M
Morteza Saberi
Researcher at University of Technology, Sydney
Publications - 183
Citations - 3676
Morteza Saberi is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Data envelopment analysis & Mean absolute percentage error. The author has an hindex of 24, co-authored 167 publications receiving 2879 citations. Previous affiliations of Morteza Saberi include Australian Defence Force Academy & Curtin University.
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Support vector regression with chaos-based firefly algorithm for stock market price forecasting
TL;DR: A forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price and performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).
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Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption
TL;DR: It is shown that neural networks dominate time series approach form the point of yielding less mean absolute percentage error (MAPE) error and utilization of ANN instead of time series to obtain better predictions for energy consumption.
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ZBWM: The Z-number extension of Best Worst Method and its application for supplier development
TL;DR: Providing BWM with Z-numbers enables the BWM method to handle the uncertainty of information of a multi-criteria decision and shows that ZBWM results lower inconsistency when compared with BWM.
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A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization
TL;DR: A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps.
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An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran
TL;DR: An integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data is presented.