Handbook Of Artificial Intelligence Applications For Industrial Sustainability 1st Edition by Vikas Garg, Richa Goel, Pooja Tiwari, Esra Sipahi Döngü 1000991520 9781000991529

1 Artificial Intelligence Application for Enterprise Sustainability

1.1 Introduction

1.2 Research Findings and Results

1.3 Design and Method

1.4 Findings and Discussion

1.5 Conclusion and Recommendations

Notes

References

2 Artificial Intelligence Models in Pattern Recognition

2.1 Introduction

2.2 Theoretical Approaches to Pattern Recognition

2.2.1 Statistical Classification

2.2.2 Structural Pattern Recognition

2.2.3 Neural Network Pattern Recognition

2.2.4 Models Used in Pattern Recognition

2.3 Real-World/Industrial Application of Pattern Recognition

2.3.1 Robotics

2.3.2 Computer Vision

2.3.3 Biomedical and Biology

2.3.4 Speech Recognition

2.3.5 Oil and Gas

2.3.6 Identification of Fingerprints

2.3.7 Financial Industry Services

2.3.8 Natural Language Processing

2.3.9 Handwriting Recognition

2.3.10 Credit Scoring

2.3.11 Anomaly Detection

2.3.12 Supporting Healthcare Professionals and Protecting Patients

2.4 Conclusion

References

3 Efficient Techniques for Disease Prediction from Medical Data

3.1 Introduction

3.2 Objectives

3.3 Methodology

3.4 Machine Learning in Disease Prediction

3.5 Data Mining in Disease Prediction

3.6 Techniques for Data Mining

3.6.1 Techniques for Data Mining

3.7 Fundamentals of Data Mining

3.7.1 Data Mining Fundamentals

3.8 Issues and Difficulties in Disease Prediction

3.9 Algorithm of Machine Learning in Medical Data

3.10 Future of Machine Learning and Data Mining in the Healthcare Sector

3.11 Conclusion

References

4 Understanding the Contribution of Artificial Intelligence

4.1 Introduction

4.2 Scope and Objectives of the Study

4.3 Literature Review

4.4 Role and Application of Technologies and Tools Powered by Artificial Intelligence

4.5 Findings

4.6 Conclusion

4.7 Managerial Implications

References

5 Artificial Intelligence and Sustainable Green Fashion Industry

5.1 Introduction

5.2 Artificial Intelligence for Sustainable Material

5.3 Artificial Intelligence for Design Process

5.4 Artificial Intelligence for Predictive Analytics and Demand Forecasting

5.5 Artificial Intelligence for Operations Automation

5.6 Product, Inventory, and Supply Chain Management

5.7 Artificial Intelligence for Customer Experience Enhancement Online and In-Store

5.7.1 Chatbots or Artificial Intelligence Smart Assistants

5.7.2 Image Search

5.7.3 Reverse Image

5.7.4 Visual Search

5.8 Subscription Services and Recommendations Engines

5.8.1 Brand Subscriptions

5.8.2 Targeted Subscriptions

5.8.3 Rental Subscriptions

5.8.4 Personal Artificial Intelligence Stylist

5.8.5 Amazon’s Echo Look

5.9 Customer Experience Enhancement In-Store

5.10 Conclusion

References

6 Artificial Intelligence for Sustainable Human Resource Management

6.1 Introduction

6.2 Artificial Intelligence

6.3 Artificial Intelligence and Human Resources Management

6.4 Sustainable Human Resources Management in the Era of Artificial Intelligence

6.5 Results

References

7 Relation of Human Resource Management and Artificial Intelligence

7.1 Introduction

7.1.1 What Is Human Resources Management?

7.1.2 What Is Artificial Intelligence?

7.1.3 Concept of Organization Culture

7.1.4 Defining Organization Culture

7.1.5 Characteristics of Organization Culture

7.1.6 Organization Culture

7.1.7 Definition of Culture

7.1.8 Seven Unique Features of Organization Culture

7.1.9 Types of Organization Culture

7.1.10 Weak Culture

7.1.11 Dimensions of Organization Culture

7.1.12 Positive Organization Culture

7.1.13 Work Culture: Dimensions and Various Studies

7.1.14 Artificial Intelligence in Human Resource Management

7.2 Methodology

7.3 Research Design

Results and Discussion

Limitation and Future Research Direction

References

8 Leveraging Artificial Intelligence for Talent Management

8.1 Introduction

8.2 Challenges in Global Talent Management and Strategic Opportunities for Talent Sustainability

8.2.1 Globalization and Allied Challenges

8.2.2 Changing Demographics

8.2.3 Demand for More Competencies and Motivation

