A
Amer Al-Rahayfeh
Researcher at Al-Hussein Bin Talal University
Publications - 20
Citations - 690
Amer Al-Rahayfeh is an academic researcher from Al-Hussein Bin Talal University. The author has contributed to research in topics: Eye tracking & Cloud computing. The author has an hindex of 10, co-authored 20 publications receiving 461 citations. Previous affiliations of Amer Al-Rahayfeh include University of Bridgeport.
Papers
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Journal ArticleDOI
Eye Tracking and Head Movement Detection: A State-of-Art Survey
Amer Al-Rahayfeh,Miad Faezipour +1 more
TL;DR: A state-of-art survey for eye tracking and head movement detection methods proposed in the literature is presented and examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are investigated.
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Deep recurrent neural network for IoT intrusion detection system
TL;DR: An artificially full-automated intrusion detection system for Fog security against cyber-attacks using multi-layered of recurrent neural networks designed to be implemented for Fog computing security that is very close to the end-users and IoT devices is presented.
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Performance evaluation of routing protocols in wireless sensor networks
TL;DR: Simulation results show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA, and the power consumption for all protocols is investigated.
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Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography
Saleh Atiewi,Amer Al-Rahayfeh,Muder Almiani,Salman Yussof,Omar Alfandi,Ahed Abugabah,Yaser Jararweh +6 more
TL;DR: The proposed cloud–IoT architecture supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system is implemented and evaluated using metrics such as computational time, security strength, encryption time, and decryption time.
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Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms
TL;DR: This work proposes a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing and evaluates the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.