Decision Tree Algorithm Final Year Projects with Source Code

Byadmin Final Year Projects

Decision Tree Algorithm Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Decision Tree Algorithm 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.

Decision Tree Algorithm Final Year Projects

  1. 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.
  2. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  3. 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.
  4. 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.
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Decision Tree Algorithm Project Synopsis & Presentation

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