To the best of my knowledge, there are no existing code libraries that directly implement neural network quantile regression. Implementing neural quantile regression from scratch using C# allows you ...

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 ...

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 ...

It is a pleasure to introduce the latest issue of The Journal of Computational Finance. The first two contributions focus on using novel neural network machinery to enhance classical financial ...

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

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 ...

Their study is centred around answering three research questions: Do ANNs perform better than the traditional multiple regression models in the prediction of lighting parameters and energy demand of ...

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 ...