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

Recurrent Neural Networks Final Year Projects

Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

This project focuses on predicting the next event in a process to help organizations work more efficiently. Current methods either change the order of events or ignore it completely, which can reduce prediction accuracy. The project proposes a new method called AXDP that keeps the order of events intact while using deep learning to predict the next step. Tests show AXDP performs better than existing models on most datasets.
Evolution of Deep Learning-Based Sequential Recommender Systems From Current Trends to New Perspectives

This project studies how modern recommendation systems work. It focuses on systems that learn users’ preferences over time to give better suggestions. The study explains how models like RNNs, CNNs, GANs, GNNs, and transformers are used to understand user behavior. It also looks at methods that handle sparse data to improve recommendations.
Multi-S3P Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model

This project focuses on predicting protein shapes, which is important for understanding biology and designing drugs. The researchers developed a model called Multi-S3P that combines two types of neural networks with an attention mechanism. This helps the model learn both local and long-range patterns in protein sequences. It was trained and tested on standard datasets and performed better than existing methods, especially in difficult regions where protein structures change.
Recurrent Residual Networks Contain Stronger Lottery Tickets

This project shows that large neural networks can be simplified without training by selecting smaller subnetworks from them. These small networks are sparse, use fewer values, and can run efficiently on hardware. The study finds that converting some networks into recurrent forms improves accuracy and reduces memory use. Using this method, a popular network can be shrunk almost 50 times while keeping good performance.

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Recurrent Neural Networks Project Synopsis & Presentation

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Recurrent Neural Networks Final Year Projects with Source Code