Siham Tabik: h-index, Total Citations, and Citation Map
Siham Tabik's h-index is 35 (59 i10-index, 20,028+ total citations across 5+ publications) according to Google Scholar as of May 2026. Siham Tabik is affiliated with Professor, Dpt. Computer Science and Artificial Intelligence, University of Granada.
Siham Tabik is a researcher affiliated with Professor, Dpt. Computer Science and Artificial Intelligence, University of Granada, specializing in Machine Learning, Computer Vision, Remote Sensing. Their work has been cited 20,028 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Siham Tabik's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 35
- i10-Index
- 59
- Total Citations
- 20,028
- Citing Countries
- 29
As of May 2026.
Siham Tabik has an h-index of 35 and 20,028 total citations across 5 publications, with research cited by institutions in 29 countries.
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Global Impact Map
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Top Cited Works
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Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible ai
202014,355
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher established a foundational framework for Explainable AI by providing a comprehensive taxonomy and analysis of concepts, opportunities, and challenges for responsible AI.
The researcher advanced shrub detection methodologies by empirically comparing deep learning against object-based image analysis using Google Earth imagery for Ziziphus lotus.
The researcher developed a deep learning framework for automatic handgun detection in video streams, establishing a foundational approach for real-time visual threat recognition.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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