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Mohammed Abo‐Zahhad

ResearcherPublications, citations & collaboration network

Mohammed Abo‐Zahhad is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 230 works, 4,246 citations, an h-index of 34 and an i10-index of 77.

230
Works
4,246
Citations
34
h-index
77
i10-index

How has Mohammed Abo‐Zahhad's publication output changed over time?

ScholarIQpublication output · 2003–2021

Output grew0% over the shown period — from 1 works in 2003 to 1 in 2021.

1
1
4
1
3
1
200320142015201720202021

What are the most-cited papers on Mohammed Abo‐Zahhad?

ScholarIQmost cited works
Recent advances in image processing techniques for automated leaf pest and disease recognition – A review
Lawrence C. Ngugi, Moataz Abelwahab, Mohammed Abo‐Zahhad
Information Processing in Agriculture. 2020492 CitationsOPEN ACCESS
Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks
Mohammed Abo‐Zahhad, Sabah M. Ahmed, Nabil Sabor, Shigenobu Sasaki
IEEE Sensors Journal. 2015195 Citations
A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions
Nabil Sabor, Shigenobu Sasaki, Mohammed Abo‐Zahhad, Sabah M. Ahmed
S235346. 2017156 CitationsOPEN ACCESS
Tomato leaf segmentation algorithms for mobile phone applications using deep learning
Lawrence C. Ngugi, Moataz M. Abdelwahab, Mohammed Abo‐Zahhad
Computers and Electronics in Agriculture. 2020145 Citations

Related on ScholarIQ

Egypt-Japan University of Science and Technology
Institution
Recent advances in image processing techniques for automated leaf pest and disease recognition – A review
Paper
Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks
Paper
A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions
Paper
Tomato leaf segmentation algorithms for mobile phone applications using deep learning
Paper
Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis
Paper
470M+ articles · free account