential impact of smart computing technologies.

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Smart computing will not only improve the speed of transaction but also will ensure a secure and reliable transaction both between the customer and the company as well as between the company and the financial organization (or bank). It will reduce the hassles of lengthy billing payment processes by cutting down the processing time incurred and saving the efforts of manpower by automating most of the trivial and clerical processes. As a result, the whole operation will be quick, easy, and secured. Through smart computing, end-to-end encrypted transactions can be made which will ensure more secure transactions between the end parties. With the use of smart computing, the need for physical presence in banks can be avoided, so, during this pandemic-like situation, a lot of industries and businesses can easily survive. The banking process will also become easier as with the help of smart computing, the required documents can be sent over Internet to the banks and an easy and smooth process would be conducted during the transaction. Here, as the process would be conducted online, therefore, a lot of time can be saved and minimal human labor will be required. Therefore, smart computing will be very effective and will certainly prove to be one of the greatest technologies that have been ever adopted for smart business transactions.30 Figure 3.3 depicts the smart transaction process that generally takes place between a customer and a firm and a firm and a bank.

FIGURE 3.3

A smart transaction process.

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In this era of emerging and transformational technologies, it is important to revamp and remodel industries and businesses with the growing technologies to survive in the competitive market. The continual and widespread integration of machine learning and artificial intelligence is an ample proof that smart computing plays a major role in dealing with data and analyzing it. Smart computing will help industries and businesses adopt the latest cultures and technologies and thus will become the foundation for various other newer technologies that will refurbish the work culture and surroundings. With smart computing, it will not be a Herculean task to skill up employees who can produce better products and thus can reap benefits for all the stakeholders. Smart computing basically focuses on providing cheaper technology for solving the problems that are prevalent in the society and the industry, and supports sustainable technologies and work culture for the future generation. Banking transactions can be made much faster using RFID cards. Radio frequency identification (RFID) tags are instruments, which share information in the form of electromagnetic fields between the reader and itself. The tags will contain the information of the account holder. When the tags are placed on the reader, the information will be loaded on the device and the person can proceed with the transaction process without much hassle.31 During money transfer, these cards can easily be used to deposit or withdraw or transfer money which makes it quite efficient and less time consuming. Efficient use of blockchain technology and cybersecurity in banking transactions will reduce frauds and will promise to ensure proper safety and security during banking transaction.32 Blockchain-based malware detection systems can prove to be effective in protecting networks and computer sytems from myriad malware attacks.33,34 The use of smart transaction process is equally important with the application of smart computing because banking transaction processes often get delayed. Hence, to reduce the transaction processing time, a smart, secure, and faster means of transaction process system is required. Such a smart transaction system is possible with the implementation of smart computing-enabled technologies. Figure 3.4 points out that smart computing is basically a sensor-based technology with a combination of the some popular emerging technologies, such as Internet of Things, machine learning, big data and artificial intelligence, to name a few. Smart computing will indeed change the scenario and give rise to newer technologies that will be adopted in the near future which will in turn become beneficial for the people in specific to the society and humanity at large.

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FIGURE 3.4

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Smart computing as an amalgamation of other emerging technologies.

In Indian Railways, it has been observed that during a particular time of the year, passenger rush would be obvious that might be due to celebration of any religious festival, vacation, or can be due to some other reason. So, with the help of ML and AI, a detailed analysis of occupancy on a particular route can be determined and suggestions for more trains can be generated. Similarly, with the help of proper information and detailed data analysis of occupancy over a year, trains can be reduced on a particular route , and as a result, wastage of time, money, electricity, and other resources could be avoided. Smart computing can also be applied to manage the traffic in the railways. With the applications of smart computing, the operation of trains can be controlled. Heavy traffic can be easily handled and managed when a particular train is approaching a station or has to be diverted through another route or directed to some particular tracks by using smart computing. Using sensor-based technology and IoT, the real-time monitoring of trains entering

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or departing a station can be done, then by using machine learning and artificial intelligence, a proper platform and route can be fixed so that trains can enter the platform and the passengers can get off the train with ease. Through the application of smart computing in railways in the traffic control unit, trains can arrive on stations at their scheduled time. Thus, it will also reduce the workload of the traffic controller. This will help to enhance the punctuality of many trains. As a result, more and more people will trust and prefer train travel and this will certainly boost the revenue of the railway industry. Thus, with such reformations in railways, an efficient, less time-consuming, and hassle-free service can be provided to the passengers. In hotel bookings, to avoid fraud and scams, unbiased customer review and rating system must be made compulsory for every visitor and/or customer who checks in the hotel or stays in. After that, the reviews and ratings will be stored in a database and then using ML there would be an analysis of proper reviews provided by the customers, and the best, average, and worstrated hotels will be shortlisted. While searching, the artificial intelligence will provide the appropriate search results as per the user search based on the machine learning-based shortlisted items in a customized and sorted order. The transaction procedure can also be optimized and faster transaction confirmation can be provided to the customer. This will help other customers in booking hotels as per their own requirements, such as budget, location, or capacity with ease. This will eventually reduce the time of booking as well as it will lead to more revenue generation as the ratings of the customers would create an impact particularly on some of the best hotels. With the help of smart computing, a report can be generated that can recommend items by analyzing the most demanded items and less demanded items required for the markets. Machine learning techniques can come to the rescue in building up such a recommender system. This process will result in proper allocation and use of raw materials in generating and manufacturing the most demanded items in large scale. This will help to reduce the wastage of raw materials for least demanded items and will also help industries and businesses to stop from exploiting the natural resources. It is a harsh reality that a lot of useless items that are no more required for the society get manufactured and at the end, it is wasted in huge amount. Also, in the case of railways, at some time of the year, some trains run on zero-percent occupancy. Through ML, the occupancy ratio can be aptly estimated so that the electricity, manpower, and other energies can be saved for future use. So, with the application of smart computing, natural resources can be saved from over exploitation. This will give rise to a term named smart productivity.

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Algorithm 3.1 explains how smart computing can aid in enhancing the performance of business processes during e-commerce and banking transactions. ALGORITHM 3.1 Steps for Developing smart computing Systems for Ecommerce and Banking Transactions.

Step 1. Take the order of product or service request from clients, user, or customers as input or if any sensor-based technology is deployed, then it will acquire or capture the necessary data as an input. Step 2. Send collected data to the cloud storage (if available) or store it in the local database. Step 3. Using machine learning, a company will be able to keep a track of what type of items are being ordered, the demand of the market and other case scenarios. Step 4. The billing process and transaction section can also be handled with ease by the use of smart computing technologies. A faster, efficient and secured transaction payment method can be developed for customers to make use of digital transactions easily and most reliably. Smart computing will give a boost to cashless transactions. Step 5. The transaction can also be performed by smart computing-enabled devices and/or systems, where the customer can pay any requisite amount and the transaction and finances will be aptly collected and handled securely. Step 6. ML and AI can also be used to manipulate data taken from user and analyze the purchase pattern. This will enable customer-specific choices to be recommended easily and whenever the same person attempts to buy any item, he/she can easily repurchase the product(s).

Step 7. At the end of any transaction or business process, the receipt or bill will be

printed and the money can be transferred to the bank directly by the use of smart

computing, thus avoiding the scam or inclusion of any third-party or middleman.

Step 8. The money can also be returned to authentic and authorized customers in

case a product is returned or the cancellation of a prepaid service takes place.

Step 9. Through the concept of big data analytics, data of numerous users can be

analyzed and a report can be published based on which the most ordered products’

production can be scaled up and the most requested services can be increased or

given more cognizance and more manpower can be allocated.

Step 10. There would a section where the user can request for new features. To fulfil

such requests, new concepts and latest technologies will be developed and engineers

and developers can add the new feature which will enhance the efficiency of the

system and make smart systems even smarter.

Step 11. Novel work and production culture will be explored through the use of smart

computing-based systems which will eventually maximize profit and productivity.

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Step 12. A lot of natural resources will be saved for future usage and thus the goal of sustainable development can be made true.

Algorithm 3.2 lists the steps for designing and developing a smart computing application deployable in the railway industry to monitor the occupancy of trains, and depending on the demand, suggests for decreasing or increasing train services on any particular route. ALGORITHM 3.2 Steps for Developing smart computing Systems for Railway

Industry for Booking and Route Estimation.

Step 1. The data of passengers and requested booking information are recorded and stored. Step 2. The stored data are sent to a database for data analysis. Step 3. Analysis of data is done depending on the months when a train runs at its full occupancy and routes of trains. Step 4. Machine learning can aid in predicting the routes on which more people plan to travel during a particular time of the year. Step 5. Thereby, artificial intelligence will provide appropriate message to the authorities to increase or decrease the number of trains plying on a particular route depending on the occupancy and demand of passengers who wish to travel during that period of time. Step 6. In railway industry, the systems built using smart computing and its allied technologies can control traffic and passengers’ rush easily and will prove to be one of the best technologies for gathering the information of all the railway stations and for setting the route and making way for the train to reach the platform comfortably and quickly. Step 7. Through new types of datasets and information, the system can be made efficient because if more amount of data will be generated and analyzed, better will be the booking prediction with enhanced system’s performance.

Smooth and efficient operation of train services will be marked with a greater profit and revenue generation. It will also reduce the problems of the passengers who are in an urgent need of traveling. The steps for designing a smart application in railway industry to manage train traffic at the station have been proposed in Algorithm 3.3. ALGORITHM 3.3 Steps for Developing Smart Computing Systems for Railway Industry for Traffic Management.

Step 1. The sensors installed at the stations will be responsible to track the trains.

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Step 2. The data fetched from the sensors will be sent to a database. Step 3. On analyzing the collected data, the platforms where a train can be accommodated is searched.

Step 4. Until successful searching, the AI-enabled system would not allow the train

to enter the station and halt the train until a vacant platform is available.

Step 5. After successful searching, the proposed system will align the tracks and

provide proper signalling to direct the train to enter the station.

Step 6. After successfully performing its duty, it will take care of the next train in

the pipeline.

Smart computing will easily handle and manage huge traffic efficiently, thereby reducing the tremendous workload of the employees. At any particular time, management of the arrival and departure schedule of multiple trains can be done with ease, thus maintaining punctuality of all the train services. This would eventually help railways to flourish and expand their network more efficiently and quickly by generating a lot of revenue. Algorithm 3.4 enlists the steps for implementing the notion of smart computing in the food ordering sector. ALGORITHM 3.4 Steps for Developing Smart Computing Systems for the Food

Ordering Industry

Step 1. The food items ordered by the customers will be recorded.

Step 2. The date and time for placing the order will also be recorded.

Step 3. With the help of machine learning, the quantity of food generally ordered by

specific customers can be recommended beforehand.

Step 4. Depending on the quantity, the food delivery servicemen would arrive at the

shop.

Step 5. Smart computing will never cause any delay in providing the delivery

services and/or during order cancellation. Step 6. Smart computing will improve the timing and efficiency of food delivery by pre-prediction of the quantity of food.

Smart computing is believed to revolutionize the world of food ordering industry and will boost the revenue and profit by providing efficient and fast services to the customers. The steps of the proposed algorithm for hotel booking using smart computing technologies are listed in Algorithm 3.5.

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ALGORITHM 3.5 Steps for Developing Smart Computing Systems for Performing Hotel Room Bookings.

Step 1. The hotel booking websites reserves and/or schedules bookings of rooms in the hotels. Step 2. The time of the month when the booking frequency is maximum for a

particular place is stored along with the total booking information.

Step 3. The types of rooms booked are also stored.

Step 4. Depending upon the responses and few other parameters, data will be

predicted by the smart system.

Step 5. Customers will be given suggestions to book their rooms in the best hotels.

Step 6. A review system will also be deployed which will help hotels with better

services to gain a better position in the search list.

Step 7. The review system will contain all genuine reviews, so the customers do not

get confused or cheated.

Step 8. Due to immense competition, the hotels will try to provide as much facilities

needed at the stipulated cost of the room and other services.

Step 9: The hotels will also be able to handle the crowd efficiently during the rush

time of the year.

The introduction of smart computing in hotel booking industry will change the scenario by scaling up its revenue generation. Proper management and crowd handling features will be the key yardstick of the proposed smart computing application. This is how smart computing is going to influence the different businesses and industries by enhancing efficiency of the system through smart systems designed using AI, ML, etc. and will pave the way for newer and advanced technologies in the near future. 3.5

DISCUSSION AND ANALYSIS

With the application of smart computing in business, productivity, profit can be maximized in a small period of time.35 Figure 3.5 shows the estimated impact of smart computing in the manufacturing industries on profitability and productivity before and after its integration in the pipeline. The scope of this research work is to estimate and predict only the nature of the graph. As it is known that the rate of productivity and profit varies sharply when the data generated and collected in traditional systems is not analyzed, but

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after the implementation of smart computing, a lot of data could be analyzed and depending on the result so obtained, appropriate decisions can be made. Optimized recommendations can be done against the demand of products in the market by analyzing the supply chain. The demand and supply can be analyzed; and then proper measurements can be taken for manufacturing the most needed items which is eventually expected to give a boost to the profit.

FIGURE 3.5 Performance comparison of production rate before and after implementation of smart computing.

With time, smart computing will increase the productivity of necessary and most sought after items. This will lead to planning and scheduling of the production pipeline for different kinds of items. It will ultimately reduce the wastage of raw materials for production of various items, thereby causing trivial damage on the ecosystem. Figure 3.6 depicts via a line chart about how the rate of production will be affected for the most and the least demanded items over time when smart computing techniques are used as an aid. The nature of the graph for the most popular items is expected to increase with time as more and more data can be analyzed, whereas the less demanded products will suffer a decreasing curve. Thus, with the help of smart computing, effective data analysis can be performed pertaining to the change in items’ demand over time. The security of transaction will be enhanced by smart computing. Transaction time would be reduced and financial operations and services will become seamless and smooth. Smart computing will open up gateways for new technologies to be embraced and adopted by the industry and various stakeholders. In railway systems, with the application of smart computing in providing services and improving infrastructure, the overall revenue will see a huge boost with the demand of more passengers traveling on trains as well as efficient use of natural resources, and energy can be done with proper management of train operations. Figure 3.7 showcases how the revenue generation of railways is

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impacted by comparing two scenarios of before and after implementation of smart computing technologies.

FIGURE 3.6 Effect of smart computing on variation of production rate based on item’s demand.

FIGURE 3.7 computing.

Revenue generation in railways before and after implementation of smart

With the introduction of smart computing in railway industry, it will be easy to handle the train traffic. Not only will it reduce the burden of employees, but it will also prove to be an efficient system for the management and operation of train traffic. The passengers will enjoy the upgarded and customized services after the revolutionary integration of smart technologies in the railway system from booking seats to traveling comfortably, planning trips based on rush and traffic and other such conveniences. Figure 3.8 illustartes the impact on the punctuality of different train services after incorporating the notion of smart computing.

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FIGURE 3.8 Comparison of punctuality percentage of trains before and after implementation of smart computing.

In the hospitality industry, during the hotel bookings instance, use of smart computing will flourish the tourism sector Pan India. Even many of the unexplored locations and destinations can be discovered with the growth of tourism industry (see Fig. 3.9). With the hotels performing at their best, tours and travels will reach a new zenith and consequently there will be an economic boost in the revenue and also our nation’s GDP. Thus, application of smart computing in hotel booking and food ordering industry will prove to be an effective move in the long run. With the help of smart computing, the peak time for tourism to flourish at any particular place can be estimated. It will be beneficial for the various stakeholders who will be hosting the tourists at a particular destination to plan and make arrangements for customers’ visit at crucial and rush hours. Over exploitation of natural resources and energy can be stopped. A lot of natural resources and energy can be saved for future use. Smart computing will make it possible with detailed analysis and prediction where wastage of natural resources and energy can be minimized and sustainable utilization of such depleting resources is encouraged. Figure 3.10 highlights that after implementing the techniques of smart computing, the natural resources and the environment can be protected. This will enable future generations to reap in the benefits of the balanced ecosystem. Sustainable development and utilization will lead to saving a lot

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of natural resources and energy from being misused and mishandled. The nature of the two lines in the graph suggests how the natural resources are expected to be handled with and without the application of smart computing. The orange line suggests that without the application of smart computing, the natural resources will be exploited and hence an over usage of natural resources is expected. With the application of smart computing, proper analysis of data will be possible. Hence, the natural resources are expected to be effectively used without any exploitation or over usage.

FIGURE 3.9

FIGURE 3.10

Growth of tourism industry with the integration of smart computing.

Impact of smart computing on natural resources.

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3.6 CONCLUSION This chapter is an attempt to highlight how smart computing and its allied technologies and methodologies are instrumental in revolutionizing and refurbishing the ecosystem and the environment and solve myriad problems existing in the surroundings that one has to face and deal with. In the process, different case studies and scenarios have been presented and discussed elaborately keeping in mind about the various domains and areas where the use of transformational technologies can drive and better the overall objective and goal of all stakeholders. The impact of various industries such as hotel booking and tourism, banking and finance, manufacturing and production, food ordering, bill payments has been presented in a lucid manner and easy-to-decipher graphs and plots have been supplemented to elucidate and expound the difference in outputs and outcomes after the implementation of smart computing paradigm. Smart computing is no doubt one of the greatest technologies that can reform the business world. More new technologies will come up and smart computing can act as the foundation for a colossal technological makeshift. It is worth mentioning that there is always a need to analyze the effect of modern and novel technologies on people and society, especially in regard to whether or not they are working as per their expectations and desirability. Necessary changes and updates should be made in the technologies, because at the end of the day, everyone expects and seeks an error-free and effective system that can make lives easier, luxurious, and worth living. This also helps in achieving outcomes and results, products, and services in a quick and smooth manner, thereby ameliorating the productivity and profitability of the overall business transaction process. Smart computing is believed to revolutionize the railway industry too. In next 5–10 years, the railways will grow on a large network which will be installed with many advanced technologies. It will not only reduce the travel time and promote faster connectivity but will also ensure more safety, more comfort, and better services. With the introduction of smart computing in hotel booking and food ordering industry, it will not only improve the revenue of these industries but also ensure genuine food quality and best services in hotels. It will flourish the tourism industry of a nation along with all the services and amenities needed for the growth of tourism and will eventually boost the revenue. Smart computing will save the natural resources from getting exploited by the industries. Thus, not only productivity and profit will be enhanced, but also the target of United Nation’s Sustainable Development Goals (SDGs) can be accomplished.

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3.7 FUTURE WORK Introduction of smart computing in business industries and traditional workflows has immense potential to boost the revenue generation and GDP of the country. Smart computing is believed to be a harbinger of success for the startup ecosystem. Smart computing will not only reduce the processing time but also will enhance the efficiency of the system. As a future scope of improvement, the real-world implementation of the proposed algorithms needs to be taken care of for multiple industries as discussed in this chapter. It also remains to be ensured as to how such smart systems fare when they are used by various stakeholders. Challenges, such as security, seamless connectivity, authentic services, and optimized recommendation need to be properly assessed once the system is deployed. Few other industries that have been not covered in this work need to be covered in regards to how smart technologies will impact the growth and revenue. With all the features and advantages of smart computing, the main aim should be at providing and applying such technologies to each and every industry and business workflow. One more significant aim of such an emerging and advanced pool of latest technologies should be to create employment opportunities and skilled workforce and certainly not to hurt the employment prospects and vacancies. Strategic and wise use of raw materials could be evaluated and thereby wastage and exploitation of raw material could be stopped. It has been rightly stressed that “Sometimes hard work does not pay, but smart work does” and with smart computing, the idea of “Smart Business,” “Smart Society,” and “Smart World” seems to become an in-depth reality and not just a skin-deep fantasy. KEYWORDS • • • • • • • • •

smart computing machine learning artificial intelligence big data Internet of things cloud computing business transaction security

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REFERENCES 1. Singh, P.; Dwivedi, Y. K.; Kahlon, K. S.; Sawhney, R. S.; Alalwan, A. A.; Rana, N. P.; Smart Monitoring and Controlling of Government Policies Using Social Media and Cloud Computing. Inf. Syst. Front. 2020, 22 (2), 315–337. DOI: 10.1007/s10796-019-09916-y. 2. Dhanabalan, T .; Sathish, A.; Transforming Indian Industries Through Artificial Intelligence and Robotics in Industry 4.0. Int. J. Mech. Eng. Technol. 2018, 9 (10), 835–845. 3. Vijaykumar, S.; Saravanakumar, S. G.; Balamurugan, M.; Unique Sense: Smart Computing Prototype for Industry 4.0 Revolution with IOT and Bigdata Implementation Model. Indian J. Sci. Technol. 2015, 8 (35). DOI: 10.17485/ijst/2015/v8i35/86698. 4. Marr, B. Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems; John Wiley & Sons, 2019. 5. Das, S. K.; Das, S. P.; Dey, N.; Hassanien, A. E. Machine Learning Algorithms for Industrial Applications; Springer 2021. 6. Bartels, A.; Parker, A.; Daley, E. Smart Computing Drives the New Era of IT Growth; Forrester 2009. 7. Zakaria, F. Ten Lessons for a Post-Pandemic World; Penguin UK 2020. 8. Jukan, A.; Masip-Bruin, X.; Amla, N. Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review. ACM Comput. Surv. 2017, 50 (1). DOI: 10.1145/3041960. 9. Bessis, N.; Dobre, C. B ig Data and Internet of Things: A Roadmap for Smart Environments; Springer International Publishing, 2014; p 546. 10. Cantarero, M. M. V. Of Renewable Energy, Energy Democracy, and Sustainable Development: A Roadmap to Accelerate the Energy Transition in Developing Countries. Energy Res. Soc. Sci. 2020, 70, 101716. DOI: 10.1016/j.erss.2020.101716. 11. Dutta, S.; Gupta, S. Machine Learning as a Game Changer for Building a Sustainable Society. Management and Sustainability: Rethinking Social Innovation and Business Ethics in Sustainability Development, Proceedings of International Conference on Sustainable Business Management Practices and Social Innovation (ICSBMPSI), 2021; pp 78–89. 12. Muralidharan, K. Green Statistics: Essence of Lean, Green, and Clean Sciences. Sustain. Dev. Qual. Life 2021. DOI: 10.1007/978-981-16-1835-2_6. 13. Li, X.; Wan, J.; Dai, H. N.; Imran, M.; Xia, M.; Celesti, A. A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. IEEE Trans. Ind. Inform. 2019, 15 (7), 4225–4234. DOI: 10.1109/TII.2019.2899679. 14. Bécue, A.; Praça, I.; Gama, J. Artificial Intelligence, Cyber-Threats and Industry 4.0: Challenges and Opportunities. Artif. Intell. Rev. 2021, 54 (5), 3849–3886. DOI: 10.1007/ s10462-020-09942-2. 15. Zhou, X.; Delicato, F. C.; Wang, K. I. K.; Huang, R. Smart Computing and Cyber Technology for Cyberization. World Wide Web 2021, 23 (2), 1089–1100. DOI: 10.1007/ s11280-019-00773-y. 16. Maddikunta, P. K. R.; Pham, Q.; Prabadevi, B.; Deepa, N.; Dev, K.; Gadekallu, T. R.; Ruby, R.; Liyanage, M. Industry 5.0: A Survey on Enabling Technologies and Potential Applications. J. Ind. Inform. Integr. 2021, 100257. DOI: 10.1016/j.jii.2021.100257.

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17. Cvar, N.; Trilar, J.; Kos, A.; Volk, M.; Duh, E. S. The Use of IoT Technology in Smart Cities and Smart Villages: Similarities, Differences, and Future Prospects. Sensors 2020, 20 (14), 3897. DOI: 10.3390/s20143897. 18. Khosravy, M.; Gupta, N.; Dey, N.; Ger, P. M. Smart Green Ocean Underwater IoT Network by ICA-Based Acoustic Blind MIMO OFDM Transceiver. Earth Sci. Inform. 2021, 14 (2), 1073–1081. 19. Ginige, A.; Javadi, B.; Calheiros, R. N.; Hendriks, S. L.; A Smart Computing Framework Centered on User and Societal Empowerment to Achieve the Sustainable Development Goals. Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST 2019, 296, 158–172. DOI: 10.1007/978-3-030-34863-2_14. 20. ElMaraghy, H.; Monostori, L.; Schuh, G.; ElMaraghy, W. Evolution and Future of Manufacturing Systems. CIRP Ann. 2021. DOI: 10.1016/j.cirp.2021.05.008. 21. Chen, B. Wan, J. Shu, L.; Li, P.; Mukherjee, M.; Yin, B. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access 2017, 6, 6505–6519. DOI: 10.1109/ACCESS.2017.2783682. 22. Chatterjee, P.; Smart Computing Applications in Railway Systems—A Case Study in Indian Railways Passenger Reservation System. Int. J. Adv. Trends Comput. Sci. Eng. 2014, 3 (4). 23. Pal, A.; Hazra, S.; Dan, A.; Gupta, S.; Smart Street Dustbin: A Smarter Way to Manage Waste. CBS J. Manage. Practices 2018, 5 (1&2), 35–50. 24. Hazra, S.; Pal, A.; Gupta, S.; Dan, A. An Efficient IOT-Based Waste Management System: One Step Closer to Smart City Planning. CBS J. Manage. Pract. 2018, 5 (1&2), 19–34. 25. Azaharuddin, S.; Bhattacharjee, A.; Adhikari, R.; Gupta, S. Smart Farming. In Project Innovations in Distributed Computing and Internet Technology, Proceedings of 8th PIC 2019, A Sister Concern Activity of 15th ICDCIT; 2019; pp 51–56. 26. Dan, A.; Gupta, S.; Rakshit, S.; Banerjee, S.; Towards an AI-Chatbot Driven Advanced Digital Locker. Proc. Int. Ethical Hacking Conf. 2018, Adv. Intell. Syst Comput 2019, 811, 37–46. DOI: 10.1007/978-981-13-1544-2_4. 27. Acharyya, S.; Hassan, H.; Gupta, S. An Improved Solution for Sustaining Health Using ADIET Recommender System. BIMTECH Busi. Prespec. 2019, 1 (2), 17–35. 28. De, D.; Mukherjee, A.; Das, S. K.; Dey, N.; Nature Inspired Computing for Wireless Sensor Networks; Springer 2020. 29. Lampert, A. Over-Exploitation of Natural Resources Is Followed by Inevitable Declines in Economic Growth and Discount Rate. Nat. Commun. 2019, 10 (1), 1–10. DOI: 10.1038/s41467-019-09246-2. 30. Mas, F. D.; Dicuonzo, G.; Massaro, M.; Dell’Atti, V. Smart Contracts to Enable Sustainable Business Models. A Case Study. Manage. Decision 2020, 58 (8), 1601–1619. DOI: 10.1108/MD-09-2019-1266. 31. Roy, M.; Minhazuddin, M.; Ghosh, S.; Sarkar, K.; Rana, T. K.; Smart Transaction and Automation System for Banks. In 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech); 2017; pp 1–5. DOI: 10.1109/IEMENTECH.2017.8077021. 32. Nath, M. D.; Bhattasali, T. Impact of Blockchain to Secure E-Banking Transaction. Int. J. Comput. Sci. Eng. 2019, 7 (18), 1–6. 33. Gupta, S.; Thakur, P.; Biswas, K.; Kumar, S.; Singh, A. P. Toward a Novel Decentralized Multi-Malware Detection Engine Based on Blockchain Technology. Emerg.

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Technol. Data Mining Inform. Security Proc. IEMIS 2020 2021, 2, 811–819. DOI: 10.1007/978-981-33-4367-2_77. 34. Gupta, S.; Thakur, P.; Biswas, K.; Kumar, S.; Singh, A. P. Developing a BlockchainBased and Distributed Database-Oriented Multi-malware Detection Engine. In Machine Intelligence and Big Data Analytics for Cybersecurity Applications; Springer; 2021, 249–275. DOI: 10.1007/978-3-030-57024-8_11. 35. Verma, P.; Sood, S. K.; Kalra, S. Smart Computing Based Student Performance Evaluation Framework for Engineering Education. Comput. Appl. Eng. Educ. 2017, 25 (6), 977–991. DOI: 10.1002/cae.21849.

CHAPTER 4

APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE RECRUITMENT PROCESS OF HRM RAMESH CHANDRA GOSWAMI1, HIREN JOSHI2, and SUNIL GAUTAM3 Department of Computer Science, Indus University, Gujarat, India

1

Department of Computer Science, Gujarat University Ahmedabad, Gujarat, India

2

Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India

3

ABSTRACT Human resources management (HRM) is a management function that focuses on hiring, motivating, and retaining employees in a company. It concentrates on the people who work in organizations. Designing management methods to ensure that human talent is utilized effectively and efficiently to achieve organizational goals is what human resource management is all about. In an organization finding the perfect candidate, that is, recruitment for the job is a crucial thing. If it fails, then it may destroy the purpose of recruitment. Artificial intelligence (AI) can play an important role in the recruitment process where there is no human interaction involved which will result in a

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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perfect candidate who is totally concerned with the requirement of the vacant position. Thus, AI technology automates recruitment processes and removes time-consuming human resource activities for hiring right candidates which gives recruiters an opportunity to pay attention to more serious issues that require human creativity. Thus AI-powered solutions will automate hiring and improve recruitment efficiency and effectiveness. Nowadays, AI is playing a significant role in the recruitment process of HRM. In this book chapter, we are focusing on the application of AI in the recruitment process of HRM. 4.1 INTRODUCTION Artificial intelligence is an emerging technology which allows machines to work in an intelligent and smart manner to facilitate the human living. It is used in verities of areas, such as health, finance, marketing, and human resource management, etc. Due to its intelligence, it gains remarkable popularity and acceptance in the past few years. The AI is defined in several ways which almost express potential to think, learn, and perform. Artificial intelligence can be expressed as “the study of how to get computers to perform things that people are better at right now.”1 Another definition is “the ability of a system to accurately understand external data, learn from it, and apply what it has learned to accomplish specific goals and tasks through flexible adaptation.”2 AI is also described as “instructions that can be processed by a machine to solve activities that would demand evident cognitive ability if completed by people.”3 Human resources are the important asset of every nation and have a significant contribution in prosperity. In every organization, HR is also playing a key role in growth and innovations. Among the organizations, there is competition for acquiring highly skilled and qualified candidates.4 The main goal of the recruitment and selection is to provide necessary human resources at minimum cost with focus on core tasks required for job to the organization. Recruitment and selection is an integral function of HRM and it is also impacted by technological growth. In the past several years, the integration of AI into the recruitment process is increasing day by day. Every innovation has its own positive and negative aspects. Sometimes due to innovation, there is increment in machines and decrement in the human resources. But, there is a need to train man power for handling the machines, modern equipment, and integrated software. This innovation is also impacting the recruitment of human resource.

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Smart automated systems are frequently used in this HRM process and they are also able to interact with job applicants to support and optimize the selection process.5 The organization success has a dependency on the quality of hiring staff. Thus, there are few questions that are arising in this scenario that how much humans will rely on AI in the recruitment process of human resources, how AI affects the recruiters, and what will be the role of HR managers in the new era of recruitment. Figure 4.1 shows the flowchart of the traditional selection process.

