Smart Spam Detection and Correction for Temperature in Heater

Abstract

The efficient operation of heating systems relies heavily on accurate temperature monitoring. However, such systems are vulnerable to data corruption, which can lead to erroneous temperature readings and potentially hazardous conditions. We propose a novel approach to address this challenge by employing machine learning techniques for spam detection and correction in heater temperature data for the detection phase. We explore various machine learning algorithms including spam detection and classification models, to identify spam temperature readings. These models are trained on the preprocessed dataset and evaluated using appropriate metrics to assess their performance.