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Abstract: With the rapid growth of residential electricity consumption data, traditional anomaly detection methods suffer from low detection rates and high false alarm rates when detecting ...

Machine Learning project to predict water potability using supervised learning algorithms with data preprocessing, model comparison, and deployment using Gradio. Gradio. data preprocessing, model ...

Abstract: In this paper, we propose an anomaly detection model based on Extended Isolation Forest and Denoising Autoencoder, which achieves unsupervised anomaly detection with good generalization ...