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Alireza Jolfaei

Researcher at Macquarie University

Publications -  180
Citations -  4410

Alireza Jolfaei is an academic researcher from Macquarie University. The author has contributed to research in topics: Computer science & Encryption. The author has an hindex of 22, co-authored 141 publications receiving 1803 citations. Previous affiliations of Alireza Jolfaei include Temple University & Griffith University.

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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

TL;DR: The current status of BCI and signal sensing technologies for collecting reliable EEG signals are reviewed and state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, are demonstrated to detect, monitor, and maintain human cognitive states and task performance in prevalent applications.
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Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications

TL;DR: An energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the quality of service (QoS) required by users.
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A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing

TL;DR: A face expression recognition method based on a convolutional neural network (CNN) and an image edge detection and the dimensionality reduction of the extracted implicit features is processed by the maximum pooling method.
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A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks

TL;DR: This paper has presented a systematic literature review of existing clone node detection schemes and provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clones in static WSNs with their drawbacks and challenges.
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Real-time cheating immune secret sharing for remote sensing images

TL;DR: A real-time cheating immune secret sharing approach is introduced that minimizes the time as well as space complexity for the secret sharing effectively and generates meaningful shares without the restriction for any fixed number participants.