Transformers Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Transformers ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Transformers Projects
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LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
This project is about detecting unusual events in computer system logs. The proposed system, called LogFiT, can learn normal log patterns on its own without needing labeled data. It uses a smart language model to understand log content and identify when something abnormal happens. Tests show that LogFiT is more accurate and flexible than existing methods. -
Transformer-Based Spatio-Temporal Analysis for Classification of Aortic Stenosis Severity From Echocardiography Cine Series
This project focuses on detecting and measuring the severity of a heart valve disease called aortic stenosis using heart ultrasound videos. The researchers built a deep learning model that can learn both the shape and movement of the valve from standard 2D heart scans. The model can pick out the most important parts of the video on its own and gives results almost as accurate as expert doctors. This method could help hospitals without specialized cardiologists diagnose the disease more easily. -
A Graph-Based Multi-Scale Approach With Knowledge Distillation for WSI Classification
This project is about teaching computers to analyze very large medical images for disease detection. Normally, labeling these images takes too much time. The researchers created a new method that looks at the images at different zoom levels and learns how parts of the image relate to each other. Their approach makes predictions more accurate and works better than previous methods on standard datasets. -
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data
This project uses artificial intelligence to predict how diseases will progress in patients over time. It looks at medical images and combines them with clinical data to make forecasts. The system learns like a doctor team, analyzing images and patient information together. It was tested on knee osteoarthritis and Alzheimer’s disease and gave better results than current methods.
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