Appointment: Deputy Director, Centre for Professional and Continuing Education, School of Computer Science and Engineering
Associate Professor, School of Computer Science and Engineering

Research topics:

• Machine Learning and Statistical Inference
• Learning with Unlabeled Data
• Meta and Ensemble learning
• Ontology for Knowledge Representation

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1 to 10 of 28 Results
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...
Jun 16, 2022
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...
ADATIME(Nanyang Technological University)
May 27, 2022
May 12, 2022
Ragab, Mohamed, 2022, "Processed version of C-MAPSS dataset", https://doi.org/10.21979/N9/FMUP9M, DR-NTU (Data), V1
C-MAPSS is a simulated turbofan engine dataset that consists of four data subsets with different operating conditions and fault modes denoted as FD001, FD002, FD003, and FD004.
Apr 23, 2022 - Lin Zhuoyi
Lin, Zhuoyi, 2022, "Related data for GLIMG: Global and local item graphs for top-N recommender systems", https://doi.org/10.21979/N9/NRRVDW, DR-NTU (Data), V2
GLIMG: Global and local item graphs for top-N recommender systems
Apr 23, 2022 - Lin Zhuoyi
Lin, Zhuoyi, 2022, "Related Data for COMET: Convolutional Dimension Interaction for Collaborative Filtering", https://doi.org/10.21979/N9/TO2HBX, DR-NTU (Data), V2
Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction informa...
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