Python
Machine Learning
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.
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 Analysis of Network based Attacks by Hybrid Machine Learning Algorithms
Predictive Analytics on Diabetes Data using Machine Learning Techniques
RanViz Ransomware Visualization and Classification Based on Time-series Categorical Representation of API Calls
Rule-Based With Machine Learning IDS for DDoS Attack Detection in Cyber-Physical Production Systems (CPPS)
Selection of Best Machine Learning Model to Predict Delay in Passenger Airlines
Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model
