Feature Extraction Final Year Projects with Source Code

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Feature Extraction Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Feature Extraction 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.

Feature Extraction Final Year Projects

  1. A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites
    This project focuses on detecting fake websites that try to steal personal information. It uses a smart learning model that studies many website patterns and learns from multiple layers of predictions. The system first picks the most useful information from each website and then checks it through several classifiers. This layered method helps the model catch phishing websites with very high accuracy.
  2. A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images
    This project focuses on identifying how severe a brain tumor is using MRI images. It uses a light and fast deep learning model to classify tumors into low-grade or high-grade groups. The model is trained on public medical datasets and data from a local hospital. It shows very high accuracy compared to other popular deep learning models.
  3. A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture
    This project helps identify diseases in fish using computer analysis of underwater images. It improves the accuracy of detection even when images are unclear. The system focuses on the important parts of the fish and learns quickly from new images. It can support farmers in keeping fish healthy and improving production.
  4. A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques
    This project focuses on detecting harmful Android apps without breaking app rules or licenses. It uses multiple detectors that check apps at different stages, before and after installation. The method is faster, uses less energy, and makes fewer mistakes than existing solutions. It helps keep Android devices safer from malware efficiently.
  5. A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition
    This project develops a lightweight system to recognize hand gestures. The system, called LHGR-Net, works on small devices like a Raspberry Pi. It can detect gestures in real time and use them to control home appliances. The method uses less memory but still performs almost as well as advanced models.
  6. 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.
  7. A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection
    This project focuses on detecting harmful content on social media. It uses machine learning methods to classify online messages as hateful or not. The study tests two models on multiple datasets and improves their accuracy using nature-inspired optimization techniques and fuzzy logic. This approach helps the system better understand the meaning behind the text.
  8. A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features
    This project focuses on detecting Android malware using machine learning. It looks at how certain permissions and app actions appear together in malicious apps compared to normal ones. The researchers created special datasets of these feature combinations and used algorithms to find the most important patterns. Their model was able to identify malware with very high accuracy, even better than existing methods.
  9. A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance
    This project focuses on detecting rumors on social media more accurately and quickly. It looks at both the hidden meaning in posts and the opinions of users who comment. The system gives more importance to trustworthy users and learns patterns over time. Tests show it can detect rumors faster and better than existing methods.
  10. A Novel Two-Stage Deep Learning Model for Network Intrusion Detection LSTM-AE
    This project focuses on improving computer systems’ ability to detect cyber-attacks automatically. It uses a combination of two advanced deep learning methods, LSTM and Auto-Encoders, to create a flexible and accurate intrusion detection system. The model is tested on publicly available datasets to find the best settings and compare its performance with other deep learning approaches. Results show that the proposed system can effectively detect attacks in modern network environments.
  11. A Swin Transformer-Based Approach for Motorcycle Helmet Detection
    This project focuses on automatically detecting whether motorcyclists are wearing helmets using video cameras. It uses advanced computer vision and machine learning techniques to identify helmets even in complex situations and with multiple passengers. The method combines a transformer model with other neural network tools to improve accuracy. Tests show that it works better than existing detection systems.
  12. A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network
    This project focuses on improving medical image segmentation, which helps computers identify regions like tumors in medical scans. Traditional methods using neural networks struggle to capture both small details and overall structures. The researchers combined ideas from Transformers and visual attention networks to create a new model called M-VAN Unet. This model uses special attention methods to better learn detailed and global features, and experiments show it performs better than existing methods.
  13. Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model
    This project focuses on creating an AI system to detect colon and lung cancer from biomedical images. The system uses advanced image processing and deep learning techniques to analyze patient scans quickly and accurately. It combines smart algorithms to improve feature extraction and classification of cancer cells. The results show it can detect cancer with very high accuracy, reaching over 99%.
  14. Bit-Plane and Correlation Spatial Attention Modules for Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It improves existing deep learning methods by adding a special attention model that focuses on the most important parts of plant images. The model detects disease areas more accurately and achieves very high accuracy on public plant disease datasets. The experiments show it works better than many commonly used methods.
  15. Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images
    This project focuses on detecting early signs of diabetic eye disease from retinal images. It creates a method to generate and highlight disease spots using a small set of open-source images. A custom neural network is developed to classify these spots accurately. The system performs very well, achieving perfect results on the test data.
  16. FieldPlant A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning
    This project focuses on improving the detection of plant diseases using images taken directly from farms. Researchers created a new dataset called FieldPlant, with over 5,000 real-field images carefully labeled by plant experts. They tested modern deep learning models on this dataset and found that these models performed better than when trained on previous datasets. The goal is to help farmers detect diseases more accurately and reduce food waste.
  17. 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.
  18. Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images
    This project develops a smart system to detect diabetic eye disease early using Internet-connected devices and deep learning. Eye images are collected with IoT devices and sent to the cloud for processing. The system cleans the images, highlights damaged regions, extracts important features, and uses an AI model to classify the disease. Tests show this method is more accurate and effective than earlier approaches.
  19. Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images
    This project focuses on using artificial intelligence to analyze chest X-rays for COVID-19 detection. The researchers developed a new method that can accurately identify infected areas, even when their size or location varies. The approach improves how the system understands both the position and details of the infection in the X-ray images. Tests on public datasets show it works better and more reliably than existing methods.
  20. 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.
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