151 to 160 of 223 Results
Jun 24, 2022 - Mahardhika Pratama
Pratama, Mahardhika; Pedrycz, Witold; Webb, Geoffrey I., 2022, "The source codes of DEVFNN", https://doi.org/10.21979/N9/LVFNVG, DR-NTU (Data), V1, UNF:6:glL3E+ZePWuvW/m8qMs2VA== [fileUNF]
the source code of DEVFNN " An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams " |
Jun 24, 2022 - Mahardhika Pratama
Ashfahani, Andri; Pratama, Mahardhika; Lughofer, Edwin; Ong, Yew-Soon, 2022, "The source codes of DEVDAN Python", https://doi.org/10.21979/N9/5QAWOV, DR-NTU (Data), V1
the source code of DEVDAN " DEVDAN: Deep evolving denoising autoencoder " |
Jun 24, 2022 - Mahardhika Pratama
Ashfahani, Andri; Pratama, Mahardhika; Lughofer, Edwin; Ong, Yew-Soon, 2022, "The source codes of DEVDAN Matlab", https://doi.org/10.21979/N9/KPULXP, DR-NTU (Data), V1
the source code of DEVDAN " DEVDAN: Deep evolving denoising autoencoder " |
Jun 24, 2022 - Mahardhika Pratama
Ashfahani, Andri; Pratama, Mahardhika, 2022, "The source codes of ADL Python", https://doi.org/10.21979/N9/PR0LV2, DR-NTU (Data), V1
the source code of ADL " Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments " |
Jun 24, 2022 - Mahardhika Pratama
Weng, Weiwei; Pratama, Mahardhika; Ashfahani, Andri; Yapp, Edward Kien Yee, 2022, "The source codes of ParsNetplus", https://doi.org/10.21979/N9/UVBUWN, DR-NTU (Data), V1
The source codes of ParsNetplus |
Jun 16, 2022 - Self-Supervised Autoregressive Domain Adaptation for Time Series Data
Ragab, Mohamed, 2022, "Paderborn Dataset Processed for Domain Adaptation Scenarios", https://doi.org/10.21979/N9/X6M827, DR-NTU (Data), V1
The dataset contains sensor readings of bearing machines under 4 different operating conditions, with each having 3 different classes, i.e., healthy, inner-bearing damage, and outer-bearing damage. Each operating condition refers to different operating parameters, including rotat... |
May 27, 2022 - ADATIME
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "UCI HAR Dataset Processed", https://doi.org/10.21979/N9/0SYHTZ, DR-NTU (Data), V1
UCIHAR is one of the most widely used datasets to evaluate performance on time series data. It contains three different sensors namely, accelerometer, gyroscope, and body sensors. These sensors have been used to collect data from 30 different persons. In our experiments, we treat... |
May 27, 2022 - ADATIME
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "Subject-wise Sleep Stage Data", https://doi.org/10.21979/N9/UD1IM9, DR-NTU (Data), V1
Sleep stage classification (SSC) problem aims to classify the electroencephalography (EEG) signals into five stages i.e. Wake (W), Non-Rapid Eye Movement stages (N1, N2, N3), and Rapid Eye Movement (REM). We adopted Sleep-EDF dataset (Goldberger et al., 2000), which contains EEG... |
May 27, 2022 - ADATIME
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "WISDM Dataset Processed", https://doi.org/10.21979/N9/KJWE5B, DR-NTU (Data), V1, UNF:6:UNCqR5C0NzMZQm8xrjVq0Q== [fileUNF]
WISDM is another popular activity recognition dataset for the evaluation of time series domain adaptation. In this dataset, accelerometer sensors were applied to collect data from 36 subjects. This data can be more challenging because of the class imbalance issue among different... |
May 27, 2022 - ADATIME
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "HHAR Processed Data", https://doi.org/10.21979/N9/OWDFXO, DR-NTU (Data), V1
The Heterogeneity Human Activity Recognition (HHAR) dataset has been collected from 9 different users using sensor readings from smartphones and smartwatches. In our experiments, we consider each user as a domain. We constructed 10 cross-domain scenarios from randomly selected us... |
