ICT and Data Sciences
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ICT and Data Sciences

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

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eBook - ePub

ICT and Data Sciences

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

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About This Book

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.

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Information

Publisher
CRC Press
Year
2022
ISBN
9781000550375
Edition
1
Topic
Computer Science
Subtopic
Information Technology
Index
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