A Comparative Analysis of Predictive Monitoring Systems Utilizing Machine Learning and Deep Learning Algorithms

Abstract

This study offers an in-depth comparative examination of predictive monitoring systems utilizing sophisticated machine learning (ML) and deep learning (DL) algorithms. The investigation delves into the efficacy, advantages, and constraints of ML algorithms, exemplified by Random Forest and Extreme Gradient Boosting will be contrasted with Deep Learning (DL) algorithms including artificial neural networks (ANNs) and LSTM, recurrent neural networks (RNNs) in this study. By evaluating these algorithms across diverse domains, the research aims to discern optimal strategies for predictive monitoring, considering factors like efficiency, real-time processing, and adaptability. The findings contribute valuable insights for practitioners and researchers, informing the selection and deployment of algorithms in predictive monitoring systems