Appointment: Associate Professor

Research topics:

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

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 33 Results
Coronavirus Neutralizing Antibodies(Nanyang Technological University)
Jun 20, 2024
CWRU Domain Generalization Version
Apr 3, 2023 - Conditional Contrastive Domain Generalization for Fault Diagnosis
Ragab, Mohamed, 2023, "CWRU Domain Generalization Version", https://doi.org/10.21979/N9/8QPQHL, DR-NTU (Data), V1
The Case Western Reserve University (CWRU) is a widely adopted dataset for rolling bearing elements (Smith and Randall 2015). Accelerometer sensors were deployed at both the drive-end and fan-end of the housing motors. Vibration signals were collected with 12 KHz sampling rate un...
Machine Fault Diagnosis
Dec 30, 2022 - ADATIME
Ragab, Mohamed, 2022, "Machine Fault Diagnosis", https://doi.org/10.21979/N9/PU85XN, DR-NTU (Data), V1
The dataset consists of sensor readings from bearing machines under four different operating conditions. Each of these conditions has three classes: healthy, inner-bearing damage, and outer-bearing damage. The operating conditions refer to different values for rotational speed, l...
Paderborn Domain Generalization Version
Oct 7, 2022 - Conditional Contrastive Domain Generalization for Fault Diagnosis
Ragab, Mohamed, 2022, "Paderborn Domain Generalization Version", https://doi.org/10.21979/N9/UCIK2K, DR-NTU (Data), V1
This dataset is generated the KAT data center in Paderborn University with the sampling rate of 64 KHz (Lessmeier et al. 2016). The damages were generated using both artificial and natural ways. More specifically, an electric discharge machine (EDM), a drilling, and an electric e...
Conditional Contrastive Domain Generalization for Fault Diagnosis(Nanyang Technological University)
Oct 7, 2022
Paderborn Dataset Processed for Domain Adaptation Scenarios
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...
Self-Supervised Autoregressive Domain Adaptation for Time Series Data(Nanyang Technological University)
Jun 16, 2022
UCI HAR Dataset Processed
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...
Subject-wise Sleep Stage Data
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...
WISDM Dataset Processed
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...
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.