Machine Learning Models Final Year Projects with Source Code

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

Machine Learning Models Final Year Projects

  1. A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques
    This project focuses on detecting harmful Android apps without breaking app rules or licenses. It uses multiple detectors that check apps at different stages, before and after installation. The method is faster, uses less energy, and makes fewer mistakes than existing solutions. It helps keep Android devices safer from malware efficiently.
  2. A Machine Learning Approach Using Statistical Models for Early Detection of Cardiac Arrest in Newborn Babies in the Cardiac Intensive Care Unit
    This project focuses on detecting cardiac arrest in newborn babies early. It uses a machine learning model to analyze babies’ vital signs in the cardiac ICU. The model can predict cardiac arrest before it happens, allowing doctors to act quickly. This approach aims to reduce deaths and complications in newborns.
  3. 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.
  4. A Signature Transform of Limit Order Book Data for Stock Price Prediction
    This project focuses on predicting stock prices using advanced machine learning techniques. It takes detailed order book data from stock exchanges and extracts key patterns called signature features. These features are then used to train models like deep neural networks and random forests. The results show that using signature features improves prediction accuracy and efficiency, especially in developed markets.
  5. A Systematic Review on Federated Learning in Medical Image Analysis
    This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI.
  6. An Attention-Based Convolutional Neural Network for Intrusion Detection Model
    This project focuses on improving network security by detecting intrusions quickly and accurately. It uses a type of artificial intelligence called convolutional neural networks with attention mechanisms. The method organizes network data into images in a smart way to make the detection process faster. Experiments show that this approach can identify threats efficiently while keeping high accuracy.
  7. A Machine Learning and Optimization Framework for the Early Diagnosis of Bovine Respiratory Disease
    This project focuses on detecting respiratory disease in dairy calves early using smart farm technologies like automatic feeders and sensors. The system uses machine learning to analyze calf behavior and identify sickness before obvious symptoms appear. A new method called CALF helps choose the best features to improve predictions within a budget. Tests on real calves show it can accurately detect illness and its severity days before traditional diagnosis.
  8. Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis
    This project builds a smarter system to predict breast cancer early. It uses different types of patient data together, such as clinical details, gene information, and genetic variations. The system learns patterns from each data type using different deep learning models and then combines them. This combined model improves prediction accuracy compared to using a single data source.
  9. 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.
  10. Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption
    This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
  11. 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.
  12. 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.
  13. IoT Network Cybersecurity Assessment With the Associated Random Neural Network
    This project develops a smart system to detect hacked devices in an IoT network. It uses a special type of neural network that looks at all devices together instead of checking them one by one. The system learns from real attack data to decide whether each device is safe or compromised. Tests show it works better than older methods.
  14. 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.
  15. Toward Secured IoT-Based Smart Systems Using Machine Learning
    This project studies how smart systems like smart cities and early warning systems use sensors and devices to collect data. Machine learning is applied to this data to make predictions and improve decision-making. The research also examines security methods to keep these systems safe. Two case studies on smart campuses and earthquake warning systems show how this works in practice.
  16. Deep vs. Shallow A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
    This project focuses on detecting fake health news on the internet. It compares two types of models: one that uses only the news text and another that also considers readability features. Different machine learning and deep learning methods were tested. The study found that using readability features improves detection, and the AdaBoost-Random Forest model gave the best results.
  17. GNNGLY Graph Neural Networks for Glycan Classification
    This project focuses on studying glycans, which are complex sugar molecules important for many biological processes and diseases. The researchers created a model called GNNGLY that treats glycans like graphs to better understand their structure. The model can classify glycans into different categories and predict their immune-related properties. It performs better than traditional methods and existing tools, helping scientists study glycans more effectively.
  18. AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things A Systematic Review
    This project studies how brain signals can help doctors detect and predict epileptic seizures. It reviews many research papers that use computer methods to understand these signals. The study looks at techniques like machine learning and deep learning to improve seizure monitoring. It also discusses current challenges and suggests directions for future research.
  19. Application of Artificial Intelligence Techniques for BrainComputer Interface in Mental Fatigue Detection A Systematic Review
    This project reviews how mental fatigue, which affects both the mind and body, can be detected using brain-computer interfaces and artificial intelligence. The study analyzed research from 2011 to 2022 and identified gaps in using these systems for automated mental fatigue monitoring. It also explains the challenges, AI techniques, and future directions for improving detection and practical implementation. The goal is to guide better and faster methods to recognize and manage mental fatigue.
  20. Cascade Windows-Based Multi-Stream Convolutional Neural Networks Framework for Early Detecting In-Sleep Stroke Using Wristbands
    This project develops a wearable system with two wristbands to monitor people while they sleep for signs of stroke. It uses motion data from both hands and a deep learning framework called EDIS to detect strokes quickly and accurately. The system processes the data with multiple neural network models and combines their results to make a final decision. This approach helps detect strokes early, allowing timely treatment and potentially reducing long-term disabilities.
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