IEEE

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto ...

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

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector ...

This project was inspired by Y. Tang's Deep Learning using Linear Support Vector Machines (2013). The full paper on this project may be read at arXiv.org. The experiments were conducted on a laptop ...
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

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled ...
Scientific Research Publishing

Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs ()

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 ...
Scientific Research Publishing

Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village ()

1 School of Computer Science & Technology, Dalian University of Technology, Dalian, China. 2 School of Computer Science & Technology, Xinjiang Normal University, Urumqi, China. Nowadays, crop diseases ...
IEEE

Electroluminescent Image Processing and Cell Degradation Type Classification via Computer ...

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 ...
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 ...