Computational Intelligence for Modern Business Systems: Emerging Applications and Strategies

✍ Scribed by Sandeep Kautish, Prasenjit Chatterjee, Dragan Pamucar, N. Pradeep, Deepmala Singh

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 523
• Series: Disruptive Technologies and Digital Transformations for Society 5.0
• Edition: 1
• Category: Library

✦ Synopsis

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations. The book redefines the computational intelligence foundations, the three pillars - neural networks, evolutionary computation, and fuzzy systems. It also discusses emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, and chaotic systems. The other areas have also been demystified in the book to strengthen the range of computational intelligence techniques such as expert systems, knowledge-based systems, and genetic algorithms. Therefore, this book will redefine the role of computational intelligence techniques in modern business system operations such as marketing, finance & accounts, operations, personnel management, supply chain management, and logistics. Besides, this book guides the readers through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone in various business system operations. This book reveals how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. The book will provide insights into research gaps, open challenges, and unsolved computational intelligence problems. The book will act as a premier reference and instant material for all the users who are contributing/practicing the adaptation of computational intelligence modern techniques in business systems.

✦ Table of Contents

Preface
Acknowledgements
Contents
Editors and Contributors
Part I Computational Intelligence for Business Finance Applications
1 Artificial Intelligence and Machine Learning in Financial Services to Improve the Business System
1 Introduction
1.1 Organization of the Chapter
2 Motivation
2.1 Challenges
3 Background Study
3.1 Role of Data Science in the Finance Sector
3.2 Benefits and Issues of Artificial Intelligence in Finance Sectors
3.3 Datasets Used in Financial Applications
3.4 How Artificial Intelligence (AI) is Changing the Financial Services Industry?
4 Role of AI in the Finance Sector
4.1 Financial Distress in Finance Sector Using Artificial Intelligence
4.2 Prediction of Credit Card Risk in the Finance Sector Using Artificial Intelligence
4.3 Sentiment Analysis in the Finance Sector Using Artificial Intelligence
4.4 Algorithm Trading for Finance Traders Using Artificial Intelligence
4.5 Prediction of Stock Price Indexing
5 Comparative Analysis
6 Conclusion
References
2 Covid-19 Related Ramifications on Financial Market: A Qualitative Study of the Pandemic’s Effects on the Stock Exchange of Big Technology Companies
1 Introduction
2 Related Work
3 Methodology
3.1 Architecture
3.2 Data Representation
3.3 Additional Variables
3.4 Linear Regression Forecasting
3.5 Correlation Between Google, Apple, General Electric, IBM & Microsoft
4 Findings and Discussions
4.1 Pre-Covid-19 (1 Jan 2015 to 1 April 2020)
4.2 During Covid-19 (1 Jan 2020 to 1 May 2020)
4.3 Post-Covid-19 Dataset (1 Nov 2020 to 19 Nov 2021)
5 Conclusion
References
3 Computational Intelligence Techniques for Behavioral Research on the Analysis of Investment Decisions in the Commercial Realty Market
1 Introduction
2 Need of Investing in the Realty Market
3 Investment in Real Estate
4 Literature Survey
5 Methodology
6 The Connection Between Behavioral Factors and Realty Market Performance
7 Data Analysis
8 Conclusion
9 Future Scope
References
4 Trust the Machine and Embrace Artificial Intelligence (AI) to Combat Money Laundering Activities
1 Introduction
2 Background
2.1 History of Money Laundering
2.2 Anti-money Laundering Policies
2.3 AML Frameworks
3 Various Artificial Intelligence (AI) Technologies for AML/CFT Compliance
3.1 AI and Machine Learning (ML)
3.2 Natural Language Processing (NLP) and Soft Computing Techniques
