Deep Belief Network Final Year Projects with Source Code

Byadmin Final Year Projects

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

Deep Belief Network Final Year Projects

  1. Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine
    This project focuses on predicting air quality using a smart computer method. The researchers improved a type of machine learning model by combining it with a genetic algorithm, which helps the model learn better and make more accurate forecasts. They tested it on real air quality data from a city in China and found that it predicts pollutants and the Air Quality Index faster and more accurately than other common methods. This can help in planning and managing air pollution more effectively.
  2. Boosted Barnacles Algorithm Optimizer Comprehensive Analysis for Social IoT Applications
    This project focuses on improving the Social Internet of Things (SIoT), where smart devices share data for health monitoring, emergency alerts, and learning systems. It introduces a new method using the Barnacles Mating Optimizer to make data transfer faster and more accurate. The method was tested on real datasets and showed better performance than existing approaches. Overall, it helps smart devices work together more efficiently.
  3. Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment
    This project focuses on protecting Internet of Things (IoT) devices from DDoS cyber-attacks that can overload them with traffic. It uses machine learning to detect attacks by selecting the most important data features. The proposed method combines a “snake optimizer” for feature selection with three deep learning models to improve detection. Tests show that this approach works better than existing methods in identifying attacks accurately.
  4. HDLNET A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease
    This project focuses on detecting similarities in software code when the original source is not available. It uses neural machine translation to analyze small code sections called basic blocks across different computer architectures. The method extracts features faster and more accurately than previous approaches. Tests show it achieves 92% accuracy and can work up to 16 times faster using GPUs.
  5. Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
    This project focuses on protecting smart systems that connect computers and physical devices. It uses a new method to detect attacks or intrusions in these systems. The approach selects important data features and combines multiple deep learning models to identify threats. Tests show it works better than traditional methods in detecting intrusions.
  6. 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.
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Deep Belief Network Project Synopsis & Presentation

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Deep Belief Network Project Thesis Writing

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