Feature Extraction Techniques Final Year Projects with Source Code

Byadmin Final Year Projects

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

  1. Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis
    This project builds a smarter system to predict breast cancer early. It uses different types of patient data together, such as clinical details, gene information, and genetic variations. The system learns patterns from each data type using different deep learning models and then combines them. This combined model improves prediction accuracy compared to using a single data source.
  2. Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset IDSAI
    This project focuses on improving computer security in networks of connected devices, like IoT systems. The researchers created a new dataset of real attacks to train and test machine learning models. They found that certain AI models can accurately detect both simple and multiple types of attacks, reaching over 90% accuracy. This work helps make network security smarter and more reliable.
  3. Deep vs. Shallow A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
    This project focuses on detecting fake health news on the internet. It compares two types of models: one that uses only the news text and another that also considers readability features. Different machine learning and deep learning methods were tested. The study found that using readability features improves detection, and the AdaBoost-Random Forest model gave the best results.
  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.
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Feature Extraction Techniques Project Synopsis & Presentation

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