missCompare R package - intuitive missing data imputation framework

A browser-based tool for speedy and correct JS performance comparisons!

Single version, Real World (Dead) Bug Fuzzer Benchmark Suite (Work-in-Progress)

Environment to comparision of evolutionary algorithms based on CEC benchmarks

This repository contains the results and comparison visualization of circRNA candidates detected by circRNA prediction softwares.

Comparison of various sorting algorithms

Comparison of branchless programming speedups (or slowdowns) in various languages

Comparison by differing nature of the input

Central repository for university projects, covering Numerical Methods, Algorithms, and Data Structures.

A Comparative Study on the Energy Consumption of Progressive Web Apps

A simple university project designed to compare these two types of data structures

Miscellaneous codes: comparing identical scripts in Python against Matlab and R; and also a pet project on term structure optimisation

A comparative review of three different basic feature extraction techniques for Reinforcement Learning with visual input.

A video-based time series anomaly detection project for classifying human activities. Includes binary (Fall vs Normal) and multi-class action recognition using CNN+LSTM, I3D, YOLOv8+ResNet models with confusion matrix results and preprocessed datasets.

Comparison of Extreme Learning Machine and MLP on the classic IRIS flower dataset.

Zend ServiceManager 3.2 refactored for much better performance.

Created to compare energy consumption of C, Java, JavaScript, TypeScript, Ruby, and Zig

A streamlined MLOps pipeline integrating version control, model tracking, and reproducibility using DagsHub. Ideal for collaborative machine learning workflows and experiment tracking. This Repo also has some Custom APIs Demo.

Repository to compare the quality of data generated from CARLA and SUMO simulators against real data from the UAH-DRIVESET-v1

An in-depth comparison and building walkthrough of two sentiment classification models, ML vs. DL —a Logistic Regression and an LSTM model— both trained on the Sentiment140 dataset. The Jupyter notebook contains every step from data preprocessing, construction, training, evaluation, and comparing the strengths and shortcomings of each approach.