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Nen Fu Huang
ResearcherPublications, citations & collaboration network
Nen Fu Huang is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 5 works, 0 citations, an h-index of 0 and an i10-index of 0.
5
Works
0
Citations
0
h-index
0
i10-index
IDs:OpenAlex
How has Nen Fu Huang's publication output changed over time?
ScholarIQpublication output · 2007–2026
Output grew200% over the shown period — from 1 works in 2007 to 3 in 2026.
1
1
3
200720142026
What are the most-cited papers on Nen Fu Huang?
ScholarIQmost cited works
The Implementation of IPv6-Enabled Locators for Location-Based Smart Marketing Service
Tseng‐Yi Chen, Fan‐Hsun Tseng, Nen Fu Huang, Wei Shih, Han Chieh Chao, Li Der Chou
20140 Citations
Special issue on "IPv6-based mobile/multimedia applications"
Nen Fu Huang, Whai En Chen, Yen Wen Chen, Chai Hien Gan
S100554201. 20070 Citations
Virtual and Augmented Reality Technology in Education and Entertainment: Current Trends and Future Outlook
Tien Chi Huang, Jian Wei Tzeng, Nen Fu Huang
Applied Sciences. 20260 CitationsOPEN ACCESS
DDAN-RGAL: A Dual-Domain Adaptive Network With Reality-Gap Aware Learning for Field-Deployable Plant Disease Detection
Syed Asif Ahmad Qadri, Nen Fu Huang, Yu-Hsiang Huang, Pin-Cheng Chan
IEEE Transactions on AgriFood Electronics. 20260 Citations
Epidemiologically-constrained severity modeling for plant disease detection via progressive multimodal fusion and dynamic multi-task learning
Syed Asif Ahmad Qadri, Nen Fu Huang
Expert Systems with Applications. 20260 Citations
Related on ScholarIQ
National Tsing Hua University
Institution
The Implementation of IPv6-Enabled Locators for Location-Based Smart Marketing Service
Paper
Special issue on "IPv6-based mobile/multimedia applications"
Paper
Virtual and Augmented Reality Technology in Education and Entertainment: Current Trends and Future Outlook
Paper
DDAN-RGAL: A Dual-Domain Adaptive Network With Reality-Gap Aware Learning for Field-Deployable Plant Disease Detection
Paper
Epidemiologically-constrained severity modeling for plant disease detection via progressive multimodal fusion and dynamic multi-task learning
Paper