Path Loss Prediction based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network and Gaussian Process.

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc.

Fasting glucose level and the risk of incident atherosclerotic cardiovascular diseases.

Objective: Although diabetes increases the risk of cardiovascular disease (CVD) and mortality, the dose-response relationship between fasting glucose levels below those diagnostic of diabetes with cardiovascular events has not been well characterized.

Research Design And Methods: A prospective cohort study of more than one million Koreans was conducted with a mean follow-up of 16 years. A total of 1,197,384 Korean adults with no specific medical conditions diagnosed were classified by baseline fasting serum glucose level.