RFF can be applicable to many other machine learning algorithms than the above. The author will provide implementations of the other algorithms soon. This module supports training/inference on GPU.

Multiplexed imaging has transformed our ability to study tissue organization by capturing thousands of cells and molecules in their native context. However, these datasets are enormous, often ...

Abstract: Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto-encoder (DAE ...

Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques ...

In the last decade, a few valuable types of research have been conducted to discriminate fractured zones from non-fractured ones. In this paper, petrophysical and image logs of eight wells were ...

Abstract: A data set of 90 60-cell module images from 5 commercial PV module brands over 6 exposure steps of damp-heat testing were analyzed. An automated data analysis pipeline was developed using ...

The initial goal of our program is to predict tuna location based on fishing vessel data and its correlation with sea surface temperature (SST) and chlorophyll concentration. We also want to see the ...

1 Faculty of Computing, Botho University-Maseru Campus, Maseru, Lesotho. 2 Department of Physics and Electronics, National University of Lesotho, Roma, Lesotho. Classical machine learning, which is at ...

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a ...

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of ...