Machine Learning
To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
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you can download these datasets from OpenML: https://www.openml.org/search?type=data&status=active&tags.tag=2019_mult...
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you can download these datasets from OpenML: https://www.openml.org/search?type=data&status=active&tags.tag=2019_mult...
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Liver cancer treatment, especially for metastatic cases, poses significant challenges in accurately targeting tumours while sparing healthy tissue. Radioembolisation with yttrium-90 (Y-90) microspheres is a promising technique, but precise imaging of microsphere distribution is crucial. This study utilises T-PEPT, a novel Positron Emission Particle Tracking (PEPT) algorithm that combines topological data analysis with machine learning to identify Y-90 microsphere clusters in a digital twin of a patient's liver.
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This dataset webpage contains datasets of exisiting and proposed models:
- centrifugalpump1.zip
- centrifugalpump2.zip
- centrifugalpump3.zip
B Model of Fault And Short-Circuit Analysis of Centrifugal Pump
presented in my last Speaker Presentation in Conference - 2025*.
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The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
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