8.2.4 Supply of High-Quality Workers

8.2.5 Building a Talent Sustainability Framework

8.3 Three Ts: Technology, Talent Intelligence, and Talent Analytics

8.3.1 Talent Attraction (Recruitment and Onboarding)

8.4 Enabling Artificial Intelligence in Talent Management

8.4.1 Artificial Intelligence Tools for Candidate Sourcing

8.4.2 Candidate Screening Using AI Tools

8.4.3 Candidate Assessment Using Artificial Intelligence Tools

8.4.4 Artificial Intelligence Reshaping Talent Acquisition Process

8.5 Effect of Artificial Intelligence in Sustainable Talent Management

8.5.1 Responsible Talent Management Model

8.6 Conclusion

References

9 Transformation of Human Resources: Future of Workforce Analytics

9.1 Introduction

9.2 Applications or Role of AI in HRM

9.2.1 Workforce Planning

9.2.2 Talent Acquisition and Recruitment

9.2.3 Employee Engagement

9.2.4 Learning and Development Programs

9.2.5 Performance Appraisal

9.3 Transformation of Traditional HR to Contemporary HR Using AI

9.3.1 Relationship between Humans and the Digital

9.3.2 Benefits, Limitations, and Challenges in Implementing the AI in HRM

9.4 Transformation of Traditional HR to Contemporary HR with AI

9.4.1 Benefits, Limitations, and Challenges in Implementing the AI in HRM

9.4.2 Limitations

9.4.3 Challenges

9.5 Policy Recommendations

9.5.1 Constructing a Marketplace

9.5.2 Creating Awareness of the Contribution of AI

9.5.3 Collaboration

9.5.4 An Aid for Start-up

9.5.5 Security and Privacy Issues

9.6 Conclusion

References

10 Fraud Detection with Machine Learning and Artificial Intelligence

10.1 Introduction

10.2 Machine Learning

10.3 Artificial Intelligence

10.4 Frauds

10.4.1 Types of Fraud

10.4.2 Fraud Detection Methods for Credit Cards

10.4.3 Machine Learning–Driven Fraud Detection

10.5 Machine Learning in Fraud Detection

10.5.3 Machine Learning Algorithms and Models for Fraud Detection

10.6 Bottom of Form

10.7 Machine Learning in Fraud Detection

10.8 Characteristics and Challenges

10.9 Conclusion

References

11 Performance of Artificial Intelligence in Fraud Detection

11.1 Introduction

11.2 Methods

11.2.1 Search Criteria (Keywords)

11.3 Analysis and Results

11.4 Conclusion

11.5 Limitation and Future Direction

References

12 Sustainable Education and Differently Abled Students

12.1 Introduction

12.2 Impact of Covid-19 on Education

12.3 Methodology

12.4 Discussion

12.4.1 Strategies to Be Adopted to Sustain Education After the Reopening of Educational Institutions After COVID-19

12.4.2 Impact of Artificial Intelligence on Education for its Sustainability in the Long Run

12.4.3 Role of Educational Data Mining and Neural Networks in Artificial Intelligence Education

12.4.4 Role of Intelligent Tutoring Systems in AIEd

12.4.5 Paradigms in AIEd

12.4.6 Technological Strategies to Be Adopted for Specially Abled Students to Make a More Inclusive Educational System

12.4.7 Solutions that Technology Can Provide for Differently Abled Students

12.5 Future Prospects of the Study

12.6 Study Limitations

12.7 Conclusion

References

13 Artificial Intelligence for Sustainable Agricultural Practices

13.1 Introduction

13.2 Theoretical Framework

13.3 Methods

13.3.1 Questionnaire Development

13.3.2 Hypothesis Development

13.3.3 Data Collection

13.4 Results

13.4.1 Profiling of Respondents

13.4.2 Reliability and Validity

13.4.3 Analysis of Hypothesis

13.5 DISCUSSION

13.6 Conclusion

References

14 Impact of Artificial Intelligence in Personalized Banking Service

14.1 Introduction

14.2 Literature Review

14.3 Study Objectives

14.4 Methodology

14.5 Data Analysis and Interpretation

14.5.1 Customers’ View

14.5.2 Bankers’ View

14.6 Summary and Conclusion

References

15 E-Commerce and Trade: The Role of Artificial Intelligence

15.1 Introduction

15.2 Artificial Intelligence for Personalization or the Internet

15.3 Artificial Intelligence: Cognitive or Perceptual

15.4 Artificial Intelligence: Decision or Business

15.5 Automated Artificial Intelligence

15.5.1 Literature Review

15.5.2 Individual Product Suggestions

15.5.3 Optimizing the Price

15.5.4 Improved Clientele Service