FIGURE 4.1

Flow chart of traditional selection process.

4.2 RECRUITMENT The research on the recruitment process is becoming more interesting day by day which is started in the last decade. It is a very important topic how

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recruitment impacts the behavior of applicant and employers. Recruitment is a very important and crucial process of finding the right candidate against the job opening.6 Thus, in the process of recruitment, it is essential that organizations have competent staff, and this is ensured by an effective recruitment process. The improper recruitment of staff can badly impact the business. However, with so much competition on the employment market, finding the most qualified and best staff is getting increasingly difficult. In the market, there is a lot of competition, so the way of recruitment has also changed. In the current scenario, the process of recruitment is different from that in earlier days. In the current situation, there are imaginative methods of enrolling representatives as an approach to stand apart from the contenders.7 4.2.1 SELECTION Selection is the process of hiring employees among the shortlisted candidates and providing them a job in the organization. The success and failure of any organization depends on its employees. When an employee is well appropriate for their job, the entire company can enjoy the benefits of their unbeatable success. It helps organizations choose the right candidates for the right positions. Selection of employees in traditional methods involves interviewing the candidates. At the time of selection, every candidate of the pool has an equal chance of selection. In this selection method, the steps involved are preselection, interviews, and assessment.8 For evaluating a proper method of selection of a candidate, three parameters are used which are the usefulness, validity, and reliability. At the end of the process final decision of election is conducted by a person generally a recruiter who has remarkable experience of the job and is able to judge a proper candidate for a mentioned job. But in case of a larger organization it is done by a group of experts.9 This interaction dispenses with the pressing factor of involvement and capacities the people need to have and can likewise assist with killing a few components of inclinations toward applicants. 4.2.2 TRADITIONAL RECRUITMENT Traditional recruitment process adopted by HRM is totally based on manual process where an organization notifies the vacancies for available posts and job seekers respond by posting the resumes. After that the HRM of the company/organization shortlists the applications and conducts interviews

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at a particular location and day, and the results are declared to the candidates. In this manual process the time and effort spent on is too high.10 This traditional recruitment process takes more time and also it is very costly. There are different suggested models of the recruitment process which are followed by a majority of HRM that consist of some common steps that are as follows. 1. 2. 3. 4. 5. 6. 7.

Vacancy declaration Job description preparation Advertisement of the vacancy Response management Short-listing of the candidate Interviews arrangement Interview conduction and final decision /result declaration

Thus, it starts from the declaration of objectives of recruitment that consists of a number of positions and required education and work experience. After that organizations develop the plan, in which they choose what kind of employee they want to recruit from which source. The recruitment’s results should be linked to the organization’s recruitment objectives from the start and apparently throughout the development of the strategy and recruitment.11 Organizations’ recruitment functions are influenced and governed by a variety of internal and external forces. The internal forces or factors are those that the organization has control over. External factors, on the other hand, are those that are beyond the organization’s control. Artificial intelligence has a major impact on human resource management and has ushered in a new way of thinking about HRM in the intelligence field. Because of the introduction of AI, it has an impact on traditional HR processes. 4.3 ARTIFICIAL INTELLIGENCE AND HUMAN RESOURCE MANAGEMENT AI is a significant and rapidly evolving technology that can be characterized in a variety of ways. One of them is the ability of a machine-based system to accurately understand external data, learn from it, and modify that knowledge to achieve specific goals and complete tasks.

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The impact of artificial intelligence (AI) on our lives can be seen from decades, but today its existence is larger than ever before. Sometimes, we do not even realize it when a new AI-powered system, tool, or product appears and outperforms us, humans. In fact, AI is affecting human life on all kinds of levels varying from: 1. The automation of time-consuming and tedious tasks. 2. The human capabilities augmentation 3. The human functions strengthening. The AI and automation is a long journey and it has different types of impact on human activities. AI has a wide range of applications within the realm of HRM. For example, AI tools have been applied to the recruitment process to facilitate job application and selection practices.12 The growing potentials of AI for HRM can be seen as an opportunity for advancement. The role of AI is continuously trying to mimick the human intelligence. AI will not help more creative, unique, intuitive, or judgmental, and it also will not help to improve reactions based on previous experience. When AI will make decision based on intuition and instincts similar to human, then we can say that revolution happens in HRM. 4.4 RECRUITMENT OBJECTIVES The objective of the recruitment is the first step toward the process of recruitment which states that it should set major recruitment that must be associated with the objective of the organization. This objective is not common for all organizations; it is very specific to the organization depending upon the activities in which the organization is involved. Some goals may attract the largest number of applications for a vacant position, while others may want to impact their post-hire outcomes from the recruitment process. It is clear that objectives are very important for an organization.12,13 The role of AI in this process is a point of discussion whether AI should play a role or not. The top managers of an organization should only be able to set objective of recruitment and AI cannot fully replace the human aspect. AI can only help to some extent in this step of recruitment. It will not replace the complete human intervention. The objective of an organization plays a significant role in the success journey of an organization. The objective is factual thing where robot and machine have a limited role, and so we can say that in the first step of the recruitment process, AI can play a limited role.

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4.4.1 DEVELOPMENT OF STRATEGY FOR RECRUITMENT A recruitment strategy is a plan that explains what roles you will recruit for, when, why, and how. It should be tied to your overall company objectives. Your strategy must be possible to implement and easy to communicate. Thus, in the strategy, recruiters are deciding how they will communicate to the candidate whom they want to hire. The formulation of a strategy should be in line with the goals that a company is attempting to achieve. Another area where the ability sets and human capabilities are required but directly cannot be replaced by artificial intelligence is strategy development. Artificial intelligence can help by maintaining the database of past applicants, AI can analyze the existing pool of applicants and identify those that would be a good fit for new roles as they open up for organization. Apart from this AI can also be used for selecting the suitable strategy for achieving the set of objectives. 4.4.2 RECRUITMENT ACTIVITIES When AI is utilized at proper section of the recruitment process, it will be beneficial. This could include efforts such as recruitment. The organization communicates a vacancy through recruitment activities, which must be simple. This is the reason why majority of job entails gathering primary candidate data, communicating with them, and gaining an overview of who the best contenders for the job are. There have been numerous theories on how to make the recruitment process and its aim more transparent, but majority have yet addressed the limited use of AI. In the initial recruitment step where there is more administrative task and less human intervention, AI can play a significant role.14–16 The preselection is the activity of this phase where by looking at the candidate profile and after a selection process where later on restrict who is the suitable candidate. AI tools can help in this routine task of screening and the selection. Thus, AI helps recruiters successfully automate time-consuming, repetitive tasks such as screening resumes or scheduling interviews with candidates. 4.4.3

INTERVIEWING JOB APPLICANT

In the interview, it is not only necessary to know the organization's view but also necessary to know the view of a job applicant. In this process, interviewers try to find out how much attention the applicant is paying, what

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is the interest of the applicant, and what is the expectation from the company. Thus, the process should focus on both sides, company as well as candidate. When artificial intelligence is implemented in this phase, it pays attention to how the job seeker will profit from it. AI also makes a candidate present themselves in a more truer manner and also in more in-depth as compared to the traditional process of recruitment.17–23 As a result, applicants have the option to express themselves and take a position in relation to the job that is being given. Applicants can do more with AI tools than they can with traditional recruitment. Apart from this, with the help of AI, information will reach applicants faster and also an earlier point of the recruitment process; they may make an informed judgment about whether they are actually interested or not. So, with the help of AI, a noninterested candidate can be eliminated easily which makes the recruitment process more faster and efficient. 4.4.4 RESULT DECLARATION In this process, there is more involvement of human process. So, the AI cannot be totally implemented but there are regions where AI can be used efficiently such as candidate can be easily informed and they can also know the reason why they are not selected and where they are lacking which will be beneficial for candidate to prepare themselves in a more efficient way so that in the next time, there is more chance of selection. Thus, AI will help as a tool that gives guarantee that the organization will get the desired candidate. The complete process of recruitment is shown in Figure 4.2.

FIGURE 4.2

Steps of recruitment in HRM.

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4.5 THE IMPORTANCE OF AI IN THE RECRUITMENT PROCESS 1. AI assists HR executives to acquire better candidates who are required by the organization. It also helps to place the right candidate at right place. 2. Screening and the selection of candidate become unbiased because there is less human and more machine involvement. 3. AI saves the organization’s time by maintaining records in a manner where there is no chance of duplication. In a traditional process, it takes huge amount of time to screen the candidates which is a repetitive task. 4. In the case of huge amount of data, AI also works in an efficient and unbiased manner which helps to hire a quality candidate by properly matching the required skill and the skill possessed by the candidate. Thus, an efficient candidate can be easily shortlisted with the help of AI. 5. Due to the implementation of AI in recruitment process, outsourcing of recruitment agency is reduced and organization also gets a quality candidate which will ultimately reduce the cost. 4.6

DRAWBACK OF AI IMPLEMENTATION

Using an artificial intelligence (AI) system on a multileveled organization for data processing, interpretation, and decision-making may appear to be expensive, and it will undoubtedly cost money to implement, maintain, update, restore, and repair. Although artificial intelligence can think like a human brain that has trained it, it will not inherit the dynamics of ethical or moral human behavior. AI will lack a sense of connection to the company or its employees, and hence will be unable to perform the most crucial duty of an HR professional. It won’t help you to be more creative, unique, intuitive, or judgmental, and it won’t help you to improve your reactions based on previous failures. 4.7

CONCLUSION AND FUTURE SCOPE

Recruitment is a process which happens in every organization and it is also a time-consuming and complicated process. For this organizations are paying a huge amount to the recruitment agency which work on the behalf of that

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particular organization. Because of this, there may be a possibility that wrong selection of candidate can happen which is not beneficial for any organization. But AI technology that tries to simulate to work like a human, helps in various steps of the recruitment process for efficient candidate without any biasness. Apart from this, there are several places where human involvement is compulsory. Thus, in the recruitment process, the involvement of human is necessary in combination with AI to produce an efficient result by saving cost and time. Thus, AI is playing a significant role in the recruitment process of HRM. Researchers predict that in the future, artificial intelligence will play a significant role in decision-making.. Grace et al. state that “artificial intelligence has a remarkable possibility of exceeding human work in a few decade years.”24 Another set of experts believes that AI will only play a supporting role and humans will never be replaced. Actually, it is a cooperation between humans and machines which will exist in the future.25–28 AI-supported applications helped organizations in improving the productivity, efficiency of an employee at work place and also to reduce the cost. By using data analytics, it automates the routine human chores and also is able to predict the future events. KEYWORDS • • • • •

human resource management artificial intelligence recruitment AI recruitment automation in HRM

REFERENCES 1. Rich, E. Users Are Individuals: Individualizing User Models. Int. J. Man Mach. Stud. 1983, 18 (3), 199–214. 2. Kaura, M.; Rekha, A. G.; Resmi, A. G. Research Landscape of Artificial Intelligence in Human Resource Management: A Bibliometric Overview. Artif. Intell. Speech Technol. 2021, 255. 3. Strohmeier, S.; Piazza, F. Artificial Intelligence Techniques in Human Resource Management—A Conceptual Exploration. Intell. Techniq. Eng. Manage. 2015, 149–172.

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4. Stahl, G. K. et al. Enhancing the Role of Human Resource Management in Corporate Sustainability and Social Responsibility: A Multi-Stakeholder, Multidimensional Approach to HRM. Human Resour. Manage. Rev. 2020, 30 (3). 5. Lopez-Cabrales, A.; Valle-Cabrera, R. Sustainable HRM Strategies and Employment Relationships as Drivers of the Triple Bottom Line. Human Resour. Manage. Rev. 2020, 30 (3). 6. Jeyanthi, N.; Barde, U.; Sravani, M.; Tiwari, V.; Iyengar, N. C. S. N. Detection of Distributed Denial of Service Attacks in Cloud Computing by Identifying Spoofed IP. Int. J. Commun. Netw. Distrib. Syst. 2013, 11 (3), 262–279. 7. Zang, S.; Ye, M. Human Resource Management in the Era of Big Data. J. Human Resour. Sustain. Stud 2015, 3 (01), 41. 8. Ved, S.; Kaundanya, N.S.; Panda, O.P. Applications and Current Achievements in the Field of Artificial Intelligence. Imperial J. Interdisc. Res., 2016, 2 (11), 932–936. 9. Kumar, N.; Garg, P.; A. C. S. Pvt. Impact of Online—Recruitment on Recruitment Performance, 2010; pp. 327–336. 10. Khosla, R. An Online Multi-Agent E-Sales Recruitment System, Proceedings of IEEE/ WIC International Conference on Web Intelligence (WI 2003), Halifax, NS, 2003; pp 111–117. 11. Leung, C. K.; Braun, P.; Cuzzocrea, A. AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning. Sensor 2019, 19 (6), art. 1345. 12. Islam, T. et al. Promoting In-Role and Extra-Role Green Behavior Through Ethical Leadership: Mediating Role of Green HRM and Moderating Role of Individual Green Values. Int. J. Manpower 2020. 13. Braun, P.; Cameron, J. J.; Cuzzocrea, A.; Jiang, F.; Leung, C. K. Effectively and Efficiently Mining Frequent Patterns from Dense Graph Streams on Disk. Procedia Computer Science 2014, 35, 338–347. 14. Lakshmanan, L. V. S.; Leung, C. K.; Ng, R. T. The Segment Support Map: Scalable Mining of Frequent Itemsets. ACM SIGKDD Explorations 2000, 2, 21–27. 15. Wu, Z.; Yin, W.; Cao, J.; Xu, G.; Cuzzocrea, A. Community Detection in MultiRelational Social Networks in WISE, 2013; pp 43–56. 16. Yang, C.; Liu, J.; Hsu, C.; Chou, W. On Improvement of Cloud Virtual Machine Availability with Virtualization Fault Tolerance Mechanism. J. Supercomput. 2014, 69 (3), 1103–1122. 17. Dierckens, K. E.; Harrison, A. B.; Leung, C. K.; Pind, A. V. A Data Science and Engineering Solution for Fast K-Means Clustering of Big Data. IEEE Trust Com-BigData SE-ICESS, 2017; pp 925–932. 18. Leung, C. K.; Tanbeer, S. K.; Cameron, J. J. Interactive Discovery of Influential Friends from Social Networks. Soc. Netw. Analy. Mining 2014, 4 (1), art.154. 19. Ringle, C. M. et al. Partial Least Squares Structural Equation Modeling in HRM Research. Int. J. Human Resour. Manage. 2020, 1617–1643. 20. Aust, I.; Matthews, B.; Muller-Camen, M. Common Good HRM: A Paradigm Shift in Sustainable HRM. Human Resour. Manage. Rev. 2020, 30 (3). 21. Budhwar, P. et al. The state of HRM in the Middle East: Challenges and Future Research Agenda. Asia Pacific J. Manage. 2019, 36 (4), 905–933. 22. Leroy, H. et al. Managing People in Organizations: Integrating the Study of HRM and Leadership, 2018; pp 249–257.

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23. Upadhyay, A. K.; Khandelwa, K. Applying Artificial Intelligence: Implications for Recruitment. Strategic HR Rev 2018. 24. Grace, K.; Salvatier, J.; Dafoe, A.; Zhang, B.; Evans, O. When will AI Exceed Human Performance? Evidence from AI Experts. J. Artif. Intell. Res. 2018, 62, 729–754. 25. Wilson, H. J.; Daugherty, P. R. Collaborative Intelligence: Humans and AI Are Joining Forces, Harvard Busi. Rev. 2018. 26. Das, S. K.; Das, S. P.; Dey, N.; Hassanien, A. E., Eds. Machine Learning Algorithms for Industrial Applications; Springer, 2021. 27. De, D.; Mukherjee, A.; Das, S. K.; Dey, N., Eds. Nature Inspired Computing for Wireless Sensor Networks; Springer, 2020. 28. Khosravy, M.; Gupta, N.; Dey, N.; Ger, P. M. Smart Green Ocean Underwater IoT Network by ICA-Based Acoustic Blind MIMO OFDM Transceiver. Earth Sci. Inform. 2021, 14 (2), 1073–1081.

SECTION II

INDUSTRY AND ORGANIZATION

MODELING

CHAPTER 5

APPLICATIONS OF THE INTERNET OF ROBOTIC THINGS IN INDUSTRY 4.0 BASED ON SEVERAL ASPECTS GARIMA JAIN1, ANKUSH JAIN2, and DIVYA MISHRA3 Department of Computer Science and Engineering,

Noida Institute of Engineering and Technology, Greater Noida, India

1

Department of Computer Science and Engineering,

Bennett University, Greater Noida, India

2

Department of Computer Science and Engineering, Subharti Institute

of Technology and Engineering, Meerut, India

3

ABSTRACT The extensive accessibility of network resources and the growth of new-age devices and cutting-edge technology permit the industry to step forward toward the Industry 4.0 era. The relationships reform the Internet of things (IoT) and robotics thoroughly in this fourth industrial rebellion. From this integration, a new technology concept took into an industry that is an Internet of robotic things (IoRT), a combination of IoT and robotics. This unrolling technology brings potentials in research, which is different from industrial, like cultivation, medical, observation, and pedagogy. The IoRT is the modern innovation that explains the next-generation IoT use case in robotics. The IoRT is an idea where devices can screen the events occurring around them, combine their sensor information, and utilize nearby and

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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conveyed information to decide the next course of action. IoT devices usually are intended to deal with explicit tasks, whereas robots need to respond to unforeseen conditions. Artificial intelligence/machine learning defines the patterns that can arise as unexpected conditions and help these robots to deal with those conditions. Despite the potential use cases of robotics, there are uncertain obstructions to the large-scale adoption of flexible automation solutions. Client experience is a vast obstruction despite innovation advancing significantly. First, this research focuses on the powerful technology of industry 4.0 and their integration architecture brings in IoRT. Also, it brings light on the effect of IoRT on researcher fields, which mainly drops on the idea of integration between innovative spaces into IoT. Second, this paper focuses on the below solutions to resolve the underlined problems. Focal point experience of the client: creative methods for putting the client at the focal point of the experience and decreasing the expectation to learn and adapt will make it simpler to utilize robots. Enhancing the learning curve: this procedure perpetually includes an operator to use an interface to program the robot to explicitly focus on the activity required. Subsequently, programming information is regularly a pre-imperative for anybody attempting to move a robot. Here, IoT uses to collect the data, and the AI/ ML algorithms will prepare the learning curve model. Streamlining the client expectations: The idea is to provide a mobile app to program the robot with the user interface. 5.1 INTRODUCTION The ROBOTIC scheme has brought incredible variations in many social and economic aspects to human culture from the last few decades.1 The coming innovations like robotics and artificial intelligence (AI) enhance and increase human abilities, increase productivity, and move from simple reasoning toward human-like intellectual skills. The impact of AI is imperative to learn from previous successes and failures and anticipate their future directions and potentially legal, moral, and financial consequences. Simulated intelligence is “the recreation of human knowledge measures by machines, particularly PC frameworks.”2 It is usually characterized as the study of causing PCs to perform errands that require insight like people. While AI is a more extensive idea of machines having the option to do assignments adroitly, AI (ML) is the current (likely the most mainstream) use of AI that empowers machines to gain from much information and act appropriately without being expressly customized.

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IoRT is a global configuration for the information civilization that enables the state-of-the-art robotic services by interconnecting built, present, and growing robotic elements with interoperable materials and communication capabilities. As a result, we can see how cloud hosting, foggy storage, and other standard communication technology are balanced from around advantages of familiar serenity. In other words, although leveraging some aspects of cloud computing, such as virtualization technologies and three primary models—program, infrastructure, and architecture—the robotic IoT devices are expected to be at the pinnacle of the cloud robotics paradigm. Moreover, using IoT and its enabling technologies allows networking robotics to have a lot of versatility in building and executing software innovations. Integrating robotic, intelligent machines, and 5G communications (GSM) will open up a world of possibilities for humans and machines to collaborate and increase efficiency and service provision for people. In recent advances in robotic technologies, machines are empowered by human intelligence, and enable them to perform human-like actions.2–5 Robots can be utilized as a rule, and for some reason. However, today, many are being used in difficult situations, industrial measures, or where humans cannot survive. Industrial robotics technology has been in massive demand over the past decade, which can be deduced from the adoption of brilliant factory structures. With the improvement of industrial robots, programmed robots have achieved high levels of real-time application performance, accuracy, power, and similarity. The accessibility of affordable, small-scale solutions for small- and medium-sized businesses influences the adoption of industrial robotics. In addition to this, linking robots, machines, and automation tools to the cloud allows manufacturers to get the highest levels of performance and uptime from their automation frameworks. The rapid adoption of automation across various verticals is driving demand in the global cloud robotics market. A critical increase in the introduction of robots in industrial automation will create new development paths for robotic technology in the cloud. Advances in artificial technology are expected to play an essential role in creating the market for cloud robotics. 5.1.1 BACKGROUND Robotic systems are self-directed technologies customized to manage and complete the given work proficiently. The incorporation of robotics frameworks in the industry has brought epic changes in numerous socioeconomical

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features of human development. Robots, robotics, and framework automation have shifted from the base of the research lab turning into a crucial cost, time, and energy-saving component of the current industry. Intelligent robots and their control frameworks have empowered the whole cycles, similar to vehicle assembly, consequently.6 By adding versatility to the blend, the possibilities to include framework automation in almost any method in almost any industry increase significantly.7 However, there is a challenge. In any case, there is a test. To design frameworks, the reason is cloud advanced mechanics empowered by 5G.8 Robots and robotics technology have been useful mostly in the manufacturing industry, especially in sequential outline systems and unsafe situations. Traditionally, robots have been intended to perform repetitious pick-and-spot undertakings and do tasks requiring a maximum degree of accuracy, just like doing risky tasks like welding and cutting. Be that as it may, things are changing. Robots are starting to show up in all enterprises and are being utilized to do a wide variety of tasks.9 Most experts say that intelligent industry manufacturing will probably signify one of the most significant areas of market review Internet of Things (IoT) sooner rather than later. Smart manufacturing is subject to modern computerization, which depends intensely on robots and machine insight. The production line will be acknowledged by digitizing the assembling cycle and plants, empowered by 5G organizations and all their structure blocks. The author considers a framework model where detachment in the organization happens between two focuses, and these two focuses cannot speak with one another. This sort of disconnectivity may happen because of street development work, renapplause work, catastrophic events, or imperceptible specialized mistakes in the organization. They will likely set up an organization and correspondence between these separated focuses.30 It has been seen that delay tolerant networks (DTNs) are an uncommon class of organizations that can work in the tested climate where regular organizations neglect to work. The fundamental justification for the disappointment of customary or conventional organization is the nonappearance of start to finish way. On several occasion that started to fade away, the customary organization cannot work as it fails to provide appropriate assistance to the end user; however on these, PC organization and web will appropriately work as they are the fundamental piece of everyones’ life. Hence, DTNs are the most plausible answer for the tested region where customary organizations cannot offer types of assistance. In actuality, everybody’s vehicles rise advanced cells and handheld gadgets; these gadgets can assume a significant part in transferring information for DTNs, that is, social DTNs. Socially mindful

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DTNs are human-driven DTNs in which hubs act like human conduct. They can act egotistically to save their assets and deny to hand-off parcels. For the arrangement of this difficulty, we have proposed IRS, a motivation-based steering system. In this methodology, transitional hubs can take an interest and procure motivating forces for forfeiting their self-centeredness.31-36 5.2 HUMAN–ROBOT INTERCTION The cutting edge in actual knowledge for robots should progress through a couple of essential degrees if research needs the robots working in our homes to go past vacuum-cleaning the floors. The chance that robots need to function effectively in homes and other nonmechanical settings, how they are instructed to do their job, and how they will be advised to stop will be of profound importance.10 The individuals who relate with them may have practically no knowledge of robotics and related technology. Thus, any interface should be enormously natural. The authors related to science native likewise expect that robots will ultimately recognize gestures, speech, and facial expressions while communicating with humans rather than CLI, that is, command-line interface.11 As it is known that speech will be the most natural way for communication between humans and computers, it is not natural in the robotics area. It will most likely be a while before robots communicate as generally as the next generation.12–14 The critical scenarios of human–robot collaboration are displayed in Fig. 5.1. Taxonomic organization for the level of mutual communication among agents can have one of the eight following values: The first is a human–robot; the second is a human–robot team; the third is a human team, more robots, a robot, more human beings, a robot; human team—robotic team; human team: more robots and more humans: robot team. An innovative standard model in worldview is collaborative robotics of human–robot interaction that depends on small, versatile machines that are intelligent, secure, and quick to program, and are designed to function in close proximity to humans.15 When compared with past versions of robots, connected devices require sensory frameworks for detecting and avoiding accidents and consequences, as well as human–robot interfaces for understanding and reading human intentions. As a result, developing sensitive layers and proximity sensors, as well as planning unique interfaces that enable various forms of commands from the customer to the robot, are major drivers of robotics and automation research and computerization research.16 One of

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the most crucial aspects of the fourth industrial revolution is the emergence of human–robot interaction. The present and future tasks are to increase the number of robots that can support humans significantly to perform control and the production program is responsible for assembling.17–20 A robot that works near a human worker must interface securely and comprehend and decipher direct client orders and support the specialist in executing various activities.

FIGURE 5.1

Humans and robots interaction.

Complex decision-making breakthroughs will be achievable thanks to robot–human collaboration in the smart factory. Robotic arms, for instance, can assist humans in a variety of ways: • In the future, the complex decision-making advancement is done by robots and human interaction in the factory.

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• For example, the human gets help by collaborative robots. • To enable better decisions evaluating complex realities to need to provide synthesized and understandable representations. • The probability of fault and fatalities are reduced by robots by understanding those risks in advance. 5.3 OVERVIEW OF INTERNET OF ROBOTIC THINGS This section provides an overview of the Internet of robotic things (IoRT). The concept of the Iot devices is presented first. Cloud robotics later merged with IoT to form the Internet of robotic things, which included a different concept. 5.3.1 DEFINITIONS The primary reason for the diversity of IoT technology conceptions, conceptions, and classifications is that, unlike some of the other scientific terms. The Internet of things is a new representation of an emerging core business model that brings together an obtainable stack of innovations to conduct government in a linked and systematic manner.19 Most of the Internet of things capabilities, such as device identification and heterogeneity, are not brand new. Instead, the Internet of things uses these capabilities to meet today’s societal demand for information technology on social, scientific, economic, and socioeconomic classes. The IoRT is a worldwide IT society framework that enables advanced industrial robots through robotic interrelationships based on established and evolving information and communications technology, with cloud computing, cloud services, and other digital advancements based on the benefits of a fully integrated cloud infrastructure. In summary, the IoRT is envisioned to be at the forefront of the cloud robotics worldview, utilizing specific aspects of cloud registering, for example, virtualization innovation, and three assistance models (i.e., programming, stage, and foundation), while applying IoT and its empowering advances to engage enormous adaptability in the planning and actualization of new applications. IoRT imparts certain viewpoints to cloud robotics and the IoT. However, it varies from them in different perspectives. Along these lines, it offers extraordinary advantages and forces particular difficulties to meet its necessities to be characterized. Shockingly, no writing yet has depicted

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this term. To make the IoRT scenario, one can particularly combine cloud robotics in IoT-aided scenario. As per the portrayal given by RoboEarth, cloud robotics might be viewed as a rising field of advanced robotics.

FIGURE 5.2

Framework for IoT-aided robotics application.

It is established in the distributed computing, distributed storage, and other existing Internet innovations revolved around the earned advantages of the met cloud foundation and shared administrations that permit robots to profit by the incredible computational stockpiling. Robotics helps in exchange of assets of current stick server farms that attempt to reduce overheads for businesses, such as maintenance, refreshments, and upgrade freedom during the personalized middleware phase. It includes extra forces that may reduce working life and impose robot versatility and increase the activity costs by including cloud data move rates to offload instructions. Figure 5.2 portrays the chart IoT-AI-aided robotics scenario created in.21 Following applications are healthcare, manufacturing plants and innovative zones, armed processes, and liberation actions. Potential applications of IoRT include the following: • Use a robotic device to check if a car can use a certain parking lot in a company parking lot. • Collaboration of IoRT and people in a manufacturing unit to make operational and other decisions.

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• Use the IoRT concept to add greater flexibility and adaptability to Intelligent Transportation Systems (ITS). • Use of IoRT for old care and organization. Figure 5.3 gives a pictographic drawing of the workings that are included in Industry 4.0 also known as “Smart Factory.”

FIGURE 5.3

Industry 4.0 paradigm.

5.4 ROBOTS WITH IOT The term “Internet of things” (IoT) is commonly used, although there is no universally accepted definition or comprehension of what this term entails. Kevin Ashton, who is now the head of the Auto-ID Center (MIT), coined the term “Internet of things” in 1999 and published it as networked “radiofrequency identification” (RFID) foundations.22–26 He coined the phrase to describe a future in which all electronic devices are organized, so each item (digital or virtual) is labeled with data specific to that piece.27 The internet of things, also known as the Internet of things, is a new advancement of the network in which the aspects make themselves detectable by transmitting data about them. They can have information compiled by numerous comments and items, or they can be part of higher level organizations.28 In the current time, the fourth industrial revolution has headed to the advancement pretended to be an Internet of robotic things, which figures out how to accomplish frameworks with dynamic autonomy, awareness,

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and management. Robotic frameworks have acquired impressive changes in different parts of human life. Mutually in the modern and the scholastic universes, robots have been utilized in playing out a wide range of complex and challenging actions, such as wrapping, collecting, repairing, and so forth. In this situation, IoT and robotics convergence happens in the improvement of new heterogeneous automated frameworks, improving the self-sufficient behavior of the robots. Besides, the coordination among robotics and systems administration is of fundamental significance in improving the IoT frameworks. In networking, robotics is the fusion of robot framework structures (including software and hardware) with network-based applications like the cloud-based computing. Networked robots have become increasingly important in the interconnection of robotic arms, distributed systems, database systems, and sometimes even individuals all over the world, due to the advancement of the Internet and robotic systems in recent years. They can be built on a local network or dispersed over a network connection. Radio-frequency identification (RFID) is an automatic identification and data capture (AIDC) technology that customs radio waves to routinely recognize substances (individuals or items), permitting the group of information about them and storing that information into computer systems. Thus, RFID innovation allows various objects to be recognized separately in the Internet of things. RFID technology is also known to be similar to barcode technology, a renowned and extensively used AIDC technology. RFID frameworks require programming, organization, and information base segments to allow data flow from tags to the association’s data foundation, where the data are handled and put away.29 Frameworks are application explicit. In the Internet of things, this robot can join as an object. Consequently, it can set up associations with different things over the Internet, either as a basis of data or as a customer. As a data customer, the robot accesses important data to assemble to accomplish specific errors. Therefore, in an operation or inactive state, the contemplated robot may be linked to the internet of subjects. The robot is not inactively connected to the Internet but can be identified solely through an RFID tag. The following features of robots can be accumulated with IoT: • In offices, homes, and several industries, robots come in all shapes and sizes, from weighty robotic arms on a workshop floor to greeting robots in retail stores, cafeterias, or guesthouses. • Robots are likewise showing up through structures of related and continuously intelligent gadgets. For instance, a self-driving associated vehicle is a robot: it incorporates different small robots or smart

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interface sensors and actuators. This total of devices, tiny robots, and intelligent sensors must all coordinate and cooperate to achieve the more enormous all-out robot objectives: the related vehicle. It can conclude from this to other extensively more excellent robots, for instance, an amassing shop floor, an intelligent structure, or a related city. • The time of robotics technology automated robotization in programming is vastly affecting the measure capability. Likewise, instead of individuals copying information between various screens or intersecting many inheritance applications, an item robot instead subsequently achieves comparative repetitive tasks: significantly faster and with fewer mistakes. A robot is a programmable machine proposed to pass on duties most operationally and productively. They can be guided with commitments from different sensors to grow their constancy to play out an endeavor with incredible ability. Robots require to investigate the field thoroughly to decide its limitation. For example, if we consider the capacity to discover positions headers at any arbitrary second in the allocated time frame, it is required to investigate the position headers at random time. The IoT is all about linking gadgets, sensors, and other devices to the existing network infrastructure to send and change data from them remotely and manage their operation in some cases. The IoRT, on the other hand, is a relatively recent notion for working an intelligent object, that is, considering a robot as an IoT device and manipulating information from sensors and other ports linked to it as if it were an Internet technology. A robot is an IoT device associated with a typical nearby worker exchanging information regarding its localization, objective, and track planning in the proposed approach. The robot informs its localization header at whatever point it crosses the 2D scanner tag imprinted on each hub (in the floor) and self-right its course to objective dependent on obstacles it might interfere in its way. This methodology intensely reduces the requirement for an actual calculation to build confinement exactness and gives more opportunity to robot to locate its way to objective dependent on its dynamic domain as briefest way from any hub to objective hub is consistently accessible from worker to a robot. For example, it is the essential soul of AI, any stratagem that endeavors to accomplish its objectives effectively by making trustworthy choices by investigating information from its condition. In this cyber-physical view of the IoRT, sensor and information technologies from the IoT are utilized to give robots a more extensive situational awareness that prompts better undertaking execution.

