Python
Neural-XGBoost A Hybrid Approach for Disaster Prediction and Management Using Machine Learning
Nyx – An Educational Assistant for the Visually Impaired
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.
Object Detection Method Using Image and Number of Objects on Image as Label
To develop an object detection model using YOLOv8 to address the limitations of existing methods and improve detection accuracy, robustness, and efficiency. The aim is to design a system that reduces the dependency on extensive labelling while ensuring adaptability to unseen environments. The model will utilize YOLOv8’s capabilities to process data efficiently and deliver high-performance results for diverse applications in object detection.
Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography
Aim:
Aim to build a reliable system that can identify different retinal diseases from OCT images. To create a practical workflow that can analyze images, compare predictions, and flag mistakes for improvement. It combine the strengths of multiple models so the final decision is more accurate and stable.
Online Exam Proctoring System Based on Artificial Intelligence
Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches
ParkNest – The Smart Parking System Using IoT
PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping
Pet Shop Management System
Phishing Detection System through Hybrid Machine Learning Based on URL
