Recurrent Neural Network Final Year Projects with Source Code

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

Recurrent Neural Network Final Year Projects

  1. A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection
    This project focuses on detecting harmful content on social media. It uses machine learning methods to classify online messages as hateful or not. The study tests two models on multiple datasets and improves their accuracy using nature-inspired optimization techniques and fuzzy logic. This approach helps the system better understand the meaning behind the text.
  2. A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance
    This project focuses on detecting rumors on social media more accurately and quickly. It looks at both the hidden meaning in posts and the opinions of users who comment. The system gives more importance to trustworthy users and learns patterns over time. Tests show it can detect rumors faster and better than existing methods.
  3. A Review of Methodologies for Fake News Analysis
    This project reviews research on detecting fake news, which is becoming more important as false information spreads online. It studies how fake news can be analyzed by looking at its knowledge, style, source, and how it spreads. Detection methods are either manual, using experts or crowds, or automatic, using machine learning. The study suggests that machine learning works well and plans to explore Bayesian methods for faster and more flexible detection in the future.
  4. A Systematic Literature Review on Multimodal Machine Learning Applications Challenges Gaps and Future Directions
    This project reviews how machine learning can use multiple types of data together, like images, text, and audio, to solve real-world problems. It studies recent research on key challenges, such as combining, translating, and aligning these different data types. The authors analyzed over 1000 articles to identify trends, gaps, and progress in this area. This work helps researchers understand the current state of multimodal machine learning and plan future studies.
  5. An Attention-Based Convolutional Neural Network for Intrusion Detection Model
    This project focuses on improving network security by detecting intrusions quickly and accurately. It uses a type of artificial intelligence called convolutional neural networks with attention mechanisms. The method organizes network data into images in a smart way to make the detection process faster. Experiments show that this approach can identify threats efficiently while keeping high accuracy.
  6. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  7. 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.
  8. Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images
    This project develops a smart system to detect diabetic eye disease early using Internet-connected devices and deep learning. Eye images are collected with IoT devices and sent to the cloud for processing. The system cleans the images, highlights damaged regions, extracts important features, and uses an AI model to classify the disease. Tests show this method is more accurate and effective than earlier approaches.
  9. 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.
  10. Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network
    This project focuses on detecting early-stage Alzheimer’s disease using patterns in people’s handwriting captured online. Since there is limited data available, the study uses a special type of artificial intelligence, called DoppelGANger, to generate realistic handwriting examples. These generated examples help train a neural network to recognize Alzheimer’s more accurately. The approach was tested on real handwriting data and showed much better results than existing methods.
  11. 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.
  12. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  13. APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System
    This project focuses on detecting advanced cyberattacks in industrial systems connected through the Internet of Things. It uses a special machine learning method called Graph Attention Networks to identify hidden attacks more accurately than traditional methods. The approach was tested on real datasets and achieved over 95% detection accuracy in just around 20 seconds. Overall, it improves cybersecurity in smart industrial systems.
  14. A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems
    This project focuses on protecting smart power grids from cyber attacks. It combines a Bayesian method with deep neural networks to detect unusual or harmful activities in the grid. The system can tell normal events from attacks more accurately than traditional methods. Tests on real industrial data show it works better than older approaches.
  15. A Deep Learning-based Intelligent Quality Detection Model for Machine Translation
    This project focuses on improving machine translation by automatically checking the quality of translations in real time. It uses a special type of neural network called Double-RNN to analyze sentences and learn from a large set of example translations. The method can evaluate translations between Chinese and English more accurately. This helps make machine translation systems smarter and more reliable.
  16. A Hybrid Deep Learning-Based Intelligent System for Sports Action Recognition via Visual Knowledge Discovery
    This project focuses on creating an intelligent system to recognize sports actions, especially in aerobics. It uses video frames to analyze human movements and understand actions. The system represents the human body as a skeleton and studies its motion over time. Experiments show that it can accurately identify actions better than existing methods.
  17. 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.
  18. A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses
    This project studies how people’s web browsing activity can be tracked and protected. It focuses on website fingerprinting, which identifies the websites a user visits. The research surveys how deep learning can be used both to perform these tracking attacks and to defend against them. It also reviews methods, challenges, and future directions in this area.
  19. 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.
  20. 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.
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