M
Mohammadhossein Ghahramani
Researcher at University College Dublin
Publications - 21
Citations - 661
Mohammadhossein Ghahramani is an academic researcher from University College Dublin. The author has contributed to research in topics: Mobile phone & Artificial neural network. The author has an hindex of 8, co-authored 20 publications receiving 333 citations. Previous affiliations of Mohammadhossein Ghahramani include Macau University of Science and Technology.
Papers
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Toward cloud computing QoS architecture: analysis of cloud systems and cloud services
TL;DR: This paper intends to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.
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AI-based modeling and data-driven evaluation for smart manufacturing processes
TL;DR: The objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
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Mobile Phone Data Analysis: A Spatial Exploration Toward Hotspot Detection
TL;DR: An exploratory spatial data analysis algorithm to detect the spatial distribution of mobile phones through the correlation of various spatial objects regarding their spatial and nonspatial dimensions to detect dependency among them is proposed.
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Urban sensing based on mobile phone data: approaches, applications, and challenges
TL;DR: This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data, classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.
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Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures
TL;DR: This paper considers the geographical context of subscribers/celltowers to discover structures of spatio-temporal interactions and communities’ patterns in Macau and implements an efficient hierarchical clustering approach to delineate relatively contiguous objects with similar attribute values.