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Sujith Samuel Mathew

Researcher at Zayed University

Publications -  44
Citations -  459

Sujith Samuel Mathew is an academic researcher from Zayed University. The author has contributed to research in topics: Sentiment analysis & Computer science. The author has an hindex of 8, co-authored 34 publications receiving 304 citations. Previous affiliations of Sujith Samuel Mathew include United Arab Emirates University & University of Adelaide.

Papers
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Journal ArticleDOI

Building a smart campus to support ubiquitous learning

TL;DR: This paper defines a model of a smart campus, and advocate learning practices in the light of new paradigms such as context-awareness, ubiquitous learning, pervasive environment, resource virtualization, autonomic computing and adaptive learning.
Book ChapterDOI

Securing the Web of Things With Role-Based Access Control

TL;DR: This paper introduces an architecture that encompasses Web-enabled things in a secure and scalable manner and utilizes the features of the well-known role-based access control (RBAC) to specify the access control policies to the WoT, and uses cryptographic keys to enforce such policies.
Proceedings ArticleDOI

Web of Things: Description, Discovery and Integration

TL;DR: This work focuses on the specification of a thing, its descriptors and functions that could participate in the process of its discovery and operations, and proposes a semantic Web-based architecture to integrate these things as Web resources to further demystify the realization of the WoT vision.
Journal ArticleDOI

Building sustainable parking lots with the Web of Things

TL;DR: This work presents a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things’ services within a parking lot, using service-oriented architecture and elevates it as a Smart Parking Spot on the Web.
Journal ArticleDOI

A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change

TL;DR: The solution stands out from existing retrospective analysis methods because it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner and can have meaningful application within social media analysis efforts.