Machine Learning
Screening tools play a vital role in sensory processing disorder detection. The automatic collection of features related to behavioral parameters and the response to given stimuli is possible with the recent technology. Real time stress related health parameters are collected as response to visual stimuli created with experts’ suggestions based on visual sensory processing related questionnaire. Body temperature and heart rate are obtained by smart watch.
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eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are all major problems. The correct eLearning Recommendation System is necessary to tailor the course recommendation according to the user's needs. To create this model, dataset of the User Profile and User Rating is needed.
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Each dataset is splitted by trainset, devset and testset.
Please read them with pytorch.
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Switching vectors of an inverter with model predictive control were collected for different conditions of an induction machine. The induction machine had a rated speed of w=3000 rpm and a rated torque of T=1.20 N·m. The switching vectors were recorded as data series for the induction machine operating at different combinations of speed and torque at healthy and faulty states. Each data series consisted of 22000 switching vectors. Inter-turn short circuit fault was introduced over 2 turns (out of 104 turns) in one phase of the machine.
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The design and implementation of an anthropomorphic robotic hand control system for the Bioengineering and Neuroimaging Laboratory LNB of the ESPOL were elaborated. The myoelectric signals were obtained using a bioelectric data acquisition board (CYTON BOARD) using six channels out of 8 available, which had an amplitude of 200 [uV] at a sampling frequency of 250 [Hz].
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Test
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The dataset contains an example of energy consumption, Functioning hours and Production KPI of different stages of the experimental open pit mine, mainly the destoning, the screening, and the train loading station. The Code is an example of the prediction algorithm, and the API can be used to apply the same algorithm used in this article.
In the proposed Dataset the energy consumption data for each station are collected from power meters and stored into a database that contains functioning hours and production.
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Este conjunto de datos es el resultado de un instrumento de medición aplicado para el desarrollo del proyecto "Aplicación de técnicas de minería de datos para la caracterización de estudiantes bajo el efecto de la pandemia de COVID-19".
En dicho instrumento se recolectaron datos sobre de variables sociodemográficas, económicas, condiciones técnicas referentes a la educación a distancia, salud emocional, así como académicas de estudiantes de un programa educativo de la Universidad Autónoma del Estado de Hidalgo.
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