Data Augmentation Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Data Augmentation ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Data Augmentation Projects
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An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network
This project focuses on detecting epilepsy using brain signals. The researchers combined two datasets and improved them to train a small, efficient neural network. The model can accurately identify epilepsy with high reliability. It works well even on low-cost devices and wearable technology, making real-time detection possible. -
BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation
This project focuses on improving how computers identify organs in CT scans, like kidneys and livers. It introduces a method called BucketAugment that helps neural networks work well on new data from different hospitals. The method uses a smart learning process to find the best way to adjust images during training. Overall, it makes medical image analysis more reliable and flexible across different datasets. -
Chronic Wound Image Augmentation and Assessment Using SemiSupervised Progressive Multi-Granularity EfficientNet
This project focuses on improving wound assessment using deep learning. The researchers started with a small set of labeled wound images and added extra unlabeled images to make the dataset bigger. They trained a neural network to score different wound features like size, depth, and tissue health. The method achieved about 90% accuracy, showing it can effectively grade wounds even with limited labeled data. -
Fetal-BET: Brain Extraction Tool for Fetal MRI
This project focuses on automatically identifying the fetal brain in MRI images. The researchers created a large dataset of fetal brain scans from different MRI types. They used deep learning to teach a computer to detect and separate the brain from other tissues. This method works accurately even when the scans are from different machines or show abnormal brains. -
FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery
This project focuses on creating a smart AI system called FetSAM that can accurately identify and outline the fetal head in ultrasound images. It uses a very large dataset to learn and improve its predictions. Compared to other existing models, FetSAM performs better in accuracy and precision. This tool can help doctors make more reliable decisions during pregnancy check-ups. -
Multi-Label Contrastive Learning for Abstract Visual Reasoning
This project teaches computers to solve reasoning puzzles like humans do. Instead of just memorizing patterns, it helps the computer understand the rules behind each puzzle. The system combines deep learning with human-style thinking to solve visual problems more accurately. It performs better than previous methods on major test datasets. -
Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery
This project focuses on improving robot-assisted surgery. It teaches a computer to recognize surgical tools and tissues in images. The method prevents the computer from forgetting old tools when learning about new ones. It also keeps patient data private by using synthetic images instead of real ones. -
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study
This project develops a smart system to detect multiple heart diseases from ECG signals. It can learn even with very few labeled examples. The system improves accuracy by creating extra training data and learning patterns of disease combinations. It works well even on new data that the system has not seen before. -
UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project uses artificial intelligence to analyze medical images. It can learn from a small number of labeled images and many unlabeled ones. The system extracts important features from unlabeled data and improves its accuracy with labeled data. It achieves very high accuracy even with only half the labeled data. -
BucketAugment Reinforced Domain Generalisation in Abdominal CT Segmentation
This project focuses on improving how computers identify organs in CT scans. The researchers created a new method called BucketAugment. It helps deep learning models work well on medical images from different hospitals and machines. This method makes organ segmentation more accurate without major changes to existing systems. -
Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation
This project focuses on improving speech analysis for people with neurological diseases. It uses computer models to break speech into individual words automatically. The study shows that adding simulated or altered speech from healthy people helps the model work better on impaired speech. This method reduces the need for real clinical recordings while keeping high accuracy. -
UKSSL Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project develops a deep learning system to analyze medical images when only a few labeled examples are available. It first learns useful features from many unlabeled images and then fine-tunes the model using the limited labeled data. The method works well on standard medical image datasets and achieves high accuracy, even better than some models trained with fully labeled data. This approach helps make medical image analysis more efficient when labeling is costly or slow. -
Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation
This project develops a method to detect diseases in chest X-rays by first creating a “normal” version of each X-ray. The system learns to erase abnormalities using synthetic image pairs. It then finds differences between the original and normal images to highlight disease areas. The method improves detection by training the model with both real and artificially generated images. -
Coronary Artery Disease Classification With Different Lesion Degree Ranges Based on Deep Learning
This project studies how artificial intelligence can help doctors analyze heart artery images. The researchers used deep learning to automatically classify parts of these images as having artery blockages or not. They tested several AI models and measured how accurate they were depending on how severe the blockages were. The results show that AI can be very accurate, but smaller blockages are harder to detect. -
Self-Training of Cyber-Threat Classification Model With Threat-Payload Centric Augmentation
This project focuses on improving security systems that detect cyber threats. It uses a smart deep learning method to automatically identify new threats without relying heavily on human analysts. The system learns from its own predictions and balances different types of threats to improve accuracy. Experiments show it is very fast and almost as accurate as manual human labeling. -
Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework
This project focuses on improving how computers detect harmful software. It combines traditional methods that use known malware signatures with artificial intelligence to find new, unseen threats. The system uses a special AI model called GAN to create fake safe files, helping the detector learn better. Tests show that this approach detects malware more accurately than existing systems.
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How We Help You with Data Augmentation Projects
At UniPhD, we provide complete guidance and support for Data Augmentation ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Data Augmentation Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Data Augmentation research and thesis development. We offer fast-track dissertation writing services to help you complete your Data Augmentation thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Data Augmentation Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Data Augmentation final year project.
Data Augmentation Research Support for PhD Scholars
UniPhD offers advanced Data Augmentation research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.