Tag: outlier detection

Environmental scientists have combined Shannon entropy and machine learning to distinguish genuine wildfire smoke plumes from faulty ground-level fine particulate ...

Researchers have developed CAMV, a framework that automatically extracts contextual information from datasets and generates coordinated multi-view visualizations to help ...

A new IPSO-Stacking integrated learning method identifies fluctuation outliers in power grid load data, with reported coverage performance stable above ...

Researchers at the University of Calabria have developed RLAX, a reinforcement learning framework that identifies compact, interpretable feature subspaces explaining ...

Researchers in Seoul have rebuilt the classic extreme studentized deviate test into an online, self-tuning algorithm that detects anomalies in ...

South African statisticians have developed a double-standardization procedure using robust statistics that simultaneously detects spikes, level shifts, and irregular diurnal ...

Italian researchers have developed a standardised workflow that cleans historical intensity data, simulates ground shaking from candidate faults, and statistically ...

Researchers at Yanshan University have developed ADPOD, an outlier detection algorithm that improves density-peak clustering with an affinity-based density measure, ...

A factorial ANOVA study across five Iranian basins shows that the most important machine learning modeling decision for precipitation prediction ...

Researchers at Xiamen University of Technology have developed an entropy-driven clustering method that automatically tunes its own thresholds to separate ...