3.3 Robotic Process Automation
3.4 Cloud-Based Solutions
4 Challenges of Using AI to Detect Money Laundering
5 Distributed Ledger Technology (DLT) to Prevent Money Laundering
6 Key Research Contributions by the Researcher
7 Discussion and Future Work
8 Conclusion
References
5 Predictive Analysis of Crowdfunding Projects
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Datasets and Preprocessing
4.1 Understanding the Datasets
4.2 Data Preprocessing
4.3 Project Attributes
5 Keyword Extraction Using Natural Language Processing
5.1 Methodology
5.2 Implementation
6 Classification of Projects Using Machine Learning
6.1 Results and Discussion
7 Prediction of Estimated Project Funding
8 Conclusion and Future Work
References
6 Stock Prediction Using Multi Deep Learning Algorithms
1 Introduction
2 Related Works
3 Methodology
3.1 The Proposed Model
3.2 Data Collection
3.3 Preprocessing
3.4 Training
4 Experiments
4.1 Dataset
5 Conclusion
References
7 House Price Prediction by Machine Learning Technique—An Empirical Study
1 Introduction
2 Background
3 Objectives
4 Methodology
4.1 Preprocessing of Data
4.2 Analysis of Data
4.3 Feature Engineering
4.4 Predictive Modeling
4.5 Methodology
5 Proposed Model
6 Analysis and Findings
7 Future Work
8 Conclusion
References
Part II Computational Intelligence for Marketing, Business Process and Human Resource Applications
8 SDN-Based Network Resource Management
1 Introduction
2 Literature Review
2.1 Edge Computing Resources Allocation via Auction Models
2.2 Use of Software Defined Networking
2.3 Blockchain Technology
2.4 Basis for the SDN-Based Broker Implementation
3 Auction Model
3.1 Negotiation Process
3.2 SDN-Based Broker
3.3 Blockchain for Registering Smart Contracts
3.4 Bidding Strategies
4 Simulations
5 Conclusions
6 Future Work
References
9 The Future of Digital Marketing: How Would Artificial Intelligence Change the Directions?
1 Introduction
2 Digital Marketing Today
3 AI and Its Capability
4 Applications of AI in Digital Marketing
4.1 Online Chatbots
4.2 AI-Assisted Customer Insights
4.3 Product Recommenders
4.4 Augmented Retailing
4.5 Market Segmentation
4.6 Market Targeting
4.7 Marketing Mix Decisions
4.8 Enhanced e-Mail Marketing
4.9 Digital Advertising
4.10 AI-Enabled Website Builders
4.11 New Online Retailing Model
4.12 Analyzing Online Customer Engagement Behavior Data
4.13 Enhancing Online Pricing Strategies
4.14 Emotional Support to Customers
5 Research Opportunities in AI-Driven Digital Marketing
5.1 Future Marketing Jobs and Skills
5.2 Change in Consumer Decision Making
5.3 AI-Driven Social Media Marketing
5.4 Privacy and Data Security
5.5 AI-Driven New Product Development
5.6 Enhanced Recommender Engine (RE)
5.7 AI-Driven Brand Positioning
5.8 Better Comprehension of Augmented Reality Marketing (ARM)
5.9 Tracking Consumer Five Senses
5.10 Integrating Psychological Theories to Sentiment Mining
6 Conclusion
References
10 Business Process Reengineering in Public Sector: A Case Study of World Book Fair
1 Background
1.1 National Book Trust, India
1.2 Publishing Industry
1.3 Book Fairs
1.4 The World Book Fair (WBF)
1.5 India Trade Promotion Organization (ITPO)
2 The Dilemma of Converting the WBF from Biennial to Annual Mode
2.1 The Predicament of the Director
2.2 Consultative Process
2.3 Identification of Factors for Evaluation of Venue Options
2.4 Idea Generation Stage
3 Collaborative Exercise with ITPO by NBT Administration
4 Business Process Re-engineering Interventions in NBT
4.1 Lack of Coordination Between Government Agencies
4.2 Nature of Interventions
4.3 Leveraging the BPR Interventions
5 Change in the Perception
6 Teaching Note
References
11 Improved Machine Learning Prediction Framework for Employees with a Focus on Function Selection
1 Introduction
2 Selection of Feature During Pre-processing
2.1 Maximum is Known Selection Characteristic Data
2.2 Example of Illustrative 1-Maximum-Out
3 Confidence Analysis Parameter
