book

Artificial Intelligence and Machine Learning in Business Management

Name: Artificial Intelligence and Machine Learning in Business Management
ISBN: 9781000432145

by Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar

November 2021

Beginner to intermediate

278 pages

8h 40m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgements
Contributors
Editors

1 Artificial Intelligence in Marketing
1.1 Introduction1.2 AI, ML and Data Science1.3 AI and Marketing1.4 Benefits and Detriments of Using AI in Marketing1.4.1 Benefits1.4.2 Detriments1.4.2.1 Amazon Go (Caselet)1.4.2.2 Technical Working of Amazon Go1.4.2.3 Issues Related to Amazon Go Technology1.5 Marketing Plan and AI’s Potential1.6 FutureReferences
2 Consumer Insights through Retail Analytics
2.1 Introduction2.2 What Value Does Analytics Bring to Retail?2.3 Types of Customer Data used in Retail Analytics2.4 Application of Consumer Data – Retail Analytics2.5 Analytics in Retail Industry – How it Works2.6 Metrics in Retail Industry2.7 Analytics in Practice in Renowned Retail Organizations2.8 Challenges and Pitfall – Retail Analytics2.9 Way Ahead2.10 Discussion QuestionsReferences
3 Multi-Agent Paradigm for B2C E-Commerce
3.1 Business Perspective3.1.1 Negotiation3.1.1.1 Types of Agent-to-Agent Negotiations3.1.1.2 Negotiation Strategies3.1.1.3 Negotiation Types3.1.2 Customer Relationship Management (CRM) and Customer Orientation (CO)3.1.3 Broker and Brokering3.1.4 Business Model3.2 Computational Perspective3.2.1 Multi-Agent System3.2.1.1 Agent: Definition and Characteristics3.2.1.2 Multi-agent Systems: Salient Features3.2.2 Cognitive and Social Parameters3.2.3 MAS Communication3.2.4 Foundation for Intelligent Physical Agents (FIPA)3.3 Machine Learning: Functions and Methods3.3.1 Supervised and Unsupervised Learning3.3.2 Decision Tree (DT)3.3.3 Neural Network3.3.4 Sensitivity Analysis (SA)3.3.5 Feature Selection3.4 ConclusionReferences
4 Artificial Intelligence and Machine Learning: Discovering New Ways of Doing Banking Business
Structure of the Chapter4.1 Introduction4.2 AI in the Banking Sector: Where It Works and What For4.2.1 AI and Customer Service4.2.1.1 Chatbots4.2.1.2 AI and Personalized Banking4.2.1.3 Smart Wallets4.2.1.4 Voice Assisted Banking4.2.1.5 Robo Advice4.2.1.6 AI Backed Blockchain for Expedite Payments4.2.2 AI and Magnifying Efficiency of Banks4.2.2.1 Determining Credit Scoring and Lending Decisions4.2.2.2 AI and CRM4.2.3 Magnifying Security and Risk Control4.2.3.1 Detection and Prevention of Financial Fraud4.2.3.2 Reducing Money Laundering4.2.3.3 Cybersecurity4.2.3.4 AI: Managing and Controlling Risk4.3 AI Applications in Indian Banks: Some Selected Examples4.3.1 State Bank of India4.3.2 HDFC Bank4.3.3 Axis Bank4.3.4 Punjab National Bank4.4 AI and its Impact on Banks’ KPIs4.4.1 Impact of AI on Profitability4.4.2 Impact of AI on Productivity and Efficiency of Banks4.4.3 Impact of AI on Improved Customer Satisfaction4.4.4 AI Helps in Offering Innovative and Tailor-Made Services4.4.5 AI Helps in Reducing Customer Attrition4.4.6 Impact of AI on Overall Performance4.5 Conclusion and Future of AIReferences
5 Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning
5.1 Introduction5.2 Related Work5.3 Proposed Method5.4 Results5.5 Conclusion and Future ScopeReferences
6 Artificial Intelligence for All: Machine Learning and Healthcare: Challenges and Perspectives in India
