Artificial Intelligence and Machine Learning in Business Management: Concepts, Challenges, and Case Studies

✍ Scribed by Sandeep Kumar Panda (editor), Vaibhav Mishra (editor), R. Balamurali (editor), Ahmed A. Elngar (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 279
• Edition: 1
• Category: Library

✦ Synopsis

The focus of this book is to introduce Artificial Intelligence (AI) and Machine Learning (ML) technologies into the context of Business Management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers.

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. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, insurance and healthcare systems.

Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgements
Contributors
Editors
Chapter 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
Chapter 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
References
Chapter 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
Chapter 4: Artificial Intelligence and Machine Learning: Discovering New Ways of Doing Banking Business
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
References
Chapter 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
References
Chapter 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
References
Chapter 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
References
Chapter 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
References
Chapter 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
9.9 Conclusion
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
References
Chapter 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
References
Chapter 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
References
Chapter 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
References
Chapter 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
Chapter 14: Ethical Issues Surrounding AI Applications
14.1 Introduction
14.2 Ethical Issues with AI Applications
14.2.1 Power Imbalance
14.2.1.1 Existing Power Imbalance in Funding Agencies and Organizations Developing AI Solutions
14.2.1.2 Biased AI Solutions Amplifying Existing Power Imbalance in Society
14.2.2 Labour Issues
14.2.2.1 What Kinds of Jobs Are Most Likely to Be Impacted by the Threat of AI?
14.2.2.2 Can AI Actually Remove All Need for Human Intervention?
14.2.3 Privacy
14.2.4 Misinformation, Disinformation and Fake News
14.3 Approaches to Address Ethical Issues in AI
14.3.1 Algorithmic Approaches for Privacy Protection
14.3.2 Non-Algorithmic Approaches to Safeguard User Privacy
14.3.3 Approaches to Handle the Spread of Disinformation
14.3.4 Addressing Bias in AI Applications
14.3.5 Addressing Risk and Security Issues in AI Applications
14.3.6 Policy and Ethical Frameworks
References
Chapter 15: Semantic Data Extraction Using Video Analysis: An AI Analytical Perspective
15.1 Introduction
15.2 Video Analytics
15.3 Need for Video Analytics
15.4 The Workflow
15.4.1 Frame Extraction
15.4.2 Segmentation Model
15.4.3 Preprocessing
15.4.4 Feature Extraction
15.4.5 Object Localization
15.4.6 Character Segmentation
15.4.7 Boundary Extraction Using Horizontal and Vertical Projection
15.4.8 Connected Component Analysis (CCA)
15.4.9 Character Recognition
15.4.10 Collecting Training Dataset
15.4.11 Machine Learning Classifier
15.5 Future Enhancement
15.6 Applications
15.7 Healthcare
15.7.1 Smart Cities/Transportation
15.7.2 Security
15.8 Conclusion
References
Index