cfDNAPro specializes in standardized and robust cfDNA fragmentomic analysis

Open source Artificial Intelligence for COVID-19 detection/early detection. Includes Convolutional Neural Networks (CNN) & Generative Adversarial Networks (GAN)

Fraud Detection for VoIP. Use SentryPeer® HQ to help prevent VoIP cyberattacks and fraudulent VoIP phone calls (toll fraud) at https://sentrypeer.com

proposed early detection method for parkinson's disease using deep learning on MRI dataset

Deep Learning Models for the Early Detection of Parkinson’s Disease using the motor-based symptoms.

Kvasir-SEG: A Segmented Polyp Dataset

This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.

Research on developing a new method for determining the warning time of Early Warning Signals. Also an attempt at removing window size uncertainty from EWS analysis

Using Image Processing and both classical and brand-new Machine Learning techniques such as SVM, k-NN, XGBoost, and also LSTM; we are trying to predict beforehand the driver's drowsiness and warn him/her by an alert before any crash happened.

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter (IJCNN 2020)

Addresses the problem of reconstructing images acquired by diffuse optical tomography using deep learning.

Methods for Advance Detection of COVID-19.

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).

EDRN Production Program for the Public/Private Portal (P5)

This project develops a predictive model to identify early signs of mental health issues in adolescents using social media activity, school performance, health records, and an AI chatbot. It analyzes emotional tone, academic changes, and health data, offering personalized recommendations and resources for mental wellness.

A collection of extension methods for validating method arguments in order to spot bugs as quickly as possible.

Heart Disease Prediction Using Machine Learning is a logistic regression model that predicts heart disease based on medical data. It analyzes features like age and cholesterol, achieving 85.24% training accuracy and 80.49% testing accuracy, facilitating early detection for timely intervention.

Early Detection of Diabetic Kidney Disease using Contrast Enhanced Ultrasound Perfusion Parameters. Explore perfusion models (Lagged Normal, Log-Normal, Gamma Variate), compare their effectiveness, and analyze their application to diabetic and control cases.

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.

Obsolete buildout for the EDRN Public Portal