Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks
This work addresses the need for comparative analysis of machine learning techniques for RF-EMF exposure estimation, which is crucial for risk assessments related to human health and environmental impacts, but it is incremental as it builds on existing methods without introducing a fundamentally new approach.
The paper tackled the problem of estimating radio-frequency electromagnetic field (RF-EMF) exposure using real-world sensor data from Lille, France, by comparing finite and infinite-width convolutional networks, resulting in performance analysis based on execution time and estimation accuracy with Root Mean Square Error (RMSE) as the evaluation metric.
Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields (RF-EMF) is essential for conducting risk assessments. These assessments aim to explore potential connections between RF-EMF exposure and its effects on human health, as well as on wildlife and plant life. Existing research has used different machine learning tools for EMF exposure estimation; however, a comparative analysis of these techniques is required to better understand their performance for real-world datasets. In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels from 70 real-world sensors in Lille, France. A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy. To improve estimation accuracy for higher-resolution grids, we utilized a preconditioned gradient descent method for kernel estimation. Root Mean Square Error (RMSE) is used as the evaluation criterion for comparing the performance of these deep learning models.