Ā Ā Ā Ā Ā To design and develop a hybrid GPT + Quantum-Inspired language model that effectively distinguishes between human-written and AI-generated text using contextual embeddings and quantum-style measurement operators.
Aim:
This study aims to develop an efficient and scalable system for multi-class classification of URLs into Phishing, Benign, Defacement, and Malware categories using the lightweight and context-aware DistilBERT model.
Aim:
To develop a custom Convolutional Neural Network (CNN) model for accurately classifying seven common canine skin diseases, thereby improving diagnostic precision and supporting veterinary care.
To develop an efficient image forgery detection system using deep learning, leveraging transfer learning models such as ConvNeXt and ResNet to enhance accuracy. The project focuses on designing a robust system that can detect forged images with high precision and recall.