4 Data Retrogression
5 Chronology
5.1 Selection of Feature
5.2 Model Final
5.3 Analysis of Confidence Parameters
6 Findings
References
12 Applications of Data Science and Artificial Intelligence Methodologies in Customer Relationship Management
1 Introduction
2 Literature Review
3 Customer Relationship Management (CRM)
4 Data Mining (DM)
4.1 Types of Data Mining
4.2 Steps in Data Mining
4.3 Important Data Mining Techniques
4.4 Advantages of Data Mining
5 Application of Datamining in Customer Relationship Management
5.1 Customer Segmentation
5.2 Click Stream Analysis
5.3 Customer Profitability
5.4 Marketing Recommendations
5.5 Targeted Marketing
6 Summary and Conclusion
References
13 AI Integrated Human Resource Management for Smart Decision in an Organization
1 Introduction
2 Related Work
3 Methodology
4 Results and Discussions
5 Conclusion
References
14 A q-ROF Based Intelligent Framework for Exploring the Interface Among the Variables of Culture Shock and Adoption Toward Organizational Effectiveness
1 Introduction
2 Literature Review
3 Preliminaries
4 Research Methodology
4.1 Force Field Analysis (FFA)
4.2 LBWA Method
4.3 The Proposed QROF-FFA Method
5 Results and Discussions
6 Research Implications
7 Conclusion and Future Direction
Appendix 1: Questionnaire
Appendix 2: Response Sheet
References
15 Personality Prediction System to Improve Employee Recruitment
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 The Big Five Model
5 Predicting Overall Personality Using Machine Learning
6 Keyword Extraction Using Natural Language Processing
7 Fuzzy Logic Interpretation
7.1 Openness to Experience
7.2 Conscientiousness
7.3 Extraversion
7.4 Agreeableness
7.5 Neuroticism
8 Result—Candidate Personality Profile
9 Conclusion and Future Work
References
Part III Computational Intelligence for Operational Excellence, Supply Chain and Project Management
16 Towards Operation Excellence in Automobile Assembly Analysis Using Hybrid Image Processing
1 Introduction
2 Motivation
3 Dataset
4 Proposed Methodology
4.1 Image Segmentation
4.2 Designed GUI for the Segmentation Task
5 Conclusions
References
17 Industry Revolution 4.0: From Industrial Automation to Industrial Autonomy
1 Introduction
1.1 Current Scenario of IR 4.0: Literature Survey
2 Structure of Industry 4.0
2.1 Industrial Automation and Monitoring Principles and Objectives
2.2 Reference-Architecture-Model-Industry 4.0 (RAMI 4.0)
2.3 Industry 4.0 Advantages and Disadvantages
3 Building Block Technologies of IR 4.0 Overview
3.1 Artificial Intelligence (AI)
3.2 Blockchain
3.3 Big Data/Statistics
3.4 Cloud
3.5 IoT
3.6 Cyber Security
3.7 Virtual Reality
3.8 Robotics and Automation
3.9 3D Printing
3.10 Simulation
3.11 System Integration
4 IR 4.0: BIM (Building Information Modeling)
5 IR 4.0: Smart Factories
5.1 Default and Robots
5.2 Monitoring and Control
5.3 Imitation
5.4 Artificial Intelligence
6 IR 4.0: Digitalization of Industries
7 IR 4.0: Automation to Autonomy
8 IR 4.0: Implementing Total Production Maintenance (TPM)
9 IR 4.0: Smart Factory Analytics SaaS Implementation
10 IR 4.0 Adoption Strategy
11 Recommendation for HR for a Successful Transition to Industry 4.0
12 IR 4.0 Global Trades and Investment
13 Conclusion and Future Work
13.1 Discussion
13.2 Comparison
13.3 Concluding Remarks
13.4 Future Work
References
18 Artificial Intelligence and Automation for Industry 4.0
1 Introduction
2 Application of AI and Automation in Business
2.1 Product Optimization
2.2 Market Adaptation
2.3 Product Development
3 Application of Artificial Intelligence and Automation in Other Fields
3.1 Artificial Intelligence and Technology
3.2 Artificial Intelligence and Agriculture
3.3 Artificial Intelligence and Manufacturing
3.4 Artificial Intelligence and Social Justice
3.5 Artificial Intelligence and Ethics
3.6 Artificial Intelligence and Government
4 Artificial Intelligence and Industry 4.0
5 Ideologies of Artificial Intelligence
5.1 Automation
