Neural Networks Final Year Projects with Source Code

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

Neural Networks 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. FieldPlant A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning
    This project focuses on improving the detection of plant diseases using images taken directly from farms. Researchers created a new dataset called FieldPlant, with over 5,000 real-field images carefully labeled by plant experts. They tested modern deep learning models on this dataset and found that these models performed better than when trained on previous datasets. The goal is to help farmers detect diseases more accurately and reduce food waste.
  3. Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques
    This project uses computer vision and machine learning to understand students’ emotions in a classroom. It tracks facial gestures to identify feelings like interest, boredom, or enthusiasm during learning. The system builds a database of real, spontaneous emotions and helps teachers evaluate students’ emotional engagement along with their learning progress. It focuses on supporting teachers in face-to-face education.
  4. Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model
    This project focuses on improving brain tumor detection using medical images. It combines two neural network models, CapsNet and VGGNet, to create a hybrid system that can automatically identify and classify tumors. The model works well even with smaller datasets and was tested on high-quality brain tumor images. It achieved very high accuracy, correctly identifying almost all tumors.
  5. Malaria Disease Cell Classification With Highlighting Small Infected Regions
    This project uses deep learning to detect malaria from images of red blood cells. The researchers created a method that focuses on the small infected regions in the cells, similar to how humans highlight important information. Their approach improved the accuracy of malaria detection on a public dataset to 97.2%, which is higher than standard models. The study shows that focusing on key areas in the images helps the neural network learn better.
  6. Feature Extraction Methods for Binary Code Similarity Detection Using Neural Machine Translation Models
    This project focuses on finding similarities in software code when the original source code is not available. It uses neural machine translation models to analyze small units of code called basic blocks across different device architectures. The method can detect similarities more accurately and extract features much faster than existing techniques. In experiments, it achieved 92% accuracy and was up to 16 times faster in feature extraction.
  7. HDLNET A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease
    This project focuses on detecting similarities in software code when the original source is not available. It uses neural machine translation to analyze small code sections called basic blocks across different computer architectures. The method extracts features faster and more accurately than previous approaches. Tests show it achieves 92% accuracy and can work up to 16 times faster using GPUs.
  8. 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.
  9. A Granular Computing-Based Deep Neural Network Approach for Automatic Evaluation of Writing Quality
    This project focuses on automatically checking how good a piece of writing is. It uses a combination of two techniques: one to organize and simplify the text, and another to understand its meaning deeply. Together, they make the evaluation fast and accurate, even for large amounts of writing. Tests show that this method works well and runs efficiently.
  10. A Novel Student Achievement Prediction Method Based on Deep Learning and Attention Mechanism
    This project focuses on predicting student grades using a smart computer model. It studies how different factors affect student performance and highlights the most important ones. The model uses advanced learning techniques to understand patterns in student data over time. Experiments show it can predict grades with high accuracy, helping teachers give better guidance to students.
  11. An Adaptive Masked Attention Mechanism to Act on the Local Text in a Global Context for Aspect-Based Sentiment Analysis
    This project studies how to understand opinions about specific parts of a sentence, such as features of a product. It introduces a new way for the model to focus on both the whole sentence and the important local words. This method reduces noise and helps the system learn useful information more efficiently. The model works well on many benchmark datasets.
  12. Automatic Voice Disorder Detection Using Self-Supervised Representations
    This project focuses on automatically detecting voice disorders using advanced machine learning. It uses deep neural networks and a transformer model to distinguish between healthy and pathological speech. The system learns patterns from audio data and improves accuracy by using extra training data. It achieved over 93% accuracy, making it highly effective for diagnosing and monitoring voice problems.
  13. Design of an Intelligent Educational Evaluation System Using Deep Learning
    This project focuses on building a smart system to evaluate students’ learning online. It uses deep learning to process information and make accurate assessments. The system is designed to be fast and efficient, using fewer parameters and less training time than traditional methods. It also works well even when the data is uneven or unbalanced.
  14. MixNet Physics Constrained Deep Neural Motion Prediction for Autonomous Racing
    This project focuses on predicting how other racecars will move around an autonomous racecar. It combines deep learning with physics rules to make predictions both accurate and safe. The method improves over traditional models by keeping predictions realistic and avoiding errors like going off-track. It was tested in simulations and used on a real autonomous racecar in a competition.
  15. Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture
    This project focuses on building a system that can automatically recognize sign language gestures. It uses cameras instead of costly sensors to capture signs. The system learns both the hand movements and facial expressions to understand gestures accurately. Advanced neural networks process these features to identify signs from Indian and Russian sign languages more reliably than existing methods.
  16. A Cross-Lingual Hybrid Neural Network With Interaction Enhancement for Grading Short-Answer Texts
    This project focuses on automatically grading short student answers using AI. It combines deep learning techniques to better understand the meaning of students’ responses. The system compares student answers with reference answers and enhances their interaction to improve scoring accuracy. Experiments show it works well for both Chinese and English answers.
  17. 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.
  18. An Artificial Neural SLAM Framework for Event-Based Vision
    This project builds a navigation system for robots using a special type of camera that reacts only to changes in brightness. The system uses neural networks to understand motion, depth, and important landmarks from this camera’s event data. It helps the robot map its surroundings and track its position more accurately. The method is tested in a simulator and shows stable and reliable performance.
  19. An Inferential Commonsense-Driven Framework for Predicting Political Bias in News Headlines
    This project focuses on detecting political bias in news headlines, which is difficult because headlines are short and often lack context. The researchers created a system called IC-BAIT that uses commonsense reasoning to better understand the meaning behind headlines. They tested it on two datasets and found it improves bias detection accuracy and reliability. The work also shows when using commonsense knowledge helps and when it may cause mistakes.
  20. Applications of Artificial Intelligence in the Economy Including Applications in Stock Trading Market Analysis and Risk Management
    This project studies how Artificial Intelligence (AI) can be used in economics. It looks at applications like stock trading, market analysis, and assessing financial risks. The research organizes different AI methods and explains how they are evaluated. It also highlights current challenges and suggests directions for future work.
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