Biomedical and Health Sciences
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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This is part of our external validation set, which contains 40 volunteers and about 80 hematological examination items. Among them, Cl, BHB, AG, RBP, HCO3, FT3, aTPO, CYSC, FT4, Folate, UA and aTG contribute more to the prediction. Because the data involves personal privacy and research confidentiality, it cannot be fully public. However, you can still make predictions by using our ML model and get a high accuracy on the external dataset.
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A flexible pressure sensing system built with a flexible pressure sensor collected footprint image video of mice in a dark environment and open field, including footprint data of Parkinson's mice and normal mice at 6 months and 9 months old. Among them, 804, 811, 812 and 825 were normal mice; Mice 1, 2, 3, 4, 653, 682 were normal mice, and the rest were PD mice.
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This dataset consists of carefully curated audio recordings that capture the distinct sounds produced by multiple individuals walking in various environments. Designed to support research in sound recognition, activity analysis, and the study of human behaviour, it provides a rich resource for understanding how group dynamics influence acoustic patterns. Each recording is accompanied by detailed metadata, including the number of participants, environmental context, and surface characteristics.
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This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598
Please cite the following paper when using this dataset:
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Brain-Computer Interface (BCI) technology facilitates a direct connection between the brain and external devices by interpreting neural signals. It is critical to have datasets that contain patient's native languages while developing BCI-based solutions for neurological disorders. However, present BCI research lacks appropriate language-specific datasets, particularly for languages such as Telugu, which is spoken by more than 90 million people in India.
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Numerous studies have demonstrated that microbes play a vital role in human health, making the identification of potential microbe-drug associations critical for drug discovery and clinical treatment. In this manuscript, we proposed a novel prediction model named GTDEKAN by integrating an aware Transformer network with a Dual Cross-Attention (DCA) module (including a Channel Cross-Attention and a Spatial Cross-Attention) and an Enhanced Kolmogorov-Arnold Network (EKAN) to infer potential microbe-drug associations.
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Persistent viruses like influenza, HIV, Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments. This study leverages a Long Short-Term Memory (LSTM) based deep learning architecture to analyze an extensive dataset of over 3.1 million unique viral spike protein sequences, with SARS-CoV-2 serving as the primary example. Our model, Escape Elite Network(EEN) outperforms existing methods in detecting escape mutations across diverse datasets.
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