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Yaser Jararweh
Researcher at Jordan University of Science and Technology
Publications - 324
Citations - 8851
Yaser Jararweh is an academic researcher from Jordan University of Science and Technology. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 44, co-authored 297 publications receiving 6045 citations. Previous affiliations of Yaser Jararweh include University of Arizona & Pennsylvania State University.
Papers
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Journal ArticleDOI
Collusion attacks mitigation in internet of things: a fog based model
Qussai Yaseen,Monther Aldwairi,Monther Aldwairi,Yaser Jararweh,Mahmoud Al-Ayyoub,Brij B. Gupta +5 more
TL;DR: A model based on Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers and claims that fog layer infrastructure can provide the required resources for the scalability of the model is introduced.
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Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications
TL;DR: This paper presents the most powerful simulation tools in this research area, including CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud and performs experiments for some of them to show their capabilities.
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A hierarchical optimization model for energy data flow in smart grid power systems
TL;DR: A three-level hierarchical optimization approach is proposed to solve scalability, computational overhead, and minimize daily electricity cost through maximizing the used percentage of renewable energy.
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Hardware Performance Evaluation of SHA-3 Candidate Algorithms
TL;DR: A comprehensive hardware evaluation for the final round SHA-3 candidates is presented, based on a comparison made between each of the finalists in terms of security level, throughput, clock frequancey, area, power consumption, and the cost.
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Transfer Learning for Arabic Named Entity Recognition With Deep Neural Networks
TL;DR: A novel Deep Learning approach for Standard Arabic Named Entity Recognition is presented that proved its out-performance when being compared to previous works and provides better fine-grained results for use in the Natural Language Processing fields.