Advances in Reliability and Statistical Computing for Intelligent Systems

Éditeurs invités

• Hoang Pham, Rutgers, The State University of New Jersey

Synthèse

This special issue focuses on recent advances in reliability and statistical computing methods for intelligent systems in real-world applications and service industries. The field has undergone significant changes due to the increasing role of artificial intelligence, which demands reliable and timely responses from intelligent systems.

The special issue welcomes articles presenting new theoretical research and practical methods in reliability and statistical computing for intelligent systems. Papers with real-world applications are preferred over purely theoretical contributions, covering topics such as mathematical reliability methods, big data modeling, machine learning approaches, system dependability, and various industrial case studies ranging from robotics to medical care and intelligent 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, including field and service robotics, medical care, education, visual surveillance, intelligent transportation