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Smart manufacturing (otherwise known as Industry 4.0), mainly because of the industrial Internet of things (IIoT), has offered rise to the adaptive digital factory. That is the industrial operational technology (OT) side. On the information technology (IT) side, as it is observed, progressively mechanical cycle robotization arrangements are utilized effectively in client relationships just as back-office activities. It is a sober-minded methodology for mechanizing work—particularly in associations that have numerous legacy frameworks. Artificial Intelligence‑Assisted Work: Now, for additional crucial and higher business esteem (and usually higher exchange volume) assignments, we have AI-helped laborers guided by progressively savvy programming that uses business rules, examination, and AI. It is like having an intelligent Siri or Alexa helping the specialists complete their assignments. Figure 5.4 explains the components of IoRT. The general start to finish value stream of various kinds of works arrangement with people, robots, virtual helpers, and robotic automation when appropriate. Including cognitive or knowledge: Knowledge laborers are the specialists. They are the cognitive laborers. Some have amassed significant information and skill precisely spaces. The thought of people losing their jobs to robotization— physical robots or robotics in programming—should be approached much comprehensively. The human factor is especially in play, and dull positions will be supplanted with considerably more energizing ones. It is present in an online program on Bots, AI, Robotics, and the workplace.

FIGURE 5.4

Internet of robotics things component.

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5.5 CLOUD ROBOTICS James Kuffner characterized the term cloud robotics in 2010, to depict another way to deal with advanced mechanics that exploits the Internet as an asset for hugely equal calculation and ongoing sharing of tremendous information assets. Several scientific organizations are looking into the possibility of robots accessing vast amounts of data and computing power via a cloud computing infrastructure. This concept, dubbed “cloud robotics” by some, would enable machines to represent reality activities like image processing and speech recognition and obtain new, simulation skills quickly. Consider a robot discovering a new thing, such as a plastic bottle, that it has never seen or used before. James Kuffner, a Carnegie Mellon professor, who now works for Google, invents the term “robotics in the cloud.” The robot can merely upload an image of the cloud and get the title of the object, a 3D model, and instructions. When a robot’s computing or memory requirements surpass its onboard capabilities, they are offloaded to the cloud, where a data center’s enormous capabilities can complement the robot’s limited available resources. Cloud robotics is also a huge step forward in robot learning. One robot’s AI resources might take 150 h to learn a task. A cooperative effort of 150 robots studying various job areas might complete the task in an hour. Google’s autonomous vehicles are cloud robots: they associate with the web to information from data set of guides and satellite symbolism, condition models (like Street view) and use sensor combination to consolidate streaming information from its numerous cameras, GPS, and 3D sensors to restrict its situation inside centimeters. Besides, every one of the self-ruling vehicles adds to this information circle by finding out about the situations, streets, or driving conditions, and sending the data over to the Google cloud, where it may improve the exhibition of different vehicles. Figure 5.5 shows the network of cloud generally speaking, and cloud robotics can be raised to improve the routine of robotic managers in five customs. • Big Data: Utilizing large datasets and data warehouses about items, layouts, and pictures that are enriched with physical and spatial data. • Cloud computing: Using cloud infrastructure’s massive storage space to offload costly asynchronous simulations. • Cooperative robot knowledge: to reuse information and distribute it across various types of robots.

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• “Transparent” and “Interactive”: Human distribution of fully accessible software, information, and ideas for computing, research, and equipment assembly is open and accessible. • Call Centers and Crowdsourcing: For exception management and error handling, person guidance (call centers) is needed. With the advancement of cloud computing, big data, and other innovations on the rise, the integration of cloud innovation and automated systems takes into consideration the structure of multirobot frameworks, with superior and elevated intrigue. The development of IoT infiltration and investments in robotics have been the real proponents of the development of industrial robotics.

FIGURE 5.5

Network of cloud.

5.5.1 BIG DATA Big data portrays huge information collection that past the abilities of the customary RDMS from investigation, catch, information duration, and

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search perspective, as it is essential for developing library of pictures, maps, and different types of information pertinent to mechanical technology on the Internet. Protocols by which big data are utilizing informational collections contain data about how to accomplish a robot activity like getting a handle on, detecting, and restricting. Models incorporate the Columbia Grasp dataset. A robot could counsel such a dataset to decide the ideal handle. Extensive data can likewise encourage learning in Computer Vision by coordinating sensor information to 3D models in an online information base. The RoboEarth venture stores information identified with object guides and assignments for applications going from object acknowledgment to portable route to getting a handle on and control. 5.5.2 CLOUD COMPUTING Infrastructure-as-a-Service and Platform-as-a-Service are cloud services, such as AWS, Google Compute Engine, Microsoft Azure, and IBM Watson. By licensing out a vast pool of resources for short-term computing workloads, these services deliver substantial advances in parallel computing. Technical and scientific elevated computing (HPC) applications have found a purpose for them. They were initially employed mainly by web software developers. While there are real-time restrictions, computing presents a problem; this is an ongoing field of research. Some robotics applications, such as cleaning a room or precomputing grip methods, really are not time-sensitive. 5.5.3 COLLECTIVE ROBOT LEARNING The cloud permits robots to share a rich information base containing past activities performed by different robots that have been transferred on the cloud. Such tasks could incorporate different conditions, similar to starting and wanted results, related control strategies and directions, and in particular, information on execution and results, and along these lines offer the robot to gain from them and apply the procedure on another condition, in this way, encouraging learning. In one model, where recently produced ways are adjusted with comparative situations, and handle the steadiness of finger contacts which obtained from the past handles on an article. Although not straightforwardly identified with IoT-supported mechanical applications, open-source equipment and programming can, regardless, prompt the headway of automated innovations. The cloud encourages sharing by people of plans for equipment,

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information, and code. The accomplishment of open-source programming is presently broadly acknowledged in the advanced mechanics and mechanization network. An essential model is ROS, the robot operating system, which gives libraries and instruments to help programming engineers make robot applications. ROS has additionally been ported to Android gadgets. ROS has gotten a standard similar to Linux and is currently utilized by practically all robot engineers in research and by numerous in industry. 5.5.4 CROWDSOURCING AND CALL CENTERS It is used to consider focuses with mechanized agents that help analyze an issue. A similar idea can be switched, wherein the robot would access such specialized emotionally supportive networks monitored by people after distinguishing blunders and exemptions. Human expertise, experience, and instinct are being tapped to take care of various issues, for example, picture naming for PC vision. Amazon Mechanical Turk is spearheading on-request publicly supporting that can draw on human calculation or social figuring frameworks. Exploration ventures investigate how this can be utilized to arrange, decide profundity layers, picture typical, and evenness from pictures, and refine picture division. Specialists are attempting to comprehend valuing models and apply public support to get a handle on them. Along these lines, if robots can turn into an aspect of the IoT framework, at that point, it could not just bridge the advantages offered by cloud robotics, yet besides trade and improve accurate data from its numerous nonautomated things. 5.6 ROBOTS WITH AI/ML AI produces a full variety of emotions. Some people see AI as a way to improve people’s quality of life, while others see it as a threat to human existence and jobs. This article demonstrates how ‘‘true” AI is designed to replace only manual and administrative activities while assisting individuals in making complex decisions, instead of completely replacing people. Expanding on the advances made in mechatronics, electrical designing, and registering, mechanical technology is growing progressively complex sensorimotor functions that enable machines to adjust to their consistently evolving climate. Up to this point, modern creation is coordinated around the machine; it is aligned by its current circumstance and endured negligible varieties. Today, it tends to be incorporated all the more effectively into a

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current climate. The self-rule of a robot in a climate can be partitioned into seeing, arranging, and execution (controlling, exploring, and teaming up). The principal thought of combining AI and robotics is to attempt to advance its degree of self-sufficiency through learning. This level of insight may be defined as an upper limit of foreseeing the future while planning a project or associating (either by controlling or exploring) with the environment. Robots with knowledge have endeavored ordinarily. Even though making a framework showing human-like insight stays subtle, robots can perform particular independent errands, like driving a vehicle, flying in common and artificial conditions, swimming, conveying boxes and material in various territories, getting items, and putting them down exist today. Another practical use of AI in advanced mechanics is for the assignment of insight. Robots can detect the climate utilizing coordinated sensors or PC vision. Somewhat recently, PC frameworks have improved the nature of both detecting and vision. Insight is not just significant for arranging yet in addition for making counterfeit self-appreciation mindfulness in the robot. This license is supporting collaborations with the robot with different elements in a similar climate. This order is known as friendly mechanical technology. It covers two expansive areas: human–robot collaborations (HCI) and psychological, mechanical technology. The vision of HCI is to improve the mechanical impression of people, for example, in getting exercises, feelings, nonverbal interchanges, and in having the option to explore climate alongside people. 5.7 ROBOT WITH IOT BASED ON FUSION OF AI AND ML IoT-based robotic frameworks likewise discover application in short-range correspondence innovation, convention design, and security confirmation in bright pervasive situations. One example of cloud robots is a self-ruling vehicle associated with the internet to access the database of maps and satellite symbolism. Utilizing sensor combinations to exploit streaming information from its camera and the worldwide situating framework (GPS), along with 3D sensors, a driverless vehicle can restrict its position precisely. The vehicle is additionally associated with an IoRT stage. • Focal point experience of client: Creative methods for putting the client at the focal point of the experience and decreasing the expectation to learn and adapt will make it simpler to utilize robots.

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• Enhancing the learning curve: This procedure usually includes an operator to use an interface to program the robot to focus on the activity required explicitly. Subsequently, programming information is regularly a pre-imperative for anybody attempting to move a robot. IoT will collect the data, and AI/ML algorithms will prepare the learning curve model. • Streamlining the client expectations: The idea is to provide a mobile app to program the robot the user interface. The IoT is a computer concept that represents the prospect of ordinary physical objects being connected to the Internet and identifying themselves from other devices. IoT entails expanding Internet accessibility beyond typical instruments, such as workstations, cell phones, and tablets to any number of generally ineffective or non-web-enabled physical objects and everyday items. These devices can impart and interface through the Internet, and they can be viewed and controlled from afar, thanks to technological advancements. AI in science and engineering of making insightful machines, brilliant programs. AI consciousness is identified with the relative error of utilizing PCs to comprehend human knowledge, yet AI does not need to limit naturally perceptible strategies. The goal of AI will likely make PCs/ PC programs clever enough to impersonate the human mind’s conduct. AI (ML) is a subset of artificial intelligence. ML is a study of planning and applying calculations that can take in things from past cases. AI permits PCs to deal with new circumstances utilizing examination, self-preparing, perception, and experience. Figure 5.6 explains the AI algorithm for big data. It is one thing to gather information; categorizing, evaluating, and setting policy based on historical evidence is quite another. As a result, AI would need to build more precise and faster algorithms and tools to be even more helpful throughout the IoT. Both AI and IoT are currently at exceptionally full-grown states, and their collaboration guarantees many advantages. IoT, which numerous industry scholars view as the driver of the fourth revolution, has enlivened an assortment of innovative advances and changes covering a broad scope of fields. Numerous masterminds accept that IoT needs AI, and in reality, that the fate of IoT is AI. They predict that soon most IoT usage will utilize AI strategies and devices.

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FIGURE 5.6

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AI algorithm for big data.

The elements among AI and IoT are analyzed. It is contended that AI/machine learning for information science is significantly more than applying accurate prescient calculations to an IoT. Consequently, it recommends a requirement for another kind of designer, namely, an architect with information on gadgets (IoT), AI/machine learning, advanced mechanics, cloud, and information of the board. It is likewise contended that information science for IoT is not quite the same as customary information science. Information science for IoT includes work with time arrangement techniques, for example, autoregressive moving regular strategies and so forth. It is clarified why IoT, big data, and AI are the three fundamental advancements, the collaboration of which will drive the up-and-coming age of uses. It is contended that important information energized by IoT is ground-breaking all alone, as is AI. However, together, they are the superpowers in the computerized universe. People cannot comprehend and deal with standard techniques, much information of the above kind. They have to grow better approaches to examine the exhibition information and data made by immense quantities of savvy gadgets/objects. The advantage of IoT information is that the speed and precision of enormous information examination should be extensively improved. Also, the nonstop advances of AI cause AI to combine with IoT to the degree that it is rapidly getting fundamental to IoT arrangements. In Figure 5.7, as AI meets with IoT carry on, the current growth of IoT is

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determined by six factors, of which the utmost influence is the start of big data and cloud computing.

FIGURE 5.7

Components responsible for the growth of IoT.

In our day, the skill of artificial intelligence in intelligent machines is progressing from handling classic repetitive tasks to the ability to carry out ever-changing tasks in an adaptive way. Figure 5.8 explains the type of artificial intelligence and its explanation in robotics. In other words, the AI application evolves through three stages, namely: • Phase 1: assisted intelligence (activities do not change, technologies study, events are robotic). • Phase 2: augmented intelligence (change-like tasks, humans notify machineries, machines inform humans). • Phase 3: autonomous intelligence (varying nature of activities, automated decisions, continuously learned machines). 5.7.1 ASSISTED INTELLIGENCE The assisted intelligence is fundamentally utilized to computerize primary cycles and errands by bridging the joined intensity of big data, cloud, and information science to help in dynamic. Another advantage of assisted

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intelligence includes its ability to perform more commonplace undertakings for helping to the loose individuals who perform more top to bottom errands. Requiring steady human information and intercession helped knowledge just work with unmistakably characterized data sources and yields. The fundamental objective of helped knowledge is improving things individuals and associations are as of now doing—thus, while the AI can caution a human about a circumstance, it leaves an official choice in possession of end clients. The particular case, would be those cases where a foreordained activity has been characterized.

FIGURE 5.8

Types of artificial intelligence.

5.7.2 AUGMENTED INTELLIGENCE The following degree of AI is enlarged knowledge, which centers on the innovation’s assistive job. This intellectual innovation is intended to improve, instead of supplanting human knowledge. This “second-level” AI is what individuals consider while examining the general idea by and large, with AI capacities layered over existing frameworks to increase human abilities. Increased insight permits associations and individuals to do things they could not, in any case, do by supporting human choices, not by mimicking free knowledge. The models included under this umbrella are AI, typical language preparing, picture acknowledgment, and neural organizations.

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5.7.3 AUTONOMOUS INTELLIGENCE The unique type of AI is self-governing knowledge, in which cycles are computerized to create the insight that permits machines, bots, and frameworks to follow-up on their own, autonomous of human intercession. When considered the stuff of sci-fi fundamentally, self-sufficient knowledge has become a reality. The idea is that similar to individuals, an AI needs self-rule to arrive at its maximum capacity. However, while self-governing knowledge applications are developing, associations are not yet and may never be prepared to hand over complete control over to machines. In light of this, AI should just be given self-sufficiency inside exacting lines of responsibility, a conviction that is in no little part due to those previously mentioned science fiction depictions. 5.8 ROBOTS WITH 5G The foundation for determining the maximum capacity of IoT is 5G. Voice was at the heart of the original versatile organization (1G). 2G was all about voice and messaging; 3G was about voice, sending messages, and knowledge; 4G was all about 3G but faster, and 5G will be much quicker; it will be fast enough to stream a full-length HD film inside a flash. Recognizing the most critical constraint of IoT begins with 5G. Voice was the subject of the first adaptive framework (1G). 2G was about voice and enlightening; 3G would be about speech, notifying, and data; 4G was something in 3G but faster; and 5G will be altogether quicker, rushing can download a full-length HD film like fire. Wireless networks in the fifth generation (5G) and post-5G (B5G) are predicted to evolve in the future, with higher data speeds, good healthcare, price, resource utilization, safety, flexibility, and scalability. AI is defined as “technology, particularly computer networks, simulating intelligence gathering operations.” Be that as it may, 5G is more than fast downloads. It will utilize IPv6, which will enable us to interface 2128 and is equivalent to 340 282 370 000 0000000 000. Quantities of gadgets associated with the web now envision these gadgets speaking with one another without humans. The potential outcomes are innumerable, for instance, self-driving vehicles thing. A vehicle can book an arrangement if the vehicle’s brake cushions are not working appropriately and let us know the closest vehicle administration focus. We are, furthermore, advising the administration community about

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the part which is to be supplanted. Furthermore, the thing is we do not have to do anything. The vehicle will do every one of these things without anyone else. It is the thing that can call IoT (Internet of things). Internet-associated objects can gather and move information over a small organization without human intercession. Presently, imagine Internet-associated ROBOTS that can gather and move information over a small organization without human mediation. This marvel is called IORT (Internet of robotic things). Its applications are stockroom robotization satisfaction focus. The place versatile robots move receptacles and beds and can facilitate their developments. Interface this application with our vehicle example. Imagine a Service community’s Warehouse where all parts are stored once the vehicle fixes, meeting with service focus to change the brake cushion and request the required part for the process. Once the vehicle requests it, the robots present in the stockroom get the part and send it to the administration place before the appointment. As the chance of IoT in ROBOTICS is countless, furthermore, the discussion is not about the future in 50 years, but rather about the future in three to quite a while since the 5G network will be operational by the end of 2020 or early in 2021. Furthermore, the continued operation of robotics in the cloud requires continuous internet connectivity which will be satisfied with the development of 5G innovation. This will ensure that the activities carried out by the robots are deeply productive, paying little attention to the environment. 5G innovation will also provide cloud robotics with a solid platform and help reach a wider crowd. However, the issues identified with information security and privacy must be addressed with utmost authenticity for the global robotic cloud technology market to grow significantly. 5.9 CONCLUSION This paper focuses on solutions to resolve the underlined problems. Customer focus is a strategy that puts customers at the center of business decision-making. Customer-focused businesses make decisions based on how those decisions impact customers—as opposed to focusing on profits above all else. It is a long-term strategy that develops loyalty and builds trust. Enhancing the learning curve: This procedure usually includes an operator to use an interface to program the robot to focus on the activity required explicitly. Subsequently, programming information is regularly a pre-imperative

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for anybody attempting to move a robot. IoT will collect the data, and AI/ML algorithms will prepare the learning curve model. Third, streamlining the client expectations: The idea is to provide a mobile app with the usefulness of programming the Robot the User Interface. KEYWORDS • • • • • •

artificial intelligence, internet of thing internet of robotic things (IoRT) industrial IoT (IIoT) big data cloud robotics machine learning

REFERENCES 1. Varga, P. et al. 5G Support for Industrial IoT Applications–Challenges, Solutions, and Research Gaps. Sensors 2020, 20 (3), 828. 2. Wang, C-X. et al. Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Commun. 2020, 27 (1), 16-23. 3. Civerchia, F. et al. Remote Control of a Robot Rover Combining 5G, AI, and GPU Image Processing at the Edge. Optical Fiber Communication Conference. Optical Society of America, 2020. 4. Siddiqi, M. A.; Yu, H.; Joung, J. 5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices. Electronics 2019, 8 (9), 981. 5. Vega, M. T. et al. Immersive Interconnected Virtual and Augmented Reality: A 5G and IoT Perspective. J. Netw. Syst. Manage. 2020, 1–31. 6. Vermesan, O. et al. Internet of Robotic Things Intelligent Connectivity and Platforms. Front. Robot. AI 2020, 7, 104. 7. Fourati, H.; Maaloul, R.; Chaari, L. A Survey of 5G Network Systems: Challenges and Machine Learning Approaches. Int. J. Mach. Learn. Cybern. 2020, 1–47. 8. Yanco, H.; Drury, J. Classifying Human-Robot Interaction: An Updated Taxonomy. In Proceedings of the IEEE International Conference Systems, Man and Cybernetics (SMC), Vol. 3; IEEE, 2004; pp 2841–2846 9. (PDF) Review on Human-Robot Interaction During Collaboration in a Shared Workspace. https://www.researchgate.net/publication/335113271_Review_on_HumanRobot_Interaction_During_Collaboration_in_a_Shared_Workspace (accessed Oct 02, 2020).

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10. Leveraging the upcoming disruption from AI and IoT: How Artificial Intelligence Will Enable Full Promise of the Internet of Things, 2015. 11. Patel, K. K.; Patel, S. M. Internet of Things-IoT: Definition, Characteristics, Architecture, Enabling Technologies, Applications, and Future Challenges. Int J. Eng. Sci. Comput. 2016, 6 (5), 6122–6131. 12. Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A Survey. Comput. Netw 2010, 54 (15), 2787–2805. 13. Sarma, S.; Brock, D.; Aston, K. The Networked Physical World. Proposals for Engineering the Next Generation of Computing, Commerce, and Automatic Identification. White Paper of the MIT Auto-ID Center, Cambridge, MA, 2000. 14. Ashton, K. That ‘Internet of Things’ Thing. RFID J. 2009, 22. (7), 97–114. 15. ITU: The Internet Executive Summary, ITU Internet Reports, 2005. 16. de Morais, C. M.; Sadok, D.; Kelner, J. An IoT Sensor and Scenario Survey for Data Researchers. J. Braz. Comput. Soc. 2019, 25 (1), 1–17. 17. Cayamcela, M. E. M.; Lim, W. Artificial Intelligence in 5G technology: A Survey. In 2018 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2018; pp 860–865. 18. Dabus, G.; Grossberg, J. A.; Cawley, C. M.; Dion, J. E.; Puri, A. S.; Wakhloo, A. K.; Gonsales, D. et al. Treatment of Complex Anterior Cerebral Artery Aneurysms with Pipeline Flow Diversion: Mid-Term Results. J. Neurointerventional Surgery 2017, 9 (2), 147–151. 19. Tzafestas, S. G. Synergy of IoT and AI in Modern Society: The Robotics and Automation Case. Robot. Automation Eng. J 2018, 31 (5), 1–15. 20. Klumpp, M. Innovation Potentials and Pathways Merging AI, CPS, and IoT. Appl. Syst. Innov. 2018, 1 (1), 5. 21. Sappin, D. E. How AI and IoT Must Work Together: R2 Uses Big Data to Develop IoT Solutions for Enterprise Customers.html, 2017. 22. Biloborodova, T. O. Part II. Data Science for IoT and IoE 6. Data Mining and Processing for the IoT. Internet of Things for Industry and Human Applications, 236. 23. Misauer, I. IoT, Big Data and AI: The New Superpowers in the Digital Universe, 2017. 24. Leveraging the Upcoming Disruption from AI and IoT: How Artificial Intelligence Will Enable Full Promise of the Internet of Things, 2015. 25. Cubbi, J.; Buyya, R.; Marasic, S.; Planiswami, M. Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Gen. Comput. Syst. 2013, 29, 1645–1660. 26. Fleerackers, T.; Bilgeri, N. Artificial Intelligence Improving CRM, Sales and Customer Experience. 27. Boyes, H.; Hallaq, J.; Cunningham, W. T. The Industrial Internet of Things (IIoT): An Analysis Framework. Comput. Ind. 2018, 101, 1–12. 28. Alba, M. Thing Worx and Deloitte Release Plans for Industry-Specific IoT Solutions. Engineering Com, 2017. 29. Breivord, H. P.; Sandström, K. Internet of Things for Industrial Automation: Challenges and Technical Solutions. Proceedings of IEEE Conference on Data Science and Data Intensive Systems (DSDIS); 2015; pp 532–533. 30. Singh, A. K.; Pamula, R. An Efficient and Intelligent Routing Strategy for Vehicular Delay Tolerant Networks. Wireless Netw. 2020, 1–18.

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31. Singh, A. K.; Pamula, R. IRS: Incentive Based Routing Strategy for Socially Aware Delay Tolerant Networks. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN); IEEE, 2018. 32. Das, S. K.; Samanta, S.; Dey, N.; Kumar, R., Eds. Design Frameworks for Wireless Networks; Springer: Singapore, 2020. 33. De, D.; Mukherjee, A.; Das, S.K.; Dey, N., Eds. Nature Inspired Computing for Wireless Sensor Networks; Springer, 2020. 34. Shandilya, S. K.; Shandilya, S.; Nagar, A. K., Eds. Advances in Nature-Inspired Computing and Applications, Vol. 1; Springer International Publishing: Switzerland, 2019. 35. Yang, W.; Wang, X.; Song, X.; Yang, Y.; Patnaik, S. Design of Intelligent Transportation System Supported by New Generation Wireless Communication Technology. Int. J. Ambient Comput. Intell. (IJACI) 2018, 9, (1), 78–94. 36. Jayakumar, L.; Dumka, A.; Janakiraman, S. Energy Aware Dynamic Mode Decision for Cellular D2D Communications by Using Integrated Multi-Criteria Decision Making Model. Int. J. Ambient Comput. Intell. (IJACI) 2020, 11 (3), 131–151.

CHAPTER 6

FABRIC DEFECT DETECTION AND FAULT IDENTIFICATION OF A FABRIC VIDEO FOR THE PURPOSE OF ERP IN INDUSTRY 4.0: AN IMAGE PROCESSING TECHNIQUE MANALI SARKAR1 and SRADDHA ROY CHOUDHURY2 School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Brahmapur, India

1

Computer Science and Engineering, Heritage Institute of Technology, Chowbaga Rd, Kolkata, India

2

ABSTRACT Nowadays, the edge of industry 4.0 uses smart technology to process and manipulate the data. It is a fusion of machine-to-machine communication based on Internet of things with big data and machine learning techniques. The concept of smart indicates self-monitoring and analyzing data based on industry. Industry 4.0 is used in textile industry that helps to analyze several types of design and production based on synthetic or natural materials. This chapter illustrates an approach for the fabric defect detection in textile industry using the machine learning technique. Since the manual fault detection is very tedious, time-consuming, and cost-consuming, this process is used to minimize the production cost and time. Here, a video of a fabric

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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material which might be consisting of some defects is taken as an input. The defect may be minor or plenty or in a scattered manner in different regions. The whole work has been divided into two parts based on the machine learning technique. First to find what portions of fabric are defective, we used the image comparison technique by using two error matrices—MSE and PSNR. In the second part, the type of defect (color bleeding, cracked point, dirty spot, fly yarn, hole, knot, misprints, scratch, slab, stretch) would be identified based on some image processing techniques, such as grayscale conversion, histogram analysis, mean square error, peak scale noise ratio, and thresholding.

6.1 INTRODUCTION Nowadays, most of the systems are based on the industry 4.0 that uses smart technology to process and manipulate the data. It is a fusion of machineto-machine communication based on Internet of things with big data and machine learning techniques. The concept of smart indicates self-monitoring and analyzing the data based on industry. This approach is also based on the purpose of ERP which is enterprise resource planning that is nothing but an important component in Industry 4.0 and grows smart and also is used in the case of data awareness. ERP basically refers to a product that might be software that has been used by various organizations to manage business in a day-to-day manner and becomes one of the most essential parts in Industry 4.0 nowadays. Industry 4.0 is used in textile industry that helps to analyze several types of design and production based on synthetic or natural materials. This chapter illustrates an approach for the fabric defect detection in textile industry using the machine learning technique. Since the manual fault detection is very tedious, time-consuming, and cost-consuming, this process is used to minimize production cost and time. Here, a video of a fabric material which might be consisting of some defects is taken as an input. The defect may be minor or plenty, or in a scattered manner in different regions. In the last few years, the textile industry is working with the fusion of Industry 4.0 for the purpose of enhancement and development. It is based on smart technology analysis and its manufacturing. Marketing principle and its conceptual framework analysis are given in Ref. [1]. In this paper, the authors analyzed the conceptual framework system of Industry 4.0. In this article, several changes and outlines are marked for the purpose of company implementation. It highlights how Industry 4.0 delivers the information based on new change and innovation. It helps in identifying market strategies and plan based on the fourth revolution system. The system of Industry 4.0 also

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incorporates in openness to system in fusion of textile industry based on the performance and analysis of different types modeling as shown in Ref. [2]. In this paper, the authors illustrated the information based on several incentives and barriers analysis. It stimulated based on financial behavior analysis for the purpose of economic and knowledge analysis that helps in several mediation modeling system. The necessary policies are incorporated based on greater knowledge enhancement and its analysis. It overcomes the limitations of the traditional manufacturing system based on several sectors and its enhancement and its analysis is shown in Ref. [3]. In this paper, the authors illustrated the system that scientific technology changes innovation in new industry for the purpose of development. It helps in several customization and optimization of systems based on smart manufacturing and production management system. It also uses new technology as Internet of things management system. Figure 6.1 shows the evolution of industrial Internet of things which is a fusion of industry with the concepts of Internet of things. It is also known as IIoT that is efficiently used in textile industry also.

FIGURE 6.1

Evolution of industrial Internet of things.

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Figure 6.2 shows different terminologies for the purpose of efficient communication that helps in IoT management with the context of industrial point of view. It is based on several intelligent concepts and information system based on some factors and parameters. This parameter helps in machine-to-machine communication to make an efficient plant for helping in business. The combination of all techniques helps to model the Industry 4.0 efficiently for the purpose of fast and efficient production and development.

FIGURE 6.2

Terminologies used in industry 4.0.

Figure 6.3 shows the analytical phases that are used in Industry 4.0 for the purpose of analysis and development of fast production and innovation system. It is based on four phases, such as operational reports analysis, financial reports analysis, data analysis, and exploration and predictive analytics based on machine learning. Machine learning is an important component that is used in Industry 4.0 for the purpose of processing data and information. Apart from machine learning, several intelligent techniques are used in Industry 4.0 that are mentioned in some books.4–7

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FIGURE 6.3

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Analytical phases of Industry 4.0.

