joepfortunato,Joe Fortunato,github

landon

A collection of tools, data structures and methods for exporting Blender data (such as meshes and armatures) and preparing it for your rendering pipeline.

latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

leflow

Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

libdshowcapture

Free and Open Source C++11 Library for capturing DirectShow video/audio devices on windows.

lime

Lime: Explaining the predictions of any machine learning classifier

linear-chain-crf

Implementation of a linear-chain CRF in PyTorch

linuxcnc

LinuxCNC controls CNC machines. It can drive milling machines, lathes, 3d printers, laser cutters, plasma cutters, robot arms, hexapods, and more.

loganalyzer

Automatic analyzer for logs generated by OBS Studio

lynis

Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.

machine-learning-based-classification-of-cervical-cancer-using-k-nearest-neighbor-random-forest-and

Cervical cancer is the second most common type of cancer that is found in the women worldwide. Generally, cancer caused due to irregular growth of cells in a particular area that or have the potential to spread to the other parts of the body as well. Identification of a cervical cancer test is an examination of the tissue taken from a particular region, which might contain cancerous cells through biopsy, is exceptionally challenging because these types of cells does not offer unusual color or texture variants from the standard cells. To identify the abnormalities in human cell the high-level digital image processing technologies are already present in the market which very costly concerning the money. Therefore, we are proposing the model which going to classify whether a female patient has cervical cancer or not. We are using various attributes from real-life and performing a feature selection algorithm Recursive Feature Elimination (RFE). Afterward, making classification models using three machine-learning algorithms like K-Nearest Neighbor (KNN), Random Forest and Multilayer Perceptron (MLP), MLP is a type of the Artificial Neural Network (ANN) algorithm whereas KNN and Random Forest is a supervised type of algorithm.