Deep Learning Models Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Deep Learning Models 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 Deep Learning Models Projects
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Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
This project focuses on teaching a computer to convert MRI scans of spines into CT-like images. It also measures how confident the computer is about its predictions. By looking at uncertainties, the model can better separate bones from soft tissues. This helps doctors and researchers trust the results more when no expert labels are available. -
Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans
This project developed an artificial intelligence system to detect and measure lung cavity lesions from CT scans. The system can automatically find and outline the affected areas. It also calculates important features like size and thickness. This tool can help doctors diagnose, monitor, and track treatment of lung lesions more quickly and accurately. -
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
This project develops an AI system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to automatically learn important features from the images. The system combines multiple models to improve accuracy and reliability. Tests show it can diagnose diseases quickly and more accurately than traditional methods. -
On-Device Smishing Classifier Resistant to Text Evasion Attack
This project focuses on detecting smishing, which are fake text messages meant to steal personal data. The researchers built a smart system that can run directly on mobile phones and tell fake messages apart from real ones. They trained the system using hundreds of thousands of real messages from users in Korea. The system is small, fast, private, and can handle even slightly changed versions of smishing messages. -
A Novel Approach for Real-Time Server-Based Attack Detection Using Meta-Learning
This project creates a realistic virtual network to collect data on different types of cyberattacks. It uses this data to train an AI model that can detect attacks in real time with very high accuracy. The model combines two machine learning methods to improve prediction performance. The goal is to make network security stronger and provide a useful dataset for future research.
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