Zeyar Aung

3.0k total citations · 1 hit paper
106 papers, 1.5k citations indexed

About

Zeyar Aung is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Zeyar Aung has authored 106 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 23 papers in Computer Vision and Pattern Recognition and 18 papers in Electrical and Electronic Engineering. Recurrent topics in Zeyar Aung's work include Energy Load and Power Forecasting (10 papers), Smart Grid Security and Resilience (10 papers) and Protein Structure and Dynamics (9 papers). Zeyar Aung is often cited by papers focused on Energy Load and Power Forecasting (10 papers), Smart Grid Security and Resilience (10 papers) and Protein Structure and Dynamics (9 papers). Zeyar Aung collaborates with scholars based in United Arab Emirates, Bangladesh and United States. Zeyar Aung's co-authors include Abel Sanchez, John Williams, Wei Lee Woon, Mohammad Abdul Azim, Kian‐Lee Tan, Zulfikar Alom, Mustafa Amir Faisal, Talal Rahwan, Depeng Li and Bijay Neupane and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Zeyar Aung

99 papers receiving 1.4k 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.

Transfer learning: a friendly introduction

2022 237 citations Zeyar Aung et al. profile →

Peers

Zeyar Aung

AI

EEE

CNC

CSE

IS

Prąsun Chakrabarti India

George A. Vouros Greece

Flavio Villanustre United States

Wei Pang China

Amgad Muneer Malaysia

Fan Lin China

Satchidananda Dehuri India

Dharmendra Singh Rajput India

Pei‐Wei Tsai China

Aimin Yang China

Prąsun Chakrabarti India

Zeyar Aung

462 ×0.9

AI

266 ×0.7

EEE

268 ×1.2

CNC

262 ×1.2

CSE

130 ×0.6

IS

Citations per year, relative to Zeyar Aung Zeyar Aung (= 1×) peers Prąsun Chakrabarti

Countries citing papers authored by Zeyar Aung

Since Specialization

Citations

This map shows the geographic impact of Zeyar Aung'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 Zeyar Aung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zeyar Aung more than expected).

Fields of papers citing papers by Zeyar Aung

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zeyar Aung. 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 Zeyar Aung. The network helps show where Zeyar Aung may publish in the future.

Co-authorship network of co-authors of Zeyar Aung

This figure shows the co-authorship network connecting the top 25 collaborators of Zeyar Aung. A scholar is included among the top collaborators of Zeyar Aung 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 Zeyar Aung. Zeyar Aung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Al-Marzouqi, Hasan, et al.. (2025). Kolmogorov-Arnold Vision Transformer for Image Reconstruction in Lung Electrical Impedance Tomography. IEEE Open Journal of the Computer Society. 6. 519–530. 1 indexed citations

2.

Mridha, M. F., et al.. (2025). Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI. IEEE Access. 13. 79609–79622. 1 indexed citations

3.

Nobel, S. M. Nuruzzaman, et al.. (2025). CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification. Computers in Biology and Medicine. 193. 110339–110339. 1 indexed citations

4.

Aung, Zeyar, et al.. (2025). TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model. PLoS ONE. 20(5). e0322171–e0322171.

5.

Masud, Md. Anwar Hossain, et al.. (2025). KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov–Arnold Classical–Quantum Dual-Channel Neural Network for Heart Disease Detection. Computers in Biology and Medicine. 197(Pt A). 110976–110976. 3 indexed citations

6.

Jaafari, Khaled Al, et al.. (2025). Real-Time Inter-Turn Short-Circuit Fault Detection and Localization in Six-Phase Induction Machines. IEEE Transactions on Industrial Electronics. 72(12). 12491–12503.

7.

Aung, Zeyar, et al.. (2025). T3SSLNet: Triple-Method Self-Supervised Learning for Enhanced Brain Tumor Classification in MRI. IEEE Access. 13. 127852–127867. 1 indexed citations

8.

Ali, Nazar, et al.. (2025). Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE. Scientific Reports. 15(1). 9797–9797. 5 indexed citations

9.

Aung, Zeyar, et al.. (2025). Multilingual sentiment analysis in restaurant reviews using aspect focused learning. Scientific Reports. 15(1). 28371–28371.

10.

Aung, Zeyar, et al.. (2024). Classification of Freshwater Fish Diseases in Bangladesh Using a Novel Ensemble Deep Learning Model: Enhancing Accuracy and Interpretability. IEEE Access. 12. 96411–96435. 4 indexed citations

11.

Musamih, Ahmad, et al.. (2024). Detection of Manufacturing Defects in Steel Using Deep Learning With Explainable Artificial Intelligence. IEEE Access. 12. 99240–99257. 13 indexed citations

12.

Mridha, M. F., et al.. (2024). Challenges and Opportunities of Text-Based Emotion Detection: A Survey. IEEE Access. 12. 18416–18450. 21 indexed citations

13.

Mehedi, Md Humaion Kabir, et al.. (2024). A lightweight deep learning method to identify different types of cervical cancer. Scientific Reports. 14(1). 29446–29446. 7 indexed citations

14.

Al-Marzouqi, Hasan, et al.. (2024). Convolutional Neural Network With Learnable Masks For EIT Based Tactile Sensing. 1609–1615. 1 indexed citations

15.

Mridha, M. F., et al.. (2024). Social Media as a Mirror: Reflecting Mental Health Through Computational Linguistics. IEEE Access. 12. 130143–130164. 2 indexed citations

16.

Aung, Zeyar, et al.. (2022). Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE. Heliyon. 9(2). e12584–e12584. 19 indexed citations

17.

Syed, Dabeeruddin, Tao Chang, Davor Svetinović, Talal Rahwan, & Zeyar Aung. (2017). Security for Complex Cyber-Physical and Industrial Control Systems: Current Trends, Limitations, and Challenges. Journal of the Association for Information Systems. 180. 4 indexed citations

18.

Aung, Zeyar, et al.. (2015). AuthorRank: A New Scheme for Identifying Field-Specific Key Researchers. CONF-IRM. 46. 2 indexed citations

19.

Oentaryo, Richard J., Ee‐Peng Lim, David Lo, et al.. (2014). Detecting click fraud in online advertising: a data mining approach. Journal of Machine Learning Research. 15(1). 99–140. 45 indexed citations

20.

Aung, Zeyar, et al.. (2012). Towards Accurate Electricity Load Forecasting in Smart Grids. 51–57. 53 indexed citations

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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