Simplified artificial neural network configuration in R programming for predictive modelling

Razak, Tajul Rosli and Jarimi, Hasila and Ahmad, Emy Zairah (2023) Simplified artificial neural network configuration in R programming for predictive modelling. In: 8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023, 2 December 2023through 3 December 2023, Kuala Lumpur.

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Abstract

The configuration of Artificial Neural Networks (ANNs) in the context of predictive modelling can provide considerable difficulty owing to the complex nature of their arrangements and the need for meticulous hyperparameter adjustment the present study addresses the issue by proposing a more straightforward methodology for configuring Artificial Neural Networks (ANNs) through the R programming language the methodology presented in this study offers a systematic and comprehensive framework, ensuring accessibility and simplicity of implementation. This approach aims to enhance the usability of Artificial Neural Networks (ANNs) for practitioners who need advanced machine learning knowledge. In order to demonstrate the applicability of the proposed methodology, a series of experiments were conducted on a case study in sustainable energy research. This study makes a valuable contribution to academic discipline by establishing a connection between artificial neural network (ANN) theory and its practical application. This study aims to enhance the accessibility of artificial neural network (ANN) setup and provide significant insights to further progress predictive modelling, specifically focusing on sustainable energy research.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Artificial neural network, Configuration, Predictive modelling; R programming
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Maizatul Najwa Ahmad
Date Deposited:	16 Oct 2024 16:47
Last Modified:	16 Oct 2024 16:47
URI:	http://eprints.utem.edu.my/id/eprint/28004
Statistic Details:	View Download Statistic

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