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 ...

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 ...

I have come across various ways of defining Artificial Neural Networks (ANNs). Many of them miss a fundamental characteristic ...

CNN and random forest model to detect multiple faults in bifacial PV systems, including dust, shading, aging, and cracks. Using simulated I-V curves and a 180-day synthetic dataset, the model achieved ...

With the onset of decentralized finance, the wave of blockchain innovations has also gotten a spark, as many projects are ...

A comprehensive framework integrates statistical modeling, machine learning, and simulation to optimize urban traffic forecasting, capacity ...