Intelligent Business Analytics: Harnessing the Power of Soft Computing for Data-Driven Insights explores the transformative role of soft computing methods in improving business analytics. It looks at how intelligent analytical methods can be applied to complex business data to derive meaningful insight. It focuses on integrating such intelligent technologies as neural networks, fuzzy logic, genetic algorithms, AI, machine learning, and deep learning, to data analytics. The book examines:
- Fundamental concepts of soft computing and its role in data analysis
- How neural networks and deep learning techniques can be used to analyze complex data patterns to make more accurate predictions
- Swarm intelligence techniques that can help identify hidden patterns in customer data and optimize marketing strategies
- Combining multiple soft computing techniques to create hybrid models for business recommendation systems
- Using machine learning techniques to analyze user behavior and brand interactions to provide personalized brand recommendations
- Advanced visualization techniques to interpret the complex results of soft computing models.
Other topics include predictive analytics, customer segmentation, real-time decision support systems, and soft computing to handle uncertainty, ambiguity, and dynamic data environments. Each chapter provides theory as well as an applied example, making the book a roadmap on how to leverage computational intelligence in diverse areas of business decision-making.
The book provides comprehensive and in-depth exploration of how soft computing techniques can be applied in the domain of business analytics. The book shows these techniques can be leveraged to extract valuable insights from vast and complex datasets to enable businesses to make data-driven decisions for improve competitive advantage.
1. Soft Computing Paradigms: A Gateway to Intelligent Data Analysis
2.
An Overview of Prophesying Line of Work through Networks and Deep Learning
3.
Machine Learning-Based Market Segmentation with Data Mining for Proficient
Data-Driven Business
4. Versatility of Neural Networks in Business Data
Analytics: Comprehensive Review and Future Directions
5. Neural Networks and
Deep Learning in Predictive Modelling
6. Swarm Intelligence in Customer
Segmentation
7. Data-Driven Insights for Decision-Making in the Stock Market
by Using Meta-Analyses
8. Hybrid Soft Computing Approaches for Business
Recommendation
9. Enhancing Personalized Brand Recommendations through
Machine Learning-Driven Analysis of User Behavior and Brand Interaction
10.
Advanced Visualization Techniques for Soft Computing Results
11. Embroilment
of Deep Learning in Business Analytics for Sustainable Growth
12. Application
of Soft Computing in Business Analytics: A Journey into Intelligent Data
Insights
13. Applications of Deep Learning Techniques in Businesses:
Challenges and Opportunities for Data Integration
14. Harnessing Artificial
Emotional Intelligence for the Improvement of Teaching Learning Process in
Digital Classroom
15. Education and Training Revolution: A Review on AR, VR,
and IoT Integration in Educational Perspective
16. Leveraging Artificial
Intelligence and Machine Learning for Enhancing Financial Inclusion
Opportunities, Challenges, and Ethical Considerations
Nitendra Kumar, PhD, is an assistant professor at the Amity Business School, Amity University, Noida, India.
Lakhwinder Kaur Dhillon, PhD, is an associate professor and specialized faculty member in corporate finance, strategic financial management, accounting for managers and international finance and Forex management at Amity University, Uttar Pradesh, India.
Mridul Dharwal, PhD, is a professor of economics in the Management Department, School of Business Studies, Sharda University, Uttar Pradesh, India.
Elena V. Korchagina, PhD, is a professor at Peter the Great St. Petersburg Polytechnic University, Institute of Industrial Management, Economics and Trade, St. Petersburg, Russia.
Vishal Jain, PhD, is an associate professor at Sharda University, Uttar Pradesh, India.
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