Activity Details
Artificial Intelligence and Digitalization Techniques in Laboratory Activities (Practical)
Objectives:
Program Structure and Key Modules:
The program has been designed to integrate both theoretical knowledge and practical application to ensure effective knowledge transfer and hands-on competency development for participants.
Theoretical Component (may include)
A comprehensive introduction to the application of Machine Learning (ML) models in inspection and testing assessment activities.
Approaches for balancing the speed of inspection processes with quality, reliability, and technical confidence through the use of artificial intelligence technologies.
Identification of risks associated with the use of artificial intelligence in laboratory operations (such as data quality issues, inappropriate model selection, and incorrect acceptance or rejection decisions), together with an overview of risk mitigation and control measures.
Practical and Applied Component (may include)
Hands-on, step-by-step training on the development of a machine learning model for predicting inspection and testing results, using air-conditioner performance testing as a case study.
A technical site visit to an air-conditioner capacity testing laboratory.
Practical application exercises based on real-world case studies involving air-conditioning units.
Analysis of raw testing data and training on the evaluation of artificial intelligence model performance and accuracy (which may exceed 98%), with emphasis on their use as a laboratory validation tool prior to conducting the actual physical test.
Attendance:
• Specialists and professionals working in inspection and testing laboratories.
• Members of the GCC Laboratories Committee.