Textile industry is developing with Industry 4.0 based on several variations and fusion for the purpose of fast development system. The abovementioned figures help to analyze several flexibilities in terms of image process application using machine learning. In automated inspection of fabric quality based on computer vision, fabric defect detection plays an important role. To locate defective regions accurately is its main function. Automated online inspection of the fabric defects based on computer vision favored high demand and efficiency over the traditional human visual inspection recently. Kaur and Dalal8 designed a method that helps to deal with several textile information systems from the fabric point of view. It helps to deal with several analyses that help to manage some quality of application. The work is based on the machine learning method that deals with several cloth material information systems. It helps to quantify several information based on quality of pixel analysis and management. It helps to determine several quantify information systems based on some parameters such as bumpy, smooth, rough, and several types function analysis based on some variation of intensities. Priya et al.9 designed a method that helps to deal with several detection systems based on image analysis and management. It helps to deal with several image processing information systems. It helps to manage several fabric information systems based on some detection analysis and management. The work is based on some inspection system management that helps to deal with some fault analysis. It is based on a

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trained inspector that deals with some very less percentage analysis. It helps to decompose analysis based on the image information system based on the plane information system. It helps to manage several shape analyses that help to deal with some mathematical morphology information system. Chan and Pang10 proposed a method that helps to deal with the fabric defect information system that is based on Fourier analysis and management. It helps to deal with several information systems based on monitoring some information system. It helps to deal with spatial analysis based on spectrum frequency analysis. It helps to manage some information based on the threedimensional information system. The information is based on some class analysis such as yarn densities, double yarn, broken and webs fabric, and missing yarn. It helps to manage several texture information systems that help to deal with several spatial and monitor information systems based on some factors. E. J. Wood11 designed a pattern analysis based on characteristic that helps to design several information based on textile information system. It is an associated information system that helps to deal with some analysis of Fourier information system. It helps to deal with image analysis and management based on visual characteristic that helps to deal with several intermediate step analyses. The work is based on cross-correlation functionbased information system that helps to manage several image processing information systems. It helps to deal with some colored pattern analysis and management that helps to deal with transformation permit. It helps in applied and pattern that deals with plain carpet analysis that helps to suppress some high and low analysis information system. Usually, image processing system includes treating images as two-dimensional signals while applying already set signal processing methods to them. Fabric fault identification has been done using various image processing techniques in the past, but in this methodology, one more process has been added where the image comparison techniques are used to find the equality of images with an ideal image frame that consists of no error that means the image of the fabric cloth has no defect. Based on the equality that is measured by using two error matrices MSE and PSNR, the image frames are classified into three parts, among them, one of the frames that is marked as defective is taken for further analysis of the defect by using various image processing techniques. 6.2 LITERATURE REVIEW During the past two decades, fabric defect detection using digital image processing has received considerable attention, and also in the literature,

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numerous approaches have been proposed. Wang et a1.12 reported that 90% of the defects in a plain fabric could be detected simply by thresholding and summarized a comparison between human visual inspection and automated inspection. In our work, we basically focus on analyzing the error part of the fabric material by online visualization. The proposed work cannot analyze or objectify the texture of the material, but it can easily identify the faulty part of a fabric image and also its fault means which defect or fault has occurred in the fabric material. T. J. Kang13 proposed an automatic method that helps to deal with some objective analysis and management. The work is based on the woven fabric information system that helps in image analysis and management. The work is based on some intersecting angle analysis that helps to deal with some variation analysis. It helps to manage several values and defect information system based on some parameters, such as cross-sectional information system, yarn crimp, cloth cover, count, weight, and fabric thickness. It helps to determine several normal analysis and management based on some experimentation information and value analysis. It helps to deal with several equalization analyses based on evaluating and information system based on some quantification analysis and management. Felzenszwalb and Huttenlocher14 addressed the problem of segmenting an image into regions by defining a predicate for measuring the evidence for a boundary between two regions using a graphbased representation of the image and developed an efficient segmentation algorithm. X. Xie15 designed a method that helps to surface defect analysis based on detection of information system. It helps to manage several information systems. It works based on the texture information system that helps to deal with several inspections based on visual inspection based on an efficient decision-making system. The work is based on visual and decision-making system that helps to deal with several application management based on abnormal analysis and information system. It helps to deal with several challenges and comprehensive analysis and management. The work helps to deal with several statistical analysis and management. It helps in classification system and management that helps to analyze some filter and statistical comprehensive analysis based on practices analysis and management. Parveen and Sathik16 used the Bit-Plane slicing method to extract the details of a Colored X-Ray Image and produced different bit level images. Bit Level 6 is evaluated for RGB colors of the Original image. It is evaluated with the Bit level 6 of the original image showing results that the colored X-Ray image Bit level 6 yields more details than the Bit level 6 Gray scale X-Ray image.

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6.3 PROBLEM FORMULATION This project illustrates an approach to detect fabric defects in textile industry for minimizing production cost and time, since the manual fault detection is very tedious, time-consuming, and costly. For this, a video of a fabric material that might be consisting of some defects is taken as an input. The defect may be minor or plenty or in a scattered manner in an indifferent region. The whole work has been divided into two parts. First to find what portions of the fabric are defective, we used the image comparison technique by using two error matrices. • Mean square error (MSE) • Peak scale-to-noise ratio (PSNR) • In the second part the type of defect (hole, scratch, stretch, fly yarn, dirty spot, knot, slab, cracked point, misprints, color bleeding) would be identified by using the following image processing techniques. • Noise remove • Gray-scale-conversion • Histogram analysis • Thresholding

FIGURE 6.4

Block diagram of error detection process.

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6.3.1 INPUT Primarily, a video of a fabric material with pixel value 320 × 420 is captured by a digital camera. The video must consist of defective parts of the fabric material. To identify the faulty part of the fabric material is the main objective of the problem. The problem is solved by using three methods as follows. i. Framing ii. Comparison process basis on the equality iii. Image processing techniques. 6.3.2

FRAMING

The solution has been started by converting the video of fabric material into frames. Each of the frames is one of the image parts of the fabric material in the video. 6.3.3

COMPARISON PROCESS

The objective of the comparison process is to compare each image frame of the fabric material with the ideal desired image of the fabric material. Each and every image frame of the video is compared with the ideal image and checked to see which video image frames are more equal with the ideal image frame. The less equal image frame can be considered a highly defective fabric image. Comparison process is going through by calculating the peak signal-to-noise ratio value and the mean square error of the images. Comparison is done by using two error metrics. The comparison process is basically occurred by calculating two error matrices MSE and PSNR on each and every image frame. Two of the error metrics are used to compare the various image compression techniques that are the mean square error (MSE) and the peak signal-to-noise ratio (PSNR). The MSE is the cumulative squared error between the compressed and the original image, whereas PSNR is a measure of the peak error. The mathematical formulas of these two errors metrics are shown in eqs 6.1 and 6.2. 2

= MSE

1 p −1 q −1 ∑ ∑ [ A( x, y) − B( x, y)] pq=x 0=y 0

PSNR =20 * log10 (255/sqrt (MSE))

(6.1) (6.2)

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where A (x,y) is the original image, B (x,y) is the approximated version of the image (which is actually the decompressed image), and p, q are the dimensions of the images. Here, we take the defect part of the fabric as the decompressed part. A lower value for MSE means lesser error, and as seen from the inverse relation between the MSE and PSNR, this translates into a high value of PSNR. Logically, a higher value of PSNR is good because this means that the ratio of signal to noise is higher. Here, the ‘‘signal” is the original image, and the ‘‘noise” is the error in reconstruction. 6.3.3.1 MEAN SQUARE ERROR (MSE) Here, we used Python 2.7 functions to find the MSE of frames. The “mean squared error” between the two images is the sum of the squared difference between the two images. The two images must have the same dimension. 6.3.3.2 PEAK SIGNAL-TO-NOISE RATIO (PSNR) Peak signal-to-noise ratio, known as PSNR, is a technical term for the ratio between the maximum possible power of a signal and the power of noise that affects the exactness of its representation. Many signals have a very wide dynamic range. The logarithmic decibel scale is used to express the value of PSNR. While comparing compression codes, PSNR is an assumption of near value to human perception of reconstruction quality. Although a higher PSNR generally denoted that the reconstruction is of higher level quality, in some cases, it may not. PSNR is most easily defined via the mean squared error (MSE). Given a noise-free p×q monochrome image A and its noisy approximation B, MSE is defined in eq 6.1. The PSNR (in dB) is defined in eq 6.2. The definition of PSNR is almost equal, except the MSE that is the sum over all squared value differences that is divided by image size. Alternately, for color images the image is converted into various color spaces and PSNR is put against each of those channels of the color space. Here, we used the Python math functions and matplotlib for plotting, numpy for numeric processing, and open cv for building to find the mean square error and peak signal-to-noise ratio.

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6.3.4 CLASSIFICATION OF FRAMES BASED ON EQUALITY After finding the PSNR values and MSE values, it is seen that the higher values of PSNR have lower MSE value and the frames that have high PSNR value have lower MSE and the equality between them is higher than which frames have high MSE value and low PSNR. In short, the data of the frames showed that the high value of PSNR and the low value of mean square error give more equality in percentage. If the mean square error value increases, then the equality of the image frame with the ideal image frame decreases. Based on their equality measurement, the frames are divided into three linguistic terms as given in Table 6.1. Based on the equality measurement value of each of the image frames, they are divided into two highly defective, less defective, and almost equal parts. The frames whose equality values lie from 84% to 85.5% are considered highly defective frames (HDE). The frames whose equality values lie from 85.6% to 86.5% are considered less defective frames (LDE) and whose equality values lie from 86.6 % to 87.5% are considered All Most Equal (AME). TABLE 6.1

Linguistic Variable Based on Equality Measurement. Abbreviation

Range (%)

Highly Defective

Linguistic variable

HDE

84–85.5

Less Defective

LDE

85.6–86.5

All Most Equal

AME

86.6–87.5

6.3.4.1 FRAME SELECTION After finding those highly defective frames (i.e., image frames having lesser equality) an image frame among the HDE part has been taken to further analyze the faulty part of the fabric in the image by applying five image processing techniques: (i) grayscale conversion, (ii) noise remove, (iii) Binary Image, (iv) histogram analysis, and (v) thresholding. 6.3.5 GRAYSCALE CONVERSION As it is known that image processing operations mainly work on grayscale images so the colored image is converted into grayscale images.

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6.3.6 NOISE REMOVAL Noise removal is a very essential part of image processing and the output that has to be achieved should be clear and should not be degraded, so, by using Python 3 code, the noise of the image is removed to analyze the defects in the fabric image. 6.3.7 CONVERSION INTO BINARY IMAGES The conversion of binary images is an important task as the image must be in machine readable format for processing. Thus, after noise removal, the image is converted into a binary image. 6.3.8 HISTOGRAM ANALYSIS Graphical expression needs to be displayed in the method by showing the visual impression of various data and the histogram analysis is doing the task here. Here it is used to classify the faults of the fabric. The distribution of color in an image represents the number in each of a fixed list of colors, and that has been represented by this histogram analysis. It enhances the contrast of images by transforming the values in an intensity image. Histogram equalization is also an important part in the analysis of graphical representation that is histogram. By stretching the contrast of each uniform distribution and the gray values, the quality of an image has been measured by the histogram equalization. The contrast enhancement can be limited to avoid amplifying the noise that might be present in the image. 6.3.9 THRESHOLDING Thresholding is a method where the color image or the grayscale image is converted into a binary image. In this process, a grayscale image is converted into a binary image on a typical value that is known as a threshold value that means, on those particular values, the image conversion into bi-level image has occurred. The pixels from some images which come as an object (either text or other line image data such as graphs, maps here in our image that can be the faults of the fabric) are basically extracted from the image frame on a particular threshold value using this thresholding.

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6.4 RESULT AND DISCUSSION In this section, the performance of the proposed method is discussed based on several intelligent techniques. 6.4.1 FRAMING Framing is the first technique that has been done in this proposed method by converting the video of the fabric cloth into 50 frames that have been described in Section 6.3.2.

FIGURE 6.5

Images of 50 frames of the fabric video.

6.4.2 COMPARISON PROCESS After finding the PSNR values and MSE values it has been observed that higher values of PSNR have lower MSE value, and the frames that have high PSNR value have lower MSE and the equality between them is higher than the frames that have high MSE value and low PSNR. In short, the data of the frames showed that the high value of PSNR and the low value of the mean square error give more equality in percentage. If the mean square error value increases, then the equality of the image frame with the ideal image frame decreases. Based on their equality measurement, the frames are divided into three linguistic terms that have been shown in Table 6.l, Section 6.3.4.

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6.4.3 RESULT OF COMPARISON PROCESS The individual PSNR, MSE, and EQ values of each frame that has been calculated have been shown using a tabular format in Table 6.2. TABLE 6.2

PSNR, MSE, EQ Values in a Tabular Format.

Frame

PSNR

MSE

EQ

Count

Term

44

14.08

2351.91

84.37

1

HDE

36

13.76

2460.49

84.39

2

HDE

3

14.11

2327.38

84.62

3

HDE

4

14.29

2255.72

84.89

4

HDE

49

14.18

2287.01

84.97

5

HDE

37

14.24

2181.07

85.01

6

HDE

43

14.38

2157.57

85.04

7

HDE

24

14.32

2202.04

85.05

8

HDE

42

14.15

2292.93

85.06

9

HDE

41

14.17

2237.66

85.09

10

HDE

23

14.53

2064.44

85.3

11

HDE

45

14.51

2134.18

85.31

12

HDE

40

14.42

2177.31

85.35

13

HDE

1

14.56

2086.59

85.46

14

HDE

38

14.45

2100.33

85.49

15

HDE

48

14.4

2161.1

85.51

16

HDE

2

14.59

2071.45

85.54

17

HDE

35

14.51

2074.1

85.56

18

HDE

46

14.57

2060.01

85.76

1

LDE

13

14.64

1980

85.8

2

LDE

6

14.87

1892

85.85

3

LDE

21

14.93

1884.93

85.86

4

LDE

33

14.8

1951.4

85.95

5

LDE

20

14.83

1917.69

85.96

6

LDE LDE

31

14.93

1877.63

86.05

7

25

14.96

1898.43

86.07

8

LDE

47

14.83

1947.08

86.1

9

LDE

19

14.95

1847.72

86.13

10

LDE

14

14.96

1857.27

86.16

11

LDE

34

14.99

1838.49

86.22

12

LDE

30

15.05

1843.5

86.25

13

LDE

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TABLE 6.2 (Continued) Frame

PSNR

MSE

EQ

Count

Term

11

15.1

1795.7

86.28

14

LDE

12

15.04

1839.35

86.35

15

LDE

32

15.11

1799.61

86.53

16

LDE

18

15.26

1722.91

86.58

17

LDE

27

15.28

1720.7

86.62

1

AMEQ

29

15.37

1712.96

86.67

2

AMEQ

10

15.3

1713.49

86.7

3

AMEQ

7

15.42

1675.34

86.76

4

AMEQ

16

15.42

1675.34

86.76

5

AMEQ

15

15.42

1673.15

86.83

6

AMEQ

8

15.42

1660.62

86.91

7

AMEQ

39

15.29

1734.18

86.94

8

AMEQ

5

15.52

1607.87

87.01

9

AMEQ

17

15.53

1615.85

87.03

10

AMEQ

26

15.55

1616.05

87.07

11

AMEQ

9

15.37

1640.09

87.16

12

AMEQ

28

15.59

1609.52

87.28

13

AMEQ

6.4.4 FRAME SELECTION FROM HDE PART BASED ON THE EQUALITY After the classification part from the frame mentioned as HDE, frame no 36 is selected for the further analysis of defects of the fabric in the image frame. 6.4.5 GRAYSCALE CONVERSION AND NOISE REMOVAL After selection of the frame numbered 36 for further analysis, it is converted into grayscale image as further analysis of the image will be done on gray image and not on colored image. Then using Python 2.7 code, the noise of the image is removed. As the noise in the image degrades the quality of the image that is why it is very much needed to remove the noise from the selected image frame as it will be easy to understand and apply the further image processing techniques on the selected image to find out the faulty part of the image.

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FIGURE 6.6

Image of selected frame.

FIGURE 6.7

Image after noise remove and grayscale conversion.

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6.4.6 HISTOGRAM ANALYSIS Next, histogram analysis has been done on a faulty image frame. Histograms have so much uses in image processing. The first use has also been discussed above. An image can be easily predictable by seeing its histogram. The histogram of the above picture of the fabric cloth would be something like this—the x-axis of the histogram shows the range of pixel values. Since it is an 8-bpp image that means it has 256 levels of gray or shades of gray in it. That is why the range of the x-axis starts from 0 and ends at 255 with a gap of 50. But on the y-axis, is the count of these intensities. By doing the histogram analysis of the image the pixels that create objects are shown in graphical x- and y-axis formats which is basically helpful to understand the faulty part of the image frame.

FIGURE 6.8

Histogram analysis of the image frame.

6.4.7 THRESHOLDING Thresholding is a type of image processing; it can be used to analyze the images by only changing the pixels of the image. In thresholding, a binary image has been developed by converting the color image or grayscale image into bilevel image, that is, one that is simply black and white. Image thresholding is simple. It can also be used to partition an image into a foreground and background. Thresholding is also a very important part of image segmentation. This method basically isolates objects by extracting those pixels from an image that creates an object in the image by converting grayscale images into

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binary images. This method can be very much effective if the image has high levels of contrast. Here, in our proposed system, after histogram analysis, the defective fabric image frame is converted into a binary image to identify the pixels that create an object considered faulty part or defect of the fabric. As a result, the pixels of the dark black region are extracted and considered defect part of the fabric image that is marked by a red circle in Figure 6.9.

FIGURE 6.9

Image after thresholding.

6.5 CONCLUSIONS In this paper, the image comparison method based on equality value and various image processing techniques has been used to identify the defect of the fabric material. The fault has been identified in the frame that is marked by a red circle in Figure 6.9. First using the comparison technique it has been identified in which portions of the fabric cloth is faulty and then the image

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processing techniques are applied to those frames that are marked as highly defective frames and less defective frames. Using this methodology, all the other frames of the fabric cloth have been examined, and if any defect is found it has been analyzed. Using online fabric, fault identification by this approach makes the overall process very smooth, accurate, and less time consuming. In this system, there are some limitations like the inputs are video of the fabric not the fabric material itself. So, the nature of the fabric is not recognized. Artificial neural network (ANN) that is a very important part of AI can also be applied for defect identification in fabric inspection in textile industry. The images that are to be analyzed can be obtained from the method that has been described in the image acquisition system and saved in relevant standard formats (JPEG, PNG, etc.). Features are extracted from the acquired image and the feature selection method can be used to reduce the dimensionality of the feature set by creating a new feature set of smaller size that is a combination of old features. There can be various algorithms used to train and test the ANN. The proposed work can also be extended by using a neural network in such a way that the result of the error image frame is given to the neural network and any type of microcontroller can be utilized and programmed such that it can detect the faulty part. And a motor of any type can also be used in the microcontroller that will be operated under normal fabric conditions and at the end after finding the defect in the fabric image, the motor will stop and the defect part of the faulty image can be identified. ACKNOWLEDGMENT Thank you for your cooperation and contribution. KEYWORDS • • • • • •

Industry 4.0 machine learning grayscale conversion histogram analysis mean square error peak scale noise ratio

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REFERENCES 1. Majumdar, A.; Garg, H.; Jain, R. Managing the Barriers of Industry 4.0 Adoption and Implementation in Textile and Clothing Industry: Interpretive Structural Model and Triple Helix Framework. Comput. Ind. 2021, 125, 103372. https://doi.org/10.1016/j. compind.2020.103372. 2. Jimeno-Morenilla, A.; Azariadis, P.; Molina-Carmona, R.; Kyratzi, S.; Moulianitis, V. Technology Enablers for the Implementation of Industry 4.0 to Traditional Manufacturing Sectors: A Review. Comput. Ind. 2021, 103390. https://doi.org/10.1016/j. compind.2020.103390. 3. Cugno, M.; Castagnoli, R.; Büchi, G. Openness to Industry 4.0 and Performance: The Impact of Barriers and Incentives. Technol. Forecast. Soc. Change 2021, 168, 120756. https://doi.org/10.1016/j.techfore.2021.120756. 4. Das, S. K.; Das, S. P.; Dey, N.; Hassanien, A. E., Eds. Machine Learning Algorithms for Industrial Applications; Springer, 2021. DOI: 10.1007/978-3-030-50641-4. 5. Das, S. K.; Samanta, S.; Dey, N.; Patel, B. S.; Hassanien, A. E., Eds. Architectural Wireless Networks Solutions and Security Issues; Springer, 2021. DOI: 10.1007/978-981-16-0386-0. 6. De, D.; Mukherjee, A.; Das, S. K.; Dey, N., Eds. Nature Inspired Computing for Wireless Sensor Networks; Springer, 2020. DOI: 10.1007/978-981-15-2125-7. 7. Das, S. K.; Dao, T. P.; Perumal, T., Eds. N ature-Inspired Computing for Smart Application Design; Springer Nature, 2021. DOI: 10.1007/978-981-33-6195-9. 8. Kaur, N.; Dalal, M. Application of Machine Vision Techniques in Textile (Fabric) Quality Analysis. IOSR J. Eng. 2012, 2 (4), 582–584. 9. Priya, S.; Kumar, T. A.; Paul, V. A Novel Approach to Fabric Defect Detection Using Digital Image Processing. In 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies; IEEE, 2011; pp. 228–232. 10. Chan, C. H.; Pang, G. K. Fabric Defect Detection by Fourier Analysis. IEEE Trans. Ind. App. 2000, 36 (5), 1267–1276. 11. Wood, E. J. Applying Fourier and Associated Transforms to Pattern Characterization in Textiles. Textile Res. J. 1990, 60 (4), 212–220. 12. Wang, J.; Campbell, R. A.; Harwood, R. J. Automated Inspection of Carpets. In Optics in Agriculture, Forestry, and Biological Processing, Vol. 2345. International Society for Optics and Photonics, 1995; pp. 180–191. 13. Kang, T. J.; Choi, S. H.; Kim, S. M.; Oh, K. W. Automatic Structure Analysis and Objective Evaluation of Woven Fabric Using Image Analysis. Textile Res. J. 2001, 71 (3), 261–270. 14. Felzenszwalb, P. F.; Huttenlocher, D. P. Efficient Graph-Based Image Segmentation. Int. J. Comput. Vision 2004, 59 (2), 167–181. 15. Xie, X. A Review of Recent Advances in Surface Defect Detection Using Texture Analysis Techniques. ELCVIA 2008, 1–22. 16. Parveen, N. R. S.; Sathik, D. M. M. Feature Extraction on Colored X-Ray Images by Bit-Plane Slicing Technique. Int. J. Eng. Sci. Technol. 2010, 2 (7), 2820–2824.

CHAPTER 7

A RF-BASED SOCIAL DISTANCE SMART BAND SYSTEM IN ORGANIZATION RANJIT KUMAR BEHERA, MOHIT MISRA, and AMRUT PATRO Department of Computer Science, National Institute of Science and Technology, Odisha, India

ABSTRACT Lately, with the surging number of COVID-19 cases in countries, such as Italy, South Korea, China, and Japan, the World Health Organization (WHO) has proclaimed this lethal coronavirus as a pandemic on 11th March 2020. The COVID-19 is a deadly wide-spreading disease, which resulted in more than 1.14 million deaths worldwide, and usually transmits via droplets generated by an infected person when he/she sneezes, coughs, or exhales. Nevertheless, WHO suggested that social distancing is the most defensive weapon need to be equipped by the entire world for fighting against COVID-19. As lockdown cannot be a permanent solution, different organizations slowly started operation with limited capability to avoid further surge in covid cases, and workers as well as the employers of those organizations are suffering from huge financial losses due to this. Hence, being conscious of such challenges, in this paper, a radio frequency-based low-cost smart band has been proposed which flags to maintain proper social distancing norms in an organization when the user comes in a close proximity with other users. The proposed band works without even the need

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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for GPS tracking or Internet connectivity. Moreover, being inexpensive and cost-effective, it can be made available to the big organizations as well as to the small organizations. 7.1 INTRODUCTION In pandemic, social distance is necessary in any organization. It helps to avoid the effect of viruses that is being spread to others. Merriam-Webster especially known for its dictionary reports that “pandemic” is the 7th most often searched word in its dictionary this year. “A pandemic is basically a global epidemic—an epidemic that spreads to more than one continent,” says Dan Epstein, a spokesman for the Pan American Health Organization.1 With the surging number of COVID-19 cases in countries, such as Italy, South Korea, China, and Japan, the World Health Organization (WHO) proclaimed this lethal coronavirus as a pandemic on 11th March 2020.2 The COVID-19,3 also known as SARS-COV-2 or nCoV, emerged in Wuhan, a city in China, has caused an epidemic on a grand scale by spreading over 215 countries and territories. More than 42 million confirmed cases have been reported all around the world.4 Usually, the virus spreads through droplet transmission, which occurs when a person comes in close contact with another person while coughing, sneezing, or exhaling. Though, WHO suggested social distancing as the most defensive weapon need to be equipped by the entire world for fighting against COVID-19, but recently, with the relaxation in lockdown all around the world, people have started disregarding social distancing. It is being said that the exponentially reproducing coronavirus may exacerbate the current pandemic situation even more if precautionary measures like social distancing is not imposed strictly. The vigorously increasing number of cases in the world is giving rise to a substantial threat in countries where the healthcare sector is still suffering for decades. Moreover, in largely populated countries, such as India and China, social distancing may seem to be unnatural for the citizens; furthermore, it is quite difficult to impose social distancing in such countries where more than around 400 persons live per square kilometer. Even the buildings are jam-packed in such a way that it can hardly allow social distancing. The street vendors have also started selling their goods on the roadsides and even the crowd at traffic lights can merely afford imposing social distance.

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One of the most sophisticated ways of imposing social distancing is the use of an alerting band, which would keep us aware to maintain physical distance all the time. Researchers have done a lot of studies on various forecasting techniques and on automated detection of COVID-19.5,6 El Majid et al.7 have proposed a smart wristband disinfectant for fighting against COVID-19. The proposed disinfectant band is made capable of disinfecting the surface of various objects in front of it and also the hand by using ultraviolet rays. The automatically as well as manually controlled smart band gets powered by the solar cells, movement of the user’s hand. Moreover, if the necessity arises, the band can be powered explicitly also. The proposed concept of automatically disinfecting not only the hands but also nearby objects is really appreciable. Though the authors have tried to minimize the cost by reducing the power consumption, yet the manufacturing cost, which includes the use of pricey components like solar cells would make the smart band quite extortionate that might make the device beyond the reach of common people. The authors Singh et al.8 have proposed a IoT-based Q-band for tracking escaped quarantine people. Their proposed model consists of a smartphone application, which tracks the escaped quarantine persons, and reports the status to the authorities. A web interface is maintained that alerts in, case of any kind of tampering is done with the band, or in case the band if found to be out of a range of around 50 m from the registered location, or in case the patient’s status is not detected for more than 10 min of time period. Though the authors have done such a painstaking study, yet the authors have missed out encountering some of the issues. In case, the smart band is dismounted by the user, their model fails to track the quarantined users, which is an unignorable drawback of the system. However, to the best of our knowledge, only a handful of studies have been done on the development of such smart bands, out of which the smart band that is mentioned in Tripathy et al.9 is the one where authors covered almost all the features a smart wristband should posses. Their proposed band is equipped with LED indicators that shows the probability of a user being in close contact with an affected person. The EasyBand requires a WiFi connection for uploading the logged data to the server. In rural areas where there is a lack of proper Internet connectivity, their proposed model may fail to serve as per the expectations. Moreover, WiFi signals in cities are subject to changing locations. The distance between two EasyBands is estimated using the received signal strength indicator (RSSI), which may not be of much competence in calculating the distance between two devices due to the change in signal strength under different

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environmental conditions. RSSI when used as a distance metric imply faults in the measured values, resulting in path loss, fading, and shadowing effects.10 Storing device logs in an attached SD card may not prove to be a good choice for a long-term use as it may exceed the memory limit. Furthermore, the proposed model has a multiplicity of features united with numerous analog and motion sensors, not only makes the system highly power-consuming but also an extortionate product that might be beyond the reach of the common man. Therefore, being mindful of such issues and challenges in this paper, a social distance imposing smart band is proposed which is not only economical but also adept. The wearable band alerts the user by making sound and vibration when he/she comes in close proximity with other users. The proposed model flags the user to maintain social distancing as soon as another user enters its sphere having a radius of around 1–2 m. Furthermore, the multipurpose, low-power consuming smart band is adept enough to work even without the presence of Internet connectivity and GPS. Moreover, the major contributions of the paper can be summarized as follows. (1) Initially, an overview of the system has been provided, followed by a comprehensive explanation of the hardware architecture of the proposed model. The proposed smart band is made up of cost-effective components and is also dexterous enough to meet the required standards without even using Internet connectivity or GPS. (2) Based on the working principles and the derived mathematical equations, a flowchart and various algorithms are designed and explained along with the design specifics. (3) Ultimately, by the heedful experiments conducted, we evaluated the efficiency and the performance of the proposed model in imposing social distancing to prevent the spread of COVID-19. The remaining parts of this chapter are organized as follows. Section 7.2 provides an overall idea about the model with the help of a wristband diagram. While in Section 7.3, the proposed architecture of the model along with various components used is explained scrupulously. Section 7.4 consists of the implementation part of the model, along with a discussion on design specifics and algorithms, followed by the experimental setup discussed in Section 7.5. Lastly, Section 7.6 concludes by showing direction toward future work, which can be done to improve the performance of the proposed model.

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7.2 SYSTEM OVERVIEW In this section, a brief overview of the proposed system is provided. As shown in Figure 7.1, the wristband basically consists of a digital display to showing device statistics, such as the time, current battery percentage, remaining working time of the device. A piezo-buzzer with an LED indicator is also present in the band to produce an alerting sound when another such similar wristband device enters its defined sphere. The antenna scans for radio waves emitted by other such similar devices to detect the presence of other users in the defined region. The sound alerts generated by the band assists the user of the device to maintain a safe interpersonal distance from another person wearing a similar band. The proposed model implements radio frequencybased transceivers to detect the presence of other users within a small range of around 1–2 m. The use of radio frequency technology in the model would not only make it budget-friendly but also it would be well-versed to serve the users without even the need for Internet connectivity and that is why this system will be an asset to any organization where everyday employees have to work and this system can help to maintain the social distance among the employees within the organization. With this, it can limit the spreading of the virus among employees if anyone gets infected with the deadly virus.

FIGURE 7.1

An overview of the proposed band.

7.3 HARDWARE ARCHITECTURE In this section, the basic hardware architecture of the proposed smart band is illustrated scrupulously with the help of a block diagram, as shown in Figure 7.2.

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FIGURE 7.2

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A block diagram for the proposed architecture for smart band.

