Artificial Intelligence and Machine Learning in Business Management

Table of contents

1. Cover
2. Half Title
3. Title Page
4. Copyright Page
5. Dedication
6. Contents
7. Preface
8. Acknowledgements
9. Contributors
10. Editors
11. 1 Artificial Intelligence in Marketing
    1. 1.1 Introduction
    2. 1.2 AI, ML and Data Science
    3. 1.3 AI and Marketing
    4. 1.4 Benefits and Detriments of Using AI in Marketing
       1. 1.4.1 Benefits
       2. 1.4.2 Detriments
          1. 1.4.2.1 Amazon Go (Caselet)
          2. 1.4.2.2 Technical Working of Amazon Go
          3. 1.4.2.3 Issues Related to Amazon Go Technology
    5. 1.5 Marketing Plan and AI’s Potential
    6. 1.6 Future
    7. References
12. 2 Consumer Insights through Retail Analytics
    1. 2.1 Introduction
    2. 2.2 What Value Does Analytics Bring to Retail?
    3. 2.3 Types of Customer Data used in Retail Analytics
    4. 2.4 Application of Consumer Data – Retail Analytics
    5. 2.5 Analytics in Retail Industry – How it Works
    6. 2.6 Metrics in Retail Industry
    7. 2.7 Analytics in Practice in Renowned Retail Organizations
    8. 2.8 Challenges and Pitfall – Retail Analytics
    9. 2.9 Way Ahead
    10. 2.10 Discussion Questions
    11. References
13. 3 Multi-Agent Paradigm for B2C E-Commerce
    1. 3.1 Business Perspective
       1. 3.1.1 Negotiation
          1. 3.1.1.1 Types of Agent-to-Agent Negotiations
          2. 3.1.1.2 Negotiation Strategies
          3. 3.1.1.3 Negotiation Types
       2. 3.1.2 Customer Relationship Management (CRM) and Customer Orientation (CO)
       3. 3.1.3 Broker and Brokering
       4. 3.1.4 Business Model
    2. 3.2 Computational Perspective
       1. 3.2.1 Multi-Agent System
          1. 3.2.1.1 Agent: Definition and Characteristics
          2. 3.2.1.2 Multi-agent Systems: Salient Features
       2. 3.2.2 Cognitive and Social Parameters
       3. 3.2.3 MAS Communication
       4. 3.2.4 Foundation for Intelligent Physical Agents (FIPA)
    3. 3.3 Machine Learning: Functions and Methods
       1. 3.3.1 Supervised and Unsupervised Learning
       2. 3.3.2 Decision Tree (DT)
       3. 3.3.3 Neural Network
       4. 3.3.4 Sensitivity Analysis (SA)
       5. 3.3.5 Feature Selection
    4. 3.4 Conclusion
    5. References
14. 4 Artificial Intelligence and Machine Learning: Discovering New Ways of Doing Banking Business
    1. Structure of the Chapter
    2. 4.1 Introduction
    3. 4.2 AI in the Banking Sector: Where It Works and What For
       1. 4.2.1 AI and Customer Service
          1. 4.2.1.1 Chatbots
          2. 4.2.1.2 AI and Personalized Banking
          3. 4.2.1.3 Smart Wallets
          4. 4.2.1.4 Voice Assisted Banking
          5. 4.2.1.5 Robo Advice
          6. 4.2.1.6 AI Backed Blockchain for Expedite Payments
       2. 4.2.2 AI and Magnifying Efficiency of Banks
          1. 4.2.2.1 Determining Credit Scoring and Lending Decisions
          2. 4.2.2.2 AI and CRM
       3. 4.2.3 Magnifying Security and Risk Control
          1. 4.2.3.1 Detection and Prevention of Financial Fraud
          2. 4.2.3.2 Reducing Money Laundering
          3. 4.2.3.3 Cybersecurity
          4. 4.2.3.4 AI: Managing and Controlling Risk
    4. 4.3 AI Applications in Indian Banks: Some Selected Examples
       1. 4.3.1 State Bank of India
       2. 4.3.2 HDFC Bank
       3. 4.3.3 Axis Bank
       4. 4.3.4 Punjab National Bank
    5. 4.4 AI and its Impact on Banks’ KPIs
       1. 4.4.1 Impact of AI on Profitability
       2. 4.4.2 Impact of AI on Productivity and Efficiency of Banks
       3. 4.4.3 Impact of AI on Improved Customer Satisfaction
       4. 4.4.4 AI Helps in Offering Innovative and Tailor-Made Services
       5. 4.4.5 AI Helps in Reducing Customer Attrition
       6. 4.4.6 Impact of AI on Overall Performance
    6. 4.5 Conclusion and Future of AI
    7. References
15. 5 Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning
    1. 5.1 Introduction
    2. 5.2 Related Work
    3. 5.3 Proposed Method
    4. 5.4 Results
    5. 5.5 Conclusion and Future Scope
    6. References
16. 6 Artificial Intelligence for All: Machine Learning and Healthcare: Challenges and Perspectives in India
    1. 6.1 Introduction
    2. 6.2 Healthcare in India: Challenges
