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Arif Ur Rahman
Researcher at Bahria University
Publications - 35
Citations - 455
Arif Ur Rahman is an academic researcher from Bahria University. The author has contributed to research in topics: Newspaper & Computer science. The author has an hindex of 8, co-authored 31 publications receiving 297 citations. Previous affiliations of Arif Ur Rahman include University of Porto & Free University of Bozen-Bolzano.
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Journal ArticleDOI
SMART TSS: Defining transportation system behavior using big data analytics in smart cities
TL;DR: This work proposes a big data analytics architecture for Intelligent Transportation Systems (ITS), which has a built-in storage and analysis capability to work with ITS data and is composed of four modules, namely (1) Big Data Acquisition and Preprocessing Unit (2)Big Data Processing Unit (3) Big data Analytics Unit and (4) Data Visualization Unit.
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Security and key management in IoT‐based wireless sensor networks: An authentication protocol using symmetric key
Anwar Ghani,Khwaja Mansoor,Shahid Mehmood,Shehzad Ashraf Chaudhry,Arif Ur Rahman,Malik Najmus Saqib +5 more
TL;DR: An enhanced symmetric key‐based authentication protocol for IoT‐based WSN has been presented that has the ability to counter user traceability, stolen verifier, and DoS attacks and has the same communication cost as the baseline protocol.
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A Multiple Criteria Approach for Negotiating Ecosystem Services Supply Targets and Forest Owners' Programs
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A Big Data Analytics Architecture for the Internet of Small Things
TL;DR: A system developed using the proposed architecture will be able to analyze and store IoST data efficiently while enabling better decisions, and a detailed analysis of a big data implementation of the IoST used to track humidity and temperature via Hadoop is presented.
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News Recommendation Systems - Accomplishments, Challenges & Future Directions
TL;DR: This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies, and presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.