Labib M. Labib
Impact in
- Artificial Intelligence top 5%
- AI in cancer detection
- Anomaly Detection Techniques and Applications
- Internet Traffic Analysis and Secure E-voting
Papers in
-
- Mobile Ad Hoc Networks 2
- Network Security and Intrusion Detection 2
- Energy Efficient Wireless Sensor Networks 1
- Co-authors
- Ahmed I. Saleh (5 shared papers)Fatma M. Talaat (1 shared paper)Ali I. El-Desouky (1 shared paper)El‐Sayed M. El‐kenawy (1 shared paper)Hesham Ali (4 shared papers)Mohamed Shehata (3 shared papers)Moumen El-Melegy (2 shared papers)Ayman El‐Baz (2 shared papers)
- Journals
- Sensors (3 papers)Neural Computing and Applications (2 papers)Biosystems (1 paper)The Computer Journal (1 paper)Multimedia Tools and Applications (1 paper)
- Partner nations
- EgyptSaudi ArabiaUnited States
In The Last Decade
Labib M. Labib
13 papers receiving 354 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 219
- Health Informatics 9
- Signal Processing 63
- Health Information Management 21
- Computer Networks and Communications 103
Countries citing papers authored by Labib M. Labib
This map shows the geographic impact of Labib M. Labib'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 Labib M. Labib with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Labib M. Labib more than expected).
Fields of papers citing papers by Labib M. Labib
This network shows the impact of papers produced by Labib M. Labib. 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 Labib M. Labib. The network helps show where Labib M. Labib may publish in the future.
Co-authors
The 18 scholars most cited alongside Labib M. Labib, 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 | 2019 | 102 | |
| 2 | 2019 | 89 | |
| 3 | 2017 | 79 | |
| 4 | 2021 | 51 | |
| 5 | 2019 | 14 | |
| 6 | 2022 | 12 | |
| 7 | 2023 | 7 | |
| 8 | 2016 | 7 | |
| 9 | 2021 | 5 | |
| 10 | 2024 | 5 | |
| 11 | 2017 | 5 | |
| 12 | 2025 | 4 | |
| 13 | 2023 | 2 | |
| 14 | 2025 | 0 |
About Labib M. Labib
Labib M. Labib is a scholar working on Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 14 papers that have together received 382 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Face and Expression Recognition (2 papers), Prostate Cancer Diagnosis and Treatment (2 papers), Mobile Ad Hoc Networks (2 papers), Gene expression and cancer classification (2 papers), Network Security and Intrusion Detection (2 papers), Fire Detection and Safety Systems (1 paper) and Energy Efficient Wireless Sensor Networks (1 paper). The work is most often cited by research in Artificial Intelligence (219 citations), Health Informatics (9 citations), Signal Processing (63 citations), Health Information Management (21 citations) and Computer Networks and Communications (103 citations). Labib M. Labib has collaborated with scholars based in Egypt, Saudi Arabia and United States. Frequent co-authors include Ahmed I. Saleh, Fatma M. Talaat, Ali I. El-Desouky, El‐Sayed M. El‐kenawy, Hesham Ali, Mohamed Shehata, Moumen El-Melegy, Ayman El‐Baz, Mohammed Ghazal and Mohamed Abou El‐Ghar. Their work appears in journals such as Sensors, Neural Computing and Applications, Biosystems, The Computer Journal and Multimedia Tools 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.