Support Vector Machines Final Year Projects with Source Code

Byadmin Final Year Projects

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

  1. A Distracted Driving Detection Model Based On Driving Performance
    This project studies how a driver behaves when fully focused and when mentally distracted. Researchers collected driving data from many people using a simulator. They trained a deep learning model to recognize whether a driver is distracted just from the way they drive. The model works very well and can help detect unsafe driving in real time.
  2. A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients
    This project predicts whether a diabetes patient will return to the hospital soon. It uses computer models to learn patterns from past patient records. The authors improve a neural network by guiding how it learns from correct and incorrect data. This guided method gives more accurate results and helps the model learn faster.
  3. A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders
    This project focuses on detecting Autism Spectrum Disorder (ASD) early using machine learning. It compares different ways of preparing data and several simple machine learning methods to see which works best. The study tests these methods on datasets for toddlers, children, adolescents, and adults. The results show high accuracy and identify the most important factors for predicting ASD, helping doctors make better decisions.
  4. A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines
    This project focuses on detecting fake or misleading online reviews that can mislead customers and harm businesses. It uses a machine learning model called Weighted Support Vector Machine combined with an optimization algorithm called Harris Hawks Optimization to improve accuracy. The method works for multiple languages, including English, Spanish, and Arabic. The system was tested with different techniques and datasets, achieving high accuracy in identifying spam reviews, especially during the COVID-19 pandemic when online reviews increased dramatically.
  5. GARL-Net Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification
    This project focuses on improving breast cancer detection using computer-based image analysis. The researchers developed a new deep learning method called GARL-Net that can learn more efficiently from large and uneven image datasets. It uses advanced techniques to reduce errors in classification and improve accuracy. Tests on popular breast cancer image datasets showed very high accuracy, precision, and recall, outperforming existing methods.
  6. Enabling IoT Service Classification A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services
    This project studies how different Internet of Things (IoT) services interact and how they can be organized efficiently. It classifies IoT services into five groups based on their key characteristics. Machine learning methods like decision trees, SVM, and voting classifiers are used to make this classification. The results show that decision trees provide accurate and reliable predictions, helping improve IoT resource management.
  7. Identifying ADHD for Children With Coexisting ASD From fNIRs Signals Using Deep Learning Approach
    This project focuses on detecting ADHD in children who also have autism. Researchers collected brain signals while children drew simple patterns. They used a deep learning method that combines CNN and Bi-LSTM to analyze these signals. The system could accurately identify children with ADHD and autism with over 90% accuracy.
  8. 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.
  9. On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments
    This project focuses on automatically assigning software bug reports to the right developers. It uses a deep learning system that studies both the context of the bug and repeating keywords in bug descriptions. The model combines these features to predict which developer can fix the bug. Tests on real-world software projects show that this method works better than previous approaches.
  10. Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks
    This project focuses on detecting and identifying power quality problems in smart grids and renewable energy systems. It uses a deep learning method called a Deep Auto-encoder to automatically learn important features from power signals. The system can accurately classify the type of disturbance and find when it starts and ends. The approach is faster and more accurate than traditional methods like SVM, even with noisy data.
  11. 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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