5.2 Optimization
6 Suggestions
7 Conclusion
References
19 Process of Combined Thinking for Long-Term Sourcing
1 Introduction
2 Literature Review
3 Research Design
3.1 Theory of Constraints (TOC)
3.2 Six Sigma (SS)
3.3 Lean Thinking (LT)
3.4 Integrated TOC-Six Sigma-Lean Methodology
4 Application, Result, and Discussion
4.1 Determine the Value of the Constraint and the Process that Has to Be Improved
4.2 Exploit the Constraint by Measuring and Analyzing the Value
4.3 Increase the Limitations’ Severity
4.4 Work Toward Perfection and Keep an Eye Out for the Next Set of Constraints
5 Sensitivity Analysis
6 Conclusion
References
20 Technological Reforms of Global Projects Using Artificial Intelligence
1 Introduction
1.1 “Artificial Intelligence is Self-evolving”
2 Artificial Intelligence Emerging Projects
2.1 AI in the Health Sector
2.2 Technology Reforms and Cyber Security
2.3 Automotive AI
2.4 AI in Scientific Searches
2.5 AI in Marketing
3 Conclusion
References
21 Choosing the Optimal Route for a Delivery Vehicle in X Express Company Using Clarke and Wright Algorithm
1 Introduction
2 About X Express and Used Algorithm
3 Using the Clark and Wright Algorithm on a Practical Example
3.1 Constructing Elementary Routes and Defining Their Lengths
3.2 Possible Savings
3.3 Merging Routes and the Final Solution
4 Conclusion and Discussion
References
22 Diet and Food Restaurant in the Covid-19 Time by Machine Learning Approaches
1 Introduction
2 Background Study
3 Methodologies
4 Result and Analysis
4.1 Processing of Data
4.2 Data Normalization (DN)
4.3 Data Noise Label Normalization (D-N-L-N)
4.4 Creating ML Model
4.5 Compare Covid-19 Versus Diet
4.6 Supervised Approaches
4.7 Principal Component Analysis (PCA)
4.8 Restaurant Business Analysis
4.9 YOY Sales Indicator
4.10 Category Sales Indicator
4.11 Top Restaurant in Best Category
4.12 Is Corona Infection Linked with Diet?
5 Conclusion
References
23 Crowd Counting via De-background Multicolumn Dynamic Convolutional Neural Network
1 Introduction
2 Literature Review
2.1 Taxonomy of Vision-Based Crowd Counting Approaches
2.2 A Brief Review of CNN-Based Crowd Counting Approaches
3 Proposed Model
3.1 Architecture Details
3.2 De-background Multicolumn Feature Extraction
3.3 Multiscale De-background Feature Fusion and Crowd Density Estimation
4 Dataset and Performance Metrics
5 Experiment Setup and Results Analysis
5.1 Results Analysis on the UCSD Dataset
5.2 Results Analysis on the Mall Dataset
6 Ablation Study
7 Conclusion
References
24 Critical Factors and Their Relationship Affecting Bundling Practices in Indian Retail Industries: An AHP Approach
1 Introduction
2 Literature Review
3 Methodology
4 Results Analysis
5 Calculation of Inconsistency Ratio
6 Discussion
7 Conclusion and Future Work
References
25 Decision Support System Modelling and Analysis for Sustainable Smart Supply Chain Network
1 Introduction
2 Mathematical Modelling
2.1 Supply Chain Network Analysis Using Probabilistic Adjacency Matrix
2.2 Intra-node Competition Model in the Supply Chain Network
3 Implementation of the Model Developed
4 Case Study—In an Orthopedic Footwear Manufacturer Perspective
4.1 Description of the Problem
4.2 Inherent Risk in the Supply Chain Network
5 Results and Discussions
6 Conclusion
References
26 Reverse Logistics: An Approach for Sustainable Development
1 Introduction
1.1 Literature
2 Methodology
2.1 TOPSIS Method
2.2 Selection of Variables for Performance-Based Sustainable Manufacturing
3 Results and Discussion
4 Conclusion
References
27 Applications of Artificial Intelligence in Public Procurement—Case Study of Nigeria
1 Introduction
2 Review of Literature
2.1 Artificial Intelligence
2.2 AI in Public Governance
2.3 AI in Government Contracting
3 Applications of AI in Government Contracting
4 Benefits of Leveraging AI in Public Procurement
5 Limitations and Recommendations for Implementing AI in Nigerian Public Procurement
6 Conclusion
References