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Healthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue---reducing these parameters to a smaller set with the most "determinant" information. However, existing proposals usually focus on classification problems---aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses.
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Bitcoin (BTC), ether (ETH), gridcoin (GRC), curecoin (CURE), and foldingcoin (FLDC) market capitalizations in USD.
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We constructed datasets by extracting different features from Android Apk files, including permissions (official definition and customization), APIs and vulnerabilities. The datasets can be used for malware detection.
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This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.
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Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data.
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This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.
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This is the market data of Bitcoin in terms of price and volume from August 2015 to August 2021. The time interval of sampling is selected as four-hour, that is to say, we choose every kind of price and volume every of four-hour as the original data. The original market data of Bitcoin are obtained from Poloniex, one of the most active crypto-asset exchanges.
Download link on XBlock: http://xblock.pro/#/dataset/5
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The accelerometer data has been collected using one smartphone carried by subjects, which are caregivers and nurses, when they were conducting daily works at a healthcare facility. The smartphone was carried in an arbitrary position such as a pocket. There are a total of 27 activities divided into 4 groups.
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Our data set has 5136 records collected in 214 days. The sampling rate of the sensors is 1 hour. Each record includes the number of vehicles entering and leaving the parking lot in an hour, the CO2 concentration of every building floor at the recording time, and the power consumption of each floor in an hour.
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