New Feature

Research on novel features focuses on improving the performance and explainability of machine learning models across diverse applications. Current efforts concentrate on developing new feature extraction techniques, such as those based on modal analysis for deformable object control and Mel-Frequency Cepstral Coefficients for bearing fault detection, and on incorporating these features into existing models like SVMs and neural networks. This work aims to enhance model accuracy, address issues like false negatives in anomaly detection, and improve the interpretability of model predictions, ultimately leading to more robust and reliable AI systems in various fields, including medical image analysis and industrial quality control.