Abstract: Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used ...

From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models. A ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

In this tutorial, we build an advanced Agentic Retrieval-Augmented Generation (RAG) system that goes beyond simple question answering. We design it to intelligently route queries to the right ...

Nearly 50 new cancer therapies are approved every year. While this positive trend is a huge benefit for patients, Altuna Akalin, PhD, head of the bioinformatics and omics data science technology ...

Junior faculty are often told to protect their time, but nobody provides instructions for how to do so. As an assistant professor at a public university, I have struggled to balance my course load, my ...

Aiming to address the complexity and uncertainty of unmanned aerial vehicle (UAV) aerial confrontation, a twin delayed deep deterministic policy gradient (TD3)–long short-term memory (LSTM) ...

Abstract: Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only ...

This model was trained and tested on a 70%/30% split (train/test result cohort), achieving an area under the receiver operator curve on the test set of 0.866 (95% CI, 0.857 to 0.875). Assigning a ...