Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

ML powered system that predicts most suitable crop using ensemble(hard voting) of Decision Tree, Random Forest, and Gradient Boosting models implemented from scratch ...

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...

Abstract: The goal of this study is to evaluate how well driver drowsiness can be detected using two different machine learning methods: the Decision Tree Classifier and the Novel Random Forest ...

The goal of this project is indentify fraudulent transactions while minimizing false positives (non-fraudulent transactions flagged as fraud) and false negatives (missed fraudulent transations). The ...

After receiving his degree in Journalism & Media Communications from CSU in 2019, Erik began building his career in online media, and found his dream job when he joined Game Rant as a staff writer.

Background: Decisions surrounding involuntary psychiatric treatment orders often involve complex clinical, legal, and ethical considerations, especially when patients lack decisional capacity and ...

Abstract: The study aims to improve the accuracy of cyberbullying detection. Compared to the Random Forest classifier, utilize XGBoost to improve accuracy. In this study, two groups were compared. The ...

A novel framework integrates urban surveillance video data with a two-stage AI pipeline: an enhanced random forest classifier detects rain streaks and selects key image regions, while a hybrid deep ...

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...