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

This repository contains Agief Prakasa Nurdien's AoL (Assurance of Learning) Case Study for COMP6065001 – Artificial Intelligence course. The project combines keyboard & mouse activity tracking with NLP-based text analysis to provide users with an accessible, interactive tool for ADHD screening and awareness (AoL score: 90, grade: A).

Semptomlardan Yola Çıkarak Diyabet Riskinin Tahmini: ML Modellerinin Performans Karşılaştırması

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

An Awesome List of the latest time series papers and code from top AI venues.

cfDNAPro specializes in standardized and robust cfDNA fragmentomic analysis

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.

Early Diabetes Diagnosis Using a Low-Complexity Deep Learning Model with Feature Reduction

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.

Breast cancer histopathology image segmentation using U-Net. This repository implements U-Net for accurate segmentation of cancerous regions. It includes data augmentation, mixed precision training, checkpointing, and evaluation metrics like Dice score to improve model performance.

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.

Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI

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.

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.

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).

A Time series Data modelling to forecast Gambling Addiction Signs in Players using K-Means Clustering, ARIMA/SARIMA and LSTM to forecast wagering patterns

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.

Kvasir-SEG: A Segmented Polyp Dataset

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.