Prashaya Fusiripong

Work place: Information and Communication Technology International College, Rangsit University, Lak Hok, Pathumthani, Thailand



Research Interests: Swarm Intelligence, Supply Chain


Dr. Prashaya Fusiripong spent more than 10 years in the IT industry in Thailand and joined Rangsit University in 2009. He graduated PhD in IT from Universiti Utara Malaysia and master’s degree in information technology in 2006 from Rangsit University. He also received a scholarship from TOT public company limited for a bachelor’s degree and received the degree of Electrical (Telecommunication) Engineer in 2000 from Kasetsart University. His research interest includes IT outsourcing, Multi-Criteria Decision-Making (MCDM) theory, swarm intelligence, and sustainability in software and supply chain management.

Author Articles
Exploring the Profound Influence of Machine Learning on Business Intelligence: A Comprehensive Review

By Herison Surbakti Prashaya Fusiripong

DOI:, Pub. Date: 8 Jun. 2024

Businesses nowadays may save a significant amount of money by using technological solutions. It is impossible to deny this when considering the expenses of acquiring and training new personnel. When faced with such difficulties, technology is virtually always able to assist. Business Intelligence/Machine Learning (BI/ML) is an essential tool in today's decision-making process because of the many issues it has created for contemporary business decision-making. A comparative study of regression models, including linear regression, random forests, and gradient boosting, could unravel their effectiveness in predictive analytics within BI. Machine learning contribution in businesses is vital as it has a strong link with business intelligence, and it helps business decision-making in businesses. Without machine learning, business intelligence is not practical while making decisions, as business owners can't make decisions effectively. This paper will comprehensively review the noteworthy contributions of Machine Learning and its Impact on Business Intelligence. Further, it will discuss the challenges and opportunities of machine learning in business intelligence. Finally, the paper will discuss future correspondence about machine learning in businesses.

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