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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Federated learning (FL), as a privacy-preserving distributed machine learning algorithm is being rapidly applied in wireless communication networks, which enables IoT clients to obtain well-trained models while keeping data local.
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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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- 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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The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.
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