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Miss Laiha Mat Kiah
Researcher at Information Technology University
Publications - 125
Citations - 4821
Miss Laiha Mat Kiah is an academic researcher from Information Technology University. The author has contributed to research in topics: Computer science & Adaptive neuro fuzzy inference system. The author has an hindex of 36, co-authored 113 publications receiving 3975 citations. Previous affiliations of Miss Laiha Mat Kiah include University of Malaya.
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A review of smart home applications based on Internet of Things
TL;DR: A review is conducted to map the research landscape of smart home based on Internet of Things into a coherent taxonomy and identifies the basic characteristics of this emerging field in the following aspects: motivation of using IoT in smart home applications, open challenges hindering utilization, and recommendations to improve the acceptance and use of smartHome IoT applications in literature.
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Towards secure mobile cloud computing: A survey
TL;DR: This literature review highlights the current state of the art work proposed to secure mobile cloud computing infrastructures, identifies the potential problems, and provides a taxonomy of the state-of-the- art.
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Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission
Dalibor Petković,Žarko Ćojbašić,Vlastimir Nikolić,Shahaboddin Shamshirband,Miss Laiha Mat Kiah,Nor Badrul Anuar,Ainuddin Wahid Abdul Wahab +6 more
TL;DR: In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based controller for variable-speed operation of a wind turbine was proposed to improve the wind energy available in an erratic wind speed regime.
Journal ArticleDOI
On cloud security attacks
Salman Iqbal,Miss Laiha Mat Kiah,Babak Dhaghighi,Muzammil Hussain,Suleman Khan,Muhammad Khurram Khan,Kim-Kwang Raymond Choo +6 more
TL;DR: This research work presents taxonomy of cloud security attacks and potential mitigation strategies with the aim of providing an in-depth understanding of security requirements in the cloud environment and highlights the importance of intrusion detection and prevention as a service.
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Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks
Shahaboddin Shamshirband,Shahaboddin Shamshirband,Ahmed Patel,Ahmed Patel,Nor Badrul Anuar,Miss Laiha Mat Kiah,Ajith Abraham +6 more
TL;DR: The proposed cooperative Game-based Fuzzy Q-learning (G-FQL) model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy, and yields a greater improvement than existing machine learning methods.