Quality Measure

Quality measures are crucial for evaluating the performance and reliability of various systems, from machine learning models to biometric authentication and industrial quality control. Current research focuses on improving the robustness and accuracy of these measures, exploring techniques like cross-validation, Pareto-compliant indicators (e.g., refined R2), and convolutional neural networks (CNNs) for enhanced performance, even with limited data. This work is significant because reliable quality measures are essential for ensuring the trustworthiness and effectiveness of diverse technologies across various fields, impacting both scientific benchmarking and practical applications like healthcare and security.