The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...

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

The Office of Undergraduate Research organizes the Symposium of Student Scholars twice per year, offering students a unique ...

Customer data platform maker GrowthLoop Inc. today introduced a composable artificial intelligence analytics platform ...

A study found a machine learning model more accurately predicted major adverse cardiac events, such as heart attacks, than ...

Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

No system was recommended for individual prognostication, and the group considered that more detail in ulcer characterization was needed and that machine learning (ML)–based models may be the solution ...

Researchers have developed an integrated framework for estimating battery state of health, or SOH, by combining incremental ...

Nine machine learning models—decision tree, random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine, elastic net, logistic ...

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...