Ensemble Learning projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Ensemble Learning projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide 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.
Latest Ensemble Learning Projects
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Enhancing DDoS Attack Detection and Mitigation in SDN Using an Ensemble Online Machine Learning Model
The project aims to improve DDoS attack detection in Software Defined Networks using machine learning. It develops an ensemble online learning model that adapts to new and evolving attacks. The model selects features dynamically to enhance detection accuracy. It is tested in SDN simulations and benchmark datasets. The goal is to provide proactive and reliable protection against diverse DDoS threats. -
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. -
Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks
This project focuses on protecting Internet of Things devices from online attacks that overload networks, known as DDoS attacks. It introduces a system called DEEPShield, which uses advanced machine learning models to detect both strong and weak attacks quickly. The system works efficiently even on small devices with limited memory. It also uses a new dataset to improve accuracy and reduce errors in detecting threats. -
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
This project develops an AI system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to automatically learn important features from the images. The system combines multiple models to improve accuracy and reliability. Tests show it can diagnose diseases quickly and more accurately than traditional methods. -
Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems A Systematic Review
This project studies how machine learning can help detect credit card fraud and payment defaults. It reviews research papers from 2016 to 2023 to understand which datasets and methods work best. The study finds that most researchers focus on fixing data imbalance but not overlapping issues. It suggests using deep learning and sampling techniques to improve fraud detection accuracy.
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