Emadeldeen Eldele

1.9k total citations · 3 hit papers
15 papers, 1.1k citations indexed

About

Emadeldeen Eldele is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Emadeldeen Eldele has authored 15 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Signal Processing and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Emadeldeen Eldele's work include Anomaly Detection Techniques and Applications (7 papers), Time Series Analysis and Forecasting (6 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Emadeldeen Eldele is often cited by papers focused on Anomaly Detection Techniques and Applications (7 papers), Time Series Analysis and Forecasting (6 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Emadeldeen Eldele collaborates with scholars based in Singapore, China and Egypt. Emadeldeen Eldele's co-authors include Min Wu, Xiaoli Li, Zhenghua Chen, Chee Keong Kwoh, Cuntai Guan, Mohamed Ragab, Chengyu Liu, Wenyu Zhang, Chuan-Sheng Foo and Qinghua Hu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Emadeldeen Eldele

13 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

2021 378 citations Emadeldeen Eldele, Zhenghua Chen et al. IEEE Transactions on Neural Systems and Rehabilitation Engineering profile →
Time-Series Representation Learning via Temporal and Contextual Contrasting

2021 289 citations Emadeldeen Eldele, Mohamed Ragab et al. DR-NTU (Nanyang Technological University) profile →
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

2023 89 citations Emadeldeen Eldele, Mohamed Ragab et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Countries citing papers authored by Emadeldeen Eldele

Since Specialization

Citations

This map shows the geographic impact of Emadeldeen Eldele's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Emadeldeen Eldele with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emadeldeen Eldele more than expected).

Fields of papers citing papers by Emadeldeen Eldele

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emadeldeen Eldele. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Emadeldeen Eldele. The network helps show where Emadeldeen Eldele may publish in the future.

Co-authorship network of co-authors of Emadeldeen Eldele

This figure shows the co-authorship network connecting the top 25 collaborators of Emadeldeen Eldele. A scholar is included among the top collaborators of Emadeldeen Eldele based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Emadeldeen Eldele. Emadeldeen Eldele is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	Evidentially Calibrated Source-Free Time-Series Domain Adaptation With Temporal Imputation 2025 ·IEEE Transactions on Knowledge and Data Engineering ·Mohamed Ragab, (unknown), Emadeldeen Eldele, Wenyu Zhang, Min Wu, Chuan-Sheng Foo, Daoqiang Zhang, Xiaoli Li, Zhenghua Chen	0
2	Hierarchical Classification Auxiliary Network for Time Series Forecasting 2025 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Zongxia Xie, Dongyue Chen, Emadeldeen Eldele, Qinghua Hu	0
3	Label-Efficient Time Series Representation Learning: A Review 2024 ·IEEE Transactions on Artificial Intelligence ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	9
4	ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer 2023 ·Biomedical Signal Processing and Control ·(unknown), Emadeldeen Eldele	39
5	ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data 2023 ·ACM Transactions on Knowledge Discovery from Data ·Mohamed Ragab, Emadeldeen Eldele, (unknown), Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	35
6	Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation 2023 ·IEEE Transactions on Neural Systems and Rehabilitation Engineering ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	18
7	Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification breakdown → 2023 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan	89
8	Contrastive Domain Adaptation for Time-Series Via Temporal Mixup 2023 ·IEEE Transactions on Artificial Intelligence ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	23
9	Source-Free Domain Adaptation with Temporal Imputation for Time Series Data 2023 ·Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, Xiaoli Li, Zhenghua Chen	11
10	Label-efficient Time Series Representation Learning: A Review 2023 ·arXiv (Cornell University) ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	1
11	ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training 2022 ·IEEE Transactions on Emerging Topics in Computational Intelligence ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan	37
12	Conditional Contrastive Domain Generalization for Fault Diagnosis 2022 ·IEEE Transactions on Instrumentation and Measurement ·Mohamed Ragab, Zhenghua Chen, Wenyu Zhang, Emadeldeen Eldele, Min Wu, Chee Keong Kwoh, Xiaoli Li	104
13	Self-Supervised Autoregressive Domain Adaptation for Time Series Data 2022 ·IEEE Transactions on Neural Networks and Learning Systems ·Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li	43
14	An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG breakdown → 2021 ·IEEE Transactions on Neural Systems and Rehabilitation Engineering ·Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan	378
15	Time-Series Representation Learning via Temporal and Contextual Contrasting breakdown → 2021 ·DR-NTU (Nanyang Technological University) ·Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan	289

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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