IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Scientific Research Publishing

Sitompul, L.R., Nababan, A.A., Manihuruk, M.L., Ponsen, W.A. and Supriyandi, S. (2025) Comparison of XGBoost, Random Forest and Logistic Regression Algorithms in Stroke Disease ...

ABSTRACT: Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often ...
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 ...
Scientific Research Publishing

Wang, J., Yu, K., Bu, J., Lin, Y. and Han, S. (2022) Multi-Classification of UWB Signal Propagation Channels Based on One-Dimensional Wavelet Packet Analysis and CNN.

ABSTRACT: Ultra-wide band (UWB) positioning technology has attracted increasing attention due to its high ranging accuracy. However, in indoor environments, non-line-of-sight (NLOS) signals ...