Nureize Arbaiy

Work place: Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johore, Malaysia



Research Interests: Decision Support System, Analysis of Algorithms, Mathematical Analysis


Nureize Arbaiy is a senior lecturer of UTHM. She received her first degree (Computer Science) at UTM and Masters Degree (Intelligent System) at Universiti Utara Malaysia in 2001 and 2004, respectively. She has published over 20 journals and conference papers in the area Fuzzy Logic. Her representative published over 50 articles. Moreover, she was a participant of International Neural Network Society (INNS) in 2015. Her research interests include Possibilistic Decision Making, Fuzzy Random Regression Analysis, Multi Criteria Decision Making, and Time-Series Analysis. In 2019, she was assigned as a Head of Department, Postgraduate Department, Faculty of Computer Science and Information Technology (FSKTM), UTHM.

Author Articles
MCS-MCMC for Optimising Architectures and Weights of Higher Order Neural Networks

By Noor Aida Husaini Rozaida Ghazali Nureize Arbaiy Ayodele Lasisi

DOI:, Pub. Date: 8 Oct. 2020

The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate that this swarm-based algorithm outperformed or at least at par with the network models with current BP algorithm in terms of lower error rate.

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