Hisham Daoud

1.0k total citations · 1 hit paper
19 papers, 655 citations indexed

About

Hisham Daoud is a scholar working on Cognitive Neuroscience, Signal Processing and Psychiatry and Mental health. According to data from OpenAlex, Hisham Daoud has authored 19 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 11 papers in Signal Processing and 5 papers in Psychiatry and Mental health. Recurrent topics in Hisham Daoud's work include EEG and Brain-Computer Interfaces (11 papers), Blind Source Separation Techniques (9 papers) and Epilepsy research and treatment (5 papers). Hisham Daoud is often cited by papers focused on EEG and Brain-Computer Interfaces (11 papers), Blind Source Separation Techniques (9 papers) and Epilepsy research and treatment (5 papers). Hisham Daoud collaborates with scholars based in United States, Egypt and Brazil. Hisham Daoud's co-authors include Magdy Bayoumi, Ahmed Abdelhameed, Phillip Williams, Sadeer Al‐Kindi, Farzad Ferdowsi, Jone Garai, Avirup Guha, Viraj Shah, Morteza Mahmoudi and Anant Madabhushi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Hypertension and Molecular Metabolism.

In The Last Decade

Hisham Daoud

18 papers receiving 627 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.

Efficient Epileptic Seizure Prediction Based on Deep Learning

2019 318 citations Hisham Daoud, Magdy Bayoumi IEEE Transactions on Biomedical Circuits and Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hisham Daoud United States	9	475	211	168	91	88	19	655
Syed Muhammad Usman Pakistan	12	559 1.2×	265 1.3×	159 0.9×	116 1.3×	106 1.2×	32	764
Mohammad-Parsa Hosseini United States	11	457 1.0×	146 0.7×	81 0.5×	110 1.2×	108 1.2×	14	766
Κostas Μ. Tsiouris Greece	11	479 1.0×	201 1.0×	149 0.9×	62 0.7×	109 1.2×	28	701
Anh Nguyen United States	8	494 1.0×	194 0.9×	153 0.9×	67 0.7×	75 0.9×	13	595
Hossein Hosseini‐Nejad Iran	8	403 0.8×	134 0.6×	105 0.6×	71 0.8×	75 0.9×	15	520
A. Sharmila India	13	454 1.0×	250 1.2×	50 0.3×	75 0.8×	115 1.3×	51	689
John Guttag United States	9	507 1.1×	245 1.2×	130 0.8×	57 0.6×	103 1.2×	13	613
Nasir Hussain Saudi Arabia	7	255 0.5×	97 0.5×	74 0.4×	78 0.9×	61 0.7×	18	438
Nhan Duy Truong Australia	11	700 1.5×	263 1.2×	242 1.4×	122 1.3×	125 1.4×	27	860
Ting Hu China	8	313 0.7×	139 0.7×	72 0.4×	109 1.2×	65 0.7×	19	452

Countries citing papers authored by Hisham Daoud

Since Specialization

Citations

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

Fields of papers citing papers by Hisham Daoud

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hisham Daoud

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

All Works

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19 of 19 papers shown

Stabellini, Nickolas, Omar Mohamed Makram, Hisham Daoud, et al.. (2025). A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer. JNCI Cancer Spectrum. 9(1). 2 indexed citations

Guha, Avirup, Viraj Shah, Omar Mohamed Makram, et al.. (2025). Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review. Current Cardiology Reports. 27(1). 56–56. 8 indexed citations

Lakkappa, Navya, Anna Maria Nuzzo, Jessie J. Guidry, et al.. (2024). UBR1 Promotes Sex-Dependent ACE2 Ubiquitination in Hypertension. Hypertension. 82(1). 84–95. 1 indexed citations

Daoud, Hisham, et al.. (2024). Efficient Epileptic Seizure Type Classification Using Hyperdimensional Computing. 1–5.

Garai, Jone, Melody Baddoo, Shi-Ping Lu, et al.. (2023). Maternal Western diet programs cardiometabolic dysfunction and hypothalamic inflammation via epigenetic mechanisms predominantly in the male offspring. Molecular Metabolism. 80. 101864–101864. 7 indexed citations

Daoud, Hisham, et al.. (2023). Enhancing EV charger resilience with reinforcement learning aided control. SHILAP Revista de lepidopterología. 5. 100276–100276. 6 indexed citations

Williams, Phillip, et al.. (2022). A survey on security in internet of things with a focus on the impact of emerging technologies. Internet of Things. 19. 100564–100564. 89 indexed citations

Daoud, Hisham & Magdy Bayoumi. (2021). Generative Adversarial Network Based Semi-supervised Learning for Epileptic Focus Localization. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 86. 1281–1285. 2 indexed citations

Daoud, Hisham, Phillip Williams, & Magdy Bayoumi. (2020). IoT based Efficient Epileptic Seizure Prediction System Using Deep Learning. 1–6. 19 indexed citations

10.

Williams, Phillip, et al.. (2020). Security Aspects of Internet of Things – A Survey. 1–6. 8 indexed citations

11.

Daoud, Hisham & Magdy Bayoumi. (2020). A Convolutional Gated Recurrent Neural Network for Seizure Onset Localization. 2572–2576. 2 indexed citations

12.

Daoud, Hisham & Magdy Bayoumi. (2019). Efficient Epileptic Seizure Prediction Based on Deep Learning. IEEE Transactions on Biomedical Circuits and Systems. 13(5). 804–813. 318 indexed citations breakdown →

13.

Daoud, Hisham & Magdy Bayoumi. (2019). Deep Learning Approach for Epileptic Focus Localization. IEEE Transactions on Biomedical Circuits and Systems. 14(2). 209–220. 49 indexed citations

14.

Daoud, Hisham & Magdy Bayoumi. (2019). Semi-supervised Learning for Epileptic Focus Localization Using Deep Convolutional Autoencoder. 1–4. 5 indexed citations

15.

Daoud, Hisham & Magdy Bayoumi. (2018). Deep Learning based Reliable Early Epileptic Seizure Predictor. 1–4. 16 indexed citations

16.

Abdelhameed, Ahmed, Hisham Daoud, & Magdy Bayoumi. (2018). Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection. 139–143. 42 indexed citations

17.

Abdelhameed, Ahmed, Hisham Daoud, & Magdy Bayoumi. (2018). Epileptic Seizure Detection using Deep Convolutional Autoencoder. 223–228. 28 indexed citations

18.

Daoud, Hisham, Ahmed Abdelhameed, & Magdy Bayoumi. (2018). FPGA Implementation of High Accuracy Automatic Epileptic Seizure Detection System. 407–410. 13 indexed citations

19.

Daoud, Hisham, Ahmed Abdelhameed, & Magdy Bayoumi. (2018). Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network. 182–186. 40 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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