Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
Tiny particles bounce light around in a unique way, a property that researchers are using to detect pollutants in water and ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Artificial intelligence is emerging as one of the most influential forces reshaping modern surgical practice. Over the past decade, surgical teams have faced rising complexity in cases, expanding ...
As social media becomes the core domain of information interaction in the era of big data, the emotional information contained in the vast amount of user-generated content provides an unprecedented ...
Conventional electronic noses rely on arrays of chemical sensors whose electrical responses are often affected by humidity, temperature fluctuations, and long-term drift. While these systems have ...
A novel nanozyme aptasensor array achieves 100 % accuracy in classifying Staphylococcus aureus strains, enhancing rapid ...
The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...
Abstract: Accurate characterization of EDFA gain ripple is crucial for optimizing WDM system performance. This work develops and validates an Artificial Neural Network (MAE 0.04 dB), trained on over 5 ...
Mount Sinai experts to present new analysis on AI models that could predict congenital heart defects and placenta accreta spectrum at the 2026 SMFM Annual Pregnancy Meeting ...
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