FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.

math with more good bits

Личный проект в области Computer Vision

The bignumber-utils package empowers developers to effortlessly perform accurate and reliable decimal and non-decimal arithmetic in JavaScript, leveraging the robust foundation of the bignumber.js library.

Developed a predictive model to classify individuals into one of seven weight categories (ranging from insufficient weight to obesity type 3) based on various personal factors using diverse neural network architectures.

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.

Build accurate and secure AI applications to unlock value faster

The content involves code that describes all the necessary steps involved in using a machine learning model for any dataset.

A descriptive and inferential statistical analysis from the Kaggle database on the data collected by an IoT smoke detection device. Machine learning techniques were also used to help build this smart device, increasing its accuracy.

Testing the consistency of binary classification performance scores reported in papers

Data quality estimations for OpenStreetMap

Multi-class confusion matrix library in Python

This project predicts diabetes using the PIMA Diabetes dataset, leveraging health metrics of Pima Indian women. Machine learning models like Logistic Regression, Decision Trees, and Random Forest are implemented to determine the likelihood of diabetes, evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.

Exploratory data analyzing of Global Financial Database and loan borrowing prediction using PySpark

This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

A simple Windows command line utility to accurately set the relative position of displays in a dual- or multi-monitor setup.

An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.

This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification

High precision and accuracy, open source, online, extensible and low cost homebrew automation on top of ESP8266 microchip.