Scientists who use imaging to understand the brain's complexity often focus on the strongest signals and ignore the rest. But this strategy, researchers warn, may reveal only the tip of the iceberg. A ...

Innovative machine learning models using routine clinical data offer superior stroke risk prediction in atrial fibrillation, ...

Researchers have developed a feature selection-based solar irradiance forecasting method to improve the operation of ...

Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...

Many technical recruiters and IT hiring managers advise that certifications carry more value for entry-level and low-level IT pros than they do for workers with more experience. The obvious exceptions ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Young, T. , Guymon, J. , Pankow, M. and Ngaile, G. (2026) A Material Removal Prediction Framework for Ball EEM Polishing in ...

Abstract: The rapid increase in cyber threats has heightened the demand for Intrusion Detection Systems (IDS) that are both accurate and efficient. While deep learning models outperform traditional ...

A new soil-moisture retrieval strategy has improved the accuracy of satellite-based moisture mapping by combining microwave reflection signals with vegetation-structure information that conventional ...

Factoring out nucleotide-level mutation biases from antibody language models dramatically improves prediction of functional mutation effects while reducing computational cost by orders of magnitude.

Figure 1: LASSO feature ranking and SHAP explanatory for Cases 1, 2, and 3 feature selection models. A positive SHAP value indicates a positive impact on prediction, leading the model to predict 1 ...