Machine Learning
This dataset encapsulates a comprehensive collection of eye movement recordings captured during sleep, exceeding 100 distinct episodes. The recordings are primarily categorized into Rapid Eye Movement (REM), Slow Eye Movement (SEM), and non-movement phases, providing a rich resource for sleep research. Each video is meticulously recorded in high-definition .mp4 format, ensuring clarity and precision in capturing subtle ocular dynamics.
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4 Views
Popularity of smartphones also popularized, reading content using smartphones. Reading using smartphones quite differs from reading using desktop system. Mouse and Keyboard are the peripherals associated with the reading in desktop systems. Study of the handling of such devices has led to provide implicit feedback of the content read. Similar study in smartphones to get implicit feedback remains to be a huge gap. Reading using smartphones involves screen gestures like pinch to zoom, tap, scroll, orientation change and screen capture.
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146 Views
The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).
All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.
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147 Views
The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).
The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).
Each file consists of a signals from one sigle ride.
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62 Views
The recognition of woven fabrics through image processing has a high value in preserving cultural heritage and assisting in identifying and classifying traditional textile products. To this end, this study proposes an approach that utilizes a Decision Tree (DT) to recognize images of woven fabrics typical of East Nusa Tenggara. DT's effectiveness in high-dimensional data classification makes it an ideal tool for modeling unique patterns in woven fabric drawings.
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7 Views
data have 16 features with 1 target value
Scope: Primarily focused on diabetes-related information.
Data Size: Contains a substantial volume of records.
Variables: Likely includes patient demographics, medical history, lab results, medications, treatments, and outcomes.
Temporal Range: Time span covered by the dataset may vary.
Privacy Measures: Anonymized to protect patient identities.
Ethical Considerations: Collected and shared adhering to ethical guidelines.
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1717 Views
SeaIceWeather Dataset
This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518
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322 Views
QuaN is a collection of specially designed datasets for exploring the impact of noise quantum machine learning and other applications. The presented work focuses on the transformation of clean datasets into noisy counterparts across diverse domains, including MNIST-handwritten digits datasets, Medical MNIST, IRIS datasets and Mobile Health datasets. The dataset is created using noise from classical and quantum domains.
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445 Views
This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases.
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2791 Views
A real world radio frequency fingerprinting (RFF) dataset for enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling technique. The dataset is collected from the cable-connected measurement and over-the-air measurement.
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387 Views