There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.
Inhaltsverzeichnis
Editors
Contributors
Part I INTRODUCTION
1 Computational Intelligence Paradigms in Business Intelligence and Analytics
Part II COMPUTAT IONAL INTELLIGENCE IN BUSINESS INTELLIGENCE AND ANALYTICS
2 Conditional Value at Risk-Based Portfolio Optimization Using Metaheuristic
Approaches
3 Big Data Analysis and Application for Video Surveillance Systems
4 Trends in Mining Biological Big Data
5 Computational Challenges in Group Membership Prediction of Highly
Imbalanced Big Data Sets
Part III DATA ANALYTICS AND PREDICTION MODELS
6 A New Paradigm in Fraud Detection Modeling Using Predictive Models,
Fuzzy Expert Systems, Social Network Analysis, and Unstructured Data
7 Speedy Data Analytics through Automatic Balancing of Big Data in MongoDB
Sharded Clusters
8 Smart Metering as a Service Using Hadoop (SMAASH
9 Service-Oriented Architecture for Big Data and Business Intelligence Analytics
in the Cloud
Part IV A PPLICAT IONS OF COMPUTAT IONAL INTELLIGENCE
10 Rough Set and Neighborhood Systems in Big Data Analysis
11 An Investigation of Fuzzy Techniques in Clustering of Big Data
12 A Survey on Learning Models with Respect to Human Behavior Analysis
for Large-Scale Surveillance Videos
13 Mining Unstructured Big Data for Competitive Intelligence and Business
Intelligence
Index
