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

Artificial Neural Networks Final Year Projects

A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients

This project predicts whether a diabetes patient will return to the hospital soon. It uses computer models to learn patterns from past patient records. The authors improve a neural network by guiding how it learns from correct and incorrect data. This guided method gives more accurate results and helps the model learn faster.
A Signature Transform of Limit Order Book Data for Stock Price Prediction

This project focuses on predicting stock prices using advanced machine learning techniques. It takes detailed order book data from stock exchanges and extracts key patterns called signature features. These features are then used to train models like deep neural networks and random forests. The results show that using signature features improves prediction accuracy and efficiency, especially in developed markets.
Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models

This project studies how neural networks for image recognition can be tricked by specially designed attacks. The researchers created two new attack methods and tested them on normal and infrared images. They also showed that using these attacks in training can make models stronger and more reliable. Finally, they explored if attacks in one type of image can affect another type without extra adjustments.
A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

This paper explains how smart group-based algorithms can help build and train artificial neural networks more effectively. These algorithms search for better network designs and improve how the network learns. The authors review many types of these algorithms and show how they are used to improve different neural networks. It gives readers a clear picture of current methods and their applications.
Application of Artificial Neural Network-Based Tool for Short Circuit Currents Estimation in Power Systems With High Penetration of Power Electronics-Based Renewables

This project focuses on predicting short circuit currents in power systems that use a lot of renewable energy sources like solar and wind. Traditional methods to calculate these currents are complex and slow. The researchers use an Artificial Neural Network to quickly estimate these currents based on how much renewable energy is connected. Tests show that this method can accurately predict different current components, making power system operation safer and faster.
Development of an Adaptive Linear Mixture Model for Decomposition of Mixed Pixels to Improve Crop Area Estimation Using Artificial Neural Network

This project focuses on accurately mapping crops using satellite images, even when fields are very small. It uses artificial intelligence to identify different land types from mixed pixels in the images. The method automatically selects key features and estimates the area covered by each crop type. Testing with drone and GPS data showed it is more accurate and efficient than previous methods.
On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

This project focuses on automatically assigning software bug reports to the right developers. It uses a deep learning system that studies both the context of the bug and repeating keywords in bug descriptions. The model combines these features to predict which developer can fix the bug. Tests on real-world software projects show that this method works better than previous approaches.
Very High Accuracy Hyperbolic Tangent Function Implementation in FPGAs

This project focuses on creating an efficient way to calculate the hyperbolic tangent function on FPGA devices. It uses polynomial approximations to achieve high accuracy while keeping computing resources and time low. The method works for both floating-point and fixed-point calculations. It can also be adapted to implement other mathematical functions easily.
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.

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Artificial Neural Networks Final Year Projects with Source Code