Machine Learning

The evolution of the Industrial Internet of Things (IIoT) introduces several benefits, such as real-time monitoring, pervasive control and self-healing. However, despite the valuable services, security and privacy issues still remain given the presence of legacy and insecure communication protocols like IEC 60870-5-104. IEC 60870-5-104 is an industrial protocol widely applied in critical infrastructures, such as the smart electrical grid and industrial healthcare systems.

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Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture calle

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Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.

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In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory.

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This dataset containg EMG data recorded with a sample rate of 1kHz. Data was collected from  the brachioradialis, flexor carpi ulnaris and common extensor digitorum muscles.

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The biographies_EN dataset contains 1000 biographies of literature writers retrieved from the english version of Wikipedia.

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Electroretinography is a non-invasive electrophysiological method standardized by the International Society for Clinical Electrophysiology of Vision (ISCEV). Electroretinography has been used for the clinical application and standardization of electrophysiological protocols for diagnosing the retina since 1989. Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. To transfer clinical practice to patients the establishment of standardized protocols is an important step.

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A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth emitters under challenging testbed setups is presented in this dataset. The dataset includes emissions from 10 COTS IoT emitters (2 laptops and 8 commercial chips) that are captured with a National Instruments Ettus USRP X300 radio outfitted with a UBX160 daughterboard and a VERT2450 antenna. The receiver is tuned to record a 2 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency.

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Roughly 16 million mobile devices are available with the majority of the population worldwide and this number going to increase exponentially. Many mobile devices do include sensors and hence can be used for other applications rather than mobile gaming. In the present work, we have collected mobile sensor data for 11 classes. Each class of data is gathered with a child or adult person performing different activities during the day or recreating the scenario of different crimes.

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The code contains two public Parkinson's speech datasets, a self-collected Parkinson's speech dataset, some common public datasets. It also contains the MATLAB code for Intra-subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Speech Data of Parkinson's Disease( JTEHM-00114-2022).

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