Data Models Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Data Models 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 Models Projects
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From Clustering to Cluster Explanations via Neural Networks
This project focuses on making machine learning easier to understand. It explains why data points are grouped into certain clusters. The method turns clustering models into neural networks to see which features influence the grouping. It helps researchers check cluster quality and find new insights in the data. -
PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep LearningBased Centroblast Cell Detection
This project created a computer program called PseudoCell that can find specific cells called centroblasts in medical tissue images. It helps doctors by pointing out important areas without needing them to label every cell manually. The program saves time and makes the diagnostic process faster and easier. It can remove most irrelevant parts of the images while keeping the important ones. -
Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction
This project improves federated learning for devices with different computing powers and network conditions. It uses a new framework called AMA-FES to make training more stable and accurate. Low-power devices only update part of the model to save computation. The system is tested with drones doing image classification and shows better results without extra cost. -
An eXtended Reality Offloading IP Traffic Dataset and Models
This project focuses on making extended reality (XR) devices lighter and more comfortable by using 5G networks to handle heavy processing remotely. The researchers created a new dataset showing how XR apps use network resources in demanding scenarios. They also developed models to simulate this network traffic and tested them using a 5G emulator. The work helps students and engineers design and improve XR systems more effectively. -
Classification of Feature Engineering Techniques for Machine Learning under the Environment of Lattice Ordered T-Bipolar Soft Rings
This project develops a new mathematical method to improve machine learning decisions. It uses T-bipolar soft rings to analyze both positive and negative data aspects. An algorithm applies this idea to choose better features for model improvement.
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