Learning Algorithms Final Year Projects with Source Code

Byadmin Final Year Projects

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

Learning Algorithms Final Year Projects

  1. A BP Neural Network-Assisted Smart Decision Method for Education Quality
    This project uses a neural network model to help universities evaluate teaching quality automatically. It collects student and expert feedback, builds an evaluation system, and trains the model to judge education performance. The method is tested on real university data to see changes before and after applying the model. The results show that it can give reliable support for improving teaching quality.
  2. A Systematic Literature Review on Multimodal Machine Learning Applications Challenges Gaps and Future Directions
    This project reviews how machine learning can use multiple types of data together, like images, text, and audio, to solve real-world problems. It studies recent research on key challenges, such as combining, translating, and aligning these different data types. The authors analyzed over 1000 articles to identify trends, gaps, and progress in this area. This work helps researchers understand the current state of multimodal machine learning and plan future studies.
  3. Boosting Performance of Visual Servoing Using Deep Reinforcement Learning From Multiple Demonstrations
    This project uses multiple expert controllers with deep reinforcement learning to improve a robot’s visual servoing, which is how a robot moves based on camera images. It creates safe action limits from expert demonstrations to reduce wasted trial-and-error learning. This approach makes training faster and improves performance in real-world scenarios. Compared to traditional methods, it achieves better accuracy and control while cutting training time almost in half.
  4. 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.
  5. DeepMist Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data
    This project focuses on predicting heart disease using advanced computing techniques. It proposes a system called DeepMist that uses deep learning on a Mist computing setup to analyze healthcare data quickly and efficiently. The model predicts heart disease with high accuracy while reducing energy use and processing delay. Testing shows it performs better than other popular machine learning methods.
  6. Eye-Tracking Image Encoding Autoencoders for the Crossing of Language Boundaries in Developmental Dyslexia Detection
    This project develops a machine learning system to detect developmental dyslexia using eye-tracking data. It converts eye movement signals into images and trains a neural network to extract features. The system was tested on two very different datasets from Serbian and Swedish readers and achieved over 82% accuracy. This approach can work across different study designs, helping combine dyslexia research from multiple sources.
  7. 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.
  8. Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking
    This project studies a way to train machine learning models without a central server, using devices that share updates with their neighbors. It focuses on keeping data private while still achieving high accuracy. The system is tested on the MNIST image dataset using a CNN model, and it shows over 95% accuracy. The method also reduces communication costs and trains faster compared to traditional centralized systems.
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Learning Algorithms Project Synopsis & Presentation

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