Cheryl Ann Alexander¹* and Lidong Wang²

¹Institute for IT Innovation and Smart Health, Mississippi, USA
²Institute for Systems Engineering Research, Mississippi State University, Vicksburg, USA

*Corresponding Author: Cheryl Ann Alexander, Institute for IT Innovation and Smart Health, Mississippi, USA.

Received: April 28, 2022; Published: July 18, 2022

Abstract

This original review paper will map out strong support for machine learning technologies for use in a brain-inspired decision-making tool that can be applied to a range of engineering management problems, including a decision support system (DSS). Studies have shown that machine learning is a solid foundation for a DSS. Big data analytics can lead to a stronger organization as decision-making skills are less dependent upon stress-related situations that can skew data. The Naïve Bayes algorithm could be an effective conclusion. In this paper, I introduced several areas of decision-making and decision support systems, including brain-inspired machine learning, smart decision-making, stress and smart decision-making, the impact of machine learning on decision-making, big data and decision support systems, COVID-19, and emerging diseases and deep technologies. In conclusion, a DSS with a data-driven strategy and machine learning methods can offer valuable experience and decision-making skills. Artificial intelligence such as machine learning facilities brain-inspired decision-making.

Keywords: Machine Learning; Decision Support Systems; Brain-inspired Decision-making; Decision Making; Naïve Bayes Algorithm; Big Data; COVID-19

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Citation

Citation: Cheryl Ann Alexander and Lidong Wang. “Trends in Decision-making: Looking at Decision Support Systems and Brain-inspired Decision-making". Acta Scientific Computer Sciences 4.8 (2022): 17-25.

Copyright

Copyright: © 2022 Cheryl Ann Alexander and Lidong Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.