Ensemble models, comprising multiple algorithms or simulations to improve prediction accuracy, are increasingly being applied ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

In performance tests, RANK achieved a dramatic 99.56% reduction in alerts requiring human review using the DARPA TC dataset, and a 95% reduction on real enterprise datasets. These results underscore ...

We’re in the midst of a paradigm shift in biomarker science. | The right biomarker signature can de-risk trials, guide ...

E-Vega Mobility Labs has developed EV Doctor, a compact, AI-powered device that diagnoses EV battery health in just 15 ...

To address these problems, in this paper, an event detection method based on robust random cut forest (RRCF) algorithm, which is an unsupervised learning method for detecting anomalous data points ...

Prediction of Failure in Lubricated Surfaces Using Acoustic Time–Frequency Features and Random Forest Algorithm Abstract: Scuffing is one of the most problematic failure mechanisms in lubricated ...

Purdue Agriculture researchers are harnessing the power of artificial intelligence (AI) and machine learning (ML) to amplify ...

Therefore, this study explores the use of prompt-based LLMs for credit risk classification using the “Give Me Some Credit” dataset. The performance of LLM is compared with traditional models, ...

This repository contains the implementation of a machine learning project aimed at predicting the likelihood of stroke occurrences based on patient data. The model employs the Random Forest Algorithm ...

This project attempts to predict whether a given passenger survived or not using machine learning algorithms. Key components of this project include: Data preprocessing and cleaning. Exploratory Data ...

Businesses are still new at this. Here are some things to know about how to go about AI integration the right way.