Abstract: In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a ...

Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.

Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...

According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...

A new Israeli study suggests that machine-learning models may soon give growers a far more precise way to predict how much water their crops use each day, while also laying the groundwork for earlier ...

New research demonstrates that ML can also predict metre-scale laboratory earthquakes, suggesting that, when scaled, similar ...

Artificial intelligence is revolutionizing drug discovery and antibody engineering, accelerating the creation of new ...

Understanding how ozone behaves indoors is vital for assessing human health risks, as people spend most of their time inside.

Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level ...

Abstract: Random forest regression is a widely used machine learning algorithm. In this study, random forest regression is employed to predict groundwater levels. Five influencing factors are ...