CLASS Vector Art - Moozonian

GitHub Repo https://github.com/EmmanuelRaj7799/EEG-Classification

EmmanuelRaj7799/EEG-Classification

This study aims to develop ML and DL methods which uses signals collected from electroencephalogram in application for detection of depression. We have extracted eleven statistical features form signal before feeding them to the model. We build three classifiers: Logistic Regression, Support Vector Machine, and 1-D Convolutional neural network. Our methods are tested of a dataset which comprise of signals form 30 healthy subjects and 34 MDD patients, these signals were collected from three different criteria: EC when eyes of subject are closed, EO when eyes of subject are open, and TASK when subject is doing some tasks. All three classifiers are applied on each of three types of signals, which gives a total of nine (3X3) experiments. Our results found that TASK signals given better accuracies of 88.4, 89.3, 90.21 for logistic regression, SVM and 1-D CNN respectively when compare to EC and EO signals, and also our results gave better accuracy than some of the available state-of-the-art methods

GitHub Repo https://github.com/Asufian1/Article-Classifier

Asufian1/Article-Classifier

We were given a dataset of text articles that had already been pre-processed into feature vectors. Each vector represented whether certain words appeared in an article, and the labels indicated the magazine it came from. Using the Naive Bayes algorithm, I trained the model to predict the most probable magazine based on the words in the article.

GitHub Repo https://github.com/ledsouza/nlp-article-classification

ledsouza/nlp-article-classification

This project aims to develop a machine learning model capable of classifying news articles into different categories based on their titles. Two different word embedding models (CBOW and Skip-gram) are trained and used to vectorize the article titles. These vectorized representations are then used to train a Logistic Regression classifier.

GitHub Repo https://github.com/frknrnn/Covid19_classification_tmempr

frknrnn/Covid19_classification_tmempr

Medical images are crucial data sources for not easily diagnosed diseases. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as the difficulties in data storage, the computational load, and the time required to process high-dimensional data. It is a vital element to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration and disease diagnosis from these images can be summarized in 2 steps as preprocessing and classification. Preprocessing step is the feature extraction process and in this step, the recently developed decomposition-based method Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is proposed as a feature extraction method. Classification of images is the second step where the Random Forest and Support Vector Machine (SVM) is applied as classifiers. Also, X-ray images have been reduced by 99,9\% with TMEMPR and with several state-of-the-art feature extraction methods which are Discrete Wavelet Transform (DWT), Discrete Cosine Transform(DCT) The results are examined under different feature extraction methods. It is observed that a higher accuracy rate of classification is achieved by using the TMEMPR method.

GitHub Repo https://github.com/opennlp/Large-Scale-Text-Classification

EmmanuelRaj7799/EEG-Classification

Asufian1/Article-Classifier

ledsouza/nlp-article-classification

frknrnn/Covid19_classification_tmempr

opennlp/Large-Scale-Text-Classification

rathnaviraj/news-article-classifier

Harshakavin/article-classification-SVM-ML

Blue-Rocket/BRPDFImage

Piyushagarwal26/News-Article-Classification-Using-SVM

shruthin4/News-Articles-Classification