Attention Mechanism Final Year Projects with Source Code

Byadmin Final Year Projects

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

Attention Mechanism Final Year Projects

  1. A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture
    This project helps identify diseases in fish using computer analysis of underwater images. It improves the accuracy of detection even when images are unclear. The system focuses on the important parts of the fish and learns quickly from new images. It can support farmers in keeping fish healthy and improving production.
  2. A Nested Attention Guided UNet Architecture for White Matter Hyperintensity Segmentation
    This project focuses on improving the detection of White Matter Hyperintensity (WMH) in brain MRI scans, which is important for predicting recovery in stroke patients. The researchers developed a new deep learning method called NAUNet++ that uses attention mechanisms and atlas images to better identify WMH regions. Their approach produces more accurate and faster segmentation results than existing methods, helping doctors assess patient prognosis more reliably.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Human Behavior Recognition Based on Multiscale Convolutional Neural Network
    This project focuses on recognizing human actions in videos using a smarter neural network. It improves how the network looks at both the overall and local details in each video. The method splits videos into segments, extracts important behavior information, and combines it over time to understand the full action. Tests show it is accurate, faster, and more efficient than older methods.
  9. Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network
    This project focuses on classifying ancient bone sticks unearthed in China using artificial intelligence. It develops a deep learning model that can accurately identify fracture locations and colors on the bone sticks. The model uses advanced attention techniques to focus on important features and reduce background interference. As a result, it achieves high accuracy, making the classification of these historical artifacts much faster and more reliable.
  10. 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.
  11. 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.
  12. HarDNet and Dual-Code Attention Mechanism Based Model for Medical Images Segmentation
    This project focuses on improving the accuracy of medical image analysis. The researchers designed a model that better identifies important features in images and separates them from the background. It uses special modules to speed up processing and highlight both position and detail information. Tests on different medical datasets showed the method works very well, helping doctors diagnose diseases faster and more accurately.
  13. Medical Image Segmentation Based on Transformer and HarDNet Structures
    This project improves medical image segmentation, which helps doctors detect diseases more accurately. It uses a new network with two encoders to capture both local details and overall image features. A special fusion module combines information from different layers to boost accuracy. Tests on several medical datasets show better results in identifying disease areas, helping in early diagnosis and treatment.
  14. 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.
  15. 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.
  16. A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism
    This project focuses on predicting epileptic seizures using brain signal data. It uses a combination of neural networks to analyze both spatial and temporal patterns in EEG signals. The model can classify signals into normal, pre-seizure, and seizure stages with high accuracy. The goal is to eventually create a wearable device that can warn patients before a seizure occurs.
  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. Agricultural Text Classification Method Based on Dynamic Fusion of Multiple Features
    This project improves how agricultural texts are classified. It combines deep learning methods to understand both the words and the important numbers in the text. The system looks at the overall meaning, local details, and numerical values. These features are fused together to make text classification more accurate.
  19. An Adaptive Masked Attention Mechanism to Act on the Local Text in a Global Context for Aspect-Based Sentiment Analysis
    This project studies how to understand opinions about specific parts of a sentence, such as features of a product. It introduces a new way for the model to focus on both the whole sentence and the important local words. This method reduces noise and helps the system learn useful information more efficiently. The model works well on many benchmark datasets.
  20. MLGNA Multi-Label Guided Network for Improving Text Classification
    This project focuses on improving how computers understand and classify text when a document can belong to multiple categories at once. The researchers created a new model that uses information about the labels to better represent the document. It also considers how labels are related to each other to make more accurate predictions. Tests show this approach works better than previous methods on standard datasets.
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