ICT and Data Sciences

Archana Singh, Vinod Kumar Shukla, Ashish Seth, Sai Sabitha, Archana Singh, Vinod Kumar Shukla, Ashish Seth, Sai Sabitha

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9780367501143
• eBook ISBN: 9781000550375
• Topic: Ciencia de la computación
• Subtopic: Ciencias computacionales general

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems.

ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things.

The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

1 Impact and Analysis of Machine Learning and IoT Application in People Analytics

Praveen Mohan Kulkarni and Santosh Saraf

DOI: 10.1201/9781003048862-1

Contents

1. 1.1 Introduction to People Analytics
2. 1.2 Purpose and Motivation of Machine Learning and Internet of Things (IoT) in People Analytics
   • 1.2.1 Machine Learning and People Analytics
   • 1.2.2 Internet of Things and People Analytics
3. 1.3 Challenges of Implementing Machine Learning and Internet of Things in People Analytics
4. 1.4 Research Design and Methodology
   • 1.4.1 Data Sources
   • 1.4.2 Screening
   • 1.4.3 Data Analysis
   • 1.4.4 Descriptive Analysis of Literature
5. 1.5 Results through Thematic Analysis of Literature
   • 1.5.1 Role of Machine Learning in People Analytics
   • 1.5.2 Role of Internet of Things (IoT) in People Analytics
6. 1.6 Practical Implications
7. 1.7 Conclusion
8. References

1.1 Introduction to People Analytics

The technology and functions of human resource management (HRM) are making efforts to combine experience and concepts of HRM for people analytics [1, 2]. People analytics supports HRM to understand the workgroups and also individuals working in the organization. Data is collected and transformed into meaningful information about the employee’s attributes, behavior, and performance, which support effective human resource decision-making [3]. Association of people analytics with HRM aims to improve recruitment, retention, assessment, promotion, salary, employee turnover, and other functions of human resource management [4]. The backbone of people analytics includes technology which is reformed and redefined to support people analytics. The Internet of Things (IoT) is one of the major technologies to support human resource practices for effective people analytics, as it is convenient for collecting information and supporting effective decision-making for human resource professionals [5, 6]. While the role of machine learning in human resource practices provides an opportunity for processing data analytics related to human resource departments in organizations, this technology collects information in advanced algorithms that predicts employee information related to human resource practices, through deep learning neural networks that are transformed for meaningful results and conclusion of data. The convergence of machine learning and IoT paves the way for improved efficiency, accuracy,productivity, and overall cost-savings for resource-constrained IoT devices. Using a combination of machine learning algorithms and IoT, we can achieve improved communication and computation, better controllability, and improved decision-making [7]. Therefore, employees are the most important resource for an organization, hence companies must make the right people-oriented decisions.Therefore, initiatives combining machine learning and the IoT with people analytics could bridge the gap between the existing judgment-based approaches with a data-driven approach for HRM.

1.2 Purpose and Motivation of Machine Learning and Internet of Things (IoT) in People Analytics

1.2.1 Machine Learning and People Analytics

Machine learning, which includes the subset of artificial intelligence, is embraced with the tools of statistics and support of computer science. Machine learning systems are instructed by programs to learninteractive data and provide meaningful information through the application of statistical interventions. This data generated through machine learning aids with decision-making for managers and other employees in anorganization [8]. Likewise, IoT supports machine learning in collecting data from various self-directed devices through nodes. These nodes have the capability to collect information through sensors and communicate with other sensors and generate data. These data are connected with machine learning algorithms, which generate meaningful information for an organization’s decision-makers [9].

Machine learning technology applies different algorithms for analysis of the data collected through IoT. Machine learning algorithms and methods of data processing for people analysis are presented in Table 1.1 [10].

