IEEE

One, Two and Three Steps Ahead Wind Speed Prediction Using Enhanced Random Forest with Bayesian Optimisation

Abstract: Wind energy-based power production worldwide adapts to meet energy demand and maintain sustainability. However, wind and maximum energy yield uncertainty require a precise and top-notch ...
Hosted on MSN

These Steps Connect Paths Inside A Forest China

This walking tour continues inside the Greenhouse Science Museum at Huizhou Botanical Garden in Huizhou, China. Stone steps surrounded by dense tropical plants guide visitors through different levels ...
GitHub

random-survival-forest

Phase III breast cancer survival modeling using Cox regression (R) and Random Survival Forest (Python) with clinical covariates and C-index evaluation.
IEEE

Data-Driven Modeling of Storm-Time Total Electron Content Using the Random Forest and Deep Learning Methods

Abstract: A high-precision storm-time ionospheric model is crucial for both ionospheric research and precise positioning applications of global navigation satellite systems (GNSSs). However, the ...
GitHub

GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code ...

Timber takes a trained ML model — XGBoost, LightGBM, scikit-learn, CatBoost, ONNX (tree ensembles, linear models, SVMs), or a URDF robot description — runs it through a multi-pass optimizing compiler, ...