Personalized algorithms may quietly sabotage how people learn, nudging them into narrow tunnels of information even when they start with zero prior knowledge. In the study, participants using ...

The goal of this article is to address the most common questions practitioners are asking today about gen AI in e-discovery.

Abstract: This study uses hyperparameter optimization to improve accuracy in classifying CKD or chronic kidney disease with the use of a Support Vector Machine algorithm or SVM. SVM is combined with ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

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An automated MATLAB application for brain tumor detection and segmentation from MRI images. This project uses image processing and a Support Vector Machine (SVM) classifier to identify and highlight ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...

In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk ...