P
Payam Barnaghi
Researcher at University of Surrey
Publications - 167
Citations - 7275
Payam Barnaghi is an academic researcher from University of Surrey. The author has contributed to research in topics: The Internet & Semantic Web. The author has an hindex of 35, co-authored 146 publications receiving 6064 citations. Previous affiliations of Payam Barnaghi include Wilmington University & University of Nottingham Malaysia Campus.
Papers
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Journal ArticleDOI
Ontology paper: The SSN ontology of the W3C semantic sensor network incubator group
Michael Compton,Payam Barnaghi,Luis Bermudez,Raúl García-Castro,Oscar Corcho,Simon Cox,John Graybeal,Manfred Hauswirth,Cory Henson,Arthur Herzog,Vincent Huang,Krzysztof Janowicz,W. David Kelsey,Danh Le Phuoc,Laurent Lefort,Myriam Leggieri,Holger Neuhaus,Andriy Nikolov,Kevin R. Page,Alexandre Passant,Amit P. Sheth,Kerry Taylor +21 more
TL;DR: The SSN ontology is described, which can describe sensors in terms of capabilities, measurement processes, observations and deployments and the use of the ontology in recent research projects is described.
Journal ArticleDOI
Machine Learning for Internet of Things Data Analysis: A Survey
Mohammad Saeid Mahdavinejad,Mohammad Saeid Mahdavinejad,Mohammadreza Rezvan,Mohammadreza Rezvan,Mohammadamin Barekatain,Peyman Adibi,Payam Barnaghi,Amit P. Sheth +7 more
TL;DR: This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case and presents a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information.
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Semantics for the Internet of Things: Early Progress and Back to the Future
TL;DR: The authors review some of the recent developments on applying the semantic technologies based on machine-interpretable representation formalism to the Internet of Things.
Journal ArticleDOI
Machine learning for Internet of Things data analysis: A survey
Mohammad Saeid Mahdavinejad,Mohammad Saeid Mahdavinejad,Mohammadreza Rezvan,Mohammadreza Rezvan,Mohammadamin Barekatain,Peyman Adibi,Payam Barnaghi,Amit P. Sheth +7 more
TL;DR: In this article, the authors present a taxonomy of machine learning algorithms that can be applied to the data in order to extract higher level information, and a use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for more detailed exploration.
Journal ArticleDOI
Probabilistic Topic Models for Learning Terminological Ontologies
TL;DR: A new approach for automatic learning of terminological ontologies from text corpus based on probabilistic topic models, which shows that the method outperforms other methods in terms of recall and precision measures.