Artificial intelligence and machine learning in business management : concepts, challenges, and case studies / edited by Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar.

Format:

Book

Contributor:

Panda, Sandeep Kumar, 1985- editor.

Language:

English

Subjects (All):

Management--Technological innovations.

Management.

Artificial intelligence--Industrial applications.

Artificial intelligence.

Physical Description:

1 online resource (353 pages)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2022.

Summary:

"The focus of this book is to introduce Artificial Intelligence (AI) and Machine Learning (ML) technologies into the context of Business Management. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines"-- Provided by publisher.

Contents:

Intro

Half Title

Title Page

Copyright Page

Dedication

Contents

Preface

Acknowledgements

Contributors

Editors

1 Artificial Intelligence in Marketing

1.1 Introduction

1.2 AI, ML and Data Science

1.3 AI and Marketing

1.4 Benefits and Detriments of Using AI in Marketing

1.4.1 Benefits

1.4.2 Detriments

1.4.2.1 Amazon Go (Caselet)

1.4.2.2 Technical Working of Amazon Go

1.4.2.3 Issues Related to Amazon Go Technology

1.5 Marketing Plan and AI's Potential

1.6 Future

References

2 Consumer Insights through Retail Analytics

2.1 Introduction

2.2 What Value Does Analytics Bring to Retail?

2.3 Types of Customer Data used in Retail Analytics

2.4 Application of Consumer Data - Retail Analytics

2.5 Analytics in Retail Industry - How it Works

2.6 Metrics in Retail Industry

2.7 Analytics in Practice in Renowned Retail Organizations

2.8 Challenges and Pitfall - Retail Analytics

2.9 Way Ahead

2.10 Discussion Questions

3 Multi-Agent Paradigm for B2C E-Commerce

3.1 Business Perspective

3.1.1 Negotiation

3.1.1.1 Types of Agent-to-Agent Negotiations

3.1.1.2 Negotiation Strategies

3.1.1.3 Negotiation Types

3.1.2 Customer Relationship Management (CRM) and Customer Orientation (CO)

3.1.3 Broker and Brokering

3.1.4 Business Model

3.2 Computational Perspective

3.2.1 Multi-Agent System

3.2.1.1 Agent: Definition and Characteristics

3.2.1.2 Multi-agent Systems: Salient Features

3.2.2 Cognitive and Social Parameters

3.2.3 MAS Communication

3.2.4 Foundation for Intelligent Physical Agents (FIPA)

3.3 Machine Learning: Functions and Methods

3.3.1 Supervised and Unsupervised Learning

3.3.2 Decision Tree (DT)

3.3.3 Neural Network

3.3.4 Sensitivity Analysis (SA)

3.3.5 Feature Selection

3.4 Conclusion.

References

4 Artificial Intelligence and Machine Learning: Discovering New Ways of Doing Banking Business

Structure of the Chapter

4.1 Introduction

4.2 AI in the Banking Sector: Where It Works and What For

4.2.1 AI and Customer Service

4.2.1.1 Chatbots

4.2.1.2 AI and Personalized Banking

4.2.1.3 Smart Wallets

4.2.1.4 Voice Assisted Banking

4.2.1.5 Robo Advice

4.2.1.6 AI Backed Blockchain for Expedite Payments

4.2.2 AI and Magnifying Efficiency of Banks

4.2.2.1 Determining Credit Scoring and Lending Decisions

4.2.2.2 AI and CRM

4.2.3 Magnifying Security and Risk Control

4.2.3.1 Detection and Prevention of Financial Fraud

4.2.3.2 Reducing Money Laundering

4.2.3.3 Cybersecurity

4.2.3.4 AI: Managing and Controlling Risk

4.3 AI Applications in Indian Banks: Some Selected Examples

4.3.1 State Bank of India

4.3.2 HDFC Bank

4.3.3 Axis Bank

4.3.4 Punjab National Bank

4.4 AI and its Impact on Banks' KPIs

4.4.1 Impact of AI on Profitability

4.4.2 Impact of AI on Productivity and Efficiency of Banks

4.4.3 Impact of AI on Improved Customer Satisfaction

4.4.4 AI Helps in Offering Innovative and Tailor-Made Services

4.4.5 AI Helps in Reducing Customer Attrition

4.4.6 Impact of AI on Overall Performance

4.5 Conclusion and Future of AI

5 Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning

5.1 Introduction

5.2 Related Work

5.3 Proposed Method

5.4 Results

5.5 Conclusion and Future Scope

6 Artificial Intelligence for All: Machine Learning and Healthcare: Challenges and Perspectives in India

6.1 Introduction

6.2 Healthcare in India: Challenges

6.3 Frameworks in Health must consider Missingness

6.3.1 Wellsprings of Missingness Must Be Painstakingly Comprehended.

6.3.2 Incorporation of Missingness

6.3.3 Settle on Careful Choices in defining Outcomes

6.3.4 Comprehend the Result in the Setting of a Social Insurance Framework

6.3.5 Be Careful with Mark Spillage

6.4 Inclined Opportunities in Healthcare

6.4.1 Automating Clinical Errands during Determination and Treatment

6.4.2 Computerizing Clinical Picture Assessment

6.4.3 Robotizing Routine Procedures

6.4.4 Streamlining Clinical Choice and Practice Support

6.4.5 Normalizing Clinical Procedures

6.4.6 Incorporating Divided Records

6.4.7 Growing Medicinal Capacities: New Skylines in Screening, Analysis and Treatment

