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Found 6 papers, 2 papers with code
Improving COVID-19 Forecasting using eXogenous Variables
We demonstrate significant enhancement in the forecasting accuracy for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 64. 58% and 59. 18% (on average) over the GCN-LSTM model in the national level data, and 58. 79% and 52. 40% (on average) over the GCN-LSTM model in the state level data.
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML
However, advances in machine learning research indicate that neural networks can be powerful data modeling techniques, as they can give higher accuracy for a plethora of learning problems and datasets.
Occupancy Detection in Room Using Sensor Data
With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments.
Gaussian Function On Response Surface Estimation
We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model.
Stereotype-Free Classification of Fictitious Faces
The proposed approach aids labeling new data (fictitious output images) by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.
Video Contents Understanding using Deep Neural Networks
We propose a novel application of Transfer Learning to classify video-frame sequences over multiple classes.
