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E-Handrawn Calculator
Published 2008“…The purpose of this project is to demonstrate an application of back-propagation network (comparison of training their algorithms and transfer function) in order to developing e-Hand-Drawn Calculator. …”
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Final Year Project -
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Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
Published 2015“…Mempertingkatkan prestasi, terutamanya dalam kejituan pengelasan yang membawa kepada perkenalan versi MWO yang telah di adaptasi; dikenali sebagai algoritma Peningkatan-MWO (E-MWO). Developing efficient training method for Neural Networks (NN) in terms of high accuracy is a challenge. …”
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Slew Control of Prolate Spinners Using Single Magnetorquer
Published 2016“…E XISTING research [1–5] on the prolate spinning spacecraft attitude maneuver has developed a series of slew algorithms using a single thruster in two categories: half-cone derived algorithms and pulse-train algorithms. …”
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The development of an automated pattern recognition based on neural network / Irni Hamiza Hamzah, Mohammad Nizam Ibrahim and Linda Mohd Kasim
Published 2006“…The capability of powerful personal computers and affordable and high resolution sensors (i.e.: CCD cameras, microphones and scanners) have fostered the development of pattern recognition algorithms in new application domains (i.e.: fuzzy logic, neural network and genetic algorithm). …”
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An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks
Published 2020“…Many algorithms have been developed for automated electrocardiogram (ECG) classification. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2024Article -
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…To achieve optimal RBFN-DDA performance, HS (or GSA) is proposed to optimize the center and the width of each hidden unit in a trained RBFN. By integrating with the HS (or GSA) algorithm, the proposed metaheuristic neural networks (i.e., RBFN-DDA-HS and RBFN-DDA-GSA) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA up to 28.69% in two benchmarks datasets, which are numerical records from a bearing and steel plate system and a condition-monitoring system in a power plant (i.e., the circulating water (CW) system). …”
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Thesis -
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Early tube leak detection system for steam boiler at KEV power plant
Published 2023Conference Paper -
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Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza
Published 2021“…Furthermore, three McR algorithms are developed and implemented in a cascaded manner to reduce the false predictions (i.e., misclassification) of the aforementioned six ML classifiers. …”
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