부산대학교 도서관

this study presents a new regression method for forecasting the occurrence of rainfall. The method includes three main steps: feature extraction, classification, and preprocessing. In the feature extraction step, all weather samples are normalized for a uniform sampling frequency, after which signal noise is removed to improve data quality and each signal is then converted to a $1 \times 34$ feature vector, capturing subtle emotional nuances. Notably, normalization precedes classifier training to improve model performance. Performance metrics including f-score, sensitivity, precision, and accuracy are used to evaluate the efficiency of the model. The proposed methodology has the potential to advance our understanding of emotional responses to music, providing a promising avenue for future research in predicting rainfall and other natural phenomenon.