As it can be seen from the block diagram, the band consists of a 433 MHz radio frequency transmitter and a receiver pair that is connected to a microcontroller unit. Moreover, a digital organic light emitting diode (OLED or Organic LED) display is also connected to the microcontroller. In the proposed hardware architecture, the microcontroller can be considered as the heart of the entire system as it is responsible for controlling the whole system. Furthermore, the microcontroller unit is also in connection with a piezo-buzzer and an LED. The entire system is powered by a 3.3 V rechargeable lithium polymer battery (Li-Po Battery). A small real-time clock (RTC) module is attached to the microcontroller in order to track the real-time data.11 Promising the multiplicity of features, a set of adjustment keys are also provided which are in connection with the microcontroller, so as to allow the user to modify or change various modes like date, time, current status of how safe the user is, and so on. The following three subsections provide enough information for having an in-depth knowledge of the proposed hardware architecture and its working principle. 7.3.1 RF-BASED COMMUNICATION Radio waves are a form of electromagnetic radiation having a lower frequency as compared with microwaves. As a general rule, the frequency

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of radio waves ranges from around 300 GHz to as low as 3 KHz, while at the same time, its wavelength generally ranges from 1 mm to around 100 km. However, the range of radio waves utterly depends on the length of the antenna used. An RF-based transceiver pair plays a pivotal role in detecting another such device entering its range. The use of radio frequency based modules for wireless data communication between the systems would not only prove to be dime a dozen but at the same time, it would also prove itself to be the most fruitful solution to be invested upon. The RF transmitter sporadically emits a signal containing the device’s unique id, whereas the RF receiver capable of detecting multiple RF signals receives the signals and decodes it. The RF receivers present in the band perform various checks and ensure that the received signal is emitted by another such similar kind of device. It is mandatory to check the received signal or the unique identification to ensure that the signal is not transmitted from the device itself. This step is crucial for avoiding a signal boomerang. The transmitter as well as the receiver operates with the same frequency band, that is, 433 MHz. However, for a short-range transmission, the length of the antenna is scalable, that is, depending upon the range to be operated upon the length of the antenna can be changed. With the use of such a scalable antenna, the device no more has to measure the distance between another device and itself using RSSI. According to WHO’s suggestion, there should be a minimum distance of 1 m or more to maintain a proper social distancing.12 And in vain to this, the device is designed to detect other user entering its sphere with a range of 1–2 m so as to avoid the spread of virus. However, the existing 433 MHz RF transmitter and receiver modules are competent enough to sense the presence of other users within a minimum default range of about 1 m or 3 ft without any extra antenna attached to it. In some cases, where the range needs to be extended13 provides enough knowledge on how to calculate the size of the antenna. The wavelength of radio waves can be represented13 as λ =

v f

(7.1)

where, v is the velocity in m/s And, f is the frequency in hertz. As a general rule, in situations when the height of the antenna is equal to the wavelength of the wave, the transmission seems to be highly efficient. On the other hand, due to the size limitation of smart bands, it does not seem to be feasible to have an antenna with height equal to the wavelength. Being

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mindful of such facts, an antenna of height around a quarter of the total wavelength can be calculated, and it can be seen if it is feasible or not. To be meticulous, the height of the antenna can be represented as

λ

(7.2) 4 where λ is the wavelength of the radio waves calculated using eq. (7.1). Now, using eq. (7.1), the wavelength for a frequency of 433 MHz can be calculated as

QuarterWaveAntenna =

λ =

VeloInAir 433, 000, 458Hz

(7.3)

where VeloInAir refers to the speed of transmission in the air which is equal to the speed of light, which is nearly equal to 299,792,458 m/s.14 Now, using the value of VeloInAir, Eq. (7.3) can be simplified as (7.4)

⇒ λ = 0.6924 meters

Since we already discussed, it is not feasible to have a smart band equipped with an antenna with height 0.6924 m, thus, using Eq. (7.2), the QuarterWaveAntenna can be calculated, that is, QuarterWaveAntenna =

0.6924 = 4

0.1731 meters

(7.5)

From, the above derivations and calculations, it can be seen that even if we try to have an antenna of height quarter wave, it is quite difficult to fit the general size of a smart band. Hence, noticing such issues, in this chapter, it is proposed to have an adjustable antenna to reach the desired range. However, in the presence of a 433 MHz transceiver having capabilities to sense within a range of 1–2 m, the need for an additional antenna becomes gratuitous. 7.3.2

RF TRANSMITTER

Saw resonator tuned in for 433 MHz operations is a part of 433 MHz RF transmitter. The transmitter uses a special technique of amplitude shift keying (ASK) for transmission of data. The binary data such as variations in the amplitude of a signal are represented by ASK and is a type of amplitude modulation. The modulated binary signal, with the technique of ASK, gives the value as zero if a low input is given as the saw resonator stop oscillating on a low data input, whereas a carrier output is received for high input. The

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switching transistors are attached to the transmitter for the transmission of data along with a few components that are passive in functioning. The transmission process occurs at the rate of 1–10 kbps.15

FIGURE 7.3

Block diagram of RF transmitter.

7.3.3 RF RECEIVER The received carrier wave from the transmitter is amplified using an RF tuned circuit along with a few pairs of operational amplifiers, and this forms a part of the RF receiver, as shown in Figure 7.4. A better-decoded output with noise immunity is received with the help of phase lock loop (PLL) that helps the decoder lock onto a stream of digital bits, and this happens only after the amplified signal is directed to the PLL.

FIGURE 7.4

Block diagram of RF receiver.

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A PLL is a control system that produces an output signal whose phase is linked to the input signal phase. Usually, a variable frequency oscillator and a phase detector in a feedback loop constitutes an electric circuit. The phase of the periodic signal generated by the oscillator is compared by the phase detector with the phase having input periodic signal and the oscillator is adjusted in order to keep the phases matched.16 7.4 IMPLEMENTATION AND DESIGN SPECIFICS In this section, the implementation of the entire model along with the design specifics is explained in a painstaking manner with the help of a flowchart and various algorithms to be followed. Also, a meticulous discussion on the working of the system is done at the end of this section. 7.4.1 TRANSMISSION THROUGH ASK In case if the amplitude of the carrier wave is altered according to the incoming data signal, then it can be referred to as ASK. Figure 7.5 depicts the transmission of waves through the ASK technique. This ASK is also sometimes classed as Binary BASK, which generally has two levels, that is, digital high and digital low. The carrier wave is driven at full strength in the case of digital high and is cut off completely in the case of digital low level. Moreover, ASK has an advantage over other modulating techniques like frequency shift keying. The bandwidth of ASK is 1.5 times less than that of frequency shift keying.17 As a consequence of using the ASK technique, being less complicated and with simple circuit design definitely makes the work done thriftily.

FIGURE 7.5

Transmission of waves through amplitude shift keying.

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7.4.2 IMPLEMENTATION OF ASK IN MICROCONTROLLERS For the transference of data, several libraries are available that perform the work by encoding it to a stream of bytes. Thereafter, the data bytes are transmitted through an RF transmitter. However, for an error-free and swift transmission, the data need to be encoded properly. Hence, in the proposed model, the device’s unique id is also transmitted through radio head packets. The transferring of data from one microcontroller to another through radio waves can be done with the help of a special Radio Head Library provided by McCauley.18 The implementation of ASK in the embedded microprocessor for transference is done by using the radio head library, which consists of an object-oriented library for sending and receiving packets of messages via a variety of common data radios. This library provides a special class known as RH_ASK Class,19 which helps to send and receive unaddressed and unreliable datagrams via inexpensive ASK or On-Off Keying RF transceivers. Furthermore, the radiohead packet consists of 36-bit training preamble consisting of 0–1-bit pairs, 12 bits start symbol 0×b38, 1 byte of message length byte count (4–30), and frame check sequence (FCS) bytes, variable length message bytes (including 4 bytes of header) and 2 bytes FCS. The FCS or cyclic redundancy check (CRC) ensures the transmission of data without any kind of errors or packet loss.20 The CRC needs to be performed on the receiver’s end, and in case the CRC fails, the radio packets are discarded. 7.4.3 SYSTEM DESIGN This subsection provides a comprehensive explanation of the working of the proposed model. The model basically consists of a transmitter as well as receiver pair for duplex communication with another device. The band sporadically transmits a unique device id with the help of a transmitter, while the receiver always senses the incoming signals from other nearby devices. The microcontroller checks whether the received signal is transmitted from the same or different device. If the signal is received from the same device the received data is discarded, whereas in case the data received is from another similar device then the microcontroller flags the user by sound with the help of a piezo-buzzer connected to it. Figure 7.6 depicts the behavior of the system and the conditions when the system alerts.

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FIGURE 7.6

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Behavior of the smart band in alerting user.

Moreover, it also notifies the user by displaying a warning message on the display. It also signals the user with the help of a red led indicator. In case there is no red light indication, then it means the user is at a safer distance from other users present nearby. The transmitter as well as the receiver paired with a dual-core microcontroller to perform the transmission as well as receiving of signals synchronously. Each device needs to be assigned with a unique device id. Assigning unique id to the devices helps in identifying and discriminating signals from two different devices. The device unceasingly alerts the user to maintain social distancing till another device is present in its sphere of sense. Working of system components in a duty cycle manner ensures that it consumes less power, which in turn makes the device performing maximum duration with a single charge of the battery.

FIGURE 7.7

Flowchart representation of data handling by the microcontroller.

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Following the working and flowchart of the system, the designed algorithms to be implemented is presented further in a pseudocode format.

ALGORITHM 7.1

Data Transferring Algorithm.

Algorithm 7.1 is a pseudocode representation of the steps to be followed for encapsulating messages into radio packets and transferring the same from one transceiver pair to another. The send( ) function used in the pseudocode (line (2)) takes a string buffer and an integer as the size of the buffer. This function is basically responsible for transmitting packets from the transmitter. The waitPacketSent( ) (line (3)) function simply waits until the radio head library has sent all the data over the transmitter. The transmitted message is in a concatenated form of the band name with the band id that is, “bandname_bandid.” While transmitting data, uint8_t typecasting is mandatory to be implemented as this ensures that data are being sent in an 8-bit character format.

ALGORITHM 7.2

Device Recognition Algorithm.

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Algorithm 7.2 is primarily responsible for recognizing the device from which signals have been received. Moreover, it also depicts the behavior of a receiver. Retrieving data from the available radio signals is done using the non-blocking function recv( ) function (line (1)). A nonblocking function is a function/method that does not halt or stop the microcontroller to process until a specific event is finished. The buffer is tokenized using the strtok( ) function, which takes a parameter as the string buffer and a delimiter and extracts the band name and band id from the received data (line (2,3)). To check whether the received signals are from an expected source or not, certain checks are made (line (4)). If the extracted data from the received buffer consists of the same band name and does not match with the current device’s unique id then True as a Boolean value is returned, which signifies that the signal is from another such user (line (5–10)). Algorithm 7.3 plays the most crucial role in monitoring social distancing and alerting the user in case of violation of social distancing. The algorithm, at first, initializes a Radio Head Object (line (2)) and also initializes a buffer variable with a string achieved by concatenating the assigned band name and band id to the system itself (line (3)). In case, the device fails to transmit the buffer, then a message is displayed for the respective problem to notify the user about the same (line (4–6)). The microcontroller stores the device name and device unique id as constants. Constants such as device name is same in all other wristbands but the device id is a unique id assigned to each of the wristband.

ALGORITHM 7.3

Distancing Monitoring and Alerting Algorithm.

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Unique device id and same band name assure that the signal received is emitted from other similar devices and this would not result in a false alert on receiving signal emitted from the same device itself or from an unexpected source. In situations, when the presence of other users within the defined range is detected, then the microcontroller directs the buzzer to trigger sound and changes the color of the led to red. Moreover, a warning message is displayed in the digital display unit to alert the user (line (7–11)). In scenarios, when no signals are received the microcontroller reflects the same to the user by displaying a message as “User in safe zone,” and no warning or indication from the LED or buzzer (line (12–17)). The microcontroller executes the algorithm unceasingly with a delay of 1 ms (line (1, 18, 19)). 7.5

EXPERIMENTAL SETUP

In this section, a detailed discussion on the experimental setup and the various hardware components used for conducting the experiment has been done. For demonstrating the working of the device, the setup needs to consists of a microcontroller unit, which can also be referred as the heart of the device. An RF 433 MHz Transmitter is used for the transmission of data and an RF 433 MHz receiver for receiving the data from the signals. The microcontroller is connected with an OLED display for displaying warning message,21 an RGB led indicator for warning indication, and a piezo-buzzer. Moreover, a RTC module is also used that keeps tracing the standard time.22 The RTC module is powered by a CMOS battery backup as it does not reset the time even if the power from the microcontroller is withdrawn. The microcontroller used in the experiment is Arduino Nano. The Arduino Nano is a tiny, cheap, and breadboard-friendly microcontroller based on the ATmega328.23 The RTC module and OLED display module use the Inter-Integrated Circuit (I2C) protocol for communication with the microcontroller. I2C protocol provides an easy, reliant, and fastest way of transferring data without any data loss as compared with other protocols. Furthermore, I2C uses only two-wire communication, that is, Serial Data and Serial Clock. 7.5.1 HARDWARE COMPONENTS SPECIFICATIONS This subsection provides details on the specifications of the primary hardware components used in the model.

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7.5.1.1 433-MHZ TRANSMITTER-RECEIVER MODULE SPECIFICATIONS The availability of 433 MHz transmitter or receivers for less than $2 makes them one of the most inexpensive data communication solutions. Moreover, these modules are diminutive in nature, with capabilities to incorporate a wireless interface into almost any project. A lot of companies use 433 MHz to make products that have a remote control because of its inexpensiveness and reliability. • • • • • • • • • •

Transmitter Operating Voltage: 5 V Transmitter Operating current: 9–40 mA Operating frequency: 433 MHz Transmission power: 25 mW (315 MHz at 12 V) Transmission distance (without antenna): 3 m (max) Modulating Technique: ASK Circuit type: Saw resonator Velocity: less than 10 Kbps Bandwidth: 2 MHz Sensitivity: −100 dBm (50 Ω)

FIGURE 7.8

433 MHz Receiver and Transmitter Pin Diagram.

7.5.1.2 MICROCONTROLLER SPECIFICATIONS The ATmega328 microcontroller with a single chip is a modified version of the 8-bit reduced instruction set computer processor, Harvard architecture. Besides this, the ATmega328 is preprogrammed with a bootloader which

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provides facilities for uploading new codes without using any kind of an external hardware programmer. Moreover, the component can communicate by using STK500 protocol. • • • • • • • • • • • • •

Microcontroller: ATmega328 Operating-Voltage: 5 V Input-Voltage: 7–12 V Digital-I/O-Pins: 14 PWM-Pins: 6 Analog-Input Pins: 8 DC Current-in I/O Pin: 40 mA Flash-Memory: 32 KB (ATmega328) Bootloader-Memory: 2 KB SRAM: 2 KB EEPROM: 1 KB Clock-Speed: 16 MHz Dimensions: 0.73″ × 1.70″

7.5.2 INTERFACING COMPONENTS WITH THE MICROCONTROLLER (ARDUINO NANO)

FIGURE 7.9

Connection diagram of the components interfaced with the microcontroller.

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The A4 and A5 pins of the Arduino Nano support I2C communication and can be used with the “Wire.h” library. As shown in Figure 7.9, the A4 pin of Arduino Nano was connected with the Serial Data pin, whereas the A5 pin was connected to the Serial Clock pin of the OLED display. A similar kind of connection was made while interfacing the RTC module with Arduino Nano. The microcontroller uses the address of the devices for establishing communication with the modules using the I2C protocol. The data pin of the 433 MHz transmitter module was connected with the Digital Pin (D2) of the Arduino Nano and the data pin of 433 MHz receiver module was connected with the Digital Pin (D3) of the Arduino Nano. The complete circuit was adequately powered by a Li-Po battery as shown in Figure 7.9. The digital pin D4 and D5 were connected to the positive terminal of LED and Piezo-buzzer, respectively. Additionally, a set of push buttons can also be connected to the microcontroller to enable the user for setting the time and date of the device. Moreover, the real-time date and time can be fetched from the RTC module using the Adafruit_SSD1306 library. The DateTime object can be created and the real-time date and time can be fetched from the object through functions and can be displayed in the OLED display module. The microcontroller was routinely put on sleep mode so as to reduce the battery consumption. Figure 7.10 represents the breadboard connection used while conducting the experiment.

FIGURE 7.10

Experimental setup using breadboard.

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7.5.3 RESULT ANALYSIS The experimental setup consists of two nodes. Each node is equipped with 433 MHz RF transceiver pair along with another peripheral device such as OLED display, led, and piezo-buzzer. An Arduino Nano is used as the microcontroller and the microcontroller’s state is monitored through the Arduino Serial Monitor. For evaluating the performance of the proposed model, three experiments were conducted and the findings can be summarized as follows. (1) A packet transmission process was initiated and the radio packet encapsulating information consisting of band name and unique id was broadcasted into the surrounding environment from node 1. Both nodes were kept within a range of around 0–1.5 m. It was observed that other node responds to the broadcasted packet and the led turned to be red, the buzzer produced a beep sound and a warning message was also displayed in the OLED display. (2) Node 2 was gradually moved away from node 1. It was observed that the other node responded to the broadcasted packet and the node kept on warning or indicating till it was within the range of around 1.5–2 m. Thereafter, when the node was moved from the range of sensing, the led turned to be green, the buzzer stopped beeping and the display did not show any kind of warning message. (3) The state of node 1 was monitored while node 2 was kept out of the range of senses. It was found that both the nodes did not produce any kind of warning indication which also concludes that the devices did not respond to signal transmitted by themselves and they are capable of distinguishing between signals produced by themselves and the signals produced from other such devices. 7.6

CONCLUSION AND FUTURE WORK

In the recent past, with the billowing number of COVID-19 (2019-nCoV) cases, the WHO has declared it as a deadly pandemic. This disseminating disease, generally transmitting via droplets generated by an infected person has created a lot of ambiguity and apprehensiveness among the people. Not long ago, though lockdown imposed by various countries has proved to be effective for preventing the exponential growth of the disease, but noticing the adverse effects of the lockdown on the economic status, countries all around the world have gradually started lifting up lockdown. However, with

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the relaxation of lockdown, it is seen that people have started disregarding social distancing in their respective organization and getting infected. According to WHO, social distancing is the most powerful weapon which can be equipped in order to win the battle against coronavirus. Noticing such a life-threatening situation in this paper, a smart band for the organization has been proposed which actively monitors and alerts the users to maintain a safe distance by triggering sound, LED indication, and by displaying a warning message. The proposed multi-purpose smart band can be an easy alternative to the wristwatches which makes it easier to wear. From the experiments conducted, it was concluded that the proposed band not only proves to be budget-friendly but also the best weapon in combating the COVID-19. If used properly by everyone, this band undeniably can not only lower the rate of community spread in different organizations but also will be effective in mitigating the disease completely from the world. In the future, we look forward to work on optimizing the performance of the proposed model, and to add more functionalities to make it the most competent weapon in fighting against the ongoing COVID-19 pandemic for any organization. ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their honest reviews and helpful comments. KEYWORDS • • • • •

radio frequency RF receiver RF transmitter smart band

organization

REFERENCES 1. https://www.webmd.com/cold-and-flu/features/what-is-pandemic#1 2. Cucinotta, D.; Vanelli, M. WHO Declares COVID-19 a Pandemic. Acta bio-medica: Atenei Parmensis 2020, 91 (1), 157–160.

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3. Zheng, Y.-Y.; et al. COVID-19 and the Cardiovascular System. Nat. Rev. Cardiol. 2020, 17 (5), 259–260. 4. https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/ (accessed on Oct 23, 2020, 4:00 pm) 5. Shinde, G. R.; et al. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Comput. Sci. 2020, 1 (4), 1–15. 6. Ahuja, S.; et al. Deep Transfer Learning-Based Automated Detection of COVID-19 from Lung CT Scan Slices. Appl. Intell. 2021, 51, 571–585. 7. El Majid, B.; et al. Preliminary Design of a Smart Wristband Disinfectant to Help in Covid-19 Fight. Inventions 2020, 5 (3), 32. 8. Singh, V.; et al. IoT-Q-Band: A Low Cost Internet of Things Based Wearable Band to Detect and Track Absconding COVID-19 Quarantine Subjects. EAI End. Trans. Internet Things 2020, 6 (21). 9. Tripathy, A. K., Mohapatra, A. G.; Mohanty, S. P.; Kougianos, E.; Joshi, A. M.; Das, G. EasyBand: A Wearable for Safety-Aware Mobility During Pandemic Outbreak. IEEE Consum. Electron. Mag. 2020. 10. Heurtefeux, K.; Valois, F. Is RSSI a good choice for localization in wireless sensor network? In 2012 IEEE 26th International Conference on Advanced Information Networking and Applications; IEEE, 2012; pp 732–739. 11. Khan, S. R., Kabir, A.; Hossain, D. A. Designing Smart Multipurpose Digital Clock Using Real Time Clock (RTC) and PIC Microcontroller. Int. J. Comput. Appl. 2012, 41 (9), 40–42. 12. World Health Organization. Modes of Transmission of Virus Causing COVID-19: Implications for IPC Precaution Recommendations: Scientific Brief, 27 March 2020. No. WHO/2019-nCoV/Sci_Brief/Transmission_modes/2020.1; World Health Organization, 2020. 13. https://www.omnicalculator.com/physics/Wavelength (accessed on Oct 23, 2020, 4:00 pm). 14. https://en.wikipedia.org/wiki/Speed_of_light (accessed on Oct 23, 2020, 4:00 pm). 15. Ahmed, F.; et al. 433 MHz (Wireless RF) Communication Between Two Arduino UNO. Am. J. Eng. Res. (AJER) 2016, 5 (10), 358–362. 16. Wolaver, D. H. Phase-Locked Loop Circuit Design. Organization 1991, 1, 7. 17. Ash, D. L. A Comparison Between OOK/ASK and FSK Modulation Techniques for Radio Links. Tech. Rep. 1992. 18. McCauley, M. Radiohead Packet Radio Library for Embedded Microprocessors, 2014. Avaliable online: http://www. airspayce. com/mikem/arduino/RadioHead/ (accessed on Nov 20, 2018). 19. https://www.airspayce.com/mikem/arduino/RadioHead/classRH__ASK.html (accessed on Oct 23, 2020, 4:00 pm). 20. Chambers, J. P. Cyclic Redundancy Data Check Encoding Method and Apparatus. U.S. Patent No. 4,283,787. Aug 11, 1981. 21. Cameron, N. Screen Displays. A rduino Applied; Apress: Berkeley, CA, 2019; pp 237–259. 22. Rosyid, A. Prototype of Automatic Fish Feeder Using Timer Module RTC DS1307 Based on Arduino UNO; Universitas Gadjah Mada: Diss. (Yogyakarta), 2014. 23. Nano, A. Arduino Nano, 2018.

SECTION III

RESOURCE OPTIMIZATION AND

MODELING

CHAPTER 8

MANPOWER OPTIMIZATION SYSTEM IN COLLEGE: A LINEAR PROGRAMMING APPROACH SANTOSH KUMAR DAS1, BIKRAM MAHAPATRA2, D. HEMA KUMAR2, MANOJ KUMAR MANDAL3, and JOYDEV GHOSH4 1

Sarala Birla University, Ranchi, Jharkhand, India

National Institute of Science and Technology (Autonomous), Odisha, India

2

Jharkhand Rai University, Jharkhand, India

3

National Research Tomsk Polytechnic University (TPU), Russia

4

ABSTRACT Nowadays, several technical colleges and universities are started for the purpose of increasing educational system of the country. It helps to increase the overall ratio of education based on different sectors of the country. Most of the institutes are based on total number of student. So, this number is used to deal with total number of teachers required in the institute. Apart from teachers, there are several manpower required to run the institute based on the requirement. Some of the manpower are named as examination department, academic staff, library staff, canteen staff, lab technician, etc. These members are known as nonteaching staff. The combination of teaching and nonteaching staff helps to run the institute for the purpose of smooth

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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education system. But it is difficult to map the ratio of student to teacher. Because number of students increases day by day, but according to this enhancement, the number of teachers does not increase. Hence, it is difficult to deal and map this strategy. Therefore, in this chapter, an efficient technique is proposed for manpower management system in technical college. The main key element of this chapter is linear programming with fuzzy logic that helps to optimize the model and produce optimal solution. 8.1 INTRODUCTION Day by day, the number of institutes and organizations is increasing rapidly due to exponential growth of the population. The main reason of this growth is the demand of the customer with respect to education in the society. With the growing nature of institutes and colleges, the number of teachers and staff are not increasing due to some constraints. So, it needs to be optimized based on several conditions. There are several works that have been done for the purpose of optimization. Francesco et al.1 designed an optimization system for manpower for the purpose of container terminal that uses several condition and analysis. It helps to deal with several planning and information system based on certain problem and formulation. Ekechukwu et al.2 proposed an optimization model for several requirement information system based on bakeries in Nigerian. Dey3 designed a method for analyzing some information based on flower optimization system that helps to deal several information along with variants of information system. Optimization is very crucial information that helps to model several nonlinear and linear information efficiently with context of proposed goal and method.4 It helps to reduce several types uncertainty based on factors and information system. It helps to deal information based on some factors that helps optimize the model efficiently. Sometime, it requires to optimize based on parameters and information that are based on machine learning information system. It is based on machine constraints and learning information. It helps to deal with several intelligent decision-making situation and information system based on industrial application and also application that are based on society problems.5,6 The rest of this chapter is organized as: Section 8.2 discussed the existing work for the purpose of literature review. Section 8.3 illustrated the main proposed method, Section 8.4 has analyzed and discussed the performance evaluation, and Section 8.5 illustrated the conclusion part with future work.

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8.2 LITERATURE REVIEW In the last few years, several works have been proposed for the purpose of optimization based on several domains, such as wireless sensor network, wireless network, smart application design, intrusion detection system, game theory analysis. Some of the works are discussed in this section as Kassan et al.7 designed a clustering method for WSN-supported distributed system. The aim of this method is to maximize the network lifetime of the WSN. The proposed method is predicated on noncooperative theory of games optimization system, which is employed to regulate decentralized system of the network efficiently. The proposed method also controls energy harvesting system of the battery. Finally, it helps to reinforce the network lifetime of the WSN alongside network metrics. Das et al.8 proposed a detailed designbased book for supported frameworks of wireless network. This book contains several frameworks, such as optimization, security and privacy, localization, and network lifetime enhancement. This book provides the essential frameworks ideas of the users and new researchers. Han et al.9 designed an intrusion detection system for WSN using theory of games. There are two basic parameters that are utilized in this method: the first one is energy of the nodes and the second parameter is the strategy of the nodes. The noncooperative theory of games model is employed during this method for optimizing the network metrics. The mixture of both parameters help to style Nash equilibrium of the sport for locating the optimal solution. Finally, it helps to scale back malicious attack and control threats of the network. Some of the works are also based on nature-inspired optimization that helps to optimize several information based on user requirement and analysis.10 Movassagh and Aghdasi11 designed a theory of games-based scheduling algorithm for WSN. It is distributed and coverage-based for handling problems with WSN. During this method, some nodes are active while some nodes are in sleep for optimizing the network lifetime and reducing the redundancy in coverage system. Finally, it helps in enhancing the network lifetime and network metrics of the WSN using the strategy management technique of WSN. There are several works that have been proposed for the purpose of wireless sensor network also help to optimize several information and parameters.12 Chengliang et al.13 proposed a distributed system for WSN that helps to regulate the power of the nodes using theory of games. The proposed theory of games technique is employed in two purposes, the first purpose is to reduce energy consumption of the battery of the node and the second purpose is to increase the network lifetime of the WSN. The proposed

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method is the fusion of two techniques like theory of games and distributed method. The combination of both techniques helps to scale back network overhead also in WSN. Arora et al.14 proposed an adaptive-based protocol for WSN. This proposed method is the fusion of two techniques like theory of games and evolutionary method. The mixture of both techniques is understood as evolutionary-based theory of games, which efficiently help in WSN for optimal route selection. This method is mixed with cryptography for enhancing the security of the network. Intelligent strategy management technique additionally helps to manage network parameters efficiently. Alskaif et al.15 designed an intelligent energy efficient routing technique for WSN. During this method, theory of games is employed as a basic key element for managing two role of the sensor nodes, the first node is sensing natural phenomenon of the environment and the second one helps to gather gathered information from neighbor nodes and send it to the sink node efficiently. The mixture of both helps to save lots of energy and also enhances the network lifetime of the WSN. Some of the works have been proposed for the purpose of smart application and design that help to deal with several informations efficiently for handling some analysis and design.16 Sun et al.17 designed a predictable routing technique for WSN using the theory of games. The proposed method is predicated on energy aware system of the network. It supported the sleep management scheduling technique of the network. It needs to manage the remaining energy of the nodes. This management also requires to handle and manage energy consumption of the sensor nodes. This formulation is achieved by the fusion of backward induction and Nash equilibrium of the sport theory technique. Several mathematical information and optimization are also based on some application that helps to model some information based on wireless networks.18 Hao et al.19 proposed a theory of games-based topology management technique for WSN. This method is predicated on Markov system that’s used to manage and handle transmitting energy of the nodes in WSN. Nash equilibrium of the sport theory is employed to handle and optimize network parameters efficiently supported by the crucial parameter energy of the sensor nodes. Finally, it outperforms the supported network lifetime of the WSN. Some of the works are based on particle swarm optimization that helps to deal and optimize several information and parameters with the context of information management. Each of the information is based on some domain that helps to manage several nature-inspired computing.20 Attiah et al.21 designed a routing technique for WSN by the fusion of theory of games and evolutionary technique. In WSN, there are several types of sensor nodes available which support the nature of

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the network and remaining energy capacity of the nodes. The mixture of nodes variation is employed to make node congestion and collision within the sensor network. So, fusion technique is employed to manage a technique of the sport and supply a Nash for managing the lifetime of the WSN. Krichen et al.22 designed an efficient technique for WSN for monitoring the network and its neighbor node-based MAC routing protocol. The proposed technique is employed for aircraft in monitoring system. It helps to detect the intensity of the vibration of the airplane using sensor nodes. So, it is also utilized in health monitoring system using sensor nodes. Finally, it helps to scale back end-to-end delay and packet loss. Some of the studies are also based on some information analysis that helps to deal with some evaluated information system based on image and other information system.23 Zhu et al.24 designed a way by fusion of theory of games and Markov chain for improving network lifetime of the WSN. The mixture of both techniques helps to regulate and manage several multimedia elements, such as image, video stream, voice. This Markov model is predicated on hidden Markov model that is used to enhance the accuracy of the sport within the network that means increase in transmitting data packet. Esposito and Choi25 designed a technique-based technique for WSN. This is often a supported localization system of the network. It helps to alert the nodes as secure location and nonsecure location with the assistance of anchor system that indicates here known position of the nodes. It also helps to scale back the value of the network by decreasing the exhaust of the network. Chen et al.26 proposed a way for WSN-supported theory of games technique. This theory of games technique is predicated on evolutionary system that will not control and manage selfish nodes of the network. In WSN, the amount of nodes is more for handling any operation. So, the behavior of the selfish nodes fluctuated frequently. The proposed method helps to manage packet forwarding system of the node by optimizing strategy of the network and increase the fitness of the WSN. Das et al.27 designed an article for the purpose of architecture analysis of wireless network based on application and security issues. It consists of several types of modeling and optimization that helps to model several information efficient based on certain factors. Each of the work in this book is based on several problems that helps to deal several types of analysis. It is based on some uncertainties information for the purpose of dealing system and its management. Sun et al.28 proposed a way for spectrum sharing in WSN where the character of the network is heterogeneous. This is often a supported theory of games optimization technique. During this network, several sorts of nodes are available where each node attempts to enhance its own profit

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which degrades the performance of the network. The sport theory optimization technique helps to determine a technique where nobody deviates the rule of the sport and optimize the network metrics efficiently. Lan et al.29 discussed clustering based on information modeling based on particle swarm optimization. It helps to deal several data monitoring and information system that helps to deal with several biomedical sensing information systems. Shamshirband et al.30 designed a cooperative technique of theory of games for managing security of the WSN. It also includes symbolic logic and learning methods. This method consists of three players (i) attacker, (ii) base station node, and (iii) sink node. The sport theory is employed to scale back attack in the WSN by cooperative manner. Finally, it helps to reinforce the network lifetime of the network and reduce energy consumption of the node. 8.3 PROPOSED METHOD In this section, manpower optimization system is designed, which is applicable in most of the colleges and institutes. In this paper, the authors used some modeling of Mandal et al.31 for the purpose of enhancement of modeling based on linear programming. The main key elements of this work are linear programming and fuzzy logic. The combination of these two methods helps to design and optimize the mathematical model efficiently based on formulations and design constraints. Equation 8.1 shows the combination of 10 departments including engineering and science where first seven departments are considered as engineering department and remaining three departments are considered as science department. Equation 8.2 shows three types of faculty members as permanent faculty, contractual faculty, and guest faculty. The variables of permanent faculty is considered as pi, the variable of contractual faculty is considered as ci, and the variable of guest faculty is considered as gi. It works for both engineering department as well as science department. These three types of faculties help in all departments for the purpose of teaching and academic purpose. The combination of Eqs. 8.3 and 8.4 is known as optimization equation of engineering department. Equation 8.3 indicates objective function and eq. 8.4 indicates constraints. The objective function denoted by MP1 indicates manpower based on three decision variables as X1, X2, and X3. Equation 8.4 indicates related constraints for seven engineering department where controlling variables of three types faculty members are x1, x2, and x3. The objective function is used for minimization purpose, that is, MP1. The variable n1 indicates the mean value of maximum range of fuzzy linguistic variables that is defined in the later section. So,

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this optimization model is used to minimize the manpower of engineering departments. The combination of eqs. 8.5 and 8.6 is known as optimization equation of science department. Equation 8.5 indicates objective function and eq. 8.6 indicates constraints. The objective function denoted by MP2 indicates the manpower based on three decision variables as X1, X2, and X3. Equation 8.6 indicates related constraints for three science department where controlling variables of three types of faculty members are x1, x2, and x3. The objective function is used for minimization purpose of, that is, MP2. The variable n1 indicates the mean value of maximum range of fuzzy linguistic variables that is defined in the later section. So, this optimization model is used to minimize the manpower of science departments. 10

D = ∑di

(8.1)

i =1

where i=1–7 indicates different engineering departments and i=8–10 indicates science departments. 10

F = ∑ pi ci gi i =1

(8.2)

where F is a combination of three types of faculties, such as permanent faculty, contractual faculty, and guest faculty. Min. MP1 = ∑

3

Xi

i =1

(8.3)

Subject to constraints: 7

∑p x i =1

i 1

+ ci x2 + gi x3 ≤ n1

(8.4)

where i varies 1–3; i = 1 indicates decision variable for permanent faculty, i = 2 indicates decision variable for contractual faculty, and i = 3 indicates decision variable for guest faculty; xi ≥ 0. 3

Min. MP2 = ∑X i i =1

(8.5)

Subject to constraints: 10

∑p x i =8

i 1

+ ci x2 + gi x3 ≤ n1

(8.6)

where i varies 1–3; i = 1 indicates decision variable for permanent faculty, i = 2 indicates decision variable for contractual faculty, and i = 3 indicates decision variable for guest faculty; xi ≤ 0.

