Advances in Reliability and Statistical Computing for Intelligent Systems

Éditeurs invités

• Hoang Pham, Rutgers University

Synthèse

The special issue addresses the growing importance of reliability and statistical computing in artificial intelligence systems used in everyday applications and service industries. It seeks contributions covering both theoretical advances and practical implementations in these areas, with emphasis on papers demonstrating real-world applicability.

The special issue welcomes research on mathematical and statistical methods for reliability, machine learning approaches for intelligent systems, big data analysis techniques, and system dependability measures. Industrial applications are particularly valued, including case studies from fields such as robotics, healthcare, education, surveillance, and transportation.

Thèmes proposés

• Mathematical reliability and statistical methods
• Big data modeling and prediction
• Statistical learning algorithms, models, and theories
• Machine learning models for intelligent systems
• Text mining and deep machine learning
• Intelligent system dependability and performability
• Reliability modeling and optimization
• High-dimensional data analysis
• Statistical inference for intelligent systems
• Industrial case studies in intelligent systems