Doing algorithms on next sets of data

MICS Geocoding is a QGIS plugin for processing household survey geospatial data, enabling cluster centroids, privacy-preserving displacement, and extraction of geospatial covariates.

kmeansc 🌼🌷🌻 : K-Means Clustering # scikit-learn framework # clustering model

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.

Assistance in the analysis of dip/direction measurements of rock mass discontinuities

Análisis de datos (aprendizaje no supervisado), clasificación de los jugadores del juego FC25 en 4 clusters, aplicando 3 medidas diferentes para valorar la similitud.

This project focuses on the development of a Recurrent Neural Network (RNN) model using Gated Recurrent Units (GRUs) for Twitter sentiment analysis, along with hyperparameter tuning. The performance of the RNN-GRU model is compared against two pre-existing models

This project focuses on developing a sentiment classification model using Multi-Layer Perceptrons (MLPs) with variations in text representation techniques and hyperparameter tuning, leveraging a balanced subset of the Kaggle Twitter Sentiment Analysis dataset. Additionally, a single instance of logistic regression was applied for comparison.

Classical Computer Vision

AI - Project 3 - This project implements Aglomerative Clustering to cluster all generated points in 2D space using: Centroid & Medoid

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"

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

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