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Deep neural network from scratch in Python – fully connected feedforward tutorial

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 ...
Frontiers

GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks

Introduction: Most farmers in Nigeria lack knowledge of their farmland’s nutrient content, often relying on intuition for crop cultivation. Even when aware, they struggle to interpret soil information ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
C&EN

RTK_RAG: Leveraging Retrieval Augmented Generation with Multi-Window Convolutional Neural Networks for Superior ATP Binding Site Prediction in Receptor Tyrosine Kinases

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Receptor tyrosine kinases (RTKs) are key regulators of cellular signaling and are ...
SciELO

Prediction of Microstructural and Mechanical Properties of Steel Welds with Artificial Neural Networks

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 ...
The University of Chicago Chronicle

AI is good at weather forecasting. Can it predict freak weather events?

Increasingly, AI models are able make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does ...
IEEE

An Effective Neural Network for Communication Signal Prediction via Propagation Path Serialized Representation

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 ...
Scientific Research Publishing

Occupational Diseases Risk Prediction by Neural Networks ()

This study explores the use of neural networks for occupational disease risk prediction based on worker and workplace characteristics. The goal is to develop a tool to assist occupational physicians ...