IEEE

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

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 ...
GitHub

Using LDA, SVM, and Decision Trees to deal with MNIST data set

This project involves the classification of handwritten digits using three different classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Decision Trees. The goal is to ...
Frontiers

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of ...
IEEE

Electroluminescent Image Processing and Cell Degradation Type Classification via Computer Vision and Statistical Learning Methodologies

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 ...
GitHub

Predicting the location of Tuna using SVM (Support Vector Machine) through Python and Dash.

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 ...
Frontiers

Machine learning for neuroimaging with scikit-learn

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 ...