Machine Learning Techniques Final Year Projects with Source Code

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

  1. A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images
    This project focuses on identifying how severe a brain tumor is using MRI images. It uses a light and fast deep learning model to classify tumors into low-grade or high-grade groups. The model is trained on public medical datasets and data from a local hospital. It shows very high accuracy compared to other popular deep learning models.
  2. 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.
  3. 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.
  4. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  5. APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System
    This project focuses on detecting advanced cyberattacks in industrial systems connected through the Internet of Things. It uses a special machine learning method called Graph Attention Networks to identify hidden attacks more accurately than traditional methods. The approach was tested on real datasets and achieved over 95% detection accuracy in just around 20 seconds. Overall, it improves cybersecurity in smart industrial systems.
  6. Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment
    This project focuses on protecting Internet of Things (IoT) devices from DDoS cyber-attacks that can overload them with traffic. It uses machine learning to detect attacks by selecting the most important data features. The proposed method combines a “snake optimizer” for feature selection with three deep learning models to improve detection. Tests show that this approach works better than existing methods in identifying attacks accurately.
  7. DeepFert An Intelligent Fertility Rate Prediction Approach for Men Based on Deep Learning Neural Networks
    This project uses artificial intelligence to predict men’s fertility. It analyzes sperm samples from men aged 18 to 50. The system looks at sperm shape and movement to assess fertility. The approach is faster and more accurate than traditional semen tests.
  8. Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model
    This project focuses on improving the detection of Android malware, which is increasingly common and hard to identify using traditional methods. It uses a combination of advanced machine learning and deep learning techniques to automatically classify malware. The model selects the most important features and optimizes its parameters to achieve high accuracy. Tests show that this approach can detect malware more effectively than existing methods, reaching almost 99% accuracy.
  9. 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.
  10. 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.
  11. A Survey on Artificial Intelligence-Based Acoustic Source Identification
    This project focuses on identifying sources of sound, like machinery noise or environmental sounds, using computers. Traditionally, experts had to analyze sound patterns manually, which is hard when there is a lot of data. The study reviews how artificial intelligence can help automatically recognize these sound sources. It also looks at applications in areas like healthcare, manufacturing, and underwater monitoring.
  12. Role of Artificial Intelligence in Online Education A Systematic Mapping Study
    This project studies how artificial intelligence, especially machine learning and deep learning, can improve online education. It looks at how these techniques help teachers track student progress and personalize learning. The study reviews research from 1961 to 2022 to understand the best methods and data sources for analyzing student performance. The goal is to provide clear insights for researchers and educators to enhance teaching and learning strategies.
  13. A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks
    This project improves the security and efficiency of wireless sensor networks (WSNs), which are used in areas like healthcare and military services. It detects and removes malicious nodes using deep learning and a blockchain-based validation system. The network is designed to prevent failures by decentralizing data handling and registering legitimate nodes securely. The results show higher accuracy, better throughput, and lower delay compared to traditional routing methods.
  14. Machine Learning Empowered Emerging Wireless Networks in 6G Recent Advancements Challenges and Future Trends
    This project explains how future 6G networks will use machine learning to make communication faster, smarter, and more reliable. It shows how ML helps the network manage resources on its own and solve problems like energy use, delays, and smooth connections. The paper reviews different ML methods and how they improve new types of networks, such as device-to-device and vehicular systems. It also discusses open challenges and future research directions for building intelligent 6G networks.
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