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In the proposed method, fuzzy logic is used, which is a part of soft computing technique that is used to deal with several types of uncertaintyrelated information and parameters. In this work, fuzzy membership function is used for the purpose of controlling uncertainty during manpower management in the college. In this work, triangular membership function is used that divides the crisp data into fuzzy data based on three linguistic variables as “Low,” “Medium,” and “High.” These fuzzy variables are used in three different types of faculty members as permanent, contractual, and guest faculty members. The details of membership function are given in Table 8.1. The crisp values of permanent, contractual, and guest faculty members are considered as 500, 100, and 50. These fuzzy linguistic variables are used to analyze the dataset for engineering and science departments based on three type’s of faculties as permanent, contractual, and guest. Tables 8.2–8.4 show the dataset of engineering department faculty member based on three linguistic variables as “Low,” “Medium,” and “High.” In these datasets, data are generated by random function based on mentioned range of fuzzy linguistic variables, and pi, ci, and gi indicate the range from i = 1 to 7 for engineering department. The same analysis is done for science department, which is shown in Tables 8.5–8.7 by random function. It is for only three science department as i = 8–10. Algorithm of the proposed method is shown in Algorithm 8.1. ALGORITHM 8.1 Evaluation of the Proposed Method. Step 1: Collect the facts related to number of students and teachers of institute Step 2: Categorized the department Step 3: Categorized the types of faculty available Step 4: Distribute the ratio between number of faculty and with fuzzy variables Step 5: Analyze the fuzzy variable based on fuzzification method Step 6: Design object function Step 7: Design related constraints Step 8: Map fuzzy variable with constraints Step 8: Validate the outcome of the proposed model Step 9: Select the optimal solution TABLE 8.1 Type

Membership Functions of Faculty Members. Low

Medium

High

Permanent

(0–200)

(180–350)

(320–500)

Contractual

(0–40)

(35–80)

(70–100)

Guest

(0–20)

(15–40)

(35–50)

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TABLE 8.2 Dataset of Engineering Department Faculty Member Under Low Manpower. pi

ci

gi

p1=70

c1=25

g1=15

p2=130

c2=33

g2=14

p3=120

c3=35

g3=8

p4=70

c4=20

g4=12

p5=86

c5=30

g5=18

p6=90

c6=38

g6=5

p7=160

c7=17

g7=3

TABLE 8.3 pi

Dataset of Engineering Department Faculty Member Under Medium Manpower. ci

gi

p1=270

c1=45

g1=25

p2=230

c2=43

g2=24

p3=220

c3=55

g3=18

p4=270

c4=60

g4=32

p5=286

c5=65

g5=38

p6=290

c6=78

g6=35

p7=260

c7=47

g7=33

TABLE 8.4 pi

Dataset of Engineering Department Under High Manpower. ci

gi

p1=370

c1=75

g1=45

p2=450

c2=83

g2=44

p3=420

c3=85

g3=48

p4=450

c4=90

g4=38

p5=486

c5=95

g5=46

p6=390

c6=88

g6=36

p7=360

c7=97

g7=40

TABLE 8.5 pi

Dataset of Science Department Faculty Member Under Low Manpower. ci

gi

p8=70

c1=25

g1=15

p9=86

c2=33

g2=14

p10=90

c3=38

g3=12

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TABLE 8.6

Dataset of Science Department Under Medium Manpower.

pi

ci

gi

p8=230

c1=43

g1=38

p9=220

c2=55

g2=35

p10=270

c3=60

g3=33

TABLE 8.7 Dataset of Science Department Under High Manpower. pi

ci

gi

p8=420

c1=83

g1=48

p9=450

c2=85

g2=38

p10=486

c3=90

g3=46

Equation 8.1 is the set of departments including engineering and science that is categorized into three types of faculties mentioned in eq. 8.3. The optimization is required for these three types of faculties, which are mentioned in eqs. 8.3–8.6. Equations 8.3 and 8.4 are the combination minimization model of engineering department for the purpose of minimization of manpower based on fuzzy linguistic variables mentioned in Table 8.1. The same thing is done for science department that is mentioned in eqs. 8.5 and 8.6 both is the combine model for minimization model for science department for the purpose of minimization of manpower based on fuzzy linguistic variables as given in Table 8.1. Both optimization models run into the simulator by using fusion of linear programming and fuzzy logic. 8.4

PERFORMANCE EVALUATION

The performance of the proposed method is evaluated in Python programming based on two types of modeling such as linear programming and fuzzy logic. Description of the parameters given in Table 8.8 is based on different types of parameters and their related descriptions. In this model, Python programming is used with graph analysis in MS Excel 2013. Pie chart is used as 3D pie chart for representing final outcomes of both departments. Linear programming is used for linear optimization with fuzzy intelligent technique. The model is iterated into three iterations based on three type’s of fuzzy variables, such as Low, Medium, and High. The analysis part of engineering department and science department is shown in Figures 8.1 and 8.2. Figure 8.1 shows the final outcome of the optimization model of engineering department where the ratio

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is calculated as 13%, 31%, and 56% for Low, Medium, and High manpower system of engineering department. The same things are calculated for science department, that is shown in Figure 8.2 as 10%, 30%, and 60% manpower ratio for Low, Medium, and High manpower system of science department. TABLE 8.8

Simulation Parameters.

Parameter

Description

Python

3.10

MS Excel version Chart Dimension of chart Optimization model Intelligent technique Iteration Total linguistic variable

2013 Pie chart 3D Linear Fuzzy logic Three 3

Name of linguistic variable

Low, Medium, High

FIGURE 8.1

Ratio of manpower of engineering department.

FIGURE 8.2

Ratio of manpower of science department.

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8.5 CONCLUSIONS AND FUTURE WORK The paper is proposed based on several analysis and management of manpower optimization based on several data. These data are based on several constraint and analysis that helps to model several information in the form of objective function and constraints. It helps to deal with several information that helps to manage some linear information in the form of constraints and objective function. Future work is to design and evaluate this model based on some factors and information management based on other model. Other model helps to analyze the information in the form of nonlinear optimization that helps to manage several information. KEYWORDS • • • •

linear programming fuzzy logic

optimization

model analysis

REFERENCES 1. Di Francesco, M.; Llorente, N. D. M.; Zanda, S.; Zuddas, P. An Optimization Model for the Short-Term Manpower Planning Problem in Transhipment Container Terminals. Comput. Ind. Eng. 2016, 97, 183–190. 2. Ekechukwu, O. V.; Madu, A. C.; Nwanya, S. C.; Agunwamba, J. C. Optimization of Energy and Manpower Requirements in Nigerian Bakeries. Energy Convers. Manag. 2011, 52 (1), 564–568. 3. Dey, N.; Ed. Applications of Flower Pollination Algorithm and Its Variants; Springer, 2021. 4. Wang, Q.; Tang, C. Deep Reinforcement Learning for Transportation Network Combinatorial Optimization: A Survey. Knowl. Based Syst. 2021, 233, 107526. 5. Das, H.; Rout, J. K.; Moharana, S. C.; Dey, N.; Eds. Applied Intelligent Decision Making in Machine Learning; CRC Press, 2020. 6. Das, S. K.; Das, S. P.; Dey, N.; Hassanien, A. E.; Eds. Machine Learning Algorithms for Industrial Applications; Springer, 2021. 7. Kassan, S.; Gaber, J.; Lorenz, P. Game Theory Based Distributed Clustering Approach to Maximize Wireless Sensors Network Lifetime. J. Netw. Comput. Appl. 2018, 123, 80–88.

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8. Das, S. K.; Samanta, S.; Dey, N.; Kumar, R. Design Frameworks for Wireless Networks; Lecture Notes in Networks and Systems; Springer, 2020. 9. Han, L.; Zhou, M.; Jia, W.; Dalil, Z.; Xu, X. Intrusion Detection Model of Wireless Sensor Networks Based on Game Theory and an Autoregressive Model. Inf. Sci. 2019, 476, 491–504. 10. Dey, N.; Rajinikanth, V.; Eds. Applications of Bat Algorithm and Its Variants; Springer, 2021. 11. Movassagh, M.; Aghdasi, H. S. Game Theory Based Node Scheduling as a Distributed Solution for Coverage Control in Wireless Sensor Networks. Eng. Appl. Artif. Intell. 2017, 65, 137–146. 12. De, D.; Mukherjee, A.; Das, S. K.; Dey, N. Nature Inspired Computing for Wireless Sensor Networks; Springer Tracts in Nature-Inspired Computing; Springer, 2017. 13. Chengliang, N.; Dongxin, L.; Tingxian, Z.; Lihong, L. Distributed Power Control Algorithm Based on Game Theory for Wireless Sensor Networks. J. Syst. Eng. Electron. 2007, 18 (3), 622–627. 14. Arora, S.; Singh, P.; Gupta, A. J. Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks Using Evolutionary Game Theory. Proc. Comput. Sci. 2016, 78 (C), 358–366. 15. AlSkaif, T.; Zapata, M. G.; Bellalta, B. Game Theory for Energy Efficiency in Wireless Sensor Networks: Latest Trends. J. Netw. Comput. Appl. 2015, 54, 33–61. 16. Das, S. K.; Dao, T. P.; Perumal, T.; Eds. Nature-Inspired Computing for Smart Application Design; Springer, 2021. DOI: https://doi.org/10.1007/978-981-33-6195-9. 17. Sun, D.; Huang, X.; Liu, Y.; Zhong, H. Predictable Energy Aware Routing Based on Dynamic Game Theory in Wireless Sensor Networks. Comput. Electr. Eng. 2013, 39 (6), 1601–1608. 18. Das, S. K.; Samanta, S.; Dey, N.; Kumar, R.; Eds. Design Frameworks for Wireless Networks; Springer: Singapore, 2020. 19. Hao, X.; Wang, L.; Yao, N.; Geng, D.; Chen, B. Topology Control Game Algorithm Based on Markov Lifetime Prediction Model for Wireless Sensor Network. Ad Hoc Netw. 2018, 78, 13–23. 20. Chakraborty, S.; Samanta, S.; Biswas, D.; Dey, N.; Chaudhuri, S. S. Particle Swarm Optimization Based Parameter Optimization Technique in Medical Information Hiding. In 2013 IEEE International Conference on Computational Intelligence and Computing Research; IEEE, 2013; pp 1–6. 21. Attiah, A.; Amjad, M. F.; Chatterjee, M.; Zou, C. An Evolutionary Routing Game for Energy Balance in Wireless Sensor Networks. Comput. Netw. 2018, 138, 31–43. 22. Krichen, D.; Abdallah, W.; Boudriga, N. On the Design of an Embedded Wireless Sensor Network for Aircraft Vibration Monitoring Using Efficient Game Theoretic Based MAC Protocol. Ad Hoc Netw. 2017, 61, 1–15. 23. Dey, N.; Rajinikanth, V.; Lin, H.; Shi, F. A Study on the Bat Algorithm Technique to Evaluate the Skin Melanoma Images. In Applications of Bat Algorithm and its Variants; Springer: Singapore, 2021; pp 45–60. 24. Zhu, J.; Jiang, D.; Ba, S.; Zhang, Y. A Game-Theoretic Power Control Mechanism Based on Hidden Markov Model in Cognitive Wireless Sensor Network with Imperfect Information. Neurocomputing 2017, 220, 76–83. 25. Esposito, C.; Choi, C. Signaling Game Based Strategy for Secure Positioning in Wireless Sensor Networks. Pervasive Mob. Comput. 2017, 40, 611–627.

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26. Chen, Z.; Qiu, Y.; Liu, J.; Xu, L. Incentive Mechanism for Selfish Nodes in Wireless Sensor Networks Based on Evolutionary Game. Comput. Math. Appl. 2011, 62 (9), 3378–3388. 27. Das, S. K.; Samanta, S.; Dey, N.; Patel, B. S.; Hassanien, A. E.; Eds. Architectural Wireless Networks Solutions and Security Issues; Springer, 2021. 28. Sun, S.; Chen, N.; Ran, T.; Xiao, J.; Tian, T. A Stackelberg Game Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks. Signal Process. 2016, 126, 18–26. 29. Lan, K.; Fong, S.; Liu, L. S.; Wong, R. K.; Dey, N.; Millham, R. C.; Wong, K. K. A Clustering Based Variable Sub-Window Approach Using Particle Swarm Optimisation for Biomedical Sensor Data Monitoring. Enterp. Inf. Syst. 2021, 15 (1), 15–35. 30. Shamshirband, S.; Patel, A.; Anuar, N. B.; Kiah, M. L. M.; Abraham, A. Cooperative Game Theoretic Approach Using Fuzzy Q-Learning for Detecting and Preventing Intrusions in Wireless Sensor Networks. Eng. Appl. Artif. Intell. 2014, 32, 228–241. 31. Mandal, M. K.; Mahatha, B. K.; Burnwal, A. P.; Das, S. K.; Ghosh, J. Maintaining Manpower in Technical College Using Fusion of Quadratic Programming and Fuzzy Logic. Nat. Inspir. Comput. Smart Appl. Des. 2021, 267.

CHAPTER 9

HOMOGENIZATION IN THE TECHNIQUE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT SUNIL GAUTAM1, KAUSHAL SINGH2, and MRUDUL BHATT2 Department of Computer Science and Engineering, Institute of Technology, Gujarat, India

1

Department of Computer Science and Engineering, Institute of Advanced Research, Gujarat, India

2

ABSTRACT Procedures of the computational insight and its subset are not new to human resource management (HRM), since they show a heterogeneous game plan of thoughts on the most capable technique to use computational power. The association of the artificial intelligence (AI) methods in HRM has been amassed. Additionally, such responsibilities offer positive pieces of information into express application prospects, a layout of the general potential is missing. Thus, this part offers a first examination of the general ability of AI strategies in HRM. To this end, a succinct foundation clarifies on the central functionalities of AI techniques and the central essentials of HRM subject to the task innovation fit philosophy. Considering this, the capacity of AI in HRM is explored in six picked circumstances. The initial one is the turnover gauge with fake neural organizations; in the second one, we see the

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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contender search with information-based chase engines; while in the third one, staff rostering with hereditary calculations; in the fourth circumstance, we see about human resource feeling investigation with text mining, the fifth circumstance is about the rundown of references information getting with information extraction, and the last circumstance is about laborer selfadministration with intuitive voice reaction. The encounters gained reliant upon the establishment and examination are inspected and summarized in this part. 9.1 INTRODUCTION Human resources (HR) are comprehensively seen as the principle resources of any affiliation, and managing this asset adequately is pondered as a huge authoritative commitment.1 Simulated intelligence can enable relationships to comprehend the most extreme limit of the capacity of the executives by building up an environment that tends to worker issues and further creates support. Such innovation can alter vocation advancement, redesign progression arranging, close capacities holes, and steer compensation approach— supporting administrators, pioneers, and chiefs in making and passing on capacity, which along these lines makes advantages for the business.2 For example, artificial intelligence (AI) can outfit a worker with quick thoughts for courses or scrutinizing that will support ordinary occupation commitments. As a decent deviation from the standard one-size-fits-all methodology, workers will feel the differentiation in an experience that records for their own destinations, necessities, and thriving, and affiliations will find placing assets into representative turns of events and satisfaction less complex and more effective.3 Regulating HR contains a broad extent of different assignments, including staffing, the executives of worker execution, advancement of representative capacities and vocations, and pay of representative undertakings. Figure 9.1 addresses the four significant jobs which AI plays in human resource management (HRM). In HRM, as in other spaces, adequate procedures are fundamental; shown strategies appreciated as deliberate rules for settling area errands enable people to adjust viably to the assorted necessary of a given space. As a grounded and dynamically professionalized area, HRM uses a wide and heterogeneous plan of strategies.3,4 Regardless area express strategies, this furthermore implies procedures that are “imported” from various spaces, for instance, psychometric tests from progressive mind science, smoothing out from errands research or online logical getting ready

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from information systems. A further unquestionable discipline offering methods that might be applied in HRM is man-made thinking AI. For AI, the composing offers a heterogeneous game plan of thoughts as to how unequivocal AI strategies could be applied for express HR assignments, for instance, how to use data mining methods in worker decision shrewd expert procedures in representative turn of events or information extraction methods in representative enlisting. Such responsibilities yield unique and nitty-gritty pieces of information into the potential outcomes of individual AI strategies for particular HR errands, and thusly they are significant.4 Be that as it may, such responsibilities cannot offer a diagram of the general potential, that is, which AI strategies generally exist, to which HR errands these could be all things considered applied, and which general conditions exist for a productive application. Any expansive examination of use prospects of unequivocal methods in a specific area should be established on wide pieces of information into the comprehensive conditions and effects of an application.5 Hence, the assignment innovation fit methodology (TTF), offers an essential and accommodating foundation. On a very basic level, the methodology plans to clarify the accomplishment of Information innovation and cases the assignment innovation fit (“correspondence between task necessities and the handiness of the innovation”) as the critical model for progress (“mix of additional created capability, further created sufficiency or conceivably better”). The methodology has been successfully applied to a far-reaching course of action of usage spaces and creative groupings and should be sensible for exploring the capacity of AI strategies (which are interpretable as advances since they are compulsorily executed as mechanical applications) in HR, the board (which is interpretable as a lot of interrelated assignments).6 As a justification behind extra examination, the general essentials of HR assignments and the general functionalities of AI procedures are quickly elucidated in the going with. Against the setting of the undertaking innovation fit methodology, the arranged hypothetical examination of potential outcomes proposes the assessment of imaginative functionalities, task credits, the normal attack of both, and the resulting results.7 Any examination, regardless, is confronted with the variety of individual AI procedures, of individual HR assignments, and subsequently, of conceivable undertaking strategy blends. The resulting examination task, consequently, is colossal and far past the degree of a lone responsibility. On this record, a discerning decision of six unmistakable application situations of AI methods in HR is discussed underneath.8 While this does not allow any last treatment of the subject, it engages distinctive first explorative

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encounters. The situations were picked reliant upon two guidelines: firstly, the course of action of utilization situations should cover the extent of AI methods similarly as the extent of HR assignments instead of spotlight on one or a few procedures just as one or two or three errands. Besides, the models should include “create” application situations, that is, uses of AI in HR that are presently elucidated, attempted, and basically rarely, moreover got eventually, rather than “current” situations with questionable reasonable credibility.9 In the going with, it is immediately inspected for each circumstance in which the functionalities of the singular AI strategy offers and whether or not and how these fit with necessities of the different HR task. Therefore, this part intends to be the principle examination of the general capacity of AI methods in HR executives. Consequently, a succinct foundation explains the central functionalities of AI strategies and the central requirements of HR, the board reliant upon the errand innovation fit methodology. Considering this, the capacity of AI procedures in HR the board is explored in six picked application situations.10 Finally, the encounters acquired ward on the foundation and examination are discussed and summarized.

FIGURE 9.1 AI role in human resource management. Source: Ref. [57].

9.2 HOW AI TECHNIQUES GET FITS IN HUMAN RESOURCE MANAGEMENT? 9.2.1 NECESSITIES OF HR TASKS As an undeniable regulatory space, HRM is portrayed and orchestrated in a startling manner all through the composing.3 Understanding the representatives as the huge wellspring of hierarchical execution and advantage and the

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efficient arrangement of all laborer-related activities for business procedure set up typical characteristics of the thought since its beginnings. As a working definition, HRM thusly can be overall portrayed as a subset of the board assignments that are related to potential or current representatives to secure responsibilities that directly or by suggestion support the approach and execution of an association.4 This proposes an enormous number of pointby-point assignments, which are characterized heterogeneously all through the composition. Zeroing in on significant buildings of undertakings with clear fundamental importance, staffing, execution the executives, improvement, and pay, involve customarily thought to be interrelated assignments of HRM. Staffing in regular implies the game plan of the sum and nature of workers indispensable for business. This proposes different subundertakings, for example, requirements arranging, enlisting, choice and on-boarding of new workers, besides, if important, moreover relocations and excusals of current representatives. Additionally, task arranging and regular undertaking of representatives similarly involve further suberrands of staffing. Execution of the board includes the efficient arranging, examination, and achievement support of gathering and individual objectives. Arranging deduces the slipping falling of (fundamental) hierarchical focuses to particular objections.7 Execution evaluation thus centers around a going with or periodical achievement appraisal of these objections, while accomplishment support centers around grouped assistance gauges that enable and work with solitary target achievement. Improvement centers around coming about the movement of individual laborer abilities similarly as agent employments. Ability headway implies the continuing to get ready of representatives to outfit them with the capacities major for the achievement of their targets, including the ability to adjust to pressure, workloads, and conflicts, among others. Past the capacities, job headway targets arranging and recognizing medium-term positions/ movements in a way that matches authoritative prerequisites similarly as individual prospects and aspirations.8 Finally, pay suggests a compensation of workers, including thoughts of advantage sharing and annuity plans. Compensation centers around a sensible and energizing portion of representatives relating to solitary ability necessities and execution responsibilities. Advantage sharing spotlights on specialist investment in the financial accomplishment of an association.11 Benefits plans expect to extend the financial sponsoring of workers in the support stage. All of these HR task classes can be maintained by shrewd techniques in two fundamental interrelated ways that are robotization and data. Robotization of an HR task centers around the (inadequate) task execution moves from people to machines. Human capacity

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and effort thusly are replaced by machines, while comparable undertakings can normally be performed speedier and at less cost. Hence, already, colossal undertakings were made to ceaselessly push the mechanization of HRM.12 Data of an HR task rely upon its past computerization and targets conveying critical pieces of information about the endeavor that was automated. These overhauled data offer decision help for human deciders, and as needs are, should additionally foster the overall decision quality. Henceforth, there have similarly been various endeavors to utilize the inherent data potential for HR decision help. In the diagram, the robotization and data of staffing, execution of the board, progression, and compensation build-up critical endeavor need groupings, as depicted in Figure 9.2

FIGURE 9.2 Major HR task categorization. Source: Ref. [57].

9.2.2 SERVICEABILITY OF AI TECHNIQUES In view of the issues of portraying general knowledge properly, AI sets up a diverse and divided space of software engineering, and there is

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heterogeneity and surprisingly certain disorder with respect to the genuine definition and arrangement of AI methods.12 Tight definitions based on underlying or social analogies with regular knowledge, that is, a sharp technique is coordinated or potentially acts as a characteristic shrewd system.13 This takes into account an unmistakable assurance of important methods (which are for the most part in the classifications of neural, fluffy, and transformative procedures, just as hybrids of them) and furthermore a reasonable outline to general PC sciences, while this restricted arrangement is progressively named as “computational knowledge.” More extensive definitions revolve around utilitarian or ability-organized analogies with regular knowledge, that is, the technique plays out specific limits and additionally has certain capacities of normal sharp systems.14 This grows the game plan of fused strategies and permits the thought of “exemplary” AI methods, similar to data portrayal; yet, this likewise disturbs a reasonable outline of “vigilant procedures” from additional computational strategies and therewith a fitting division of AI from general software engineering. To cover the scope of existing vigilant strategies, the current section takes on an expansive arrangement and describes AI procedures as machineprocessable rules to settle undertakings that would require clear intellectual capacities at whatever point tended to by people.15 In view of this definition, it becomes possible to order AI strategies dependent on the intellectual ability they imply. In such a manner, data, thought, and language set up major intellectual limits that contain one-of-a-kind classes of related AI methods, which are immediately introduced in the going. Understanding the data as mindfulness and understanding certain pertinent realities, the age, safeguarding, and preparing of dataset up clear intellectual capacities.16 Significant AI procedures that are identified with data can be sorted into data revelation, data portrayal, and data preparation. Data revelation (moreover “AI,” “design acknowledgment,” or “information mining”) insinuates the way toward recognizing novel, conceivably accommodating, and substantial data in information.17 Consequently, an expansive scope of data divulgence procedures is accessible, with the order, affiliation, division, and figure strategies including observable classes. Data portrayal insinuates the planning of a lot of significant suggestions (“data”) to formal pictures in a way that permits PCs to use these conventional pictures when tackling the task. Significant assortments are decisive (portrayal of basic realities) and procedural (portrayal of methods to utilize data) data portrayal. For data portrayal, there is a bigger plan of strategies, with outlines, semantic nets, and ontologies containing observable model classifications. Data handling

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(moreover “thinking” or “inferencing”) targets utilizing data addressed in a PC to make new data. Data preparing therewith is liable to existing data portrayals as a premise and contribution for thinking. There are different methods for thinking, while deductive, inductive, and abductive thinking involve significant classifications.18 Understanding thought as the deliberate interior preparing of existing data to convey new data and tackle issues, thought contains a further unmistakably intellectual capacity significant in AI. As is unequivocally described here, thought is identified with data in a twofold way since it utilizes existing data as information and targets making new data as yield. Consequently, especially methods that cycle data must be furthermore delegated thought-related also. Moreover, methods for looking through courses of action (too “tackling advancement issues”) include a second urgent class of thought-related procedures.19 Essentially, these methods target formalizing “difficult issues” and addressing them dependent on splendidly looking through a quest space for an ideal, or on the other hand, if nothing else a plausible, course of action. Therefore, a more extensive plan of canny procedures, for instance, the A* search calculation, slope climbing calculations, molecule swarm advancement, and hereditary calculations, is suggested. Finally, understanding the language as the use of an unusual course of action of spoken or encoded parts for correspondence, language utilization contains a further obviously intellectual ability. Suggesting language, text handling, and discourse preparing contain significant classifications of sharp language-related strategies [likewise subsumed as “normal language handling (NLP)”].20 Text handling strategies target supporting errands identified with created language, similar to subject extraction, text synopsis, text interpretation, or text arrangement, among others. Consequently, a lot of text preparing methods, similar to tokenization, lemmatization, and grammatical form labeling, are accessible. Discourse preparing strategies target supporting errands identified with imparted in language, specifically programmed discourse acknowledgment and programmed discourse blend yet also further undertakings, like speaker acknowledgment and check or amplifier arrangement, among others.21 A more extensive course of action of different procedures is accessible, with Hidden Markov Models building up an obvious model in the space of discourse acknowledgment. In outline, the revelation, portrayal, and handling of data, the quest for plans, and the preparing of text and discourse set up significant classifications of AI usefulness, as depicted in Figure 9.3. As it is doable to understand an AI application dependent on a solitary strategy from one classification, a few procedures are progressively merged and thusly include cross variety of strategies.

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FIGURE 9.3 AI major features categorization. Source: Ref. [57].

