Random Forest Final Year Projects with Source Code

Byadmin Final Year Projects

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

  1. A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders
    This project focuses on detecting Autism Spectrum Disorder (ASD) early using machine learning. It compares different ways of preparing data and several simple machine learning methods to see which works best. The study tests these methods on datasets for toddlers, children, adolescents, and adults. The results show high accuracy and identify the most important factors for predicting ASD, helping doctors make better decisions.
  2. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets
    This project studies public reactions to the recent monkeypox outbreak by analyzing social media posts. Researchers collected over 500,000 tweets and labeled them as positive, negative, or neutral. They tested many machine learning models to find the best way to predict public sentiment. The study found that a model using TextBlob, lemmatization, CountVectorizer, and SVM gave the most accurate results, helping health authorities understand public concerns.
  3. Design and Development of an Efficient Risk Prediction Model for Cervical Cancer
    This study focuses on predicting the risk of cervical cancer in women based on lifestyle and health factors. The researchers developed a computer model that analyzes data such as age, sexual history, and habits like smoking. The model identifies women at higher risk and achieved very high accuracy of 98.9%. This tool can help doctors prioritize screening and improve prevention and early management of cervical cancer.
  4. Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning
    This project focuses on predicting heart failure early using patient health data and machine learning. The researchers developed a new method called Principal Component Heart Failure (PCHF) to select the most important features from the data. They tested several machine learning algorithms and found that a decision tree model performed the best, achieving very high accuracy. The study can help doctors detect heart failure sooner and improve patient care.
  5. Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
    This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
  6. 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.
  7. Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality
    This project focuses on monitoring and predicting water quality using smart sensors and machine learning. Sensors collect data on temperature, pH, turbidity, and dissolved solids from a canal in Pakistan. Machine learning models then analyze this data to predict water quality levels and classify water conditions. The study shows that some models, like MLP for prediction and Random Forest for classification, give very accurate results.
  8. Automatic Generation Control Strategy Based on Deep Forest
    This project improves how electricity grids maintain stable power supply. It uses a smart system called a deep forest network to choose the best control method in real time. The system adjusts power output efficiently with fewer control actions. Simulations show it works better than traditional methods.
  9. 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.
  10. Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes Processes
    This project studies how mobile phone call and text records can reveal personal information. It shows that a person’s personality and relationships with friends can be predicted from these logs. The research analyzed data from over 900 students and used models to track communication patterns. The results suggest that even data people consider anonymous can expose private details.
  11. Artificial Intelligence in Cosmetic Dermatology A Systematic Literature Review
    This project reviews how artificial intelligence (AI) is being used in cosmetic dermatology. It looks at studies from 2018 to 2023 and organizes them into areas like product development, skin assessment, diagnosis, treatment advice, and predicting results. The study helps researchers understand trends in AI applications and provides guidance for doctors and practitioners in improving cosmetic skin treatments. It highlights both current achievements and opportunities for future research in aesthetic medicine.
  12. Smart Healthcare Hand Gesture Recognition Using CNN-Based Detector and Deep Belief Network
    This project develops a system that can accurately track and recognize hand gestures from videos in real-world environments. It processes video frames, cleans the images, and uses neural networks to identify hand movements. The system then extracts detailed features, optimizes them to reduce errors, and classifies gestures using a deep learning model. Tests on standard datasets show it achieves high accuracy and works well compared to existing methods.
  13. Using PBL and Agile to Teach Artificial Intelligence to Undergraduate Computing Students
    This project explored a hands-on learning approach where students solve real-world problems while learning Artificial Intelligence. Thirty undergraduate students used Agile and Scrum methods to build five machine learning models for predicting breast cancer. The approach helped them improve teamwork, problem-solving, and communication skills. It shows that combining project-based learning with Agile methods can make computing education more effective and practical.
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