15.5.5 Client Segmentation

15.5.6 Smart Logistics

15.5.7 Forecasting of Demand and Sales

15.6 Artificial Intelligence in E-Commerce Statistics and Facts

15.6.1 Statistics on Artificial Intelligence Adoption in E-Commerce

15.6.2 Statistics on the Role of Artificial Intelligence in Online Sales

15.6.3 Statistics on the Use of Artificial Intelligence in Customer Service

15.7 Conclusion

References

16 Artificial Intelligence Tools for Reshaping E-Business and Trade

16.1 Introduction

16.1.1 Global Leaders: Artificial Intelligence

16.2 Literature Review: Theoretical Background of Electronic Commerce and Artificial Intelligence

16.2.1 Electronic Commerce

16.2.2 Artificial Intelligence: Concepts and its Development

16.3 Emerging Artificial Intelligence Technologies: Reshaping E-Business

16.3.1 Computer Vision

16.3.2 Speech, Text, and Image Recognition

16.4 Artificial Intelligence Transforming E–Business and Economic Sectors: Artificial Intelligence Methods and Functions

16.4.1 Heuristics Artificial Intelligence Method

16.4.2 Machines that Support Vectors

16.4.3 Artificial Neural Networks

16.4.4 Markov Decision-Making

16.4.5 Computer-Aided Natural Language Processing

16.4.6 Fuzzy Logic

16.5 Emerging Artificial Intelligence Trends: Implementing and Promoting E-Business and Trade

16.5.1 Chatbots

16.5.2 Management of Customer Relationships

16.5.3 Achieving Sales Goals

16.5.4 Product Content Management

16.5.5 Customer Service

16.5.6 Automation

16.5.7 Artificial Intelligence Is Everywhere

16.6 Artificial Intelligence Technology: Its Applications in Electronic Business and Trade

16.6.1 Artificial Intelligence Assistant

16.6.2 Engine of Recommendations

16.6.3 Advanced Supply Chain

16.6.4 Maximum Profits

16.7 Artificial Intelligence in E-Business: Case Studies

16.7.1 Amazon

16.7.2 JD.com

16.7.3 Alibaba

16.7.4 eBay

16.7.5 ASOS

16.7.6 Rakuten

16.8 New Electricity in E-Business: Artificial Intelligence

16.8.1 Consumer-Focused Visual Search

16.8.2 Improve Sales Process and Retarget Potential Clients

16.8.3 Higher Degree of Customization

16.8.4 Chatbots and Virtual Helpers

16.8.5 Increase Customer Recommendations

16.8.6 Knowledgeable Actors

16.8.7 Remove Fake Reviews

16.8.8 Automation

16.9 Electronic Commerce and Related Business Domains: Artificial Intelligence Applications

16.9.1 E-Commerce

16.9.2 Retailing

16.9.3 Manufacturing

16.9.4 Marketing

16.9.5 Payments

16.9.6 Supply Chain

16.9.7 Cybersecurity

16.10 Challenges and Issues: Artificial Intelligence

16.10.1 Finding Use Cases that Are Valid

16.10.2 Sorting Out Data

16.10.3 Creating a Department for Artificial Intelligence

16.10.4 Culture and Risk Management

16.11 E-Business and Trade: The Future of Artificial Intelligence

16.12 Discussion and Conclusion

References

17 Robotic Process AutomationThe Emerging Technology

17.1 Concept of Robotic Process Automation

17.2 Evolution of Robotic Process Automation

17.2.1 Technology

17.2.2 Workflow Automation and Management Tools

17.2.3 Artificial Intelligence

17.2.4 Robotic Process Automation: The Emerging Technology

17.3 Distinction Among Manual, Scripted Automation, and Robotic Process Automation

17.3.1 Manual Process

17.3.2 Scripted Automation

17.3.3 Robotic Process Automation

17.3.4 Heavy Weight Vs. Light Weight Automation

17.4 Types of Business Processes Wherein Robotic Process Automation can be Used

17.4.1 Repetitive Tasks That Employees Perform 50–60 Times in an 8-Hour Working Day

17.4.2 Reporting and Analysis

17.4.3 Mass Mail Generation, Archiving, and Extracting

17.4.4 Conversion of Data Formats and Graphics

17.4.5 Enterprise Resource Planning Transactions

17.4.6 Process Lists and File Storage

17.5 Characteristics and Strengths of Robotic Process Automation

17.6 Impact of Robotic Process Automation and Artificial Intelligence

17.6.1 Capgemini (2016) Research Work

17.6.2 Deloitte (2017) Research Findings

17.6.3 Research Works of Lacity and Willcocks (2017)

17.6.4 Research Work of Ian Sandholm (2019)

17.6.5 Research Work of Hindle J, Lacity M, Willcocks L, and Khan S (2018)

17.7 Bottlenecks and Risks

17.8 Conclusion

References

Index