Abstract: This study develops an Artificial Neural Network (ANN)-based prediction model to estimate the total project cost (TPC) of residential dwellings in Quezon City, the largest local government ...
An evnet driven model that uses financial time series data with New York Times information to form a LSTM recurrent neural network. There are 3 models. The first 2 models are based on price and volume ...
Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...
Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from ...
A high-performance neural network implementation written in Rust from scratch. This project demonstrates building AI/ML components in Rust for superior speed and memory safety compared to traditional ...
This study investigates the mechanical performance of Steel Fibre-Reinforced Concrete (SFRC) subjected to elevated temperatures using artificial neural network (ANN) modeling. While existing ...
Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...
In the field of materials science, the application of machine learning, particularly neural networks inspired by the human brain, has gained significant traction in recent years. One of the key ...
Abstract: Communication signal prediction holds great significance for the optimization and deployment of B5G networks. In this letter, we propose a neural network model with Propagation Path ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results