transfer learning
The dataset aims to compile images of buildings with structural damage for analysis. The images can be classified by the severity of damage to building facades after seismic events using deep learning techniques, particularly pre-trained convolutional neural networks and transfer learning. The analysis can precisely identify structural damage levels, aiding in effective evaluation and response strategies.
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This is the MRI scan database used in the research work of classifying Meningioma Tumor in humans by using hybrid Ensemble Deep Learning Network AlGoRes. It consist of two sets; one for training and another one for testing the Deep Learning Network AlGoRes.
Training data set consist of 822 imagers with meningioma_tumor and 395 images without tumor.
Testing data set consist of 115 imagers with meningioma_tumor and 104 images without tumor.
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This dataset is derived from Sentinel-2 satellite imagery.
The main goal is to employ this dataset to train and classify images into two classes: with trees, and without trees.
The structure of the dataset is 2 folders named: "tree" (images containing trees) and "no-trees" (images without presence of trees).
Each folder contains 5200 images of this type.
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369 Views
This dataset is developed to support research on early warning of pilots' cognitive collapse. It encompasses the recordings of 10 participants over 3 separate sessions in the simulated flight experiment paradigm. The experiment aims to elicit the pilots' cognitive collapse state and to early warn of the tipping points, for details you can refer to the paper "An Early Warning Approach for Pilots’ Cognitive Tipping Points Based Multi-modal Signals". Each trial consists of 3 stages that can induce cognitive states at low, medium, and high level with a cognitive collapse.
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The dataset encompasses the recordings of 10 participants over 3 separate sessions in the simulated flight experiment paradigm. The experiment aims to elicit the pilots' cognitive collapse state and to early warn of the tipping points, for details you can refer to the paper "An Early Warning Approach for Pilots’ Cognitive Tipping Points Based Multi-modal Signals". The dataset includes EEG, eye movement, task performance data, and experimenter's recording. It has a total of 30 trials and is approximately 4.7G in size.EEG signal.
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110 Views
The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.
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1701 Views
This dataset concludes source data which is professional fact descriptions from Chinese law office website and target data which is non-professional fact descriptions from daily spoken language.
The dataset is for transfer learning in law domain.
The dataset also concludes processed dictionary and .npy files.
The task of transfer learning is to predict the accusation based on the description.
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