M
Mohammadreza Amirian
Researcher at University of Ulm
Publications - 27
Citations - 547
Mohammadreza Amirian is an academic researcher from University of Ulm. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 11, co-authored 27 publications receiving 371 citations. Previous affiliations of Mohammadreza Amirian include Zürcher Fachhochschule & Zurich University of Applied Sciences/ZHAW.
Papers
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Journal ArticleDOI
Adaptive confidence learning for the personalization of pain intensity estimation systems
Markus Kächele,Mohammadreza Amirian,Patrick Thiam,Philipp Werner,Steffen Walter,Günther Palm,Friedhelm Schwenker +6 more
TL;DR: A method is presented for the continuous estimation of pain intensity based on fusion of bio-physiological and video features and a method is proposed for the adaptation of the system to unknown test persons based on unlabeled data.
Journal ArticleDOI
Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels
TL;DR: Methods for the personalization of a system for the continuous estimation of pain intensity from bio-physiological channels are presented and it is shown that the system is capable of running in real-time and issues that arise when dealing with incremental data processing are discussed.
Book ChapterDOI
Multimodal Data Fusion for Person-Independent, Continuous Estimation of Pain Intensity
Markus Kächele,Patrick Thiam,Mohammadreza Amirian,Philipp Werner,Steffen Walter,Friedhelm Schwenker,Günther Palm +6 more
TL;DR: The focus of the paper is to analyse which modalities and feature sets are suited best for the task of recognizing pain levels in a person-independent setting to improve the continuous estimation of pain intensity given unseen persons.
Journal ArticleDOI
Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database
Patrick Thiam,Viktor Kessler,Mohammadreza Amirian,Peter Bellmann,Georg Layher,Yan Zhang,Maria Velana,Sascha Gruss,Steffen Walter,Harald C. Traue,Daniel Schork,Jonghwa Kim,Elisabeth André,Heiko Neumann,Friedhelm Schwenker +14 more
TL;DR: Three distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation and an extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system.
Proceedings ArticleDOI
Automated Machine Learning in Practice: State of the Art and Recent Results
Lukas Tuggener,Mohammadreza Amirian,Katharina Rombach,Stefan Lörwald,Anastasia Varlet,Christian Westermann,Thilo Stadelmann +6 more
TL;DR: An overview of the state of the art in AutoML with a focus on practical applicability in a business context, and recent benchmark results of the most important AutoML algorithms are provided in this article.