Artificial Intelligence
Many of the publicly available electrocardiogram (ECG) databases either have a low number of people in the database, each with longer recordings, or have more people, each with shorter recordings. As a result, attempting to split a single database into training, testing, and, optionally, validation datasets is challenging. Some models seem to do well with larger training sets, but that leaves only a small set of data for testing. Moreover, if the ECG is segmented by heartbeat, the data are further limited by the number of heartbeats in the recording.
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This dataset is used for sign language emotion recognition and contains five emotions from 12 participants (6 males and 6 females) with high-positive, low-positive, high-negative, low-negative, and neutral emotions. The surface electromyography (sEMG) and inertial measurement unit (IMU) sensors were used to capture 30 sign language sentence signals. Participants' emotions were activated by film clips.
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The dataset includes annotated Computed Tomography (CT) scanned images. The labels consist of three types:
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The dataset contains short video clips of four shoulder exercises.
- Arm flexion and extension
- Arm abduction and adduction
- Arm lateral and medial rotation
- Arm circumduction
The videos are labeled as either correct or incorrect.
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<p>This multilingual Twitter dataset spans over 2 years from October 2019 to the end of 2021, including 3 months before the outbreak of the COVID-19 pandemic.</p>
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This dataset is a private foot pressure image dataset containing 317 images of high arches (H), 217 images of flat feet (L) and 362 images of normal feet (N).
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- There are six folders corresponding to 6 types of BPPV disorders.
- Each folder has one sample.
Each class is specified by the typical movement of the eye.
+) Lt_Geo_BPPV: eye beats toward the ground, beats stronger to the left side (turn head left).
+) Rt_Geo_BPPV: eye beats toward the ground, beats stronger to the right side (turn head right).
+) Lt_Apo_BPPV: eye beats toward the sky, beats stronger to the left side (turn head right).
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Multi-label event classification label of each sample-document is done with nine bits. The first bit signifies whether an event is present or absent with 1 or 0 respectively. The remaining eight bits signifies presence or absence of (i) covid, (ii) flood, (iii) storm, (iv) heavy rain, (v) cloudburst, (vi) landslide, (vii) earthquake, (viii) Tsunami with 1 or 0. The location and the impact sentence classification labeling are similar.
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A synthetic data for low power (P ≤10 mW) InGaAsP MQW-DFB lasers operating at a wavelength (λ) ranging from 1.53 to 1.57 µm at a case temperature laying between -40 ℃ to 85 ℃ with side mode suppression ratio of more than 35 dB is generated and can be used for laser lifetime prediction using machine learning based approaches.
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