Transfer Learning Technique Final Year Projects with Source Code
Transfer Learning Technique Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Transfer Learning Technique projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.
Transfer Learning Technique Final Year Projects
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EfficientNetB3-Adaptive Augmented Deep Learning AADL for Multi-Class Plant Disease Classification
This project focuses on automatically identifying plant diseases using artificial intelligence. It uses advanced deep learning models that have already been trained on large datasets to recognize 52 types of diseases and healthy leaves. The study tested several models and found that one called EfficientNetB3-AADL gave the most accurate results, correctly identifying diseases 98.7% of the time. This approach can help farmers quickly and accurately detect plant diseases to protect crops. -
Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption
This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
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Transfer Learning Technique Project Synopsis & Presentation
Final Year Projects helps prepare Transfer Learning Technique project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.
Transfer Learning Technique Project Thesis Writing
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