Researchers at Pennsylvania State University examined whether machine learning could predict the risk and contributing ...

Ink authentication is often complicated by tampering, aging, and chemical variability. Now, forensic scientists are turning ...

Let’s say there are 100 records in the training dataset. The observations are arranged in decreasing order of probability ...

Background Restarting direct oral anticoagulants (DOACs) after a serious bleeding event in patients with atrial fibrillation ...

Finally, we developed models using various classifiers and compared their effectiveness. Among these, the models trained with stochastic gradient descent classifier, ridge classifier, and logistic ...

Abstract This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, K ...

On the other hand, transforming the training samples into a strict binary label matrix makes the generalization ability of the classifier limited. To address these challenges, this article constructs ...

This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning ...

The new enrollment-based setup will be put in place for the 2025-2026 academic year.

Among the ML models that perform classification, one is based on logistic regression 7. Logistic regression is a statistical method employed to predict the likelihood of a specific outcome by ...