6.1 Introduction6.2 Healthcare in India: Challenges6.3 Frameworks in Health must consider Missingness6.3.1 Wellsprings of Missingness Must Be Painstakingly Comprehended6.3.2 Incorporation of Missingness6.3.3 Settle on Careful Choices in defining Outcomes6.3.4 Comprehend the Result in the Setting of a Social Insurance Framework6.3.5 Be Careful with Mark Spillage6.4 Inclined Opportunities in Healthcare6.4.1 Automating Clinical Errands during Determination and Treatment6.4.2 Computerizing Clinical Picture Assessment6.4.3 Robotizing Routine Procedures6.4.4 Streamlining Clinical Choice and Practice Support6.4.5 Normalizing Clinical Procedures6.4.6 Incorporating Divided Records6.4.7 Growing Medicinal Capacities: New Skylines in Screening, Analysis and Treatment6.4.8 Growing the Inclusion of Proof6.4.9 Moving towards Constant Social Checking6.5 Population Protection (Crowd Surveillance)6.6 Marketing Strategy6.7 Population Screening6.8 Patient Advocacy6.9 Role of Machine Learning in Society6.10 Ayushman Bharat: A Step Forward6.11 The National E-Health Authority (Neha)6.12 Cancer Screening and Machine Learning6.13 “Sick” Care to “Health” Care: Moving Forward6.14 Machine Learning and Healthcare Opportunities6.14.1 Computerizing Clinical Assignments during Determination and Treatment6.14.2 Robotizing Clinical Picture Assessment6.14.3 Robotizing Routine Procedures6.14.4 Clinical Support and Augmentation6.14.5 Expanding Clinical Capacities6.14.6 Precision Medicine for Early Individualized Treatment6.14.7 Open Doors for Innovative Research6.14.8 Adding Communication to AI and Assessment6.14.9 Distinguishing Representations in a Large and Multi-source Network6.15 Common Machine Learning Applications in Healthcare6.15.1 Machine Learning Application in Drug Discovery6.15.2 Neuroscience and Image Computing6.15.3 Cloud Computing Frameworks in building Machine Learning-based Healthcare6.15.4 Machine Learning in Personalized Healthcare6.15.5 Machine Learning in Outbreak Prediction6.15.6 Machine Learning in Patient Risk Stratification6.15.7 Machine Learning in Telemedicine6.15.8 Multimodal Machine Learning for Data Fusion in Medical Imaging6.16 Incorporating Expectations and Learning Significant Portrayals for the Space6.17 ConclusionReferences
7 Demystifying the Capabilities of Machine Learning and Artificial Intelligence for Personalized Care
7.1 Introduction7.2 Temporal Displacement of Care7.3 AI/ML use in Healthcare7.4 Wearable Health Devices7.5 ConclusionReferences
8 Artificial Intelligence and the 4th Industrial Revolution
8.1 Introduction8.2 The Industrial Revolutions8.3 The Technologies of the 4th Industrial Revolution8.3.1 Internet of Things8.3.2 4th Industrial Revolution: New Technologies8.3.3 Machine Learning and Artificial Intelligence8.3.4 Internet of Things, Microelectro-sensors and Biosensor Tech8.3.5 Robotics8.3.6 Virtual Reality, Augmented Reality and Mixed Reality8.3.7 3D Printing and Additive Manufacturing8.3.8 Neuromorphic Computing8.3.9 Biochips8.4 AI Applications in the 4th Industrial Revolution8.4.1 Gaming Industry8.4.2 Surveillance and Human Behavioural Marketing8.4.3 Identity Management8.4.4 Chatbots8.4.5 Healthcare8.4.6 Wearable Wellbeing Monitors8.4.7 Asset Monitoring and Maintenance8.4.8 Monitoring Fake News on Social Media8.4.9 Furniture Design8.4.10 Engineering Design in Aeronautics8.4.11 Self-Driving Vehicles8.4.12 AI-enabled Smart Grids8.5 ConclusionReferences
9 AI-Based Evaluation to Assist Students Studying through Online Systems
9.1 Problem Description9.2 The Online Learning Environment9.2.1 Content Delivery Process9.2.2 Evaluation Process9.3 Question and Answer Model9.3.1 Most Widely-used Question Types9.4 A Short Introduction to AI and Machine Learning9.5 Selection of Machine Learning Algorithms to address our Problem9.5.1 Reinforced Learning (RL)9.6 Evaluation Process9.6.1 Question Delivery9.6.2 Question Attributes9.7 Evaluator States and Actions9.8 Implementation9.8.1 Listing 19.8.2 Listing 29.8.3 Implementation Details9.8.4 Testing the Evaluator9.8.5 TestCase Output9.9 ConclusionReferences
10 Investigating Artificial Intelligence Usage for Revolution in E-Learning during COVID-19
