While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. The model’s ...

This is a potentially valuable modeling study on sequence generation in the hippocampus in a variety of behavioral contexts. While the scope of the model is ambitious, its presentation is incomplete ...

This project allows the user to extract static features from Windows PE files, which have been proven effective for malware family classification. Specifically, the list of the chosen features and the ...

Abstract: Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning ... of model updates. To address this shortcoming, in this work we ...

When the IBM PC was new, I served as the president of the San Francisco PC User Group for three years. That’s how I met PCMag’s editorial team, who brought me on board in 1986. In the years ...

Training deep learning models at scale requires distributed training capabilities. This repository demonstrates how to leverage different distributed training frameworks on Databricks to accelerate ...

aArtificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA bDepartment of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer ...

Researchers compare advanced genetic methods that pinpoint a tree’s origin on a continuous scale, refining seed sourcing, ...