Table 1.1 Machine Learning Algorithms and Data Processing Tasks for People Analytics

Sr. No.	Machine Learning Algorithms	Data Processing Tasks	People Analytics
1	K-Nearest Neighbors	Classification	Employee turnover, employee demographics, salary grades
2	Naive Bayes	Classification	Employee performance, talent management, leave management
3	Support Vector Machine	Classification	Talent classification, abilities, skills, knowledge
4	Linear Regression	Regression	recruitment, selection, training, compensation
5	Support Vector Regression	Regression	Working hours, employee productivity analysis, training effectiveness
6	Classification and Regression Trees	Classification/Regression	Succession planning, leadership management
7	Random Forests	Classification/Regression	Human resource (HR) planning, job analysis
8	Bagging	Classification/Regression	HR process, training performance
9	K-Means	Clustering	Personal management systems, employee engagement
10	Density-Based Spatial Clustering of Applications with Noise	Clustering	Talent evaluation and management, Performance management systems, job descriptions
11	Principal Component Analysis	Feature extraction	Salary survey, performance productivity analysis
12	Canonical Correlation Analysis	Feature extraction	Analysis of past performance to present performance of employee
13	Feed Forward Neural Network	Regression/Classification/Clustering/FeatureExtraction	Employee performance forecasting, human resource forecasting
14	One-class Support Vector Machines	Anomaly detection	Job turnover analysis, HR decision-making, strategic HRM

1.2.2 Internet of Things and People Analytics

IoT is internet-based smart technology which connects people and devices, and generates meaningful information for decision-makers. As IoT becomesmore integral to employees, its relationship would be more related to human resource practices. IoT enables one to influence anorganizational culture by understanding employees from remote locations, monitor performance, and also provide flexibility on work-timings to employees [11]. The application of IoT in HRM is presented in Table 1.2.

Table 1.2 IoT in People Analytics

Sr. No.	IoT in HRM	Descriptions
1	Recruitment	Employees are more likely to have a real experience of their future office spaces, before taking a major step. IoT-enabled artificial intelligence can make the selection process more impartial and introduce more diversity of employees.
2	Measuring Employee Behavior and Identification	However, these badges can serve as sociometric badges and are used to track workplace, or employee stress levels with their voice and heart rate, etc. In the service delivery industry, it can help to track the speed of driver monitoring, eliminate downtime between delivery, gauge efficient and secure routes, etc., in all with IoT software.
3	Enabling an Insightful and Agile Organization	IoT will also enable organizations to make real-time employee statistics possible. It will give them insight into employee productivity, their time horizons and skill sets, among others. This will also improve HR performance and HR response to staffing issues.

1.3 Challenges of Implementing Machine Learning and Internet of Things in People Analytics

The next part of the chapter will cover problems related to acceptance of machine learning and IoT f...

Table of contents

• Cover Page
• Half Title page
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• 1 Impact and Analysis of Machine Learning and IoT Application in People Analytics
• 2 Augmented Reality in Online Shopping
• 3 Internet of Things (IoT) Their Ethics and Privacy Concerns
• 4 Artificial Intelligence and Deep Learning are Changing the Healthcare Industry
• 5 Application of Disruptive Technology in Food Trackability
• 6 Analyzing Cyber Security Breaches
• 7 Industrial Internet of Things (IoT) and Cyber Manufacturing Systems Industry 4.0 Implementation and Impact on Business Strategy and Value Chain
• 8 Artificial Intelligence-Based Hiring in Data Science Driven Management Context
• 9 New Patterns in Cyber Crime with the Confluence of IoT and Machine Learning
• 10 A Review for Cyber Security Challenges on Big Data Using Machine Learning Techniques
• 11 Research Agenda for Use of Machine Learning and Internet of Things in “People Analytics”
• 12 IoT-Integrated Photovoltaic System for Improved System Performance
• 13 Metaheuristic Optimization in Routing Protocol for Cluster-Based Wireless Sensor Networks and Wireless Ad-Hoc Networks
• 14 Artificial Intelligence A Threat to Human Dignity
• 15 Device Programming for IoT In Defense of Python as the Beginner’s Language of Choice for IoT Programming
• 16 Enhancing Real-Time Learning Experiences through Information Communication Technology, Augmented Reality, and Virtual Reality
• 17 Topic-Based Classification for Aggression Detection in a Social Network
• 18 Role of ICT in Online Education during COVID-19 Pandemic and beyond Issues, Challenges, and Infrastructure
• Index