    3. 6.3 Frameworks in Health must consider Missingness
       1. 6.3.1 Wellsprings of Missingness Must Be Painstakingly Comprehended
       2. 6.3.2 Incorporation of Missingness
       3. 6.3.3 Settle on Careful Choices in defining Outcomes
       4. 6.3.4 Comprehend the Result in the Setting of a Social Insurance Framework
       5. 6.3.5 Be Careful with Mark Spillage
    4. 6.4 Inclined Opportunities in Healthcare
       1. 6.4.1 Automating Clinical Errands during Determination and Treatment
       2. 6.4.2 Computerizing Clinical Picture Assessment
       3. 6.4.3 Robotizing Routine Procedures
       4. 6.4.4 Streamlining Clinical Choice and Practice Support
       5. 6.4.5 Normalizing Clinical Procedures
       6. 6.4.6 Incorporating Divided Records
       7. 6.4.7 Growing Medicinal Capacities: New Skylines in Screening, Analysis and Treatment
       8. 6.4.8 Growing the Inclusion of Proof
       9. 6.4.9 Moving towards Constant Social Checking
    5. 6.5 Population Protection (Crowd Surveillance)
    6. 6.6 Marketing Strategy
    7. 6.7 Population Screening
    8. 6.8 Patient Advocacy
    9. 6.9 Role of Machine Learning in Society
    10. 6.10 Ayushman Bharat: A Step Forward
    11. 6.11 The National E-Health Authority (Neha)
    12. 6.12 Cancer Screening and Machine Learning
    13. 6.13 “Sick” Care to “Health” Care: Moving Forward
    14. 6.14 Machine Learning and Healthcare Opportunities
        1. 6.14.1 Computerizing Clinical Assignments during Determination and Treatment
        2. 6.14.2 Robotizing Clinical Picture Assessment
        3. 6.14.3 Robotizing Routine Procedures
        4. 6.14.4 Clinical Support and Augmentation
        5. 6.14.5 Expanding Clinical Capacities
        6. 6.14.6 Precision Medicine for Early Individualized Treatment
        7. 6.14.7 Open Doors for Innovative Research
        8. 6.14.8 Adding Communication to AI and Assessment
        9. 6.14.9 Distinguishing Representations in a Large and Multi-source Network
    15. 6.15 Common Machine Learning Applications in Healthcare
        1. 6.15.1 Machine Learning Application in Drug Discovery
        2. 6.15.2 Neuroscience and Image Computing
        3. 6.15.3 Cloud Computing Frameworks in building Machine Learning-based Healthcare
        4. 6.15.4 Machine Learning in Personalized Healthcare
        5. 6.15.5 Machine Learning in Outbreak Prediction
        6. 6.15.6 Machine Learning in Patient Risk Stratification
        7. 6.15.7 Machine Learning in Telemedicine
        8. 6.15.8 Multimodal Machine Learning for Data Fusion in Medical Imaging
    16. 6.16 Incorporating Expectations and Learning Significant Portrayals for the Space
    17. 6.17 Conclusion
    18. References
17. 7 Demystifying the Capabilities of Machine Learning and Artificial Intelligence for Personalized Care
    1. 7.1 Introduction
    2. 7.2 Temporal Displacement of Care
    3. 7.3 AI/ML use in Healthcare
    4. 7.4 Wearable Health Devices
    5. 7.5 Conclusion
    6. References
18. 8 Artificial Intelligence and the 4th Industrial Revolution
    1. 8.1 Introduction
    2. 8.2 The Industrial Revolutions
    3. 8.3 The Technologies of the 4th Industrial Revolution
       1. 8.3.1 Internet of Things
       2. 8.3.2 4th Industrial Revolution: New Technologies
       3. 8.3.3 Machine Learning and Artificial Intelligence
       4. 8.3.4 Internet of Things, Microelectro-sensors and Biosensor Tech
       5. 8.3.5 Robotics
       6. 8.3.6 Virtual Reality, Augmented Reality and Mixed Reality
       7. 8.3.7 3D Printing and Additive Manufacturing
       8. 8.3.8 Neuromorphic Computing
       9. 8.3.9 Biochips
    4. 8.4 AI Applications in the 4th Industrial Revolution
       1. 8.4.1 Gaming Industry
       2. 8.4.2 Surveillance and Human Behavioural Marketing
       3. 8.4.3 Identity Management
       4. 8.4.4 Chatbots
       5. 8.4.5 Healthcare
       6. 8.4.6 Wearable Wellbeing Monitors
       7. 8.4.7 Asset Monitoring and Maintenance
       8. 8.4.8 Monitoring Fake News on Social Media
       9. 8.4.9 Furniture Design
       10. 8.4.10 Engineering Design in Aeronautics
       11. 8.4.11 Self-Driving Vehicles
       12. 8.4.12 AI-enabled Smart Grids
    5. 8.5 Conclusion
    6. References
