Noha A. Hikal
-
- Network Security and Intrusion Detection 10
- Energy Efficient Wireless Sensor Networks 4
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- Digital Imaging for Blood Diseases 3
- Chaos-based Image/Signal Encryption 3
- Artificial Intelligence top 10%
- AI in cancer detection 5
- Anomaly Detection Techniques and Applications 5
- Signal Processing top 10%
- Advanced Malware Detection Techniques 6
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- COVID-19 diagnosis using AI 4
- Co-authors
- Esraa HassanMahmoud Y. ShamsSamir ElmougyHassan SolimanMarwa M. EidSomula RamasubbareddyMohamed AbouhawwashAnand Nayyar
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- SHILAP Revista de lepidopterología (1 paper)IEEE Access (3 papers)Neural Computing and Applications (1 paper)
- Partner nations
- EgyptUnited StatesBulgaria
In The Last Decade
Noha A. Hikal
31 papers receiving 453 citations
Peers
Comparison fields: 5 of 96
- Computer Networks and Communications 175
- Computer Vision and Pattern Recognition 118
- Artificial Intelligence 146
- Signal Processing 47
- Radiology, Nuclear Medicine and Imaging 76
Countries citing papers authored by Noha A. Hikal
This map shows the geographic impact of Noha A. Hikal'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 Noha A. Hikal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noha A. Hikal more than expected).
Fields of papers citing papers by Noha A. Hikal
This network shows the impact of papers produced by Noha A. Hikal. 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 Noha A. Hikal. The network helps show where Noha A. Hikal may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Noha A. Hikal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 24 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 5 | |
| 8 | 2022 | 54 | |
| 9 | 2022 | 120 | |
| 10 | 2021 | 8 | |
| 11 | 2021 | 2 | |
| 12 | 2021 | 47 | |
| 13 | 2021 | 2 | |
| 14 | 2021 | 11 | |
| 15 | 2021 | 10 | |
| 16 | 2019 | 7 | |
| 17 | 2013 | 3 | |
| 18 | Skin color segmentation using adaptive PCA and modified elliptic boundary model | 2011 | 1 |
| 19 | 2006 | 0 | |
| 20 | 2006 | 1 |
About Noha A. Hikal
Noha A. Hikal is a scholar working on Signal Processing, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 33 papers that have together received 479 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (10 papers), Advanced Malware Detection Techniques (6 papers), AI in cancer detection (5 papers), Anomaly Detection Techniques and Applications (5 papers), Energy Efficient Wireless Sensor Networks (4 papers), COVID-19 diagnosis using AI (4 papers), Digital Imaging for Blood Diseases (3 papers) and Chaos-based Image/Signal Encryption (3 papers). The work is most often cited by research in Computer Networks and Communications (175 citations), Computer Vision and Pattern Recognition (118 citations) and Artificial Intelligence (146 citations). Noha A. Hikal has collaborated with scholars based in Egypt, United States and Bulgaria. Frequent co-authors include Esraa Hassan, Mahmoud Y. Shams, Samir Elmougy, Hassan Soliman, Marwa M. Eid, Somula Ramasubbareddy, Mohamed Abouhawwash, Anand Nayyar, S. Sankar and Mohamed Hesham Farouk. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Neural Computing and Applications.
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.