Mohd Kasihmuddin, Mohd Shareduwan and Abdul Halim, Nur Shahira and Mohd Jamaludin, Siti Zulaikha and Mansor, Mohd. Asyraf and Alway, Alyaa and Zamri, Nur Ezlin and Azhar, Siti Aishah and Marsani, Muhammad Fadhil (2023) Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm. Journal of Information and Communication Technology, 22 (03). pp. 309-335. ISSN 2180-3862

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Abstract

Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset.

Item Type:	Article
Uncontrolled Keywords:	2-satisfiability, genetic algorithm, Hopfield neural network, logic mining, online shopping
Subjects:	T Technology > T Technology (General)
Divisions:	School of Computing
Depositing User:	Mrs Nurin Jazlina Hamid
Date Deposited:	31 Jul 2023 09:52
Last Modified:	31 Jul 2023 09:52
URI:	https://repo.uum.edu.my/id/eprint/29664

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