6.4.8 Growing the Inclusion of Proof

6.4.9 Moving towards Constant Social Checking

6.5 Population Protection (Crowd Surveillance)

6.6 Marketing Strategy

6.7 Population Screening

6.8 Patient Advocacy

6.9 Role of Machine Learning in Society

6.10 Ayushman Bharat: A Step Forward

6.11 The National E-Health Authority (Neha)

6.12 Cancer Screening and Machine Learning

6.13 "Sick" Care to "Health" Care: Moving Forward

6.14 Machine Learning and Healthcare Opportunities

6.14.1 Computerizing Clinical Assignments during Determination and Treatment

6.14.2 Robotizing Clinical Picture Assessment

6.14.3 Robotizing Routine Procedures

6.14.4 Clinical Support and Augmentation

6.14.5 Expanding Clinical Capacities

6.14.6 Precision Medicine for Early Individualized Treatment

6.14.7 Open Doors for Innovative Research

6.14.8 Adding Communication to AI and Assessment

6.14.9 Distinguishing Representations in a Large and Multi-source Network

6.15 Common Machine Learning Applications in Healthcare

6.15.1 Machine Learning Application in Drug Discovery

6.15.2 Neuroscience and Image Computing

6.15.3 Cloud Computing Frameworks in building Machine Learning-based Healthcare.

6.15.4 Machine Learning in Personalized Healthcare

6.15.5 Machine Learning in Outbreak Prediction

6.15.6 Machine Learning in Patient Risk Stratification

6.15.7 Machine Learning in Telemedicine

6.15.8 Multimodal Machine Learning for Data Fusion in Medical Imaging

6.16 Incorporating Expectations and Learning Significant Portrayals for the Space

6.17 Conclusion

7 Demystifying the Capabilities of Machine Learning and Artificial Intelligence for Personalized Care

7.1 Introduction

7.2 Temporal Displacement of Care

7.3 AI/ML use in Healthcare

7.4 Wearable Health Devices

7.5 Conclusion

8 Artificial Intelligence and the 4th Industrial Revolution

8.1 Introduction

8.2 The Industrial Revolutions

8.3 The Technologies of the 4th Industrial Revolution

8.3.1 Internet of Things

8.3.2 4th Industrial Revolution: New Technologies

8.3.3 Machine Learning and Artificial Intelligence

8.3.4 Internet of Things, Microelectro-sensors and Biosensor Tech

8.3.5 Robotics

8.3.6 Virtual Reality, Augmented Reality and Mixed Reality

8.3.7 3D Printing and Additive Manufacturing

8.3.8 Neuromorphic Computing

8.3.9 Biochips

8.4 AI Applications in the 4th Industrial Revolution

8.4.1 Gaming Industry

8.4.2 Surveillance and Human Behavioural Marketing

8.4.3 Identity Management

8.4.4 Chatbots

8.4.5 Healthcare

8.4.6 Wearable Wellbeing Monitors

8.4.7 Asset Monitoring and Maintenance

8.4.8 Monitoring Fake News on Social Media

8.4.9 Furniture Design

8.4.10 Engineering Design in Aeronautics

8.4.11 Self-Driving Vehicles

8.4.12 AI-enabled Smart Grids

8.5 Conclusion

9 AI-Based Evaluation to Assist Students Studying through Online Systems

9.1 Problem Description

9.2 The Online Learning Environment

9.2.1 Content Delivery Process.

9.2.2 Evaluation Process

9.3 Question and Answer Model

9.3.1 Most Widely-used Question Types

9.4 A Short Introduction to AI and Machine Learning

9.5 Selection of Machine Learning Algorithms to address our Problem

9.5.1 Reinforced Learning (RL)

9.6 Evaluation Process

9.6.1 Question Delivery

9.6.2 Question Attributes

9.7 Evaluator States and Actions

9.8 Implementation

9.8.1 Listing 1

9.8.2 Listing 2

9.8.3 Implementation Details

9.8.4 Testing the Evaluator

9.8.5 TestCase Output

9.9 Conclusion

10 Investigating Artificial Intelligence Usage for Revolution in E-Learning during COVID-19

10.1 Introduction

10.2 Review of Existing Literature

10.3 Objective of the Study

10.4 Research Methodology

10.5 Data Analysis and Discussion

10.6 Implications and Conclusion

10.7 Limitation and Future Scope

Acknowledgement

11 Employee Churn Management Using AI

11.1 Introduction

11.2 Proposed Methodology

11.2.1 Dataset Review

11.3 Model Building

11.3.1 Train Test Split

11.3.2 Model Building

11.3.3 Random Forest Classifier

11.3.4 XGBoost

11.4 Comparison

11.4.1 AUC-ROC Curve

11.5 Conclusion

12 Machine Learning: Beginning of a New Era in the Dominance of Statistical Methods of Forecasting

12.1 Introduction

12.2 Analyzing Prominent Studies

12.3 Tabulation of prominent studies forecasting Time Series Data using Machine Learnings Techniques

12.4 Conclusion

13 Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction

13.1 Introduction

13.2 Related Work

13.3 Working Principle of LSTM

13.4 Results and Simulations Study

13.4.1 Data Preparation

13.4.2 Performance Measure

13.5 Results and Discussion

13.6 Conclusion

References.

14 Ethical Issues Surrounding AI Applications.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781003125129

1003125123

9781000432114

1000432114

9781000432145

1000432149

OCLC:

1272998106