IEEE

Synthetic Biological Signals Machine-Generated by GPT-2 Improve the Classification of EEG and EMG Through Data Augmentation

Abstract: Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and with a scarcity of ...
GitHub

c-lasso: a Python package for constrained sparse regression and classification

c-lasso is a Python package that enables sparse and robust linear regression and classification with linear equality constraints on the model parameters. For detailed info, one can check the ...
IEEE

ANN Based High Spatial Resolution Remote Sensing Wetland Classification

Abstract: RS (Remote Sensing) image classification based on ANN (Artificial Neural Network) is carried out with high spatial resolution images of the wetland, which is the most important ecological ...
Frontiers

Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests

Prognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging. We retrospectively reviewed the ...
GitHub

transforEmotion: Sentiment Analysis for Text, Image and Video Using Transformer Models

With transforEmotion you can use cutting-edge transformer models for zero-shot emotion classification of text, image, and video in R — without the need for a GPU, subscriptions, or paid services, and ...
Frontiers

tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data

tascCODA posits a Dirichlet-Multinomial model for Y i,⋅ for each sample i ∈ 1…, n, thus accounting for the compositional nature of the count data. The covariates are associated with the features ...