benign

ExeRay AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.

Extracting features from URLs to build a data set for machine learning. The purpose is to find a machine learning model to predict phishing URLs, which are targeted to the Brazilian population.

🌸 Breast epithelium segmentation through IHC-guided supervision

Official implementation of the paper: "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions", IEEE WACV, 2022

Collect and download benign Windows PE executables from marketplace sources for research and machine learning

Using Mammography and Histopathology (ResNet for classification and UNet for detection)

This ML-based Network Intrusion Detection System classifies traffic as Benign, DDoS, or Port-Scan. Powered by a Random Forest model and 22 OSI-layer features, it achieves 93% accuracy with a low 1.84% false-positive rate. The end-to-end pipeline includes synthetic dataset generation, automated preprocessing, model training, and deep evaluation.

Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithm Logistic Regression

Gallbladder Rokitansky-Aschoff Sinus - Safra Kesesi Rokitansky-Aschoff Sinus

Esophagus Granular Cell Tumor - Özofagus Granüler Hücreli Tümör

Reactive atypia in an ulcerated colon polyp - Ülsere Kolon Polibinde Reaktif Atipi

Setup IPFS and Host Simple Benign HTML and Executable

Transfer learning with DenseNet201 for binary classification of breast cancer histopathology images using BreakHis dataset. Advanced augmentation + class weights. Accuracy: 90.48%.

Benign Prostate Hyperplasia - Benign Prostat Hiperplazisi

Liver Hemangioma - Karaciğer Hemangioma

Endometriosis - Endometriozis

Exostosis: Osteochondroma - Ekzostoz: Osteokondrom

Morphea - Morfea

Ochronosis - Okronozis

Pancreas Solid Pseudopapillary Neoplasm - Pankreas Solid Psödopapiller Neoplazm