A
Azizah Abdul Manaf
Researcher at Universiti Teknologi Malaysia
Publications - 127
Citations - 2534
Azizah Abdul Manaf is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Digital watermarking & Steganography. The author has an hindex of 24, co-authored 125 publications receiving 2064 citations. Previous affiliations of Azizah Abdul Manaf include Information Technology University.
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An overview of principal component analysis
TL;DR: The principal component analysis is a kind of algorithms in biometrics that covers standard deviation, covariance, and eigenvectors and is a tool to reduce multidimensional data to lower dimensions while retaining most of the information.
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Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters
Shahaboddin Shamshirband,Ehsan Jafari Nodoushan,Jason E. Adolf,Azizah Abdul Manaf,Amir Mosavi,Amir Mosavi,Kwok Wing Chau +6 more
TL;DR: In this paper, ensemble models using the Bates-Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models for predicting chlorophyll a and salinity with different lead.
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Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach
Mehrbakhsh Nilashi,Sarminah Samad,Azizah Abdul Manaf,Hossein Ahmadi,Tarik A. Rashid,Asmaa Munshi,Wafa Almukadi,Othman Ibrahim,Omed Hassan Ahmed +8 more
TL;DR: The results showed that human and technological factors are the most important factors for medical tourism adoption in Malaysia.
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Extreme learning machine for prediction of heat load in district heating systems
Shahin Sajjadi,Shahaboddin Shamshirband,Meysam Alizamir,Por Lip Yee,Zulkefli Mansor,Azizah Abdul Manaf,Torki A. Altameem,Ali Mostafaeipour +7 more
TL;DR: In this article, a short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created using the novel method based on Extreme Learning Machine (ELM).
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Efficient intelligent energy routing protocol in wireless sensor networks
TL;DR: A new protocol to reach energy efficiency is proposed an intelligent routing protocol algorithm based on reinforcement learning techniques that has improvement in different parameters such as network lifetime, packet delivery, packet delay, and network balance.