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DR-LabStack is a React–Flask web system that integrates four pretrained diabetic retinopathy prediction models into a common clinician-facing interface, demonstrating a reusable workflow for heterogeneous ML models.
This paper evaluates machine learning and ARIMA models for adaptive public health forecasting using Ontario COVID-19 data, proposing an ensemble method called MLAMA for improved performance across different conditions.
This paper proposes a multi-stage training pipeline using language-based preprocessing and an ensemble of models to detect abusive comments in Indic languages, aiming to minimize false positives while preserving freedom of expression.
A developer shares practical lessons from moving from a single AI image detection model to an ensemble of six models plus non-ML signals in production, highlighting the roles each model plays and the value of disagreement signals. The post also asks the community about retraining cadence and model retirement strategies.