Python
Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data using GPT
Measuring the Heart Attack Possibility using Different Types of Machine Learning Algorithms
Medical Chatbot
Mobile crowd sensing approaches to address the COVID-19 pandemic in Spain
Motorized Solar Scarecrow Bird Animal Repellent
Mulberry Leaf Disease Detection Using CNN-Based Smart Android Application
Multi-Fruit Classification and Grading Using a Same-Domain Transfer Learning Approach
Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path
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.
