Noor Aida Husaini

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



Research Interests: Computer systems and computational processes, Multimedia Information System, Data Structures and Algorithms


Noor Aida Husaini pursued her degree at Universiti Tun Hussein Onn Malaysia (UTHM) and graduated with the Bachelor of Information Technology in 2009. Upon graduation, she was a research assistant at Software and Multimedia Centre where the research is sponsored by Ministry of Science, Technology and Innovation. She then enrolled at UTHM in 2009 for Master of Information Technology. After completing her Master, she was working as a Teaching Fellow in UTHM. In 2013, she then enrolled for the Ph.D programme in the same university.

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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