Python
Advanced Heart Attack Risk Prediction Using Stacked Hybrid Machine Learning
Diagnosis of Liver Disease using ANN and MLAlgorithms with Hyperparameter Tuning
The aim of this project is to develop a system for the diagnosis of liver disease using Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms with hyperparameter tuning. The project focuses on leveraging advanced models and optimization techniques to enhance predictive capabilities, aiding in the early detection and effective management of liver disease.
DroneGuard: An Explainable and Efficient Machine Learning Framework for Intrusion Detection in Drone Networks
Early Detection of Childhood Malnutrition using Survey Data and Machine Learning Approaches
Efficient Machine Learning Approach for Crime Detection in India
Enhancing Smishing Detection A Deep Learning Approach for Improved Accuracy and Reduced False Positives
The aim of this work is to explore and develop advanced methods for enhancing the detection and prevention of smishing attacks. This involves utilizing cutting-edge technologies such as machine learning, artificial intelligence, and behavioral analysis to identify and block fraudulent SMS messages, protecting users from financial and personal data theft. The goal is to create more effective, real-time detection systems to mitigate the growing threat of smishing attack
Leveraging Machine Learning Techniques of Real Time Detection of UPI Fraud
Machine Learning in Money Laundering Detection Over Blockchain Technology
Neural-XGBoost A Hybrid Approach for Disaster Prediction and Management Using Machine Learning
