Python
Deep Learning
Medical Chatbot
Multi-Fruit Classification and Grading Using a Same-Domain Transfer Learning Approach
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
PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping
Plant Disease Detection and Classification by Deep Learning: A Review
Predicting Market Performance Using Machine and Deep Learning Techniques
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors
Product Recommendation System Using Large Language Model Llama 3
To develop a chatbot that integrates Retrieval-Augmented Generation (RAG) and Llama-3 API for product recommendation by leveraging a vector database with embeddings created using SBERT. This aim involves addressing limitations in traditional recommender systems, such as cold start problems and lack of personalization, by combining state-of-the-art language models with efficient data retrieval mechanisms.