10.1 Introduction10.2 Review of Existing Literature10.3 Objective of the Study10.4 Research Methodology10.5 Data Analysis and Discussion10.6 Implications and Conclusion10.7 Limitation and Future ScopeAcknowledgementReferences
11 Employee Churn Management Using AI
11.1 Introduction11.2 Proposed Methodology11.2.1 Dataset Review11.3 Model Building11.3.1 Train Test Split11.3.2 Model Building11.3.3 Random Forest Classifier11.3.4 XGBoost11.4 Comparison11.4.1 AUC–ROC Curve11.5 ConclusionReferences
12 Machine Learning: Beginning of a New Era in the Dominance of Statistical Methods of Forecasting
12.1 Introduction12.2 Analyzing Prominent Studies12.3 Tabulation of prominent studies forecasting Time Series Data using Machine Learnings Techniques12.4 ConclusionReferences
13 Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction
13.1 Introduction13.2 Related Work13.3 Working Principle of LSTM13.4 Results and Simulations Study13.4.1 Data Preparation13.4.2 Performance Measure13.5 Results and Discussion13.6 ConclusionReferences
14 Ethical Issues Surrounding AI Applications
14.1 Introduction14.2 Ethical Issues with AI Applications14.2.1 Power Imbalance14.2.1.1 Existing Power Imbalance in Funding Agencies and Organizations Developing AI Solutions14.2.1.2 Biased AI Solutions Amplifying Existing Power Imbalance in Society14.2.2 Labour Issues14.2.2.1 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 Privacy14.2.4 Misinformation, Disinformation and Fake News14.3 Approaches to Address Ethical Issues in AI14.3.1 Algorithmic Approaches for Privacy Protection14.3.2 Non-Algorithmic Approaches to Safeguard User Privacy14.3.3 Approaches to Handle the Spread of Disinformation14.3.4 Addressing Bias in AI Applications14.3.5 Addressing Risk and Security Issues in AI Applications14.3.6 Policy and Ethical FrameworksReferences
15 Semantic Data Extraction Using Video Analysis: An AI Analytical Perspective
15.1 Introduction15.2 Video Analytics15.3 Need for Video Analytics15.4 The Workflow15.4.1 Frame Extraction15.4.2 Segmentation Model15.4.3 Preprocessing15.4.4 Feature Extraction15.4.5 Object Localization15.4.6 Character Segmentation15.4.7 Boundary Extraction Using Horizontal and Vertical Projection15.4.8 Connected Component Analysis (CCA)15.4.9 Character Recognition15.4.10 Collecting Training Dataset15.4.11 Machine Learning Classifier15.5 Future Enhancement15.6 Applications15.7 Healthcare15.7.1 Smart Cities/Transportation15.7.2 Security15.8 ConclusionReferences
Index

Content preview from Artificial Intelligence and Machine Learning in Business Management

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

Minakhi Rout, Dhiraj Bhattarai, and Ajay Kumar Jena

School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be) University, Bhubaneswar, Odisha

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

13.1 Introduction

The prediction of forex data is a crucial and challenging task in short- and long-run financial decision making. The accurate prediction of foreign currency exchange rate can be beneficial to the financial institutions to deal with global import and export ...

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Artificial Intelligence and Machine Learning in Industry

Publisher Resources

ISBN: 9781000432145

Cloud Computing

Data Engineering

Data Science

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IT/Ops

Security

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Business

Soft Skills

Artificial Intelligence and Machine Learning in Business Management

by Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar

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

Contents

13.1 Introduction

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