计算机科学
集成学习
人工智能
独创性
机器学习
元组
商业智能
航程(航空)
大数据
运筹学
知识管理
数据挖掘
工程类
数学
离散数学
创造力
政治学
法学
航空航天工程
作者
Chairote Yaiprasert,Achmad Nizar Hidayanto
标识
DOI:10.1016/j.jjimei.2023.100209
摘要
This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.
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