Support Vector Regression Final Year Projects with Source Code

Byadmin Final Year Projects

Support Vector Regression Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Support Vector Regression 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.

Support Vector Regression Final Year Projects

  1. Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network
    This project focuses on predicting a person’s brain age using MRI scans. It uses a special type of neural network to compare brain images from different people and measure their similarity. The model learns important features from the images and predicts brain age with good accuracy. Tests show it works well on a dataset of brain scans over time.
  2. A Robust Hybrid Classical and Quantum Model for Short-Term Wind Speed Forecasting
    This project focuses on predicting wind speeds more accurately to help power utilities manage renewable energy better. It combines a traditional LSTM model with a quantum neural network to improve forecasting. The model is tuned using the Taguchi method to handle seasonal changes in data. Tests using historical wind data from multiple countries show that this hybrid approach predicts wind speeds better than existing methods.
  3. An Elitist Artificial Electric Field Algorithm Based Random Vector Functional Link Network for Cryptocurrency Prices Forecasting
    This project builds a model to predict the prices of popular cryptocurrencies. It improves an existing learning method by using an algorithm that finds better internal settings automatically. The model learns patterns in past price data and tries to make more accurate predictions. Tests show that it performs better than several well-known forecasting methods.
  4. Application of Artificial Intelligence Techniques for BrainComputer Interface in Mental Fatigue Detection A Systematic Review
    This project reviews how mental fatigue, which affects both the mind and body, can be detected using brain-computer interfaces and artificial intelligence. The study analyzed research from 2011 to 2022 and identified gaps in using these systems for automated mental fatigue monitoring. It also explains the challenges, AI techniques, and future directions for improving detection and practical implementation. The goal is to guide better and faster methods to recognize and manage mental fatigue.
  5. Link Characterization and Edge-Centric Predictive Modeling in an Ocean Network
    This project focuses on improving internet communication for fishing vessels at sea. It studies how weather, waves, and antenna movement affect wireless signals. The researchers use a combination of past and real-time data to predict signal strength using a smart learning system. This helps make sea communication more reliable and efficient.
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Support Vector Regression Project Synopsis & Presentation

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