Support Vector Machine Final Year Projects with Source Code

Byadmin Final Year Projects

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

  1. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets
    This project studies public reactions to the recent monkeypox outbreak by analyzing social media posts. Researchers collected over 500,000 tweets and labeled them as positive, negative, or neutral. They tested many machine learning models to find the best way to predict public sentiment. The study found that a model using TextBlob, lemmatization, CountVectorizer, and SVM gave the most accurate results, helping health authorities understand public concerns.
  2. A Review of Methodologies for Fake News Analysis
    This project reviews research on detecting fake news, which is becoming more important as false information spreads online. It studies how fake news can be analyzed by looking at its knowledge, style, source, and how it spreads. Detection methods are either manual, using experts or crowds, or automatic, using machine learning. The study suggests that machine learning works well and plans to explore Bayesian methods for faster and more flexible detection in the future.
  3. A Stock Price Prediction Method Based on BiLSTM and Improved Transformer
    This project develops a new method to predict stock prices more accurately and reliably. It combines three advanced models—BiLSTM, Transformer, and TCN—to use their strengths in analyzing stock data. The method was tested on multiple stocks and consistently outperformed existing prediction approaches. It provides stable, precise forecasts without timing issues.
  4. A Systematic Literature Review on Multimodal Machine Learning Applications Challenges Gaps and Future Directions
    This project reviews how machine learning can use multiple types of data together, like images, text, and audio, to solve real-world problems. It studies recent research on key challenges, such as combining, translating, and aligning these different data types. The authors analyzed over 1000 articles to identify trends, gaps, and progress in this area. This work helps researchers understand the current state of multimodal machine learning and plan future studies.
  5. 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.
  6. Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention
    This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
  7. Automatic Liver Cancer Detection Using Deep Convolution Neural Network
    This project focuses on automatically detecting liver cancer from CT scans. It uses a new method called ESP-UNet to accurately separate the liver from the rest of the image, avoiding errors in segmentation. After that, a lightweight deep learning model analyzes the segmented liver to detect cancer. The method shows better results than previous approaches in terms of accuracy and reliability.
  8. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  9. DeepSkin: A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using computer-based methods. It uses images of different skin lesions from a large dataset to teach a computer to recognize cancer types. Advanced deep learning models, like CNNs with DenseNet169 and ResNet50, are applied to improve accuracy. The goal is to help doctors identify skin cancer more reliably and quickly.
  10. Human Behavior Recognition Based on Multiscale Convolutional Neural Network
    This project focuses on recognizing human actions in videos using a smarter neural network. It improves how the network looks at both the overall and local details in each video. The method splits videos into segments, extracts important behavior information, and combines it over time to understand the full action. Tests show it is accurate, faster, and more efficient than older methods.
  11. Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques
    This project uses computer vision and machine learning to understand students’ emotions in a classroom. It tracks facial gestures to identify feelings like interest, boredom, or enthusiasm during learning. The system builds a database of real, spontaneous emotions and helps teachers evaluate students’ emotional engagement along with their learning progress. It focuses on supporting teachers in face-to-face education.
  12. A Machine Learning and Optimization Framework for the Early Diagnosis of Bovine Respiratory Disease
    This project focuses on detecting respiratory disease in dairy calves early using smart farm technologies like automatic feeders and sensors. The system uses machine learning to analyze calf behavior and identify sickness before obvious symptoms appear. A new method called CALF helps choose the best features to improve predictions within a budget. Tests on real calves show it can accurately detect illness and its severity days before traditional diagnosis.
  13. Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection
    This project focuses on detecting insider threats, which are harmful actions by people who have authorized access to an organization’s network. Traditional security tools often fail, so the study uses machine learning to improve detection. It combines supervised and semi-supervised learning, analyzes data streams, and retrains models periodically. The best results were achieved using the Isolation Forest algorithm, showing good accuracy in identifying both harmful and safe activities.
  14. Classification and Prediction of Drivers Mental Workload Based on Long Time Sequences and Multiple Physiological Factors
    This project focuses on understanding a driver’s mental workload to improve road safety. The researchers collected physiological data like heart rate and skin activity while driving. They developed a model called LTS-MPF that looks at all these signals over time to predict how stressed or focused a driver is. The model can classify the current mental state and even predict the next second, achieving over 93% accuracy.
  15. DeepCurvMRI Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimers Disease
    This project aims to detect Alzheimer’s Disease early using MRI brain images. The researchers first enhanced the images and then trained a deep learning model to recognize patterns linked to different stages of the disease. The model learned these patterns with very high accuracy. This approach could help doctors identify Alzheimer’s much earlier and more reliably.
  16. Design and Development of an Efficient Risk Prediction Model for Cervical Cancer
    This study focuses on predicting the risk of cervical cancer in women based on lifestyle and health factors. The researchers developed a computer model that analyzes data such as age, sexual history, and habits like smoking. The model identifies women at higher risk and achieved very high accuracy of 98.9%. This tool can help doctors prioritize screening and improve prevention and early management of cervical cancer.
  17. Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning
    This project focuses on predicting heart failure early using patient health data and machine learning. The researchers developed a new method called Principal Component Heart Failure (PCHF) to select the most important features from the data. They tested several machine learning algorithms and found that a decision tree model performed the best, achieving very high accuracy. The study can help doctors detect heart failure sooner and improve patient care.
  18. Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering
    This project focuses on helping doctors quickly understand medical images by identifying the type of imaging technique used, like X-rays or skin scans. The researchers combine visual patterns and texture details from images using deep learning to extract important features. They then merge these features to improve accuracy in classifying the images. Their method shows high precision and recall, making medical image analysis faster and more reliable.
  19. Leveraging Brain MRI for Biomedical Alzheimers Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning
    This project focuses on improving the diagnosis of Alzheimer’s disease using brain MRI scans. It uses deep learning to automatically analyze images and extract important features, reducing the need for manual input from experts. The method combines a DenseNet model for feature extraction with an optimized neural network for classification. Tests show that this approach gives more accurate results than existing techniques.
  20. Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images
    This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
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