M
Mohsen Kahani
Researcher at Ferdowsi University of Mashhad
Publications - 120
Citations - 2004
Mohsen Kahani is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Ontology (information science) & Semantic Web. The author has an hindex of 17, co-authored 113 publications receiving 1576 citations. Previous affiliations of Mohsen Kahani include Ryerson University & University of Melbourne.
Papers
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HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
Shreshth Tuli,Shreshth Tuli,Nipam Basumatary,Nipam Basumatary,Sukhpal Singh Gill,Mohsen Kahani,Mohsen Kahani,Rajesh Chand Arya,Gurpreet Singh Wander,Rajkumar Buyya +9 more
TL;DR: A novel framework called HealthFog is proposed for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis.
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A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers
TL;DR: Several soft computing techniques are incorporated into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network, including neuro-fuzzy networks, fuzzy inference approach and genetic algorithms.
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Customizing ISO 9126 quality model for evaluation of B2B applications
TL;DR: This model, then, is customized in accordance with special characteristics of B2B applications, and is evaluated as a case study, ISACO portal is evaluated by the proposed model.
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Decentralised approaches for network management
Mohsen Kahani,H. W. Peter Beadle +1 more
TL;DR: This paper is a review of decentralised network management techniques and technologies, and explains distributed architectures for network management, and discusses some of the most important implemented distributed network management systems.
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Mining user interests over active topics on social networks
TL;DR: Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of the proposed graph-based link prediction schema in inferring users’ interests in terms of perplexity and in the context of retweet prediction application.