Support Vector Machine Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Support Vector Machine ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Support Vector Machine Projects for ME, MTech, and PhD Students

Latest Support Vector Machine Projects

  1. A Deep Learning Based Induced GNSS Spoof Detection Framework
    This project focuses on keeping GPS signals safe from fake or spoofed signals. It uses advanced deep learning methods to learn what normal GPS data looks like. The system can then detect unusual or fake signals, even ones it has not seen before. Tests show it can identify almost all spoofed signals accurately, making GPS systems more secure and reliable.
  2. A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis
    This project focuses on detecting brain disorders using brain activity data. It combines diagnosis and explanation in a single system to make results more reliable. The system learns which brain connections are important for each person. It can also simulate changes in brain activity to understand how disorders affect the brain.
  3. An Overview of Data Integration in Neuroscience With Focus on Alzheimer’s Disease
    This project explores how to combine different types of medical and scientific data to better understand complex brain diseases. It explains the challenges of working with large and noisy datasets. The study gives guidance for beginners in data integration. It also shows how these methods can help in early detection of Alzheimer's Disease using machine learning.
  4. Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning
    This project focuses on checking how useful predictions are, not just how accurate they are. It gives more importance to predictions that matter most in real situations. The method looks at different types of mistakes and weighs them by their impact. It tests this approach on predictions for pollution, space weather, stock prices, and IoT data, showing better results overall.
  5. Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients
    This project focuses on predicting serious health problems in hospitalized patients before they happen. It uses a computer-generated score called the deterioration index to track patient condition over time. A special learning method helps the computer understand patterns in these scores and make accurate predictions. Hospitals can use this system as an early warning to provide timely care and prevent complications.
  6. GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status
    This project uses computer analysis of CT scans to predict a gene mutation called EGFR in lung cancer patients. It can highlight the most suspicious area in the tumor, helping doctors take more accurate biopsies. The method uses a special deep learning model that learns from both the images and tumor features. Tests showed it works better than older techniques and can help guide treatment decisions.
  7. Identifying Biases in a Multicenter MRI Database for Parkinson’s Disease Classification: Is the Disease Classifier a Secret Site Classifier
    This project studies how deep learning models for Parkinson's disease (PD) classification can unintentionally learn unrelated information from MRI scans. The researchers tested if the model could detect patient sex, scanner type, or hospital location even though it was only trained to detect PD. They found that the model could do this with high accuracy, showing it may rely on shortcuts instead of true disease patterns. This explains why such models sometimes fail on data from new hospitals.
  8. LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
    This project is about detecting unusual events in computer system logs. The proposed system, called LogFiT, can learn normal log patterns on its own without needing labeled data. It uses a smart language model to understand log content and identify when something abnormal happens. Tests show that LogFiT is more accurate and flexible than existing methods.
  9. Identifying Biases in a Multicenter MRI Database for Parkinsons Disease Classification Is the Disease Classifier a Secret Site Classifier
    This project studied how a deep learning model for Parkinson's disease might rely on unintended shortcuts in brain MRI data. The researchers tested whether the model's internal features could reveal the patient’s sex, the scanner used, or the hospital the scan came from. They found that the model could predict these factors even though it was only trained to detect Parkinson's. The study shows that such shortcuts can affect the model’s reliability on new data from other centers.
  10. Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor
    This project develops a wearable device to measure hand tremors in people with essential tremor. The device records movements while participants draw a spiral. Data from sensors are used to estimate tremor severity and classify tremor types. The method showed high accuracy and could help doctors monitor tremors and test new treatments.
  11. Multiclass Counterfactual Explanations Using Support Vector Data Description
    This project focuses on making complex AI models easier to understand. It develops a method to show how small changes in data can change the model’s prediction. The approach finds multiple alternative scenarios to explain decisions clearly. The method was tested on real datasets and gave useful results for practical applications.
  12. Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques Recent Research Advancements
    This project studies ways to keep cloud networks safe from unusual or harmful activity. It looks at how machine learning and deep learning can detect problems like intrusions or attacks. The research compares different methods and suggests better ways to find anomalies. The goal is to make cloud networks more secure and reliable.
  13. Toward a Conflict Resolution Protocol for Cloud Forensics Investigation
    This project focuses on resolving conflicts in cloud forensics. When cloud providers and users collect evidence, their results can sometimes disagree. The project proposes a protocol that uses a peer-to-peer system and AI to automatically identify and resolve these differences. A case study shows that this method works well without needing a human mediator.
  14. Channel-Agnostic Radio Frequency Fingerprint Identification Using Spectral Quotient Constellation Errors
    This project focuses on identifying individual wireless devices by analyzing tiny hardware imperfections in their signals. The system processes the signal, extracts unique patterns, and then classifies the device using a machine learning model. It works well even when the signal is affected by noise or interference. Tests with WiFi devices showed very high accuracy and better performance than previous methods.
  15. Intrusion Detection in Cyber-Physical Grid Using Incremental ML With Adaptive Moment Estimation
    This project develops a smart system to detect cyber-attacks in power grids. Unlike traditional methods, it can learn and adapt to new, unknown attacks without starting from scratch. The system uses a neural network that updates itself with new threat information. Tests show it can detect attacks with very high accuracy in both simulated and real datasets.
  16. RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification
    This project focuses on detecting harmful software that can attack computers and networks. It uses artificial intelligence, especially deep learning, to analyze large amounts of data and identify malware. The researchers designed a new system called RMDNet that can classify different types of malware accurately. Tests show it works better than existing methods on several malware datasets.
  17. Advanced Machine Learning Based Malware Detection Systems
    This project focuses on making machine learning faster and simpler. It creates smaller, optimized datasets that keep almost the same accuracy as the full data. The method helps train AI systems more efficiently and makes them easier to understand. It was tested on malware detection and kept 99% accuracy while using much less data.
  18. A Benchmarking on Optofluidic Microplastic Pattern Recognition: A Systematic Comparison Between Statistical Detection Models and ML-Based Algorithms
    This project focuses on detecting tiny plastic particles called microplastics using computer models. It compares traditional statistical methods with modern machine learning techniques to see which works best. The study finds that as more data is analyzed, detection becomes more accurate. Among all models, Support Vector Machine, Linear Discriminant Analysis, and Naive Bayes perform the best.
  19. A Comparative Analysis of Word Embeddings Techniques for Italian News Categorization
    This project focuses on teaching computers to automatically classify Italian news articles into different topics. It uses various language models to understand the meaning of Italian words and compares their accuracy. The researchers tested many algorithms and found that some worked much better than others. The study also introduced new Italian language models to help future research in text classification.
  20. A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers
    This project focuses on improving how malware is detected in computer systems. It changes malware images into grayscale and studies their texture patterns to find useful features. Different machine learning models are tested to identify which gives the best results. The study shows that the KNN model with SFTA and Gabor features gives the highest accuracy in detecting malware efficiently.
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At UniPhD, we provide complete guidance and support for Support Vector Machine ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

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UniPhD offers advanced Support Vector Machine research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

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