9.3 SYNOPSIS OF AI IN HR MANAGEMENT 9.3.1 EMPLOYEE REVENUE PREDICTION USING AI Artificial neural networks (ANNs) are data preparing structures that include a particular number of data handling units (as well “cells,” “neurons”) that join mathematical limits and are related by facilitated weighted associations.22 ANNs contain a characterization of information disclosure that is skilled to address grouping, arrangement, assessment, and expectation errands. The data preparing units are commonly planned in layers, with an info layer to give the information, something like one mystery layer to deal with the information through the neural organization besides, a yield layer to give the result. ANNs are invigorated by a relationship to the cerebrum, where data preparing relies upon neurons that are related to one another and impart the level of enactment through nerve strands to various neurons. They acquire from getting ready models and patch up their internal construction (plan) including the units, layers, and directed weighted associations with make incredible yields. Right when the yield meets a particular quality measure, the designing of the ANN is fixed and can be used for expectation undertakings.23 Further sharp procedures are from time to time used to recognize ANNs, for instance, innate computations are applied to change the heaps of the associations during the learning cooperation. In light of their inside structure, ANNs can assess any mathematical limit, and along

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these lines, to discover complex models inside information. In any case, the ANNs themselves are flighty and nonstraightforward and fill in as “secret elements,” passing on extraordinary yields anyway two or three explanations. An application circumstance of ANNs in HRM is the expectation of worker turnover. Worker turnover implies the intentional renunciation of representatives subject to their own reasons and unequivocally bars excusals reliant upon manager reasons or unavoidable separations, similar to retirement, demise, or enduring handicap.24 This marvel, especially useless turnover (extraordinary performing representatives leave, while vulnerable performing workers stay), is of fundamental interest for relationships because of the lessened productivity related to it. Moreover, broken turnover prompts extending staffing costs considering the way that new representatives should be searched for, enrolled, and ready to fill void positions. We can find it in Figure 9.4.

FIGURE 9.4 Role of AI techniques in HR management. Source: Ref. [57].

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Against this establishment, turnover forecast offers the likelihood to perceive the workers that are presumably going to leave and therewith enables the headway of individual representative upkeep measures. The turnover forecast undertaking can be shown as a grouping task where the yield variable comprehends the two discrete classes “yes” and “no” relating to turnover. To apply the ANN, a planning dataset is required that contains vital worker information concerning turnover similarly as other conceivably critical information affecting turnover, similar to age, rank, pay, abilities, position, sexual orientation, family status, etc., in an underlying advance, the ANN is ready on a getting ready set, that is, a fragment of the open representative information, to reveal the precise connection between the info factors affecting turnover and the singular yield variable tending to turnover.25 As they can vague any limit, ANNs are furthermore prepared to discover uncommonly complex instances of worker turnover. The nature of the made ANN can be overviewed by using a test set, a section of the worker dataset left out from the readiness cycle, which can reveal pieces of information into the error, for instance, the level of representatives wrongly assigned “leavers.” An affectability examination can also show the meaning of the effect factors and considers unmistakable confirmation of the components that most effect the worker turnover.26 The made ANN, therefore, can be applied to expect which representatives are presumably going to leave and besides can pass on data about the relevant factors influencing turnover ANNs unmistakably fit with the task of turnover forecast as they address the idea of the basic order and expectation task. An ANN can expect which significant workers are likely going to leave similarly as reveal obscure components that sway turnover. Therewith, significant judicious data for staffing that engages proactive administration of the turnover of representatives are offered and that cannot be presented by customary strategies, for instance, basically addressing worker information bases. Expecting and proactively managing turnover can avoid, or perhaps ease up, the genuine downsides of “broken turnover,” recollecting drops for various leveled proficiency and the costs of choosing and introducing new workers.27 In the diagram, turnover forecast with ANNs maintains the data of staffing and thinks about a proactive HRM. While the certain practical use of ANNs in the turnover forecast is not revealed in the composition, a couple of evaluations likewise, models uncover “application development” of the circumstance in applying an information divulgence technique in HRM.

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9.3.2 USING KNOWLEDGE-BASED SEARCH ENGINES TO FIND THE BEST CANDIDATE Information-based web search tools (furthermore customarily called “semantic web search tools”) basically offer value for scanning the web for content. Stood out from regular pursuit, in any case, the inquiry string entered just as semantically related ideas, similar to equivalent words, hypernyms, and hyponyms, are thusly considered.28 Subsequently, an information-based inquiry functions like it would understand the semantic meaning of the looked-through content, and therefore, further fosters the query item quality. Therewith, it also diminishes the routinely astounding and monotonous hunt measure. Ontologies as a methodology of information portrayal and associated reasoners as a technique of information preparation are two essential AI processes that are employed to participate in information-based inquiry.29 Ontologies are collections of knowledge organized into categories, such as concepts, relations, cases, and rules. The term “ideas” refers to groups of articles that are acceptable for the space. The term “relations” denotes a link between two or more ideas. Any ideal association may be created depending on the space, while describing different types of relationships between ideas suggests power and coercion of ideas, thinking about the perception of subideas, ideas, and superideas.30 Events are significant individual people from a specific class portrayed by a specific idea. Rules, finally, address causal associations among ideas and furthermore models that can be used for deducing new information. By and large, ontologies besides incorporate equivalent words for ideas and models.31 Thusly, information on a specific area can be tended to and ontologies subsequently build up a particular information depiction methodology. Reasoners use the information on the mysticism, and ward on the fundamental application objective, furthermore outside case subordinate data to create new information. For instance, applying a norm (A ⇒ B) from the way of thinking on an article that meets the reason “A” licenses deducing that it moreover meets the end “B”; thusly, new information can be made.32 The mix of ontologies and reasoners licenses for an information-based inquiry that yields results comparable to that of shrewd people with significant area information. An application circumstance of information-based web crawlers in HRM is looking for up-and-comers. In view of work market insufficiencies, various associations successfully look for sensible up-and-comers on the web, for example, on online worksheets. Due to the abundance and heterogeneity of human language, looking for sensible applicants dependent on standard web crawlers as regularly as

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potential winds up being both insufficient and effortful. Issues of standard inquiry arise, explicitly, if the material phrasing of the association and the up-and-comer wander off-track from each other—which is a conventional occasion in e-enlisting. The looking through association needs to then utilize an abundance of search terms, yet it actually cannot know with sureness that sensible applicants are not being dismissed. An information-based hunt engine utilizing an area theory and a reasoner can additionally foster contender search measures, as conveyed in Figure 9.5.

FIGURE 9.5 Knowledge-based search engine. Source: Ref. [57].

To portray key viewpoints, such as offered and needed jobs, necessary and offered talents, and so on, the association and candidate employ a going awry wording. The response on an area of mysticism lets the search engine to “perceive” that the unfilled job “deals chief” semantically differs with a searched position “advertising director,” among others, and it provides a suitable output that a regular search engine would not have. If a reasoner is employed in the selection process, it may provide extra information, such as assuming French language abilities based on the fact that the candidate was educated in France.33 Information-based web crawlers are clearly suited to the task of locating candidates who describe their ideal position and provided capabilities in everyday language, and therefore in a diverse and varied phrasing that information-based pursuit can handle. Informationbased online search tools are designed to automate parts of the search job

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while also providing useful data to aid in predecision making; as a result, the computerization and information of personnel is maintained.34 Central updates are related to the efficacy (faster and lowered effort) and quality (extended correctness of outcomes) of up-and-comer search. They seem differently in comparison to conventional inquiry. Information-based pursuit is a well-developed trend that is frequently used in HRM, for example, a few online job sheets that extend to the semantic search of company opportunities and rivals. 9.3.3 USING GENETIC ALGORITHM’S STAFF SCHEDULING Hereditary calculations are critical thinking procedures motivated by natural cycles containing variety and choice to propel “natural selection.” As a critical application space, limit improvement issues are those wherein the factors addressing the limits are regularly encoded by bit strings.35 Hereditary calculations create arrangements as indicated by a predefined target capacity and issue express imperatives. In the underlying advance, an underlying populace is created (haphazardly, for example) where each person from the populace is addressed by a piece string (furthermore implied as a “genotype” or “chromosome”). Calculations further play out the periods of choice, hybrid, and change. Inside the choice stage, hands down the fittest individuals in a populace suffer to give their hereditary material to the future. Wellness is a motivation for each not set in the stone ward on the target capacity of the improvement issue. The better the wellness regard near with different individuals, the more copies get by to the future. The size of the populace stays steady starting with one age then onto the next.36 Hence, the fittest individuals are picked and imitated, while those with the most insignificant wellness regard do not persevere. The resulting stage, hybrid, is the stage similar to multiplication in nature and targets making new individuals from the populace from existing ones by consolidating pieces of them. There are a couple of hybrid frameworks, similar to single-point, two-point, n-point, or uniform hybrid. The new individuals are not the same as the current ones yet do not really fit better. Regardless, when another blend ends up having a high wellness regard, it is presumably going to be rehashed in individuals in the future.37 Transformation targets acknowledging changes that cannot result from choice and hybrid alone, for example, by flipping an arbitrarily picked bit. Determination and hybrid rely upon introductory conditions and haphazardness that might keep likely compelling mixes from being created

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and considered in succeeding ages. Like in nature, transformations are at risk to be perilous and ruinous; thusly, change should be rarely applied. If the underlying populace gives a fair consideration of the arrangement space, determination and hybrid are adequate. Determination, hybrid (and change) structure another age, which will be evaluated once more, prompting an iterative cycle. Normal stop models for hereditary calculations are a decent number of ages, a period limit, or the shortfall of upgrades.38 Hereditary calculations are stochastic heuristic pursuit methods that at the same time consider many focuses in the pursuit space, and subsequently the likelihood of discovering just nearby optima is decreased. Hereditary calculations can thusly be arranged as a presumed related shrewd procedure. An application situation of hereditary calculations in HRM is staff rostering (as well “agent planning”). Staff rostering addresses the age of ideal tasks of workers to shifts coordinating with the subjective and quantitative necessities of the endeavors with the subjective and quantitative pointlessness of the representatives. In many branches, such as assembling, administration, or medical care, a versatile and productive age of significant staff programs is a fundamental assignment.39 The subsequent enhancement issue suggests different measures, similar to costs, work individual fit, and specialist inclinations and is depicted by various limitations with respect to space unequivocal points, as most outrageous working time, entertainment times and capability prerequisites, among others. For example, each agent works all things considered one shift every day, while the overall month-to-month working time should meet a particular resistance limit around the target working time. Further models for requirements are that a most outrageous number of sequential working days should not be outperformed, that the evening and end of the week developments should be passed on among the representatives as indicated by their agreements and with respect to a sensible dissemination and that specialist inclinations should be thought about whatever amount as could sensibly be anticipated. Requirements can be consolidated in the estimation of the wellness regard as punishment costs by bringing down the wellness regard if a higher wellness regard shows a predominant part and the opposite way around.40 Lists can be encoded as strings addressing the singular individuals from the populace. Playing out the hereditary estimation, that is, choice, hybrid, and transformation, drives then to ages of additional created lists, and the rundown with the best wellness worth can be at long last picked. Hereditary calculations unmistakably fit with the task of staff rostering as they address its very nature as an advancement issue and give feasible lists thinking about an immense number of limitations. They

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can robotize the rostering task and to informatic with respect to authentic programs. Hereditary calculations clearly beat any manual booking. Considering that various distributions show the productive use of hereditary calculations in staff rostering and considering that hereditary calculations are truly coordinated in business staff rostering programming, a foster situation of applying a canny arrangement search method in HRM can be introduced. 9.3.4 USING TEXT MINING SENTIMENT ANALYSIS Text mining offers different functionalities related to unstructured text archives: topic acknowledgment and following perceives subjects in reports, records text archives that are related to a similar point, and designates new reports to successfully perceived themes.41 Text rundown summarizes the substance of a text report in a short framework. Text grouping portrays text records into predefined classes. An application delineation of message order is feeling examination: sentiment investigation (besides “appraisal mining”) centers around the modified extraction of feelings and conclusions that are conveyed in unstructured message reports, and accordingly, orchestrates message archives into the classes “good opinions” and “negative opinions.” Subsequently, it becomes possible to accumulate opinions conveyed in different texts, for instance, manager studies on boss rating sites.42 The fundamental clever techniques used to recognize opinion examination is a blend of text prepreparing and following text grouping. Text prehandling implies the root of the text into single terms (“tokenization”), the etymological classification of these terms (“labeling”), their decline to the root structure (“lemmatization”), and their change into a vector that conveys the overall repeat of every perceived term (“vector space model”).43 These vector models would then have the option to be used as a commitment for text grouping, while support vector machines are calculations that are frequently used for the order. As is normal in data divulgence, the grouping calculation needs to at first be “prepared” considering getting ready records. Thusly, these arrangement archives are first preprepared to get a vector space model of each report that can be used by the calculation to start rules for records expressing positive or negative opinions or perspectives. After the order calculation setting up, the records that ought to be penniless down in like manner should be moved into vector space models through prepreparing before the standards are used to describe the examination reports as imparting either certain or negative feelings. The mix of prepreparing as a

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keen message handling technique and grouping as a shrewd data revelation methodology considers taking apart and summarizing feelings conveyed in archives, while message mining therewith can be uncovered as a characteristically cream strategy.44 An application circumstance of message mining in HRM is feeling investigation. Knowing feelings of representatives, directors, up-and-comers besides, further HR partners relating to different HR-significant points, for instance, pay extents, job possibilities, nature of getting ready, authority style, workplace, etc., involves critical information on the characteristics and weaknesses of HRM as seen by the huge partners. Such assessments and feelings are logically conveyed in different web-set up records concerning manager rating sites, relational associations, online diaries, etc. Text mining can comprehend the task of analyzing opinions, as conveyed in Figure 9.6. From the get-go, sensible getting ready archives ought to be crawled, preprepared, and moved into vectors that an arrangement calculation can use as the commitment to learn grouping concludes that render normal vectors for the specific feeling classes, for instance, “opinions staffing: negative” or “opinions pay: good.” These rules are then applied on preprepared examination reports to describe them into a specific class. These singular results can be gathered in a bar outline that shows positive and negative evaluations. If text records for a couple of associations c1, c2, … are inspected, results can in like manner have the goods. Additionally, dependent upon existing text archives, more unmistakable and refined information can be gained.

FIGURE 9.6 Sentimental analytics. Source: Ref. [57].

For instance, the refined data on how the compensation procedure (i.e., types, total, and development of compensation) are chosen by representatives

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and up-and-comers will offer significant encounters into characteristics and inadequacies and subsequently support imperative compensation decisions. Message mining therewith fits with the task of the investigation of HR-related opinions imparted in unstructured message data.45 Diverged from a manual affirmation, central upgrades relate to efficiency because even an uncommonly colossal number of texts that are past manual getting ready can be easily inspected; regardless, the individual planning of the structure moreover deduces starting effort before any application. Observably, the circumstance therewith targets supporting the information in all HR limits. By and by, there are various thoughts and models of HR feeling investigation and moreover leading business offers of HR programming merchants. Text mining in this manner contains a sharp procedure that is practically application in HRM. 9.3.5 USING EXTRACTION OF THE INFORMATION RESUME DATA ACQUISITION Data extraction centers around thus finding and removing, coordinated data from unstructured or semicoordinated text. The critical tasks of data extraction are named element acknowledgment and connection extraction.46 A named substance is a token (i.e., an individual or a social event of characters) or a gathering of tokens that show an authentic element, for instance, someone in particular, affiliation or capacity. Relations range from something like two substances related to a specific goal in mind, for instance, “is illustrative of.” Information extraction requires a couple of texts prepreparing steps, similar to tokenization, linguistic component labeling, and lemmatization. There are a couple of techniques to perform data extraction that can be by and large arranged into rule-based and measurable strategies. Rule-set-up extraction strategies depend as for a (truly portrayed of course normally academic) set of rules with hard predicates while measurable techniques rely upon a weighted number of predicates to recognize and eliminate substances and element connections.47 Data extraction enables the customized conspicuous confirmation and extraction of named substances and element relations in a text and therewith builds up a particular grouping of shrewd language technique focusing in on text taking care of. An application circumstance in HRM is rundown of references information obtained (also “CV parsing”). Inside the selecting connection, affiliations regularly get a lot of rundown of capabilities to the extent of text records. These text archives then should be ready

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by people, that is, the significant data should be truly removed and gone into HR data systems to continue with the enrolling collaboration. Data extraction targets motorizing this cycle by a modified conspicuous verification and extraction of significant data from the rundown of capabilities of occupation up-and-comers, similar to name, address, work titles, work periods, names of past affiliations, capacities, etc., to give likewise, measure these data in HR data structures.48 Rundown of capabilities are typically semicoordinated text archives giving data in different squares, similar to individual data, enlightening data, work understanding, etc. Thus, as opposed to glancing through the whole text, arranging the report into the different squares of data works with the customized recognizing verification and extraction of the specific substances. For example, the element “up-and-comer name” can be found inside the singular data block; however, substances of abilities can be found inside the educational data block. Since a rundown of references is given in various text information designs, for instance, pdf, txt, etc., rundown of capabilities of data extraction should have the alternative to manage different arrangements. The ID of a specific single substance would then have the option to be performed, for example, with rule-based techniques where the standard then again gets the setting before the start moreover, after the completion of an element and matches the tokens in the element.49 Recognizing a singular’s certification from college would thus be able to be based, for example, on rules getting the string “college” around “ace” or “solitary officer” inside the educational data block. Rundown of references data extraction may similarly combine information depiction strategies, for instance, ontologies, to think about semantic perspectives in data extraction, therewith building up a chipped away at cross variety approach. Rundown of capabilities data extraction typically gives the removed data in grouped standard configurations, for instance, HR-XML, XML, or JSON, which can be easily brought into HR data systems, for instance, enlisting structures. Rundown of capabilities information procurement with data extraction unmistakably fits with the HR task of taking in rundown of capabilities information from text records and entering them into HR data structures. It automates the dreary manual ascertainment by people, including scrutinizing rundown of references, extricating huge information, and entering them into individual HR data structures. Accordingly, rundown of references taking care of with data extraction unmistakably targets robotizing staffing.50 Central redesigns are the accelerated extra planning of applicant information, offering the likelihood to decay individual costs. Rundown of references information procurement with data extraction shows an obvious level of advancement

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considering the way that various space express systems have been presented by various shippers for a surprisingly long time. 9.3.6 UTILIZING INTERACTIVE VOICE RESPONSE IN EMPLOYEE SELF-ADMINISTRATION Intuitive voice reaction (IVR) centers around the cooperation of people and PCs through voice. Such voice-based cooperations can be recognized through direct voice contact of the human and the PC or mediated voice contact through telephone or associations, similar to the web. Principle-wise progressions that underlie IVR are robotized discourse affirmation and computerized discourse blend.51 Robotized discourse affirmation [additionally “discourse to message (STT)”] centers around the change of conveyed language into machine-intelligible strings. The discourse affirmation measure contains different advances: initially, the human discourse signal should be gotten and put away in a sound record. Using differentiating extraction calculations, normal features of discourse signals are isolated and changed into numerical models of the sign as a vector. These vectors are used as the commitment for affirmation calculations that accomplish the vector to message; for example, Hidden Markov Models are as frequently as conceivable used for affirmation (for a framework of unmistakable extraction and affirmation methodologies). On account of computerized discourse affirmation, the discourse articulation of the human customer is changed into its text-based relationship, which is machine-significant, and thusly, can be used by the PC for additional movement.52 To change PC yield into voice, robotized discourse amalgamation [as well “text to discourse (TTS)”] is used. Computerized discourse blend is recognized in different advances. Initially, the data message record should be preprepared, which joins tasks, for instance, message structure distinguishing proof (e.g., number and sort of sentences) or message standardization (e.g., treatment of truncations and shortenings). A subsequent phonetic examination prepares the discourse by grapheme-to-phoneme change, that is, choosing the best approach to communicate each word. In light of this, a prosodic examination chooses the palatable pitch, term, and commotion, among different perspectives. The still symbolic yield of these examination steps is used by discourse synthesizers that truly play out the verbalization.53 Significant kinds of synthesizers are articulatory (the synthesizer uses a PC model of the human vocal package and its parts to replicated verbalization), formant (the synthesizer calculates the waveform of the proposed acoustic

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yield), and interfacing (the synthesizer joins units of recorded sounds from an information base). An application circumstance of intelligent voice reaction in HRM is worker self-administration (ESS). ESS centers around the development-based moving of HR tasks from HR experts to workers. Basically, ESS is viewed as a thought that trades functional tasks, such as invigorating individual information, advancing advantages, or enlisting for getting ready measures to workers, with the critical objective of usefulness gains.54 Significant developments used to recognize ESS are communication and electronic systems. Correspondence-based ESS engages representatives to pass on out tasks remotely using adaptable and fixed-net telephones. A normal application model is time arrangements of representatives that work outside the association inside the edge of cooperation with the chiefs. It ends up being rapidly obvious that IVR comprises the fundamental enabling development of correspondence-based ESS. IVR engages the representative to interface with various HR backend structures, similar to time and investment the load-up systems, to fulfill the specific task. Data sources, for instance, requests, information input, etc., can be directly made by voice, and individual yields of the system can again be presented by voice. While correspondence-based ESS apparently builds up the central application circumstance, IVR might be particularly used also in web-based ESS, for instance, for discourse put together quest for content with respect to an HR passage or for recognizing chatbots that answer HR-related requests. As a smart discourse handling strategy, IVR therewith perceptibly fits with the task of enabling the voice-based participation of representatives with a more broad game plan of HR backend structures. For more direct functional HR endeavors all through the singular HR limits, it becomes possible to robotize the correspondence endeavors of human HR experts, and thusly, to recognize ESS thoughts. Huge improvements relate to effectiveness gains, explicitly cost, and time hold assets in the HR office. Furthermore, the never-ending availability of HR administrations “nonstop” moreover involves an improvement. IVR has been a grown-up advancement in HR for a couple of times and is—for certain overall differences—moreover broadly applied. 9.4 INHERENT OF AI TECHNIQUES IN HUMAN RESOURCE MANAGEMENT Against the foundation of the undertaking innovation fit approach, a utilization of AI methods is productive if the AI procedures offer functionalities

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that contrast and the necessities of the HR task. The discussion of foster application situations could reveal that across different procedure and assignment classes, fitting blends can be found. This basically features the doubt of the wide application capacity of AI strategies in all arrangements of HR errands. Regardless, it furthermore ended up being amazingly obvious that these application prospects are far from being researched, and significantly more, far from being basically mishandled; also, only one out of every odd AI method is sensible in HR the board, and only one out of every odd HR assignment can be tended to by an AI strategy. While, in the long run, the attack of method and errand should be strenuously clarified on a singular reason, some concretizations can be made on the absolute level in the going with. Starting with information disclosure methods, the above circumstance of using fake neural organizations for turnover forecast uncovers that this class fundamentally fits with the essential of in arranging HR all through the different undertakings. In explicit, information divulgence strategies can be used to enhance regular addressing techniques that yield noteworthy enlightening data (that portrays existing miracles) with explanative data (that gives purposes behind existing wonders) and prescient data (that predicts future marvels).55 This capacity of enhancing existing HR data procedures is similarly featured by the creating assessment on information revelation strategies in HRM that implies an incredibly extensive game plan of individual method task-blends. Joining information depiction and information preparing strategies, somewhat recently of the last century, there were suppositions toward setting up information-based “master frameworks” in HRM. Regardless, these presumptions could not be met all things being equal given that master system innovation was not even more broadly made and applied. In any case, inside the arrangement of semantic (web) progresses, information-based methods encountered a time of recuperation in HRM, as also displayed with the application situation of information-based up-and-comer search. The scattered investigation on semantic progressions in HRM generally implies semantic chase, recuperation, and organizing in selecting (by and large competitors and occupations) or advancement (generally understudies and courses), while express assessment reviews are absent. Given this, the ability of information disclosure and preparing procedures should be settled even more powerfully as fitting the errand of setting up interoperability among people and machines or between different machines that use wandering tasks, therewith engaging further correspondence and “comprehension.” Clearly, this hypothetical potential might apply to an extensive plan of generous mechanization similarly as data undertakings across all HR limits.56

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Arrangement looking through strategies implies quantifiable streamlining undertakings, while in HR particular assignment errands can be subsumed under this order. As exemplified with the circumstance of staff rostering, task errands exist basically in staffing (assignment of workers to undertakings, projects, shifts, position, units, etc.) Besides, calling and movement masterminding as a subcategory of advancement incorporates the connected errand of naming representatives to different calling positions as time goes on. Further HR-related errand undertakings, for instance, assignment of instructors, rooms, and understudies in representative turn of events, are conceivable yet not analyzed as yet. For an extremely drawn-out period of time, such undertakings have been at this point tended to by streamlining procedures from exercises research. Notwithstanding, considering that these issues every now and again qualify as NP-hard, they are not attainable by advancement. Arrangement looking through procedures from AI in this manner includes a critical heuristic alternative for HR task issues. Text handling strategies contrast and the presence of a wide grouping of HR-huge substance reports, for instance, worker mailings, application records, references, composed reminders, or execution evaluations, among others, and related HR errands. A first wide potential is the mechanization of a broad collection of documents related to practical assignments, such as looking, situating, requesting, eliminating, taking a gander at or summarizing text reports, among others. The circumstance of consequently eliminating CV information sets up a model for this mechanization potential. A second expansive potential is in giving decision-supporting data by analyzing text files. The circumstance of feeling investigation in web records involves an outline of this potential. Seeking after a general course in business information, hence, data subject to organized information can be enhanced with data reliant upon unstructured information similarly in HR the executives. Finally, discourse preparing strategies offer the fundamental capacity of discourse-based human machine correspondence as clarified on in the circumstance of keen voice response for worker self-organization. Basically, the capacity of discourse preparing exists in conditions where control center worked figuring is abnormal or troublesome, for instance, in compact handling. 9.5

CONCLUSION

Evaluating the accomplishment potential does first thing gather an intensive appraisal of whether the providable handiness truly fits with HR

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undertakings that are basically appropriate. Furthermore, considering that HRM at this point disposes of a more broad plan of grounded methods for a more broad course of action of HR undertakings, the normal utilization of an AI strategy should be differentiated and at this point existing HR strategies. Any AI method ought to be more fruitful (further developed results) and moreover more powerful (less execution effort) than the inside and out set up HR strategies; something different, an application is worthless. Accepting, in any case, settled strategies, for instance, fundamental gatherings of workers and line executives, pass on something practically the same or even further developed results, a usage of an ANN is not useful. Subsequently, AI methods not simply need to fit with HR task necessities yet moreover need to defeat the current strategies. Cultivating an area-driven application sets up the second indispensable development. Basically giving the “pure” AI method and expecting that HR specialists change the procedure to their necessities and a short time later use it does not normally work. A stunning opportunity to comprehend this is to directly embed AI value in area express HR data frameworks. This licenses HR specialists to apply the AI procedure inside their regular space setting without having complex particular or possibly exact AI capacities. KEYWORDS • • •

human resource management (HRM) artificial intelligence (AI) artificial neural networks (ANNs)

REFERENCES 1. Devanna, M. A.; Fombrun, C. J.; Tichy, N. A Framework for Strategic Human Resource Management. In Strategic Human Resource Management; Fombrun, C. J.; Tichy, N.; Devanna, M. A., Eds.; Wiley: New York, 2014. 2. Wolf, A.; Jenkins, A. Explaining Greater Test Use for Selection: The Role of HR Professionals in a World of Expanding Regulation. Hum. Resour. Manage 2016. 3. Ernst, A. T.; Jiang, H.; Krishnamoorthy, M.; Sier, D. Staff Scheduling and Rostering: A Review of Applications, Methods and Models. Eur. J. Oper. 2014. 4. Burgard, M.; Piazza, F. D ata Warehouse and Business Intelligence Systems in the Context of e-HRM, 2009

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5. Janev, V.; Vraneš, S. Applicability Assessment of Semantic Web Technologies in Human Resources Domain. Inf. Res. Manage. J. 2010. 6. Chien, C. F.; Chen, L. F. Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industry, 2008. 7. Giotopoulos, K. C.; Alexakos, C. E.; Beligiannis, G. N.; Likothanassis, S. D. Integrating Agents and Computational Intelligence Techniques in E-Learning Environments. 2017 8. Gonçalves, J. F.; de Magalhães Mendes, J. J.; Resende, M. G. C. A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem. Eur. J. Oper. 2015 9. Goodhue, D. L.; Thompson, R. L. Task-Technology Fit and Individual Performance, 2018. 10. Furneaux, B. Task-Technology Fit Theory: A Survey and Synopsis of the Literature; In-formation Systems Theory; Springer, 2012. 11. Jantan, H.; Hamdan, A. R.; Othman, Z. A. Intelligent Techniques for Decision Support System in Human Resource Management, 2010 12. Zuboff, S. Automate/Informate: The Two Faces of Intelligent Technology, 2005. 13. Duch, W. What is Computational Intelligence and Where Is It Going? Challenges for Computational Intelligence; Springer, Berlin, 2007. 14. Wang, P. What Do You Mean by “AI”? In Artificial General Intelligence, 2008. 15. Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. From Data Mining to Knowledge Discovery in Databases, 2016. 16. Wu, X.; Kumar, V.; Quinlan, J. R.; Ghosh, J.; Yang, Q.; Motoda, H.; Steinberg, D. Top 10 Algorithms in Data Mining, 2008. 17. Tanwar, P.; Prasad, T. V .; Aswal, M. S. Comparative Study of Three Declarative Knowledge Representation Techniques, 2010. 18. Luger, G. F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 2005. 19. Jurafsky, D.; Martin, J. H. Speech and Language Processing, 2008. 20. Benesty, J.; Sondhi, M. M.; Huang, Y. A. Introduction to Speech Processing; Benesty, J., Sondhi, M. M., Huang, Y., Eds.; Springer, 2008. 21. Kahraman, C.; Kaya, I.; Çinar, D. Computational Intelligence: Past, Today, and Future; Ruan, D., Ed.; 2010 22. Rojas, R. Neural Networks—A Systematic Introduction; Springer: Berlin, 1996. 23. Sexton, R. S.; McMurtrey, S.; Michalopoulos, J. O.; Smith, A. M. Employee Turnover: A Neural Network Solution, 2005. 24. Hornik, K.; Stinchcombe, M.; White, H. U niversal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks, 1990. 25. Leshno, M.; Lin, V. Y.; Pinkus, A.; Schocken, S. Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function, 1993. 26. Fan, C. Y.; Fan, P. S.; Chan, T. Y.; Chang, S. H. Using Hybrid Data Mining and Machine Learning Clustering Analysis to Predict the Turnover Rate for Technology Professionals, 2012. 27. Somers, M. J. Application of Two Neural Network Paradigms to the Study of Voluntary Employee Turnover, 1999. 28. Mangold, C. A Survey and Classification of Semantic Search Approaches. I nt. J. Metadata Semant. Ontol. 2007. 29. Guarino, N.; Oberle, D.; Staab, S. What Is an Ontology? In Handbook on Ontologies; Staab, S., Studer, R., Eds.; Springer: Berlin, 2009.

