Mongabay News on MSN

Scientists can’t agree on where the world’s forests are

By Rhett Ayers Butler [( A deceptively simple question underlies many global environmental policies: where, exactly, are the world’s forests? A new study suggests the answer depends heavily on which ...
Risk.net

Demand deposit balance prediction models under the interest rate risk in the banking book guidelines: an empirical analysis integrating time-series models and machine learning ...

The authors analyze the interest rate risk in the banking book regulations, arguing that financial institutions must develop robust models for forecasting ...
Tech Xplore

Smarter machine-learning models can improve phishing website detection

Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New ...
The Southern Maryland Chronicle on MSN

How are QA teams using machine learning to predict test failures in real time?

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Fentanyl or phony? Machine learning algorithm learns to pick out opioid signatures

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...
University of Birmingham

Machine‑learning framework could transform prediction of drug side effects

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.
Rheumatology Advisor

Novel Machine Learning Model Accurately Predicts OUD Risk in Chronic Noncancer Pain

The Opioid Risk Tool for Opioid Use Disorder may help identify patients with chronic noncancer pain at increased risk for OUD ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
Frontiers

Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States 2 Department of Animal Sciences, Cornell University, Ithaca, NY, United States The lack of ...
GitHub

RFX-Fuse: Breiman and Cutler's Random Forests as a Forest Unified Learning and Similarity Engine - Extended with Native Explainable Similarity

RFX-Fuse (Random Forests X [X=compression] — Forest Unified Learning and Similarity Engine) delivers Breiman and Cutler's complete vision for Random Forests as a Forests Unified Machine Learning and ...
IEEE

Epileptic Seizure Detection in Rodent iEEG using Machine Learning: A Comparative Study of Random Forest, SVM, and Gradient Boosting Models

Abstract: Epilepsy is a neurological condition because seizures occur at random and thus patients require automated and accurate detection mechanisms. The study design employs an evaluation of ...