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Israel N. Chaparro-Cruz

ResearcherPublications, citations & collaboration network

Israel N. Chaparro-Cruz is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 5 works, 14 citations, an h-index of 2 and an i10-index of 0.

5
Works
14
Citations
2
h-index
0
i10-index

How has Israel N. Chaparro-Cruz's publication output changed over time?

ScholarIQpublication output · 2021–2025

Output grew100% over the shown period — from 1 works in 2021 to 2 in 2025.

1
1
1
2
2021202320242025

What are the most-cited papers on Israel N. Chaparro-Cruz?

ScholarIQmost cited works
Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u
Erbert F. Osco Mamani, Israel N. Chaparro-Cruz
International Journal of Advanced Computer Science and Applications. 20237 CitationsOPEN ACCESS
The Detection and Counting of Olive Tree Fruits Using Deep Learning Models in Tacna, Perú
Erbert F. Osco Mamani, Oliver I. Santana-Carbajal, Israel N. Chaparro-Cruz, Daniel Ochoa, Sylvia C. Alcázar-Alay
S4210200511. 20255 CitationsOPEN ACCESS
BORDE: Boundary and Sub-Region Denormalization for Semantic Brain Image Synthesis
Israel N. Chaparro-Cruz, Javier A. Montoya‐Zegarra
20212 Citations
A Comparative Study of Convolutional Neural Networks for Oregano Diseases Classification in A Novel Dataset
Israel N. Chaparro-Cruz, Oliver I. Santana-Carbajal, Erbert F. Osco Mamani
20240 Citations
Data Science Framework for Evaluation of Entrance Exams and Identification of Academic Patterns
Luciana Nikole Huertas-Condori, Silvana Beatriz Cabana-Yupanqui, Israel N. Chaparro-Cruz, Americo Chaparro-Guerra
20250 Citations

Related on ScholarIQ

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Institution
Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u
Paper
The Detection and Counting of Olive Tree Fruits Using Deep Learning Models in Tacna, Perú
Paper
BORDE: Boundary and Sub-Region Denormalization for Semantic Brain Image Synthesis
Paper
A Comparative Study of Convolutional Neural Networks for Oregano Diseases Classification in A Novel Dataset
Paper
Data Science Framework for Evaluation of Entrance Exams and Identification of Academic Patterns
Paper
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