Transfer Learning projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Transfer Learning projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide 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 Transfer Learning Projects
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A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
This project aims to develop an intelligent deep learning system for classifying diabetic retinopathy based on fundus images. It focuses on identifying both early and severe retinal lesions that previous models often ignored. The approach combines GoogleNet and ResNet with an adaptive particle swarm optimizer to improve feature extraction. The extracted features are then tested with machine learning models such as random forest and SVM. The hybrid model achieves high accuracy and shows strong potential for improving diagnosis of DR severity levels. -
A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System
This project created a new CPR training system that listens to the sounds made during chest compressions. The system uses these sounds to check how deep, fast, and well the compressions are done. It uses a smartphone and special sounds from a training device to give accurate feedback. This helps people practice CPR at home without expensive equipment. -
BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs
This project created BeatProfiler, a computer tool that studies heart cell function in the lab. It measures how heart cells contract, handle calcium, and respond to drugs. The tool works faster and more accurately than older methods. It can also use machine learning to identify heart diseases and classify drug effects. -
Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients
This project focuses on predicting serious health problems in hospitalized patients before they happen. It uses a computer-generated score called the deterioration index to track patient condition over time. A special learning method helps the computer understand patterns in these scores and make accurate predictions. Hospitals can use this system as an early warning to provide timely care and prevent complications. -
Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
This project uses data from wearable sensors to predict a person’s blood sugar levels. It applies deep learning models to understand which methods and inputs give the most accurate results. The study compares different model types and personal data amounts to find what works best. It also provides a new dataset to help other researchers study glucose prediction. -
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
This project focuses on making mobile networks smarter and cheaper to run. It studies how to set up and manage different parts of the network, like base stations and virtual units, depending on traffic and resources. The researchers used a type of artificial intelligence called deep reinforcement learning to find the best setup automatically. Their method reduces network costs a lot compared to older approaches. -
Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images
This project focuses on detecting liver cancer using CT scans. It uses smart computer programs that learn from many hospitals’ data without sharing patient details. The system can accurately find the liver and tumor areas in images. It aims to make cancer detection faster, safer, and more reliable for doctors. -
Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations
This project focuses on improving eye scans called OCTA, which show the blood vessels in the retina. The researchers created a way to make realistic fake images of these vessels to help train computers. Their method helps the computer find even the smallest blood vessels more accurately. They also shared all their code and data so others can use it. -
BeatProfiler Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs
This project created BeatProfiler, a software tool that studies how heart cells beat and handle calcium. It can quickly and accurately measure heart cell contraction and response to drugs without expensive equipment. The tool also uses machine learning to identify heart diseases and how cells react to different treatments. Overall, it makes heart cell research faster, easier, and more reliable. -
From Scalp to Ear-EEG A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People
This project studies how to improve sleep monitoring using a small ear sensor instead of bulky head sensors. It uses existing brainwave data and trained models from scalp recordings to teach the ear-based system. By fine-tuning these models, the research achieved more accurate sleep stage detection in older adults. This could help develop simple, comfortable tools for tracking sleep at home. -
GenHPF General Healthcare Predictive Framework for Multi-Task MultiSource Learning
This project creates a system that can easily use hospital data from different sources to predict patient outcomes. It turns medical records into readable text so computers can understand them better. The system works well even when data formats change across hospitals. This helps doctors and researchers use AI models more effectively for many medical prediction tasks. -
Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection
This project uses machine learning to study how common heart-related lab tests change over time in patients. It first learns general patterns from many patients and then applies this knowledge to detect specific heart problems in smaller patient groups. The method improves prediction accuracy and helps doctors better plan tests and treatments. It shows potential for use in other diseases too. -
Is Attention all You Need in Medical Image Analysis? A Review
This project reviews the use of hybrid models that combine CNNs and Transformers for medical image analysis. CNNs capture local details in images, while Transformers capture global patterns. By combining both, these models can better understand complex medical images. The study analyzes existing designs, their strengths, and future opportunities for improving medical diagnosis and research. -
A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework
This project uses artificial intelligence to help detect breast cancer from medical images. It keeps patient data private by using federated learning, which trains models without sharing personal data. The system improves accuracy by enhancing images, balancing the data, and using advanced AI models. Tests show it performs better than traditional methods and can be used in healthcare safely. -
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
This project develops an AI system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to automatically learn important features from the images. The system combines multiple models to improve accuracy and reliability. Tests show it can diagnose diseases quickly and more accurately than traditional methods. -
Advancing UAV Communications: A Comprehensive Survey of CuttingEdge Machine Learning Techniques
This project studies how machine learning can help drones communicate better and work smarter in mobile networks. It explains how drones can act as flying users or base stations to improve network coverage. The paper reviews different learning methods that help drones save energy and find the best flight paths. It also explores how new AI techniques can connect with cloud and edge systems for better performance. -
Advancing UAV Communications A Comprehensive Survey of CuttingEdge Machine Learning Techniques
This project reviews how machine learning is used with drones in mobile networks. It explains how drones can act like flying users or mini base stations. The study looks at how AI can help improve coverage, energy use, and network performance. It also explores new AI methods and how they can work with cloud or edge computing. -
Rethinking Membership Inference Attacks Against Transfer Learning
This project studies privacy risks in transfer learning, a type of machine learning where knowledge is shared between models. The researchers show that even if someone only has access to the “student” model, they can infer whether specific data was used to train the original “teacher” model. They propose a method to detect this by comparing hidden information inside models. The work highlights a hidden privacy risk in using transfer learning. -
Transferability of Machine Learning Algorithm for IoT Device Profiling and Identification
This project focuses on improving security for Internet of Things (IoT) devices. It uses machine learning to identify device types and detect potential vulnerabilities. The system transfers knowledge from one dataset to another to work better on new devices. Finally, it shows security risks and results on a clear dashboard. -
A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages
This project focuses on identifying the different growth stages of pomegranates using images. It uses machine learning to recognize stages like bud, flower, and ripe fruit. The model helps farmers know the exact stage of their crops early. This can improve yield, quality, and reduce losses from pests or diseases.
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How We Help You with Transfer Learning Projects
At UniPhD, we provide complete guidance and support for Transfer Learning 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 have extensive experience guiding students in computer science, electronics, and electrical domains, ensuring successful completion of academic and research projects.
Transfer Learning Thesis and Dissertation Writing
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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.
Transfer Learning 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 Transfer Learning final year project.
Transfer Learning Research Support for PhD Scholars
UniPhD offers advanced Transfer Learning 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.