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Centroid-UNet is deep neural network model to detect centroids from satellite images.
Center points of world countries - CSV and GeoJSON
object detections on polygonal roi using yolo
Most of the problems I solved and algorithms I grinded while prepairing for the Russian Olympiad in Informatics.
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.
Parallellization of the Kmeans algorithm with OpenMP
Creates a new feature class with the centroid of all polygons for each category provided by a field.
We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
Simple object tracking by using the centroid tracking algorithm
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.
Unsupervised Machine Learning and Cryptocurrencies
Clustering algorithm with other functions (Laplacian Norm, Jacobi algorithm - Eigenvalues and Eigenvectors extractor, etc)
Analyse d'un groupement de pays cible pour l'exportation de poulet (clustering, CAH, k-means, ACP)
Um exemplo que mostra o cálculo do número e a distâncias aos centroides utilizando um dataset de flores iris.
function in R for calculating a centroid matrix
Using Supervised Machine Learning algorithms to identify credit risks
Jython does not have the GIL problem that CPython has, so we use this to easily create a threaded K-Means program.
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.
Implementaion of K-Means & Page Rank algorithms. (extend of "IR-CosineSimilarity-vs-Freq" repository)
From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. Use R or Python to perform this task
Supervised Learning Recap
Classical Computer Vision
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.
Data Analysis, EDA and Unsupervised Machine Learning Models on Uber NY Dataset
Doing algorithms on next sets of data