Machine Learning
The pressure sensors are represented by black circles, which are located in the three zones of each foot. For the left foot: S1 and S2 cover the forefoot area. S3, S4, and S5 the midfoot area. S6 and S7 the rearfoot or heel area. Similarly, for the right foot: S8 and S9 represent the forefoot area. S10, S11, S12 the midfoot area. S13 and S14 the heel area. The values of each sensor are read by the analog inputs of an Arduino mega 2560.
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1130 Views
This dataset collection contains eleven datasets used in Locally Linear Embedding and fMRI feature selection in psychiatric classification.
The datasets given in the Links section are reduced subsets of those contained in their respective tar files (a consequence of Mendeley Data's 10GB limitation).
The Linked datasets (not the tar files) contain just the MATLAB file and the resting state image (or block-design fMRI for the MRN dataset), where appropriate.
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842 Views
Device identification using network traffic analysis is being researched for IoT and non-IoT devices against cyber-attacks. The idea is to define a device specific unique fingerprint by analyzing the solely inter-arrival time (IAT) of packets as feature to identify a device. Deep learning is used on IAT signature for device fingerprinting of 58 non-IoT devices. We observed maximum recall and accuracy of 97.9% and 97.7% to identify device. A comparitive research GTID found using defined IAT signature that models of device identification are better than device type identification.
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1208 Views
The dataset is used in machine learning method of the "A distributed Front-end Edge node assessment model by using Fuzzy and a learning-to-rank method" paper
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157 Views
This dataset is related to the paper "A distributed Front-end Edge node assessment method by using a learning-to-rank method"
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185 Views
This dataset is benchmark dataset we use in our research for Intrusion Detection System.
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3617 Views
This repository contains a data base of Cell Signal Quality samples obtained from 4 COTS cellphones. Data shows the dynamics of the cellular signal, and how these can be affected by the presence of human body nearby.
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473 Views
This dataset includes the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior.
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554 Views
This dataset includes the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014.
There are 4 columns in the file, which represent viewer ID, the current channel number, th next channel number, the date of the month, respectively.
The first column, the ID code of a viewter, ranks with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger
the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior.
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150 Views
Electroencephalography (EEG) signal data was collected from twelve healthy subjects with no known musculoskeletal or neurological deficits (mean age 25.5 ± 3.7, 11 male, 1 female, 1 left handed, 11 right handed) using an EGI Geodesics© Hydrocel EEG 64-Channel spongeless sensor net. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Wisconsin-Milwaukee (17.352).
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1420 Views