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This presentation focuses on addressing corona discharge issues through data-driven analysis and modeling. It begins with identifying challenges and setting clear research objectives, followed by gathering and understanding an electric spectrum dataset. The data preparation phase involves image resizing, normalization, and dataset splitting. A CRNN architecture is then built, trained, and validated to model the phenomena accurately. Evaluation is conducted using a Confusion Matrix and...