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Lhassane Idoumghar

ResearcherPublications, citations & collaboration network

Lhassane Idoumghar is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 241 works, 5,871 citations, an h-index of 25 and an i10-index of 60.

241
Works
5,871
Citations
25
h-index
60
i10-index

How has Lhassane Idoumghar's publication output changed over time?

ScholarIQpublication output · 2017–2021

Output grew0% over the shown period — from 1 works in 2017 to 1 in 2021.

1
5
4
1
2017201820192021

What are the most-cited papers on Lhassane Idoumghar?

ScholarIQmost cited works
Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Müller
S121920818. 20193,273 CitationsOPEN ACCESS
Transfer learning for time series classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Müller
2018272 CitationsOPEN ACCESS
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Müller
2019139 CitationsOPEN ACCESS
Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Müller
Lecture notes in computer science. 2018121 CitationsOPEN ACCESS
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Müller
S4306402449. 2019105 CitationsOPEN ACCESS

Related on ScholarIQ

Université de Haute-Alsace
Institution
Deep learning for time series classification: a review
Paper
Transfer learning for time series classification
Paper
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Paper
Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks
Paper
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
Paper
470M+ articles · free account