Fariya Bintay Shafi

Abstract It’s important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets.

Abstract Accurate, fast, and interpretable fault identification on electrical transmission lines is essential for maintaining power system stability and reducing outage durations. In this study, we propose a hybrid 1D convolutional neural network–Decision Tree (1D-CNN–DT) for transmission line fault detection and classification, in which the 1D-CNN acts solely as a feature extractor. During this process, the Decision Tree performs the final, interpretable classification.