19. 9 AI-Based Evaluation to Assist Students Studying through Online Systems
    1. 9.1 Problem Description
    2. 9.2 The Online Learning Environment
       1. 9.2.1 Content Delivery Process
       2. 9.2.2 Evaluation Process
    3. 9.3 Question and Answer Model
       1. 9.3.1 Most Widely-used Question Types
    4. 9.4 A Short Introduction to AI and Machine Learning
    5. 9.5 Selection of Machine Learning Algorithms to address our Problem
       1. 9.5.1 Reinforced Learning (RL)
    6. 9.6 Evaluation Process
       1. 9.6.1 Question Delivery
       2. 9.6.2 Question Attributes
    7. 9.7 Evaluator States and Actions
    8. 9.8 Implementation
       1. 9.8.1 Listing 1
       2. 9.8.2 Listing 2
       3. 9.8.3 Implementation Details
       4. 9.8.4 Testing the Evaluator
       5. 9.8.5 TestCase Output
    9. 9.9 Conclusion
    10. References
20. 10 Investigating Artificial Intelligence Usage for Revolution in E-Learning during COVID-19
    1. 10.1 Introduction
    2. 10.2 Review of Existing Literature
    3. 10.3 Objective of the Study
    4. 10.4 Research Methodology
    5. 10.5 Data Analysis and Discussion
    6. 10.6 Implications and Conclusion
    7. 10.7 Limitation and Future Scope
    8. Acknowledgement
    9. References
21. 11 Employee Churn Management Using AI
    1. 11.1 Introduction
    2. 11.2 Proposed Methodology
       1. 11.2.1 Dataset Review
    3. 11.3 Model Building
       1. 11.3.1 Train Test Split
       2. 11.3.2 Model Building
       3. 11.3.3 Random Forest Classifier
       4. 11.3.4 XGBoost
    4. 11.4 Comparison
       1. 11.4.1 AUC–ROC Curve
    5. 11.5 Conclusion
    6. References
22. 12 Machine Learning: Beginning of a New Era in the Dominance of Statistical Methods of Forecasting
    1. 12.1 Introduction
    2. 12.2 Analyzing Prominent Studies
    3. 12.3 Tabulation of prominent studies forecasting Time Series Data using Machine Learnings Techniques
    4. 12.4 Conclusion
    5. References
23. 13 Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction
    1. 13.1 Introduction
    2. 13.2 Related Work
    3. 13.3 Working Principle of LSTM
    4. 13.4 Results and Simulations Study
       1. 13.4.1 Data Preparation
       2. 13.4.2 Performance Measure
    5. 13.5 Results and Discussion
    6. 13.6 Conclusion
    7. References
24. 14 Ethical Issues Surrounding AI Applications
    1. 14.1 Introduction
    2. 14.2 Ethical Issues with AI Applications
       1. 14.2.1 Power Imbalance
          1. 14.2.1.1 Existing Power Imbalance in Funding Agencies and Organizations Developing AI Solutions
          2. 14.2.1.2 Biased AI Solutions Amplifying Existing Power Imbalance in Society
       2. 14.2.2 Labour Issues
          1. 14.2.2.1 What Kinds of Jobs Are Most Likely to Be Impacted by the Threat of AI?
          2. 14.2.2.2 Can AI Actually Remove All Need for Human Intervention?
       3. 14.2.3 Privacy
       4. 14.2.4 Misinformation, Disinformation and Fake News
    3. 14.3 Approaches to Address Ethical Issues in AI
       1. 14.3.1 Algorithmic Approaches for Privacy Protection
       2. 14.3.2 Non-Algorithmic Approaches to Safeguard User Privacy
       3. 14.3.3 Approaches to Handle the Spread of Disinformation
       4. 14.3.4 Addressing Bias in AI Applications
       5. 14.3.5 Addressing Risk and Security Issues in AI Applications
       6. 14.3.6 Policy and Ethical Frameworks
    4. References
25. 15 Semantic Data Extraction Using Video Analysis: An AI Analytical Perspective
    1. 15.1 Introduction
    2. 15.2 Video Analytics
    3. 15.3 Need for Video Analytics
    4. 15.4 The Workflow
       1. 15.4.1 Frame Extraction
       2. 15.4.2 Segmentation Model
       3. 15.4.3 Preprocessing
       4. 15.4.4 Feature Extraction
       5. 15.4.5 Object Localization
       6. 15.4.6 Character Segmentation
       7. 15.4.7 Boundary Extraction Using Horizontal and Vertical Projection
       8. 15.4.8 Connected Component Analysis (CCA)
       9. 15.4.9 Character Recognition
       10. 15.4.10 Collecting Training Dataset
       11. 15.4.11 Machine Learning Classifier
    5. 15.5 Future Enhancement
    6. 15.6 Applications
    7. 15.7 Healthcare
       1. 15.7.1 Smart Cities/Transportation
       2. 15.7.2 Security
    8. 15.8 Conclusion
    9. References
26. Index