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30. Abburu, S. A Survey on Ontology Reasoners and Comparison, 2012 31. Bock, J.; Haase, P.; Ji, Q.; Volz, R. Benchmarking OWL Reasoners, 2008. 32. Guha, R.; McCool, R.; Miller, E. Semantic Search, 2003 33. Mochol, M.; Jentzsch, A.; Wache, H. Suitable Employees Wanted? Find Them with Semantic Techniques, 2007. 34. Strohmeier, S.; Gasper, C.; Müller, D. Entwicklung und Evaluation semantischer Jobportale—Ein “Design Science”—Ansatz, 2011. 35. Sivanandam, S. N.; Deepa, S. N. Introduction to Genetic Algorithms; Springer, 2008. 36. Whitley, D. A Genetic Algorithm Tutorial. Stat. Comput. 1994, 4 (2), 65–85. 37. Linoff, G. S.; Berry, M. J. A. Data Mining Techniques, 2011. 38. Aickelin, U.; Dowsland, K. A. Exploiting Problem Structure in a Genetic Algorithm Approach to a Nurse Rostering Problem, 2000. 39. Gonçalves, J. F.; de Magalhães Mendes, J. J.; Resende, M. G. C. A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem, 2005. 40. Kim, S.; Ko, Y.; Uhmn, S.; Kim, J. A Strategy to Improve Performance of Genetic Algorithm for Nurse Scheduling Problem, 2014. 41. Moz, M.; Vaz Pato, M. A Genetic Algorithm Approach to a Nurse Rerostering Problem, 2007. 42. Aggarwal, C. C.; Zhai, C. X. An Introduction to Text Mining. In Mining Text Data; Aggarwal, C. C., Zhai, C. X., Eds.; Springer, 2012. 43. Liu, B.; Zhang, L. A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data; Aggarwal, C. C., Zhai, C. X., Eds.; Springer, 2012. 44. Pang, B.; Lee, L. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2008. 45. Strohmeier, S.; Piazza, F.; Eds. Human Resource Intelligence und Analytics; Springer, 2015. 46. Aqel, D.; Vadera, S. A Framework for Employee Appraisals Based on Sentiment Analysis, 2010 47. Jiang, J. Information Extraction from Text. In Mining Text Data; Aggarwal, C. C., Zhai, C., Eds.; Springer; Sarawagi, S. Information extraction. Found. Trends Databases 2008. 48. Kaczmarek, T.; Kowalkiewicz, M.; Pikorski, J. Information Extraction from CV, 2005. 49. Yu, K.; Guan, G.; Zhou, M. Resume Information Extraction with Cascaded Hybrid Model, 2005. 50. Çelik, D.; Elçi, A. An Ontology-Based Information Extraction Approach for Résumés; Springer, 2013. 51. Deng, L.; Li, X. Machine Learning Paradigms for Speech Recognition: An Overview. IEEE Trans. Audio Speech Lang. 2013. 52. Gulzar, T.; Singh, A.; Rajoriya, D. K.; Farooq, N. A Systematic Analysis of Automatic Speech Recognition: An Overview, 2014 53. Nature-Inspired Computing for Smart Application Design; Springer Nature. 54. Smart Design and Its Applications: Challenges and Techniques. Natur e-Inspired Computing for Smart Application Design. 55. Energy Aware Dynamic Mode Decision for Cellular D2D Communications by Using Integrated Multi-Criteria Decision Making model. Int. J. Ambient Comput. Intell. IGI Glob. 2020.

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56. Design of Intelligent Transportation Systems Supported by New Generation Wireless Communication Technology. In Intelligent Systems: Concepts, Methodologies, Tools, and Applications; IGI Global, 2020. 57. Strohmeier, S.; Piazza, F. Artificial Intelligence Techniques in Human Resource Management—A Conceptual Exploration, 2015.

CHAPTER 10

CONFLICTING STRATEGY MANAGEMENT TECHNIQUE FOR COMPANIES: AN INTELLIGENT OPTIMIZATION TECHNIQUE SANTOSH KUMAR DAS1, KIMMI KUMARI2, SAGARIKA DARIPA2, AMIT KUMAR SINGH3, and ADITYA SHARMA4 Department of Computer Science and Engineering, Sarala Birla University, Jharkhand, India 1

National Institute of Science and Technology (Autonomous), Institute Park, Odisha, India

2

SRM University AP, Andhara Pradesh, India

3

Institute of Nanoengineering and Microsystems, National Tsing Hua University, No. 101, Taiwan, R.O.C.

4

ABSTRACT In the current era, most of the applications are based on the services and their usage. The applications are dealt with and maintained by several types of companies and organizations. Applications are based on the demand of the customers and their requirement analysis. They help to manage several information based on certain factors that help the customers. There are several types of companies that are available where the nature of most of

Artificial Intelligence Techniques in Human Resource Management. Soumi Ghosh, PhD, Soumi Majumder & Santosh Kumar Das, PhD (Eds.) © 2023 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)

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the companies are the same. But it vary depends of the choices and strategies management that deal with some information management. The main reason for this confliction is customer demand and its variation that helps to model several information. This modeling is based on some different type’s strategies for management. It helps to model several applications and their usage based on service management. In this chapter, a formulation model is proposed based on intelligent algorithm that helps to map several strategies of the company. It is formulated with the help of fuzzy logic and its related linguistic fuzzy variable helps to map several analyses. Finally, it helps to produce optimal solutions based on several factors of the company. 10.1 INTRODUCTION In the last few decades, the application of several innovations and inventions increases rapidly for the purpose of new demands of the customers. So, there are several companies that are growing rapidly that provide several services to the customers. Most of the companies are based on the same product with some variations that help to provide some choice-based service to the customers. It creates some conflicting between two or more companies. Conflict is still not defined generally in the literature. There are, instead, two general approaches that are defined in the article.1 It is defined based on some approaches, the first approach has concentrated on phenomena associated with competitive intentions, such as deliberate interference with the other’s goals. The proposed definition seems to be closer to the common usage of the term “conflict” and is especially popular in the literature on industrial relations. L. R. Pondy2 defined the second approach the has been to adopting more general definitions that move “upstream” in the conflict process in order to include events that occur before choices are made about conflicthandling modes. By allowing conflict processes to include “branches” that involve conflict-handling styles other than competition, these definitions encompass a broader range of phenomena. This makes them more theoretically valuable for capturing the range of options available to conflict parties since they focus not only on the choice but also on the factors that influence it. So, there is a need of proper optimization that helps to model several applications efficiently that help to take care of several parameters and information. The most of efficient and intelligent optimization is based on nature-inspired optimization technique that helps to model several applications. This type’s optimization is based on several phenomena that are

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based on environmental information.3,4 It helps to optimize several types of conflicting based on some emphasized information. There are several works that have been designed based on company and market analysis that helps to deal with some strategies. Compared with the product-marketplace method, the firm’s political strategy focuses on local cultural and political contexts. Some argue that “nonmarket place approaches are less worldwide and more multinational, that is, tailored to the US’s particular issues, institutions, and interests.” Similarly, Hansen and Mitchell found that overseas subsidiaries of multinational corporations had to adapt their political strategies to fit hostcountry conditions, even if they avoided high-profile activities.5,6 However, this focus on multi-home nonmarketplace strategies might not hold for industries that are more global in scope. Lin’s examination of American chemical multinationals in Asia indicated that they followed nonmarketplace techniques primarily based totally on their home-United States environment. Numerous conflicts are being explored in several control areas. However, there has been little research into conflicts related to inventory control. So we conducted a research study to investigate these conflicts, their reasons, and the rise of these conflicts during the process of shaping, implementing, and utilizing stock methods.7 By doing so, a framework for assessing specific forms of conflicts is made available for consideration. It is typically said that enforcing stock structures can be a complicated and dynamic process owing to their multidimensional nature. For instance, when it comes to stock control, several business methods are often involved, including sales, planning, and shopping. The proper conflicting management is based on efficient routing strategy management and with several tolerant parameters that help to manage several communication of the product of the company. There are several authors mentioned some works that deal with delay-tolerant applications, strategy management systems based on smart networking application such as vehicular network or wireless ad hoc network (WANET).8–11 Because the wireless network is necessary for any type of communication in the business along with supply chain management. Sometimes, it uses fuzzy logic to deal with and manage several types of uncertainties based on application management. It helps to deal with and manage several information for the purpose of managing some strategies. The concept of comparative graded membership, derived from human cognition and perception, lies at the heart of fuzzy logic theory. In 1965, Lotfi A. Zadeh released his seminal study on fuzzy sets.12 Fuzzy logic can handle information derived from computational cognition and perception that is uncertain, ambiguous, imprecise, partially true, or lacking sharp limits.

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In computing challenges, fuzzy logic enables the integration of ambiguous human judgments. It also provides an excellent method for resolving multiple criterion conflicts and better evaluating solutions.13 In the creation of smart applications for strategic planning, pattern recognition, optimization, and management, new computing approaches centered on fuzzy logic can be applied. It also helps to solve several issues and problems in the application areas of vehicular network as well as industrial applications.14,15 Various professionals participating in the research and innovation, notably those in the engineering field, such as electrical, electronics, chemical, civil, mechanical, aerospace, computer, biomedical, environment, mechatronics, and in the science field, such as physics, mathematics, biology, chemistry, earth science, medical, geology, management, political science, economics, and psychology analysts of public policy, business analysts, and lawyers, find fuzzy logic incredibly.16,17 Its aim is to look at how fuzzy logic has progressed in a wide range of real-world uses and commercial goods in a wide range of disciplines. While fuzzy logic has uses in a wide variety of fields, those who are inexperienced with smart applications are unaware of how it may be used in many goods already on the industry. The scientific and technological definition of the word fuzzy is still a mystery to the many experts. It is critical that these individuals comprehend how and when fuzzy logic may be applied.18 In this chapter, one model is illustrated that helps to deal with and manage several types of strategic management based on company analysis. It is based on an optimization technique that used fuzzy logic applications for handling several conflicting strategies of the company. The rest of this chapter is illustrated as: Section 10.2 discussed several existing works based on a literature review. Section 10.3 discussed the main proposed method. Section 10.4 discussed conclusion with future work. 10.2 LITERATURE REVIEW In the last few years, several works have been proposed for the purpose of optimization along with conflicting strategy management system. Some of the authors also illustrated some applications based on the fuzzy logic system that helps to deal with several variations along with modeling. Matinmikko et al.19 designed a novel fuzzy merging technique for cooperative spectrum sensing (CSS) in the cognitive radio system (CRS). CRS is gaining traction as a new approach for maximizing the utilization of network and radio resources.

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Such systems are capable of gathering information about the internal radio working technology and environment, dynamically adapting their actions in response, and studying from the outcomes to enhance the effectiveness even more. The ability to make resource management decisions in the upcoming CRS is highly reliant on the information of the operating environment. Strategies for gaining data on present resource usage and the status of nature will be critical for environmental awareness. Upcoming CRSs will require advanced environment-awareness and decision-making approaches that can handle contradictory, multidimensional, and often unpredictable decision-making situations where an ideal answer cannot be discovered. The findings suggest that fuzzy logic may be utilized in CSS to provide existing merging approaches more versatility. Das et al.20 proposed a details design-based book for supported frameworks of wireless network. This book contains several frameworks, such as optimization, security and privacy, localization, and network lifetime enhancement. This book provides the essential frameworks ideas of the users and new researchers. Niittymaki et al. proposed fuzzy logic for regulating pedestrian crossing signal timing in 1998. Regulating the timings of a traffic light is an example of this type of issue.21 The controller is meant to mimic an accomplished crossing guard’s decision-making process. These control results have been compared with two types of classic demandactuated controls: one that utilizes typical green extension criteria, and another, that employs modified extension rules. The proportion of cars that are blocked, as well as delays to pedestrians and automobiles, is the criterion for assessment. The control algorithm strikes a balance between two opposing goals: reduction of pedestrian delay and vehicular delay. The examination was carried out with the use of HUTSIM, a microsimulation created by the Helsinki University of Technology. Without needing numerous parameter adjustments, the fuzzy logic controller works as good as or better than traditional demand-actuated control. Additionally, the control principles are straightforward, and a collection of logical decision-making procedures is presented in natural language. The findings suggest that the algorithm not only just to successfully regulates signal timing but also provides performance that is at least as good or even better than traditional demand-actuated signal management. Movassagh and Aghdasi22 designed a theory of games-based scheduling algorithms for WSN. It is distributed and coverage based for handling problems with WSN. In this method, some nodes are active and some nodes are in sleep mode for optimizing the network lifetime and reducing the redundancy in coverage system. Finally, it helps in enhancing the network lifetime and network metrics of the WSN using the strategy

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management technique of WSN. Chengliang et al.23 proposed a distributed system for WSN that helps to regulate the power of the nodes using the theory of games. The proposed theory of games technique is employed in two purposes: the first is to reduce energy consumption of the battery of the node and the second is that to increase the network lifetime of the WSN. Chakraborty et al.24 designed a method for the purpose of particle swarm optimization that helps to deal with several medical information systems based on information hiding information systems. It helps to model some analysis based on some parameters optimization. It helps to manage several data and information of medical systems based on image analysis and prediction. Barnawi et al.25 designed a method for COVID-19 screening and analysis for the purpose of the pandemic. It uses a thermal imaging system that uses the fusion of artificial intelligence and Internet of things. This method uses several metrics for the purpose of evaluation. It uses several sensor for analysis purposes based on the classifier method of machine leaning with fusion of artificial intelligence. Sharma et al.26 used a soft computing method which is a fitness function and fuzzy logic to create this suggested protocol. The WANET is a form of wireless network that is decentralized. Since it does not rely on what was before infrastructures like routers or access points, the network is ad hoc. Routing is accomplished by each node passing data to the other. As a result, the decision as to which nodes send data is decided dynamically based on the network connection. The primary issue with this network is its energy efficiency, as nodes rely on a finite amount of battery power, which is adequate for any real-world use. This fitness rating aids in determining the route’s characteristics, as well as the energy-efficient route. It also contributes to the network’s longevity. Upcoming work will involve determining the statistic of network lifespan based on various characteristics and determining a fitness threshold data for each route. After a given amount of time has passed, evaluate all data to see what threshold values are best for energy-efficient routing. Finally, based on the network statistics, select the most energy-efficient routes as well as routes along with the most energy-efficient routes. Das et al.27 designed an application management system based on an intelligent decisionmaking system. It helps to model several information based on some applied applications. It helps to deal with and model several information and parameter for the purpose of information management. Cheema et al.28 designed a method for the purpose of COVID-19. In this method, the author needs to analyze several information based on medical analysis POCUS based on ICU. In this work, the model is based on point-of-care ultrasound analysis and its management. It used artificial intelligence for the purpose of enabling

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system and services. In this method, cardiac ultrasound is used for medical diagnosis. Sucharitha and Chary29 designed prediction effect of COVID-19 based on several variations and its analysis. It is based on several facilitate and striving system based on different collaboration. The work is based on cognitive systems and different integration systems. It is based on real-time analysis and its management that helps in depth exploratory data analysis system for the purpose of management. Several analyses and features are used in this system model for handling global datasets several predicting analysis and management. The work is based on biomedical analysis system and its management for handling several information based on decease analysis. Dey et al.30 designed a study that helps to manage several information for the purpose of analysis and design that helps to deal with some evaluation of information. It helps to deal with some analysis of evaluation based on skin analysis. It helps to manage several melanoma analysis of the image that helps to deal with some applications with the context of image. Wang et al.31 designed a method risk analysis and stratification for the purpose of patient analysis in hospital during COVID-19. It is based on artificial intelligence for the purpose of echocardiography technique. It helps to manage several prognosis information of COVID-19 that helps in several measurement and its adjustment for different information. The work is based on different types clinical tools based on hospital and patient information by managing different systems. Soltan et al.32 designed a clinical data for the purpose of patient analysis and different type’s validation systems for handling the information with the help of artificial intelligence. There are several information that are analyzed and dealt with based on factors of management. It helps in health care data analysis. It uses several parameters, such as blood test, blood gas, and different types of vital information analysis. Lan et al.33 designed a clustering modeling that helps to deal with several information based on subwindow application. It helps to manage several optimizations that help to model several data monitoring information and application management. It helps to manage several information based on particle swarm optimization system that helps in information modeling. Ramella et al.34 designed a method for the purpose of radiation analysis for pneumonitis for handling several types of issues based on COVID-19. The work is based on several types of diagnosis and information radiation pneumonitis for handling lung radiation analysis. The work is based on statistical analysis for performing different types of roles. It is based on patient analysis for the combination of classification management. Dey and Rajinikanth35 designed a bat optimization modeling for handling several variants and information system. It helps to deal with and

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analysis of several information system based on certain factors and information modeling. It helps to deal with several linear and nonlinear optimizations. Tiwari et al.36 designed switching system based on uncertainty management for handling several types of revolutions. It is based on industrial revolution for the purpose of pandemic handling. It deals with several types of analysis and management for handling different types of negative analysis. It uses emission trading analysis based on asymmetric analysis based on cryptography. It deals with several types of policy uncertainty management based on different variations for the purpose of equity analysis. 10.3 PROPOSED METHOD In this section, detailed illustration of the proposed method is discussed based on several information of the company. In this paper, two companies are considered as Company A and Company B based on the same product. The confliction is measured in the market based on some consequences, such as low efficiency, quality of work and low productivity and negative impact analysis. The details of this work are shown in Algorithm 10.1. ALGORITHM 10.1

Pseudocode of the Proposed Method.

Step 1: Start Step 2: Design a model based on the company Step 3: Find out the different strategies of the company Step 4: Decide crisp value of all the strategies Step 5: Fuzzified the crisp value Step 6: Design payoff matrix Step 7: Assume probability value of the strategies for both company Step 8: Decide objective function and its constraints Step 9: Run the model Step 10: Analyze the outcomes Step 11: Decide feasible and optimal solution Step 12: Stop

This method is based on fusion of fuzzy logic, game theory, and linear programming. Fuzzy logic is used to deal with several types of uncertainty parameters and reduces its imprecise information. Linear programming is used to optimize several variables and parameters based on objective

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function along with related constraints. Game theory is used to deal with several types of optimization based on payoff matrix analyzed information. The proposed model is based on eq. 10.1, where A indicates the set of action needed to deploy product into the market and S is the set of strategy that helps to map several parameters into the system modeling of the company. G = ( A, S )

(10.1)

The action of the company is based on crisp model which is fuzzify based on required information system of fuzzy logic. Because, in most of the cases, during dealing of strategy, several types of confusion or uncertainty are raising. It increases the complexity into system that needs to resolve by fuzzy logic by fuzzification system in the fuzzy logic that is shown in eq. 10.2. F = { x, µA ( x )}

(10.2)

where F is the fuzzification function, x is the crisp element, and µA(x) is the membership value that is generated after fuzzification system. Figure 10.1. shows the membership function of fuzzy logic which is used to evaluate the modeling of crisp data for the purpose of fuzzification. Table 10.1 shows the strategy mapping of both companies based on certain factors and parameters that help to map several information efficiently.

FIGURE 10.1

Membership function.

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TABLE 10.1

Strategy of Both Companies. Company A

Company B

Sa1

Sa2

Sa3

Sa4

Sb1

P11

P12

P13

P14

Sb2

P21

P22

P23

P24

Sb3

P31

P32

P33

P34

Sb4

P41

P42

P43

P44

Table 10.1 contains four rows and columns based on 4 × 4 cell that indicates payoff values which is decided by game theory optimization. Column value indicates strategy of Company A, and row value indicates strategy of Company B. Cell value indicates the compromise value of both companies. Initially, this value is considered as crisp value after that this value fuzzify by eq. 10.2 by modeling with eq. 10.1. Table 10.2 shows linguistic variables of different strategies of both companies. TABLE 10.2

Linguistic Variable of Strategy of Both Companies.

Strategy

Low

Medium

High

Sa1/Sb1

(0–10)

(3–20)

(10–30)

Sa2/Sb2

(0–15)

(11–30)

(15–40)

Sa3/Sb3

(0–20)

(14–40)

(18–50)

Sa4/Sb4

(0–30)

(22–35)

(28–60)

TABLE 10.3 Strategy

Crisp Value of Company A. Low

Medium

High

Sa1

4

10

16

Sa2

13

25

17

Sa3

4

18

27

Sa4

15

33

57

Medium

High

TABLE 10.4 Strategy

Crisp Value of Company B. Low

Sb1

9

7

15

Sb2

12

30

40

Sb3

2

39

50

Sb4

9

29

40

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U = Mean ( µSa ( x ) , µSb ( y ) ) TABLE 10.5

(10.3)

Payoff Matrix of the Proposed Method.

Company B

Company A Sa1

Sa2

Sa3

Sa4

Sb1

[0, 1]

[0, 1]

[0, 1]

[0, 1]

Sb2

[0, 1]

[0, 1]

[0, 1]

[0, 1]

Sb3

[0, 1]

[0, 1]

[0, 1]

[0, 1]

Sb4

[0, 1]

[0, 1]

[0, 1]

[0, 1]

TABLE 10.6

Fusion of Linear Programming with Fuzzy Logic.

Company B

Company A Sa1

Sa2

Sa3

Sa4

Sb1

U11

U12

U13

U14

a1

Sb2

U21

U22

U23

U24

a2

Sb3

U31

U32

U33

U34

a3

Sb4

U41

U42

U43

U44

a4

b1

b2

b3

b4

Tables 10.3 and 10.4 indicate crisp values of both companies that are based on the calculated strategy that is mapped by mathematical modeling, and data are given in Table 10.2. Table 10.5 indicates payoff value range of fuzzification which always lies between 0 and 1. It helps to generate fuzzy values based on data 0 and 1 by eq. 10.2. Finally, it helps to map both values and apply aggregation method which is shown in eq. 10.3 for evaluating feasible and optimal solutions. Equation 10.3 shows the utility function that is used to deal with combination value of membership function of both player’s membership value. The variables x and y deal with membership value of Company A and Company B which act as Player A and Player B for the purpose of evaluation. Table 10.6 presents the utility function mapping system of both companies. In this mapping, two variables are used for analyzing and evaluating linear formulation as a and b value. The value a indicates the decision variable of linear programming of Company A. The value b indicates decision variable of linear programming of Company B. The combination of both variables along with several data and equations helps to map objective function and constraint. Finally, the value of a and

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b helps to select the proper strategy of the company based on proper ration. It helps to reduce conflicting among multiple strategies of the company and helps to increase productivity. 10.4

CONCLUSION AND FUTURE WORK

The proposed method helps to model several conflicting strategies of the company based on some analysis of mathematical formulation. It helps to model several information and its modeling is based on formulation and analysis. It helps to model some analysis of the method that helps to reduce several types of uncertainty. The proposed goal is achieved by the fusion of fuzzy logic, game theory, and linear programming. Fuzzy logic helps to map imprecise parameters into precise values. Game theory is used to model the payoff matrix based on both company’s strategy. Linear programming is used to deal with objective values and constraint that are received by payoff matrix. Future work is to deal with several payoff value based on linear and quadratic programming. It also helps to analyze the model in a simulation platform based on optimization software that helps to deal with several metrics. KEYWORDS • • • • •

fuzzy logic linear programming game theory linguistic variable fuzzification

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INDEX

A Adaptive Cognitive Manufacturing System (ACMS), 91

Amplitude shift keying (ASK), 180

Artificial intelligence (AI), 29, 113

benefits, 67–68 concept, 31–37 different problems, 70–71 human resource management (HRM), 117–118

implementation, 121

implementing, 51

performance, 62–64

recruitment process, 52–53

talent acquisition, 51–52

training, 57–62

importance, 121

legal implementation, 73–74

literature survey, 39–43

major challenges, 68–70

major revolution, 43–46

need, 37–39

objectives, 118, 119

job applicant, 119–120

strategy, 119

recruitment process, 64–68, 115–116

screening, 53–54

selection, 54–57, 116

traditional, 116–117

reinventing HR, 46–51 relevance, 71–73

Artificial neural networks (ANNs), 219

Augmented reality (AR), 37

B Big data, 140–141

Block diagram

for smart band, 178

Branding internally, 13

C Cloud robotics, 139–140

big data, 140–141

call centers, 142

computing, 141

crowdsourcing, 142

robot operating system (ROT), 141–142

Cognitive radio system (CRS), 242

Conflicting strategy management

intelligent optimization technique, 239

cognitive radio system (CRS), 242

cooperative spectrum sensing (CSS), 242

literature review, 242–246

method, 246–250

proposed method, 246–250

wireless adhoc network (WANET), 239

Cooperative spectrum sensing (CSS), 242

D Delay tolerant networks (DTNs), 129–131,

130

Design specifics, 182

implementation in microcontroller, 183

microcontroller, 183

system design, 183–187

transmission through ASK, 182

F Fabric defect detection and fault

identification, 153

classification of frames based

frame selection, 163

comparison process

mean square error (MSE), 162

peak signal-to-noise ratio (PSNR), 162

conversion, binary images, 164

discussion

comparison process, 165

framing, 165

grayscale conversion and noise

removal, 167–168

HDE part based, frame selection, 167

histogram analysis, 169

254

Index

result, 166–167

thresholding, 169–170

equality, 163

frame selection, 163

grayscale conversion, 163

linguistic variable, 163

literature review, 159

noise removal, 164

problem formulation, 160

classification of frames based, 163

comparison process, 161–162

conversion into binary images, 164

framing, 161

grayscale conversion, 163

histogram analysis, 164

input, 161

noise removal, 164

thresholding, 164

review, literature, 158–159

thresholding, 164

H Hardware architecture, 177–178 communication, 178–180 radio waves, 178–180 receiver, 181–182 transmitter, 180–181 Homogenization in technique, 211

inherent, 231–233

necessities of HR tasks, 214–216

serviceability of AI techniques, 216–219

synopsis in HR management

data acquisition, 228–230

employee revenue prediction using AI,

219–221 genetic algorithm’s staff, 224–226 knowledge-based search engines, 222–224 text mining sentiment analysis, 226–228 utilizing interactive voice response, 230–231

Human resource management (HRM), 1

advantages of, 12

awareness creation, 13

branding internally, 13

conflict, 13

goals, 12

maintaining conducive work environment, 14

mission, 12

organizational culture, 14

strategic, 12

talent, 13

values, 12

vision, 12

advisory

departmental head, 12

top management, 12

employee

ensuring fair, 6

focusing on level satisfaction, 6

inducting, 5–6

recruitment, 5

evolution, 3

few examples, 20

CISCO, 21

Google, 20–21

Nissan, 21–22

functions, 6–7

advisory, 12

managerial, 7

operational, 8

homogenization in technique, 211

data acquisition, 228–230

employee revenue prediction using AI,

219–221

genetic algorithm’s staff, 224–226

inherent, 231–233

knowledge-based search engines,

222–224 necessities of HR tasks, 214–216 serviceability of AI techniques, 216–219 text mining sentiment analysis, 226–228 utilizing interactive voice response, 230–231

homogenization technique, 211

importance of

employee, 5

inherent of AI techniques, 231–233

managerial

controlling, 8

directing, 8

organizing, 8

planning, 7

necessities of HR tasks, 214–216

normal language handling (NLP), 218

Index

255

operational

compensation, 10

design, 9

development, 9–10

information efficiently, 11–12

job analysis, 9

learning, 9–10

performance management, 9

policies, 10–11

selection, 8–9

welfare, 11

serviceability of AI techniques, 216–219 SHRM, 16

application, 19–20

capacity, 18

corrective actions, 20

estimation of, 18

evaluation of capacity, 17–18

follow-up, 20

identify, 16–17

useful tools, 19

strategy, 14

aligning HR activities, 15–16

emphasis on data, 16

plan for business, 15

synopsis in HR management

artificial neural networks (ANNs), 219

employee revenue prediction using AI,

219–221 extraction of resume data acquisition, 228–230

genetic algorithm’s staff, 224–226

intuitive voice reaction (IVR), 230

knowledge-based search engines,

222–224 text mining sentiment analysis, 226–228 utilizing interactive voice response, 230–231

I

Intelligent optimization technique, 239

cognitive radio system (CRS), 242

cooperative spectrum sensing (CSS), 242

literature review, 242–246

method, 246–250

proposed method, 246–250

wireless Aadhoc network (WANET), 239

Internet of robotic things (IoRT), 127

AI/ML, 142–143

cloud robotics, 139–140

big data, 140–141

call centers, 142

computing, 141

crowdsourcing, 142

robot operating system (ROT), 141–142

delay tolerant networks (DTNs), 129–131 fusion, 143–146

assisted intelligence, 146–147

augmented, 147

autonomous, 148

5G, 148–149

human robot, 131–133

overview, 133

definitions, 133–135

robots with, 135–138

Intuitive voice reaction (IVR), 230

L Lithium polymer battery (Li-Po Battery), 178

M Manpower optimization system, 197

dataset, 206

engineering department, 205

under high, 205

literature review, 199–202

medium, 205

membership functions, 204

method, 203–206

performance evaluation, 206–207

proposed method, 203–206

science department, 205

N Normal language handling (NLP), 218

O Organic light emitting diode (OLED), 178

P Peak signal-to-noise ratio (PSNR), 162

Phase lock loop (PLL), 181

Problem formulation, 160

classification of frames based, 163

comparison process, 161–162

conversion into binary images, 164

256

Index

framing, 161

grayscale conversion, 163

histogram analysis, 164

input, 161

noise removal, 164

thresholding, 164

R Radio frequency identification (RFID) tags, 97 Real-time clock (RTC), 178 Received signal strength indicator (RSSI), 175 Recruitment AI roles in, 115–116

process, 64–68

screening, 53–54

selection, 54–57, 116

traditional, 116–117

RF-based social distance, 173 amplitude shift keying (ASK), 180 block diagram for smart band, 178 design specifics, 182 implementation in microcontroller, 183 microcontroller, 183 system design, 183–187 transmission through ASK, 182 experimental setup, 187 interfacing components with Arduino Nano, 189–190 primary hardware components, 187 hardware architecture, 177–178

communication, 178–180

radio waves, 178–180

receiver, 181–182

transmitter, 180–181

lithium polymer battery (Li-Po Battery), 178 organic light emitting diode (OLED), 178 overview, system, 177 phase lock loop (PLL), 181 primary hardware components 433 MHz transmitter or receivers, 188 433 MHz transmitter or receivers, 188 ATmega328 microcontroller, 188–189 ATmega328 microcontroller, 188–189 real-time clock (RTC), 178 received signal strength indicator (RSSI), 175 World Health Organization (WHO), 173 Robot operating system (ROT), 141–142

S Smart computing, 87, 95, 96 analysis, 103–107 challenges, 92–94 discussion, 103–107 hotel booking, 99 in Indian railways, 98 issues, 92–94 proposed work, 94–103 radio frequency identification (RFID) tags, 97

related works, 90–92

Synopsis in HR management artificial neural networks (ANNs), 219 employee revenue prediction using AI, 219–221 extraction of resume data acquisition, 228–230 genetic algorithm’s staff, 224–226 intuitive voice reaction (IVR), 230 knowledge-based search engines, 222–224 text mining sentiment analysis, 226–228 utilizing interactive voice response, 230–231

T Text to discourse (TTS), 230 Traditional businesses, 87 analysis, 103–107 challenges, 92–94 discussion, 103–107 issues, 92–94 proposed work, 94–103 related works, 90–92

U Utilizing interactive voice response, 230–231

V Virtual reality (VR), 37

W Wireless Adhoc network (WANET), 239 Wireless Sensor Networks, 92 World Health Organization (WHO), 173