Python
Deep Learning
A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities
A Deep-Learning-Based Lightweight Model for Ship Localizations in SAR Images
A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images
A Novel Transformer Model With Multiple Instance Learning for Diabetic Retinopathy Classification
A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID
Advanced YOLO DeepSort Based System for Drainage Pipeline Defects Intelligent Detection
AI-Generated vs. Human Text: Introducing a New Dataset for Benchmarking and Analysis
Aim: The aim of this project is to enhance the ability to distinguish between AI-generated and human-authored text by utilizing a fine-tuned BERT classifier. This approach emphasizes contextual understanding and deep language representation to outperform traditional machine learning systems in identifying AI-generated content.
An Integrated Multi-Task Model for Fake News Detection
Android Malware Detection Using Informative Syscall Subsequences
ATT Squeeze U-Net A Lightweight Network for Forest Fire Detection and Recognition
Automated Brain Tumor Segmentation and Classification in MRI using YOLO-based Deep Learning
Python, Deep Learning, Generative AI, Projects, Artificial Intelligence, Deep Learning, Generative AI
The aim of this research is to develop a more effective and efficient brain tumor segmentation system using the YOLOv11 architecture. The focus is on enhancing the accuracy and reliability of tumor identification in brain imaging, specifically through advanced segmentation techniques. By leveraging deep learning models, the study seeks to provide an automated solution for real-time tumor segmentation, assisting in clinical decision-making and early diagnosis.