There are 6 repositories under machine-learning-algorithm topic.
Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
GeneWalk identifies relevant gene functions for a biological context using network representation learning
FL-HDC: Hyperdimensional Computing Design for the Application of Federated Learning (IEEE AICAS2021)
Openwrt 18.06.5 featured with the Exein's security framework
A comprehensive curated list of algorithms🤠🏆
A project to survey the possibilities of a graph database Neo4j in building decision tree algorithms using stored procedures.
The data-set is related with direct marketing campaigns (were based on phone calls) of a banking institution. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The goal is to predict if the client will subscribe a term deposit
This project aims to reduce the time delay caused due to the unnecessary back and forth shuttling between the hospital and the pathology lab. Here a machine learning algorithm will be trained to predict a liver disease in patients using a data-set collected from North East of Andhra Pradesh, India.
This source code is a MATLAB implementation of a haze removal algorithm that can deal with the post-dehazing false enlargement of white objects effectively. The work was published in MDPI Sensors journal under the title "Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator".
Ozone Day AdaBoostClassifier and Random Forest Tree Classifier with Machine Learning
A log-based Threat Hunting tool
Welcome to Guilherme Yuji Fernandes' Portfolio. Here you will find data science projects with problems that many companies are trying to solve nowadays. The solutions given to them were obtained through data.
Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.
A Classification Method in Machine Learning Based on Soft Decision-Making via Fuzzy Parameterized Fuzzy Soft Matrices
The program is written in R which analysis patient's health condition using sentiment analysis and classifies as exist, deteriorate and recover using machine learning algorithm - Naive bayes
A java library providing a configurable neural network. Supports supervised learning and genetic algorithm.
Using long short term memory networks to analysis the pollution of Beijing, China.
A New Classification Method Using Soft Decision-Making Based on an Aggregation Operator of Fuzzy Parameterized Fuzzy Soft Matrices
Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices
In this repository, You will find the documentations on a daily basis on Machine Learning
Music Genre Classification and Recommendation
A simple Machine Learning algorithm that calculates the price of houses based on weighted attributes and a large training dataset.
I have used machine learning algorithm(linear regression) in this project for predicting house price.
Artificial Intelligence, Computer Network, Machine Learning, Python, DAA, Distributed System, Internet of Things (IoT), Data Science and Analysis.
The hyperparameters of XGBoost was found using the DE algorithm. The fraud detection challenge was used for this project. The model accuracy on test data was found 89%.
Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task.
A machine learning algorithm based on Gaussian processes for MODIS multilayer cloud and thermodynamic phase classification using CALIOP and CloudSat
Comparison of the logistic regression, decision tree, and random forest models to predict red wine quality in R.
Classification based machine learning algorithm to classify the strength of passwords within predefined categories.
Final project progress will be posted here.
A classifier inspired by electrostatics. Works with weighted datasets.
Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;
This is my attempt for the #AndroidDevChallenge
Code playground for commonly used machine learning models and algorithms