Muhammad Raza
University of Technology, Sydney
14 Papers
53 Citations
Muhammad Raza is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Business ecosystem & Software as a service. The author has an hindex of 5, co-authored 14 publications. Previous affiliations of Muhammad Raza include Curtin University.
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Papers
Internet of Things 2.0: Concepts, Applications, and Future Directions
Ian Zhou,Imran Makhdoom,Negin Shariati,Muhammad Raza,Rasool Keshavarz,Justin Lipman,Mehran Abolhasan,Abbas Jamalipour +7 more
TL;DR: In this article, the authors discuss the evolution of the Internet of Things and present the vision for IoT 2.0 development across seven major fields including machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security.
A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews
TL;DR: A systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer is adopted to address the drawback of no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar.
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A methodology for quality-based mashup of data sources
Muhammad Raza,Farookh Khadeer Hussain,Elizabeth Chang +2 more
- 24 Nov 2008
TL;DR: This paper reviews the concept of mashup in different domains and proposes a conceptual solution framework for providing quality based mashup process and a methodology by which to make use of the quality of the input to the Mashup process as a governing principle.
Neural Network-Based Approach for Predicting Trust Values Based on Non-uniform Input in Mobile Applications
TL;DR: Results indicate that the proposed Multi-layer Feed Forward Artificial Neural Network model is reliable in predicting trust values even in scenarios where there are only limited data available on training the neural network and a high level of distortion is present in the input series.
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Maturity, distance and density (MD2) metrics for optimizing trust prediction for business intelligence
TL;DR: This paper proposes a methodology which comprises a suite of metrics—maturity, distance and density (MD2) which are capable of capturing various aspects of the confidence level in the predicted trust value.
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