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Wan Amizah Wan Jusoh
Researcher at Universiti Tun Hussein Onn Malaysia
Publications - 13
Citations - 468
Wan Amizah Wan Jusoh is an academic researcher from Universiti Tun Hussein Onn Malaysia. The author has contributed to research in topics: Compressive strength & Ultimate tensile strength. The author has an hindex of 7, co-authored 12 publications receiving 278 citations. Previous affiliations of Wan Amizah Wan Jusoh include Universiti Teknologi Malaysia.
Papers
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Optimization of ANFIS with GA and PSO estimating α ratio in driven piles
TL;DR: This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts.
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Optimizing ANN models with PSO for predicting short building seismic response
Hoang Nguyen,Hossein Moayedi,Loke Kok Foong,Husam Abdulrasool H. Al Najjar,Wan Amizah Wan Jusoh,Ahmad Safuan A. Rashid,J. Jamali +6 more
TL;DR: The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.
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A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
Hossein Moayedi,Mansour Mosallanezhad,Ahmad Safuan A. Rashid,Wan Amizah Wan Jusoh,Mohammed Abdullahi Mu’azu +4 more
TL;DR: The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.
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Prediction of Flyrock in Mine Blasting: A New Computational Intelligence Approach
TL;DR: The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models.
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Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system
TL;DR: The introduced PSO-M5Rules model is proposed as the best predictive network in approximating C L of an EEB system and higher reliability is indicated.