Deep Neural Network Model Final Year Projects with Source Code

Byadmin Final Year Projects

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

Deep Neural Network Model Final Year Projects

  1. A Novel Two-Stage Deep Learning Model for Network Intrusion Detection LSTM-AE
    This project focuses on improving computer systems’ ability to detect cyber-attacks automatically. It uses a combination of two advanced deep learning methods, LSTM and Auto-Encoders, to create a flexible and accurate intrusion detection system. The model is tested on publicly available datasets to find the best settings and compare its performance with other deep learning approaches. Results show that the proposed system can effectively detect attacks in modern network environments.
  2. A Signature Transform of Limit Order Book Data for Stock Price Prediction
    This project focuses on predicting stock prices using advanced machine learning techniques. It takes detailed order book data from stock exchanges and extracts key patterns called signature features. These features are then used to train models like deep neural networks and random forests. The results show that using signature features improves prediction accuracy and efficiency, especially in developed markets.
  3. A Swin Transformer-Based Approach for Motorcycle Helmet Detection
    This project focuses on automatically detecting whether motorcyclists are wearing helmets using video cameras. It uses advanced computer vision and machine learning techniques to identify helmets even in complex situations and with multiple passengers. The method combines a transformer model with other neural network tools to improve accuracy. Tests show that it works better than existing detection systems.
  4. Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network
    This project uses Internet of Things (IoT) devices in farms to monitor environmental conditions like temperature, humidity, rainfall, wind, and sunshine. A deep learning model uses this data to predict pest outbreaks in crops. The system generates weekly predictions with high accuracy, helping farmers take timely action to protect crops. Over five years of data, the model achieved 94% accuracy and improves predictions over time.
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At Final Year Projects, we provide complete guidance for Deep Neural Network Model IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

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Deep Neural Network Model Project Synopsis & Presentation

Final Year Projects helps prepare Deep Neural Network Model 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.

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