Skip to main content
Statistics
STAT
Statistics
Study
Prospective Students
Current Students
Research
Research Areas
Research Groups
People
All People
Faculty
Affiliate Faculty
Instructional Faculty
Research Scientists
Research Staff
Postdoctoral Fellows
Administrative Staff
Alumni
Students
News
Events
History
Al-Kindi
Al-Kindi Distinguished Statistics Lectures
Al-Kindi Student Awards
About
CEMSE Division
Apply
anomaly detection
Data-driven Anomaly Detection in Industrial Processes
Fouzi Harrou, Senior Research Scientist, Statistics
Feb 12, 12:00
-
13:00
B9 L2 R2325
anomaly detection
multivariate statistics
artificial intelligence
AI
This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.
