Quality Control

Quality control (QC) in diverse fields is increasingly reliant on automated methods, aiming to improve accuracy, efficiency, and reliability. Current research focuses on developing and integrating machine learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs like LSTMs), to detect anomalies and assess the quality of data and model outputs across various applications, from medical image analysis to manufacturing processes. These advancements are crucial for enhancing the trustworthiness of data-driven decisions in scientific research and improving the performance and safety of industrial systems. The development of robust QC frameworks is essential for ensuring the validity and reliability of results across numerous domains.