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The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized ...

Real-time intelligent connectivity is a necessity, yet telecommunications infrastructure—built for a different era—struggles to keep pace. As data traffic explodes and latency-sensitive applications ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

The delayed coker unit (DCU) at TotalEnergies’ Port Arthur refinery's is a complex system that thermally cracks heavy residual oils into lighter, more valuable oil products and petroleum coke. As ...

Monoclonal antibody (mAb) manufacturing must continually improve to keep up with increasing demands. To do this, biomanufacturers can deploy machine learning tools to augment traditional process ...

Researchers sought to develop a machine learning-based prognostic model that can predict all-cause mortality or liver transplant among patients with autoimmune hepatitis. A machine learning model ...

In this tutorial, we explore LitServe, a lightweight and powerful serving framework that allows us to deploy machine learning models as APIs with minimal effort. We build and test multiple endpoints ...

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict ...

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...