Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter (IJCNN 2020)
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Updated
Jun 22, 2020 - Python
Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter (IJCNN 2020)
This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.
This project primarily focuses on addressing the issue of early detection of learning disabilities in students, with a specific focus on dyslexia and attention deficit hyperactivity disorder (ADHD).
This project uses YOLO for real-time leukemia detection in blood samples and CNNs for classifying brain hemorrhages in MRI scans. It aims to support faster, more accurate medical diagnostics through deep learning.
Classification of Alzheimer's Disease stages from Magnetic Resonance Images using Deep Learning
📊 Multiple Disease Prediction System 🏥 An intelligent healthcare system for predicting and diagnosing multiple diseases using machine learning and data analysis. Empowering early detection and better patient care. Disease Prediction: Predict the likelihood of various diseases, including heart diseases, diabetes, and more.
This repository houses a workflow that uses biological feature trees to segregate cancer RNA-seq datasets, then it trains machine learning models to predict the presence or absence of known, cancer-associated DNA-level mutations.
EDRN's knowledge using the Resource Description Format (RDF)
Obsolete buildout for the EDRN Public Portal
Addresses the problem of reconstructing images acquired by diffuse optical tomography using deep learning.
Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI
Built an end-to-end deep learning pipeline using ResNet-50 to classify retinal images into five stages of Diabetic Retinopathy. Applied transfer learning, image preprocessing, and AUC-based evaluation on the APTOS 2019 Kaggle dataset, achieving a 94% validation AUC—offering real-world potential in clinical diagnosis automation.
This project uses YOLO for real-time leukemia detection in blood samples and CNNs for classifying brain hemorrhages in MRI scans. It aims to support faster, more accurate medical diagnostics through deep learning.
Methods for Advance Detection of COVID-19.
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