IEEE

AI-Driven Classification of Tsunami-Generating Earthquakes: Harnessing Random Forest, SVM, and Logistic Regression for Early Detection

Abstract: Predicting whether an earthquake will generate a tsunami is critical for early warning systems and disaster mitigation. In this study, we present an AI-driven approach to classify ...
SciELO

Obesity classification: a comparative study of machine learning models excluding weight and height data

OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity ...
GitHub

Tutorial on seismic signal/noise classification; from linear to deep classifiers

This jupyter notebook tutorial is meant to be a general introduction to machine and deep learning. We use seismic time series data from i) real earthquakes and ii) nuisance signals to train a suite of ...
IEEE

Comparison of some Logistic Regression Methodologies in Supervised Classification for Functional Data

Abstract: Logistic regression for functional data is a statistical technique for modeling the relationship between functional predictor variables and a binary or multiclass outcome variable. The model ...