Hybrid deep learning model for efficient prediction of telecom data using EMF radiation

Abstract

EMF has a variety of biological impacts and has an impact on the metabolic process in the human body. Antenna towers, anechoic chambers, and other sources can all produce this. Some of the human populations live very close to the EMF-emitting antenna towers. We can make humans aware of the EMF radiation and protect from diseases if there is a proper method to anticipate the EMF radiation of antennas installed in different places. For the study of telecom data and EMF emission, many machine learning and deep learning techniques have been developed in recent years. Predictive analytics played a bigger part in this. For prediction, it comprises advanced statistics, modeling and more machine learning methodologies. However, the appropriate hyper parameters must be established for the model’s effective prediction, but this cannot be guaranteed in a dynamic environment where the data is always changing. The learning model’s performance improves when these parameters are optimized. The goal of this study is to use the Telecom dataset to create a novel hybrid deep learning model for forecasting the trend of EMF radiations. The patterns were first discovered using Artificial Neural Networks (ANN) and Multilayer Perceptron (MLP) combined with the Particle Swarm Optimization method (PSO). Later to boost its performance the hybrid approach (MLP-RFD-PSO) was developed and 98.8% accuracy was achieved.