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Shahram Jamali
Researcher at University of Mohaghegh Ardabili
Publications - 207
Citations - 1474
Shahram Jamali is an academic researcher from University of Mohaghegh Ardabili. The author has contributed to research in topics: Flexible AC transmission system & Electrical impedance. The author has an hindex of 17, co-authored 200 publications receiving 1300 citations. Previous affiliations of Shahram Jamali include Iran University of Science and Technology.
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DAWA: Defending against wormhole attack in MANETs by using fuzzy logic and artificial immune system
Shahram Jamali,Reza Fotohi +1 more
TL;DR: The proposed protocol called defending against worm hole attack (DAWA) employs fuzzy logic system and artificial immune system to defend against wormhole attacks and outperforms other existing solutions in terms of false negative ratio, false positive ratio, detection ratio, packet delivery ratio, packets loss ratio and packets drop ratio.
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An Energy-efficient Routing Protocol for MANETs: a Particle Swarm Optimization Approach
TL;DR: This paper employs the Binary Particle Swarm Optimization algorithm (BPSO) to add the energyawareness feature to the TORA routing protocol and shows that the proposed routing protocol, BPSO-TORA, prolongs the network lifetime remarkably and outperforms TORA in terms of network life time, system life time and total delivered data.
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Defending against Wormhole Attack in MANET Using an Artificial Immune System
Shahram Jamali,Reza Fotohi +1 more
TL;DR: The use of an Artificial Immune System (AIS) to defend against wormhole attack is investigated and it is shown that the proposed approach offers better performance than other schemes defending against the worm hole attack.
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A novel keyed parallel hashing scheme based on a new chaotic system
TL;DR: A new chaotic system is proposed and employed to design a secure and fast hash function, which has a dynamic random array of functions and can be implemented by a parallel architecture and proves security of the proposed function.
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Online reputation measurement of companies based on user-generated content in online social networks
TL;DR: Evaluation of the proposed reputation management system indicates that this method has a better efficiency and performance in terms of recall and precision compared to the previous methods such as Neural Network and Bayesian method.