Which Machine Learning Models Are Most Used In Crypto Signal Generation?

Machine learning is transforming how crypto traders create and understand signals. From supervised models such as Random Forests and Gradient Boosting Machines to sophisticated deep learning hybrids ...
Devdiscourse

New AI forecasting model promises greater stability for Kenya’s volatile food prices

Kenya’s food markets are known for extreme volatility influenced by weather shocks, inflation, currency fluctuations, and ...
Scientific Research Publishing

Research on Prediction of Air Quality CO Concentration Based on Python Machine Learning ()

Wang, Z. (2025) Research on Prediction of Air Quality CO Concentration Based on Python Machine Learning. Advances in Internet ...
IT-Online

Senior Data Scientist at Datonomy Solutions

Cape Town based candidates only. The Senior Data Scientist will be responsible for developing, validating, and operationalizing advanced machine learning and AI models that support strategic business ...
IT-Online

Senior Data Scientist at Datonomy Solutions – Western Cape Kenilworth

Cape Town based candidates only. The Senior Data Scientist will be responsible for developing, validating, and operationalizing advanced machine learning and AI models that support strategic business ...
pv magazine International

Machine learning-based fault detection technique for bifacial PV

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 ...
Devdiscourse

AI-powered forecasting boosts retail efficiency: LSTM outshines traditional models

Demand forecasting remains one of the most complex challenges in retail management. As consumer behavior evolves rapidly, ...
IEEE

Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability

Abstract: The increasing integration of solar photovoltaic (PV) systems into modern energy grids presents significant challenges due to the intermittent and weather-dependent nature of solar energy ...
Scientific Research Publishing

Goodfellow, I., Bengio, Y. and Courville, A. (2016) Machine Learning Basics. In: Deep Learning, The MIT Press, 95-161.

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
Visual Studio Magazine

Kernel Ridge Regression with Cholesky Inverse Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses the kernel matrix inverse (Cholesky ...
The EMBO Journal

Predicting natural variation in the yeast phenotypic landscape with machine learning

Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. Most organismal traits result from the complex ...
Yale Environment 360

SoFiE Financial Machine Learning Summer School

The Society for Financial Econometrics (SoFiE) Summer School is an annual week-long research-based course for PhD students, new faculty, and professionals in financial econometrics. For the first two ...