Del i serien Advances in Computational Collective Intelligence

Intelligent Business Analytics

Harnessing the Power of Soft Computing for Data-Driven Insights

Inbunden, Engelska, 2025

AvNitendra Kumar,Lakhwinder Kaur Dhillon,idul Dharwal,Elena Korchagina,Vishal Jain

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This book explores the transformative role of soft computing methods in increasing business analytics, providing a comprehensive look into how these advanced methods can be applied to complex business data for meaningful insights. Through the integration of neural network, fuzzy logic, genetic algorithms, artificial intelligence, machine learning, deep learning, and other innovative approaches, Intelligent Business Analytics: Harnessing the Power of Soft Computing for Data- Driven Insights presents a roadmap for leveraging computational intelligence in diverse areas of business decision- making.Readers will venture from predictive analytics and customer segmentation to real- time decision support systems and many other applications. Soft computing’s flexibility and applicability in the handling of uncertainty, ambiguity, and dynamic data environments shine throughout the book. Each chapter is created to be a base of theory and, at the same time, provide an applied example, so the book is appropriate for students, researchers, and professionals in the field.This book also discusses where the markets are heading and new applications that are in store for intelligent analytics to create a competitive advantage that also supports sustainable growth. At the end, this book is for those who want to learn more about using data-driven approaches and those who are ready to face the changes of the fast-evolving digital world.

Produktinformation

Utgivningsdatum2025-08-26
Mått178 x 254 x 21 mm
Vikt730 g
FormatInbunden
SpråkEngelska
SerieAdvances in Computational Collective Intelligence
Antal sidor293
FörlagTaylor & Francis Ltd
ISBN9781032751788

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Artificiell intelligens inom Data och IT
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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.

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