Python
Measuring the Heart Attack Possibility using Different Types of Machine Learning Algorithms
Neural-XGBoost A Hybrid Approach for Disaster Prediction and Management Using Machine Learning
Obfuscated Privacy Malware Classification Using Machine Learning and Deep Learning Techniques
Python, Cybersecurity, Deep Learning, Machine Learning, Artificial Intelligence, Cyber Security, Deep Learning, Machine Learning
Aim
The aim of this research is to develop an intelligent system capable of detecting and classifying obfuscated privacy malware into various categories and families. This system leverages machine learning and deep learning models trained on the CIC-MalMem-2022 dataset to improve accuracy and address the challenges posed by data imbalance and complex malware behaviour.
PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping
Phishing Detection System through Hybrid Machine Learning Based on URL
Phishing URL Detection: A Real-Case Scenario Through Login URLs
Plant Disease Detection Using Machine Learning Techniques
Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning
Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques
Predictive Analytics on Diabetes Data using Machine Learning Techniques
Ransomware Classification and Detection with Machine Learning Algorithms
