Artificial Intelligence
brats 中的训练集（ training set） 有285个病例，每个病例有四个模态（t1、t2、flair、t1ce），需要分割三个部分：whole tumor（WT）， enhance tumor（ET）， and tumor core（TC）.
- Categories:
136 Views
In this study, a primary IMU-based gait dataset has been collected from 30 participants using a MOTI goniometer. This device collects movement data related to specific joints depending on the location of the device. The MOTI sensor contains an IMU consisting of an accelerometer, gyroscope, and magnetometer. Accelerometers measure acceleration the acceleration of the device, gyroscopes measure angular velocity of the device, and magnetometers measure the magnetic field of the Earth. In our study, the device was attached to arm and leg (thigh) positions.
- Categories:
604 Views
This is a data set resulting from the collaboration of the user and Phantom Premium $1.5$A Haptic Device, Geomagic Inc. Five right-handed users performed the task of tracking squiggly line in 6 trials. Since there is a significant difference between the performance of the trials done by the dominant hand and with ones performed by the non-dominant hand, we consider trajectories of the dominant hand of the users as expert data and trajectories coming from the other hand as novice data. This assumption is due to the fact that our dominant hand is fully trained to skillfully perform elaborate
- Categories:
117 Views
Please cite the following paper when using this dataset:
N. Thakur, “MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkeypox outbreak, findings from analysis of Tweets, and open research questions,” Infect. Dis. Rep., vol. 14, no. 6, pp. 855–883, 2022, DOI: https://doi.org/10.3390/idr14060087.
Abstract
- Categories:
3547 Views
We have developed three datasets, referred to as ER-C, Mito-C and Nucleus-C, respectively, for benchmarking robustness of DNN models against corruptions and adversarial attacks in semantic segmentation of fluorescence microscopy images. Degraded images in these three datasets are synthesized from raw images along with their manually annotated segmentation labels in the ER, Mito, and Nucleus datasets [1,2], respectively. They are synthesized with controlled corruptions and adversarial attacks. An updated version of the dataset has been released at
- Categories:
271 Views
In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.
- Categories:
354 Views
This dataset is created for neural network-based surrogate modeling of the power conversion losses. The dataset includes two sets of training and test data (for AC/DC and DC/DC converters respectively) for the neural network. The raw data is generated using PLECS Blockset Packages in MATLAB-Simulink environment.
- Categories:
99 Views
Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.
- Categories:
3600 Views
Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, A dataset containing images of indoor scenes and annotations of instance segmentation is developed in this research.
- Categories:
1530 Views
The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park.
In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics. Please cite the following original article where this work was initially presented:
- Categories:
3180 Views