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Minhao Jin

ResearcherPublications, citations & collaboration network

Minhao Jin is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 20 works, 199 citations, an h-index of 5 and an i10-index of 2.

20
Works
199
Citations
5
h-index
2
i10-index

How has Minhao Jin's publication output changed over time?

ScholarIQpublication output · 2020–2025

Output grew0% over the shown period — from 1 works in 2020 to 1 in 2025.

1
2
3
3
1
20202021202220242025

What are the most-cited papers on Minhao Jin?

ScholarIQmost cited works
Practical GAN-based synthetic IP header trace generation using NetShare
Yucheng Yin, Zinan Lin, Minhao Jin, Giulia Fanti, Vyas Sekar
202292 CitationsOPEN ACCESS
Multimodal approach to analysing big social and news media data
Kay L. O’Halloran, Gautam Pal, Minhao Jin
Discourse Context & Media. 202181 Citations
Towards accurate and interpretable surgical skill assessment: a video-based method for skill score prediction and guiding feedback generation
Tianyu Wang, Minhao Jin, Mian Li
International Journal of Computer Assisted Radiology and Surgery. 20218 Citations
Collaborative optimization control of hybrid distribution transformers integrated with photovoltaics in unbalanced distribution networks
Lishi Zhang, Zihan Kong, Yibin Liu, Deliang Liang, Libo Fan, Minhao Jin, Jiabin Huang
S168377509. 20247 CitationsOPEN ACCESS
Towards a data-driven method for RGB video-based hand action quality assessment in real time
Tianyu Wang, Minhao Jin, Jingying Wang, Yijie Wang, Mian Li
20206 Citations

Related on ScholarIQ

Shanghai Electric (China)
Institution
Practical GAN-based synthetic IP header trace generation using NetShare
Paper
Multimodal approach to analysing big social and news media data
Paper
Towards accurate and interpretable surgical skill assessment: a video-based method for skill score prediction and guiding feedback generation
Paper
Collaborative optimization control of hybrid distribution transformers integrated with photovoltaics in unbalanced distribution networks
Paper
Towards a data-driven method for RGB video-based hand action quality assessment in real time
Paper
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