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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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Journal ArticleDOI
Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
Haijun Liao,Zhenyu Zhou,Xiongwen Zhao,Lei Zhang,Shahid Mumtaz,Alireza Jolfaei,Syed Hassan Ahmed,Ali Kashif Bashir +7 more
TL;DR: This article proposes a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory, and proves that the proposed framework can achieve guaranteed performance.
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Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT
TL;DR: A class of n-order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel, and an intelligent-classic control system is developed to compensate cyber-attacks.
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A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU
Thippa Reddy Gadekallu,Dharmendra Singh Rajput,M. Praveen Kumar Reddy,Kuruva Lakshmanna,Sweta Bhattacharya,Saurabh Singh,Alireza Jolfaei,Mamoun Alazab +7 more
TL;DR: This present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis.
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On the Security of Permutation-Only Image Encryption Schemes
TL;DR: It is proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack, which significantly outperforms the state-of-theart cryptanalytic methods.
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Data mining and machine learning methods for sustainable smart cities traffic classification: a survey
TL;DR: Challenges and recommendations for SSC network traffic classification with the dataset of features are presented and some well-known and most used datasets with details statistical features are described.