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Abstract: This research aims to compare the performance of Logistic Regression and Random Forest algorithms in classifying cyber-attack types. Using a data set consisting of 494,021 data points with ...

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

Preprocessing of text data (tokenization, normalization, etc.). Multi-class emotion classification using Logistic Regression and SVM. Detailed evaluation using classification metrics and ...