Ensemble Learning Final Year Projects with Source Code

Byadmin Final Year Projects

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

Ensemble Learning 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. An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
    This project develops an AI-based system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to analyze images and identify diseases more accurately than traditional methods. The system improves image data with techniques like rotation and flipping and combines multiple models to make reliable predictions. Experiments show it achieves around 97% accuracy, helping doctors make faster and better treatment decisions.
  3. BukaGini A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance
    This project develops a new algorithm called BukaGini to study how different features in data interact with each other. It uses a special technique based on the Gini index to capture both simple and complex relationships between features. The method was tested on datasets about student performance, cancer types, spam emails, and network attacks. Results show that BukaGini improves accuracy compared to traditional methods, making it useful for many machine learning applications.
  4. Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging
    This project focuses on automatically detecting brain bleeding from CT scans using artificial intelligence. It creates a smart system that learns important patterns in images to classify different types of bleeding. The model combines multiple AI techniques to improve accuracy and speed. This helps doctors diagnose and treat patients faster while reducing manual effort.
  5. Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
    This project focuses on protecting smart systems that connect computers and physical devices. It uses a new method to detect attacks or intrusions in these systems. The approach selects important data features and combines multiple deep learning models to identify threats. Tests show it works better than traditional methods in detecting intrusions.
  6. Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks
    This project focuses on reducing electricity losses that are not caused by technical faults, especially in developing countries. It combines data from both electricity customers and distribution staff to better understand why losses occur. A deep learning model called NTLCONVNET was developed to predict these losses and explain which factors are most important. The study found that staff-related factors play a significant role, suggesting policies should include human resource monitoring to reduce losses.
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At Final Year Projects, we provide complete guidance for Ensemble Learning IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

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Ensemble Learning Project Synopsis & Presentation

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