This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. To classify chest X-ray images into three categories: COVID-19 positive, pneumonia positive, and normal. To achieve this, we utilize the COVID-19 Chest X-ray dataset available on Kaggle.

Country centroids based on data from geoBoundaries.org. Useful for bubble maps and arc maps.

Code for the paper "DUCK: Distance-based Unlearning via Centroid Kinematics"

Doing algorithms on next sets of data

Creates a new feature class with the centroid of all polygons for each category provided by a field.

The Similarity Search Tree is an efficient method for indexing high dimensional feature vectors. The main objective of this data structure is to obtain the nearest neighbors given a certain query vector in a reasonable amount of time. In this project, the k-NN algorithm was adapted for supporting image retrieval.

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

object detections on polygonal roi using yolo

From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. Use R or Python to perform this task

With this Python code it's possible to find the centroid of a regular or irregular geometric figures wich are solid or have holes, using Open CV library

Classical Computer Vision

Clustering algorithm with other functions (Laplacian Norm, Jacobi algorithm - Eigenvalues and Eigenvectors extractor, etc)

Parallellization of the Kmeans algorithm with OpenMP

Center points of world countries - CSV and GeoJSON

Um exemplo que mostra o cálculo do número e a distâncias aos centroides utilizando um dataset de flores iris.

Analyse d'un groupement de pays cible pour l'exportation de poulet (clustering, CAH, k-means, ACP)

Using Supervised Machine Learning algorithms to identify credit risks

Unsupervised Machine Learning and Cryptocurrencies

Centroid-UNet is deep neural network model to detect centroids from satellite images.