Anomaly Detection in Time Series Data using Autoencoders approach.
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Updated
Apr 20, 2023
Anomaly Detection in Time Series Data using Autoencoders approach.
This repo contains the code used for the CARSS external validation project.
Prognóstico de sobrevida em cativeiro de Tityus bahiensis capturados em Americana/SP
Prognóstico de componentes hematológicos após ATQ bilateral simultânea em centro cirúrgico de referência
Implant failure rates in a knee prosthesis sub-population of the Helios Klinikum Berlin-Buch hospitals
Consultorias em Estatística Médica e Epidemiologia Clínica. CNPJ:42.154.074/0001-22
Survival analysis of events attributed to PJI in patients that undergone TJA surgeries
Reproduce results from the paper "Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis. K. Chalkou et. al. Diagn Progn Res . 2021 Oct 27;5(1):17. doi: 10.1186/s41512-021-00106-6."
Effect of socioeconomic status in mortality rates after brain injury: cohort study
Conceptual models of oceanic diurnal warm layer dynamics
NASA Turbofan Jet Engine Propagation modeling
Time-adjusted effect of socioeconomic status in mortality rates after brain injury: cohort study
Sensitivity of mortality rates to the imputation of missing socioeconomic data: cohort study
This is the official repository of the R package metamisc
The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computation of remaining useful life) of engineering systems, and provides a set of models and algorithms for select components developed within this framework, suitable for use in prognostic applications.
The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.
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