Feature Selection with Integrated Gaussian Seahorse Optimization Data Mining for Cross-border Business Cooperation between the Malaysian Medical Industry and Tourism Industry
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Abstract
The cross-border collaboration between the medical industry and the tourism industry has gained significant attention as a promising avenue for economic growth and development. Data mining techniques are employed to extract valuable patterns and insights from large-scale datasets, shedding light on the opportunities and challenges associated with this collaborative effort. This study proposes an integrated approach that combines feature selection with Gaussian Seahorse Optimization Data Mining (GSH-DM) to identify the most relevant features and optimize the data mining process. The GSH-DM assembling comprehensive datasets encompassing information from both the Malaysian medical industry and tourism industry. The integrated GSH-DM model then applies the Gaussian Seahorse Optimization algorithm to optimize the data mining process, enhancing the accuracy and efficiency of pattern discovery. the GSH-DM model, this study aims to uncover hidden patterns, relationships, and predictive models that can guide decision-making and strategy development for cross-border business cooperation. The findings of this study contribute to a deeper understanding of the factors that influence cross-border business cooperation between the Malaysian medical industry and the tourism industry. The integrated GSH-DM approach showcases the potential of combining feature selection techniques with advanced optimization algorithms in data mining applications. The results of GSH-DM provide actionable insights for stakeholders, enabling them to make informed decisions and foster successful cross-border collaborations between the Malaysian medical industry and the tourism industry. The analysis of the results demonstrated that GSH-DM exhibits improved performance for feature selection and classification.
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