Rajitha Meka

TL;DR: In this paper, a robust machine learning methodology is used to generate a site-specific power-curve of a full-scale isolated wind turbine operating in an atmospheric boundary layer to drastically improve the power predictions, and, thus, the forecasting of the monthly energy production estimates.

TL;DR: In this paper, a robust deep learning model is developed for short-term forecasts of wind turbine generated power in a wind farm using the state-of-the-art temporal convolutional networks (TCN) to simultaneously capture the temporal dynamics of the wind turbine power and relationship among the local meteorological variables.

TL;DR: A deep learning model based on long short-term memory–based recurrent neural networks was developed to forecast the next-day glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before.

TL;DR: This article proposes an active learning methodology based on the fundamental idea of adding a ridge and a Laplacian penalty to the V-optimal design to shrink the weight of less significant factors, while looking for the most informative settings to be tested.

TL;DR: An active learning methodology based on the integration of Laplacian regularization and active learning - Cohn (ALC) measure for identification of the most informative points for efficient estimation of noisy black-box functions using Gaussian processes is proposed.