Python
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 Approach to Control the PC with Hand Gesture Recognition using Computer Vision Technique
An Efficient and Generic Construction of Public Key Encryption with Equality Test Under the Random Oracle Model
An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier
An Integrated Multi-Task Model for Fake News Detection
ANALYSIS OF CHRONIC LIVER DISEASE DETECTION BY USING MACHINE LEARNING TECHNIQUES
Android Malware Detection Using Informative Syscall Subsequences
Artificial Intelligence in Agriculture: A Systematic Review of Crop Yield Prediction and Optimization
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.
Automated Smart Attendance System Using Face Recognition
