centroids

gicait/centroid-unet

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

DougUOT/Credit_Risk_Analysis

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.

OcraM17/DUCK

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

bharath5673/Bounding-boxes-inside-PolyROI

object detections on polygonal roi using yolo

abarankab/competitive-programming

Most of the problems I solved and algorithms I grinded while prepairing for the Russian Olympiad in Informatics.

ByJuanDiego/ss-tree

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.

ParsaD23/kmeans_parallel_OpenMP

Parallellization of the Kmeans algorithm with OpenMP

360-info/country-centroids

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

alejo1630/centroids

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

dal-gis/find-centroids-toolbox

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

huytranvan2010/Simple-Object-Tracking-with-OpenCV

Simple object tracking by using the centroid tracking algorithm

juan0x/FC25-Clusters-w-K-Means

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

Scrayil/k-means

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.

Akotovets1/Cryptocurrencies

Unsupervised Machine Learning and Cryptocurrencies

amyylin1/Cryptocurrencies

antonioualex/Twitter-Sentiment-Analysis-RNN-GRU-Development

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

AvivR94/Normalized-Spectral-Clustering-Project

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

BECHEUR/Etude_de_marche_exportation_alimentaire

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

geovanimelo/AppDistCentroide

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

J-Stephen-Brewer/centroids-in-R

function in R for calculating a centroid matrix

Peteresis/Credit_Risk_Analysis

Using Supervised Machine Learning algorithms to identify credit risks

RedChow/ThreadedKMeans

Jython does not have the GIL problem that CPython has, so we use this to easily create a threaded K-Means program.

RikoAppDev/agglomerative-clustering

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

AlexandrosPlessias/IR-Kmeans-PageRank

Implementaion of K-Means & Page Rank algorithms. (extend of "IR-CosineSimilarity-vs-Freq" repository)

aniketpanda18/Prediction-using-Unsupervised-ML

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

antonioualex/Tweet-Sentiment-Classifier-using-MLPs

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.

Baylex/Cryptocurrencies

Supervised Learning Recap

chandra-ps612/Set_of_shortest_distance_rectangles

Classical Computer Vision

Samahussien7/COVID-19-Chest-X-ray-Classification-KNN

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.

shinanna/Uber_NY_Machine_Learning

Data Analysis, EDA and Unsupervised Machine Learning Models on Uber NY Dataset

stu115/Machine-learning-assignment-pt.2

Doing algorithms on next sets of data