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

Artificial Neural Network Final Year Projects

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.
BSANet High-Performance 3D Medical Image Segmentation

This project focuses on improving medical image analysis, especially for tasks like brain tumor and organ segmentation. It introduces BSANet, a 3D network that can better understand images by focusing on important areas and combining information at different scales. This helps the system capture more details and make more accurate predictions. The model is tested on standard medical datasets and shows strong performance.
Classification of Diabetic Retinopathy Disease Levels by Extracting Topological Features Using Graph Neural Networks

This project focuses on improving the detection of diabetic retinopathy, a major cause of blindness, from retinal images. It uses a new deep learning approach that combines feature extraction and graph-based analysis to better capture important details in the images. The model was tested on public datasets and showed higher accuracy and reliability than existing methods. It helps doctors by making disease diagnosis faster and more precise.
Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning

This project aims to detect stomach and digestive cancers early using computer analysis of medical images. It cleans the images and then uses advanced AI models to learn important patterns. The system chooses the best settings automatically to improve accuracy. This helps doctors identify cancer sooner and make better treatment decisions.
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.
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.
Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval

This project focuses on securely storing and retrieving medical images like X-rays, MRIs, and CT scans. It uses advanced deep learning techniques to extract important features from the images. The system encrypts images to keep them safe while allowing accurate searching and matching. Tests show that this method works better than existing approaches.
Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network

This project uses Internet of Things (IoT) devices in farms to monitor environmental conditions like temperature, humidity, rainfall, wind, and sunshine. A deep learning model uses this data to predict pest outbreaks in crops. The system generates weekly predictions with high accuracy, helping farmers take timely action to protect crops. Over five years of data, the model achieved 94% accuracy and improves predictions over time.
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.
A hybrid method for identifying the feeding behavior of tilapia

This project focuses on monitoring how tilapia fish eat in real time. The researchers improved a computer vision model called ResNet34 to better recognize fish feeding behavior. They added a module to help the model focus on important image features and used transfer learning to speed up training. The final model achieved very high accuracy, helping farmers decide the right amount of feed scientifically.
A Novel Spatio Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

This project focuses on improving driver assistance systems using only GPS data. The researchers developed a method to turn GPS trajectories into images and trained a neural network to analyze them. The system can accurately detect when a car is turning or going straight. It works better than existing methods and helps make driving safer and smarter.
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.
A Systematic Review of Facial Expression Detection Methods

This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph

This project improves how online recommendation systems understand users. It uses both the text of user reviews and user ratings to learn what people like. The model studies the full structure of review sentences, not just nearby words. It then combines this with rating patterns to give more accurate recommendations.
Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms

This project focuses on keeping smart power grids safe from cyberattacks. It uses deep learning methods to detect fake or harmful data that hackers might send to the system. The study proposes a new algorithm that combines machine learning with nature-inspired optimization to improve detection. Tests show it works better than existing methods, achieving high accuracy in identifying attacks.
Deep Learning Using Context Vectors to Identify Implicit Aspects

This project focuses on finding the hidden topics that people talk about in their reviews. It looks for meanings that are not directly written but are implied through the words people use. The system learns from examples and understands the surrounding text to detect these hidden ideas. It helps improve sentiment analysis by making it more accurate and closer to real human understanding.
Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

This project focuses on detecting a dangerous electrical problem called chaotic ferroresonance, which can cause high voltages and damage equipment. The researchers used deep learning models to quickly identify when this problem occurs. They trained the models on images of voltage patterns and achieved high accuracy. This helps power networks respond faster and protect equipment from damage.
End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model

This project focuses on automatically reading Tamil handwritten text and converting it into digital text. It uses deep learning to first improve image quality and then separate lines and words. A MobileNet-based model extracts features, and a BiGRU model with optimization identifies each character. Tests show it can recognize Tamil handwriting accurately, achieving nearly 98.5% accuracy.
Intelligent Deployment Solution for Tabling Adapting Deep Learning

This project develops a smart system to improve mineral processing. It uses deep learning to analyze images and identify features of mineral ore belts. Then, it predicts how operating conditions affect these minerals using an advanced regression model. The system makes processing faster and more accurate, offering new possibilities for research.
Using Deep Learning Model to Identify Iron Chlorosis in Plants

This project uses artificial intelligence to detect nutrient deficiencies in plant leaves. It analyzes leaf images and the soil to find the cause of the deficiency. Two deep learning models, SSD MobileNet v2 and EfficientDet D0, are tested. The models can classify leaves with high accuracy, and EfficientDet D0 gives the most precise results, though it takes more time to process.

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Artificial Neural Network Final Year Projects with Source Code