However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...

Under the influence of global warming, the Arctic is transitioning from a state dominated by multi-year thick ice to a "New ...

Researchers sought to develop and validate artificial neural networks for overall survival and progression-free survival in older adults with HNSCC following definitive chemoradiation.

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for ...

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field ...

Researchers use a machine learning (ML) approach to obtain the EM-aware aging prediction of the power grid (PG) network. They use neural network–based regression as their core ML technique to ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...