Performance comparison of Android ORM Frameworks

A fastest, exception-safety and pure C++17 thread pool.

A demo for the performace evaluation of different kinds of Kalman filters, including the conventional Kalman filter (KF), the unscented Kalman filter (UKF), the extended Kalman filter (EKF), the embedded/imbedded cubature Kalman filter (ICKF/ECKF), the third-degree cubature Kalman filter (CKF) and the fifth-degree cubature Kalman filter (FCKF).

Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10

Performance comparison of the MRPT algorithm to other approximate nearest neighbor search libraries

performance comparison among bunch of sorting algorithms it computes the time used in each algorithm to sort list of: Sorted numbers and Unsorted numbers

Performance Comparison of Prim's Algorithm using Different Priority Queues

A performance comparison of standard matrix functions between CPU and GPU using Nvidia CUDA on Visual Studio using C++

This software package accompanies the paper "A Methodology for Comparing Auto-Tuning Optimization Algorithms" (https://doi.org/10.1016/j.future.2024.05.021), making the guidelines in the methodology easy to apply.

Performance Testing and comparison between spring Boot and spring Webflux using JMeter in IntelliJ over a relational database

Performance Comparision of Machine Learning Algorithms

🧬 Implementation of a pipeline to compare the performance of Machine Learning classifiers on Gene Expression Omnibus data.

Compare performance between Python, R, Go, Rust, Julia

A performance comparison of various JVM web frameworks for building microservices with Kotlin.

It is Internship program having the objective of developing a multiclass classification model for the prediction of post partum hemorrhage causes in Ethiopia

Benchmark C, C++, Rust, Go. Raw numbers. No fluff 🏁

Performance Comparison Between Base Simulators for Minecraft in Reinforcement Learning

Execute R, Go, Python, Rust, Julia performance test and show result with PySide graph to compare with each other

This project uses Python, Hugging Face (sentence-transformers), Milvus + Docker (container running Vector DB) to create a vector database, populate it with details of many people (names, ages, salaries, addresses and their introductions) and enable searching and querying on the database contents using Cosine-Similarity distances on IVF Flat index.

Comparative analysis of Weaviate and Milvus vector databases using a dataset of over 1K resumes. Includes build time, search time, and throughput benchmarks with varying data and query loads.