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

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.
A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition

This project develops a lightweight system to recognize hand gestures. The system, called LHGR-Net, works on small devices like a Raspberry Pi. It can detect gestures in real time and use them to control home appliances. The method uses less memory but still performs almost as well as advanced models.
A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines

This project focuses on detecting fake or misleading online reviews that can mislead customers and harm businesses. It uses a machine learning model called Weighted Support Vector Machine combined with an optimization algorithm called Harris Hawks Optimization to improve accuracy. The method works for multiple languages, including English, Spanish, and Arabic. The system was tested with different techniques and datasets, achieving high accuracy in identifying spam reviews, especially during the COVID-19 pandemic when online reviews increased dramatically.
A Network Intrusion Detection System for Building Automation and Control Systems

This project focuses on improving security for building automation systems, like smart lighting and HVAC controls. The researchers designed a network intrusion detection system that can detect attacks across different building system protocols, not just one. They built and tested a working version for KNX, a common building automation protocol, using a real installation to show it works. The system aims to make smart buildings safer from cyber threats.
A Stock Price Prediction Method Based on BiLSTM and Improved Transformer

This project develops a new method to predict stock prices more accurately and reliably. It combines three advanced models—BiLSTM, Transformer, and TCN—to use their strengths in analyzing stock data. The method was tested on multiple stocks and consistently outperformed existing prediction approaches. It provides stable, precise forecasts without timing issues.
A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network

This project focuses on improving medical image segmentation, which helps computers identify regions like tumors in medical scans. Traditional methods using neural networks struggle to capture both small details and overall structures. The researchers combined ideas from Transformers and visual attention networks to create a new model called M-VAN Unet. This model uses special attention methods to better learn detailed and global features, and experiments show it performs better than existing methods.
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images

This project focuses on detecting tuberculosis (TB) from chest X-ray images using a new deep learning model called CBAMWDnet. The model combines advanced techniques to better understand important features in the images. Tests on large datasets show it is very accurate and performs better than many existing models. This approach can help doctors diagnose TB earlier and more reliably.
BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

This project develops a computer system that can automatically detect breast cancer from ultrasound images. It uses artificial intelligence to tell apart dangerous tumors from harmless ones. The system also explains its decisions using medical features that doctors rely on. Tests show it improves diagnosis accuracy and helps doctors understand its reasoning.
Bit-Plane and Correlation Spatial Attention Modules for Plant Disease Classification

This project focuses on automatically identifying plant diseases using artificial intelligence. It improves existing deep learning methods by adding a special attention model that focuses on the most important parts of plant images. The model detects disease areas more accurately and achieves very high accuracy on public plant disease datasets. The experiments show it works better than many commonly used methods.
DeepSkin: A Deep Learning Approach for Skin Cancer Classification

This project focuses on detecting skin cancer early using computer-based methods. It uses images of different skin lesions from a large dataset to teach a computer to recognize cancer types. Advanced deep learning models, like CNNs with DenseNet169 and ResNet50, are applied to improve accuracy. The goal is to help doctors identify skin cancer more reliably and quickly.
Anchor-Free Feature Aggregation Network for Instrument Detection in Endoscopic Surgery

This project focuses on helping surgeons see and track their tools during delicate nasal surgeries. It develops a smart computer system that can detect surgical instruments in real time from endoscopic videos. The system uses advanced image analysis techniques to identify and locate tools accurately. Tests show it works better than existing methods, making surgeries safer and easier to perform.
Classification and Prediction of Drivers Mental Workload Based on Long Time Sequences and Multiple Physiological Factors

This project focuses on understanding a driver’s mental workload to improve road safety. The researchers collected physiological data like heart rate and skin activity while driving. They developed a model called LTS-MPF that looks at all these signals over time to predict how stressed or focused a driver is. The model can classify the current mental state and even predict the next second, achieving over 93% accuracy.
Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

This project focuses on detecting brain diseases using electrical impedance tomography, even when electrodes cannot be evenly placed. The researchers developed new ways to arrange electrodes and a smart method that considers these arrangements to locate brain bleeding accurately. Their approach was tested under many challenging conditions and showed very high accuracy and reliability. It performs better than traditional neural network methods for this task.
Clinical Errors From Acronym Use in Electronic Health Record A Review of NLP-Based Disambiguation Techniques

This study looks at how medical records often contain confusing acronyms that can cause errors in patient care. It explains why electronic health records (EHRs) sometimes increase mistakes and why understanding these acronyms is important. The research also explores how artificial intelligence, especially machine learning, can help automatically clarify the meaning of these acronyms. Finally, it reviews how EHRs are used worldwide and the latest AI methods for reducing errors caused by unclear medical terms.
Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

This project focuses on improving brain tumor detection from MRI images using artificial intelligence. The researchers developed a deep learning model that automatically processes and classifies images, reducing errors and saving time. They used special filters and mathematical techniques to extract important features from the images. The model achieved very high accuracy of 98.8% when tested on brain tumor data.
Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning

This study uses deep learning to automatically detect Temporomandibular Disorder (TMD) from MRI scans. Researchers collected over 2500 images from 200 patients and tested several advanced neural network models to classify them. The performance of these models was measured using accuracy and other medical metrics. The results show that deep learning can successfully assist in diagnosing TMD.
Medical Ultrasound Image Segmentation With Deep Learning Models

This project focuses on improving the analysis of medical ultrasound images. The researchers created a new model called ConvTrans-Net that combines a transformer and a deep neural network. It helps accurately identify and segment lesion areas in ultrasound scans. The model showed high precision and recall, making it more effective than some existing methods.
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.
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.

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Neural Network Final Year Projects with Source Code