AlphaGalileo

Satellite data reveal hidden crop planting timelines

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale.

Machine learning tracks methane emissions from orbit

Methane is one of the most powerful greenhouse gases, yet quantifying its emissions remains difficult at large scales. A new framework, CH4Vision, addresses this problem by estimating methane flux ...
IEEE

An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability

Abstract: Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, ...
The Lancet

When to and when not to use machine learning in risk prediction models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
Visual Studio Magazine

Linear Regression with Two-Way Interactions Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Standard linear regression predicts a single numeric value ...
Arizona State Press

ASU’s Volunteer Open Projects turns real datasets into real-world wins

ASU Library’s Data Science and Analytics Unit relaunched Volunteer Open Projects in a hybrid kickoff. In-person seats were limited, and a Zoom option made the program accessible to students across ...
Scientific Research Publishing

Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1, 140-147.

ABSTRACT: Nowadays, understanding and predicting revenue trends is highly competitive, in the food and beverage industry. It can be difficult to determine which aspects of everyday operations have the ...
Scientific Research Publishing

Hope, T. M. H. (2020). Linear Regression. In A. Mechelli, & S. Vieira (Eds.), Machine Learning (pp. 67-81). Elsevier.

ABSTRACT: Nowadays, understanding and predicting revenue trends is highly competitive, in the food and beverage industry. It can be difficult to determine which aspects of everyday operations have the ...