Random Forest Classifier Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Random Forest Classifier 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.

Random Forest Classifier Final Year Projects

A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients

This project predicts whether a diabetes patient will return to the hospital soon. It uses computer models to learn patterns from past patient records. The authors improve a neural network by guiding how it learns from correct and incorrect data. This guided method gives more accurate results and helps the model learn faster.
Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention

This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

This project studies how people’s web browsing activity can be tracked and protected. It focuses on website fingerprinting, which identifies the websites a user visits. The research surveys how deep learning can be used both to perform these tracking attacks and to defend against them. It also reviews methods, challenges, and future directions in this area.
Lightweight Deep Learning Framework for Speech Emotion Recognition

This project is about creating a system that can detect human emotions from speech. It uses a smart model that combines deep learning and simpler machine learning methods to work efficiently. The system is designed to run fast even on devices with limited resources. Tests on several speech datasets showed that it can recognize emotions like happy, sad, angry, and calm with very high accuracy.
A Methodological Framework for AI-Assisted Security Assessments of Active Directory Environments

This project focuses on improving the security of complex technological systems. It uses artificial intelligence to check if a system is safe or vulnerable. The method represents system components and weaknesses as graphs, then uses machine learning to analyze possible attack paths. Experiments showed that the approach can accurately identify risky networks, making automated security assessment possible.
A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare

This project focuses on detecting diseases like cancer, diabetes, and heart problems at an early stage using smart data analysis. It combines artificial intelligence with a Random Forest algorithm to identify subtle patterns in patient data and make accurate diagnoses. The system also uses secure data encryption to protect patient information. Tests show it is highly accurate, fast, and can help doctors make timely treatment decisions.
Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis

This project focuses on using muscle signals from the arm to control prosthetic hands for people with physical disabilities. The researchers collected signals from volunteers performing different hand movements and processed the data to remove noise and reduce complexity. They then used machine learning and neural networks to classify these movements accurately. The system achieved high accuracy, showing it can reliably recognize and control hand motions in real time.

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Random Forest Classifier Project Synopsis & Presentation

Final Year Projects helps prepare Random Forest Classifier project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Random Forest Classifier Project Thesis Writing

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Random Forest Classifier Final Year Projects with Source Code