Acta Scientific COMPUTER SCIENCES

RReview Article Volume 4 Issue 8

Trends in Decision-making: Looking at Decision Support Systems and Brain-inspired Decision-making

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

References

  1. Falih N., et al. “Structural Analysis using Galois Lattice Concept for Strategic Business Processes Alignment”. In 2021 7th International Conference on Optimization and Applications (ICOA) IEEE (2021): 1-5.
  2. Simion D O and Vasile E. “Applications for Businesses that Use Relational Databases”. Internal Auditing and Risk Management1 (2017).
  3. Mora M., et al. “Evaluating analytics DSS for the COVID-19 pandemic through WHO-INTEGRATE EtD for health policy”. Journal of Decision Systems1-2 (2022): 19-39.
  4. Lupei M I., et al. “A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19”. PloS One1 (2022): e0262193.
  5. Gupta S., et al. “Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research”. Annals of Operations Research (2021): 1-60.
  6. Flisberg P., et al. “Spatial optimization of ground-based primary extraction routes using the BestWay decision support system”. Canadian Journal of Forest Research5 (2021): 675-691.
  7. Lee A. “Towards Informatic Personhood: understanding contemporary subjects in a data-driven society”. Information, Communication and Society2 (2021): 167-182.
  8. Ghonim M A., et al. “Strategic alignment and its impact on decision effectiveness: a comprehensive model”. International Journal of Emerging Markets (2020).
  9. Brynjolfsson E., et al. “The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance”. Workplace Complements, and Business Performance (2021).
  10. Palamarchuk I S and Vaillancourt T. “Mental resilience and coping with stress: A comprehensive, multi-level model of cognitive processing, decision making, and behavior”. Frontiers in Behavioral Neuroscience (2021): 176.
  11. Shim D. “Capturing heterogeneous decision-making processes: the case with the E-book reader market”. International Journal of Market Research2 (2021): 216-235.
  12. Daylamani-Zad D., et al. “A framework and serious game for decision making in stressful situations; a fire evacuation scenario”. International Journal of Human-Computer Studies162 (2022): 102790.
  13. Molins F., et al. “Early stages of the acute physical stress response increase loss aversion and learning on decision making: A Bayesian approach”. Physiology and Behavior237 (2021): 113459.
  14. Chandler R C. “Anticipatory foresight and adaptive decision-making are crucial characteristics for business continuity, crisis, and emergency leadership”. Journal of Business Continuity and Emergency Planning3 (2022): 255-269.
  15. Bokhari S A A and Myeong S. “Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective”. Sustainability2 (2022): 620.
  16. Lunenburg F C. “The decision making process”. In National Forum of Educational Administration and Supervision Journal 27.4 (2010): 1-12. 
  17. Rane SB and Narvel YAM. “Data-driven decision making with Blockchain-IoT integrated architecture: a project resource management agility perspective of industry 4.0”. International Journal of System Assurance Engineering and Management (2021): 1-19.
  18. O’Neill M., et al. “Process Visualization of Manufacturing Execution System (MES) Data”. In 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) IEEE (2021): 659-664.
  19. Arena S., et al. “A novel decision support system for managing predictive maintenance strategies based on machine learning approaches”. Safety Science146 (2022): 105529.
  20. Patalay S and Bandlamudi MR. “Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning”. Ingénierie des Systèmes d Inf., 26.1 (2021): 87-93.
  21. Mathoho S and Pillay K. “The Potential of Big Data Analytics to replace Managerial Decision-Making: Findings of a Systematic Review”. In 2021 IST-Africa Conference (IST-Africa) IEEE (2021): 1-12.
  22. Kasimatis C N., et al. “Implementation of a decision support system for prediction of the total soluble solids of industrial tomato using machine learning models”. Computers and Electronics in Agriculture193 (2022): 106688.
  23. Salazar R., et al. “Tomato yield prediction in a semi-closed greenhouse”. In XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): 1107 (2014): 263-270.
  24. Bhosale S V., et al. “Crop yield prediction using data analytics and a hybrid approach”. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)IEEE (2018): 1-5.
  25. Kluttz D N and Mulligan D K. “Automated decision support technologies and the legal profession”. Berkeley Technology LJ, 34 (2019): 853.
  26. Rajesh R. “A novel advanced grey incidence analysis for investigating the level of resilience in supply chains”. Annals of Operations Research (2020): 1-50.
  27. Gautam V. “Qualitative model to enhance the quality of metadata for data warehouse”. International Journal of Information Technology4 (2020): 1025-1036.
  28. Chu X., et al. “Big data and its V’s with IoT to develop sustainability”. Scientific Programming, (2021).
  29. Al-Amin., et al. “Efficient machine learning on data science languages with parallel data summarization”. Data and Knowledge Engineering, 136 (2021): 101930.
  30. Ahouz F and Golabpour A. “Predicting the incidence of COVID-19 using data mining”. BMC Public Health1 (2021). 1-12.
  31. Gupta A and Katarya R. “PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning”. Computers in Biology and Medicine138 (2021): 104920.

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.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is May 30, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US