Teachers are worried about students cheating with AI, but my survey suggests the deeper issue is learning

The risk of students using AI to cheat tends to get a lot of attention—with good reason. A student can simply copy and paste ...
British Journal of Ophthalmology

Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...
Frontiers

Evaluating XAI techniques under class imbalance using CPRD data

The need for eXplainable Artificial Intelligence (XAI) in healthcare is more critical than ever, especially as regulatory frameworks such as the European Union Artificial Intelligence (EU AI) Act ...
IEEE

Shape Penalized Decision Forests for Imbalanced Data Classification

Abstract: Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional ...
Scientific Research Publishing

Credit Score Classification Using Advanced Machine Learning: A Comprehensive Approach ()

1 Department of Computer Science, Rutgers University, New Brunswick, NJ, USA. 2 Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA. This paper presents a ...
GitHub

SMOTE-variants for imbalanced learning

The package implements 86 variants of the Synthetic Minority Oversampling Technique (SMOTE). Besides the implementations, an easy to use model selection framework is supplied to enable the rapid ...
GitHub

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Official Pytorch implementation of ICML 2022 paper "TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification" This work investigates the phenomenon that imbalance handling algorithms ...