Back Propagation Neural Network Final Year Projects with Source Code

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Back Propagation Neural Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Back Propagation Neural Network 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.

Back Propagation Neural Network Final Year Projects

  1. A BP Neural Network-Assisted Smart Decision Method for Education Quality
    This project uses a neural network model to help universities evaluate teaching quality automatically. It collects student and expert feedback, builds an evaluation system, and trains the model to judge education performance. The method is tested on real university data to see changes before and after applying the model. The results show that it can give reliable support for improving teaching quality.
  2. Leveraging Brain MRI for Biomedical Alzheimers Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning
    This project focuses on improving the diagnosis of Alzheimer’s disease using brain MRI scans. It uses deep learning to automatically analyze images and extract important features, reducing the need for manual input from experts. The method combines a DenseNet model for feature extraction with an optimized neural network for classification. Tests show that this approach gives more accurate results than existing techniques.
  3. Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network
    This project uses deep learning to identify patterns in electrical partial discharges from images. It combines two networks, CNN and LSTM, to improve detection accuracy. Data augmentation increases the number of training images. The proposed hybrid model achieves almost perfect accuracy in recognizing different PD types.
  4. Smart Healthcare Hand Gesture Recognition Using CNN-Based Detector and Deep Belief Network
    This project develops a system that can accurately track and recognize hand gestures from videos in real-world environments. It processes video frames, cleans the images, and uses neural networks to identify hand movements. The system then extracts detailed features, optimizes them to reduce errors, and classifies gestures using a deep learning model. Tests on standard datasets show it achieves high accuracy and works well compared to existing methods.
  5. Improved Sparrow Search Algorithm Optimized DV-Hop for Wireless Sensor Network Coverage
    This project improves how wireless sensor networks find the positions of nodes. It uses a new algorithm called GSSADV-Hop, which reduces location errors by adjusting node hop calculations and using a smart search strategy. The method is faster and more accurate than traditional techniques, achieving very low positioning errors. It helps make sensor networks more reliable and efficient for practical use.
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Back Propagation Neural Network Project Synopsis & Presentation

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