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Acta Scientific Computer Sciences

Research Article Volume 3 Issue 1

Brain-inspired Decision-making: A Reduction of the Stress and Anxiety Factor in Business Intelligence

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: November 03, 2020; Published: December 30, 2020

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Abstract

  Decision-making is critical for every manager at all levels. Decision-making is necessary for managers to decide when to increase sales, improve customer service, develop new technologies, or conduct a project. However, decision-making can be subjected to many variables such as stress, emotions, morals, situations, etc. But brain-inspired decision-making (BIDM), using technologies such as artificial neural networks (ANNs) and artificial intelligence (AI), can eliminate indecision and stress, emotion, situations, etc., in the decision-making process. Stress has been discovered to be the most corrosive element in the decision-making process. Stress can cause managers to make the wrong decision in critical situations. Stress can affect decision-making by skewing critical decisions, causing emotional distress in individuals under stress, and leading to bad decision-making. On the other hand, using AI and ANNs to develop BIDM can lead to effective decision-making under the most corrosive stress. In this paper, we have proposed a method to test and evaluate BIDM using 25 engineering managers under stress. A literature review and history of BIDM is also given.

Keywords: Brain-inspired decision-making; Decision-making; Business Intelligence; Artificial Intelligence; Artificial Neural Networks; Cognitive Dynamic System

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References

  1. Herli M and Tjahjadi B. “Effectiveness of the Business Intelligence System in the manufacturing decision-making process: The Case in fertilizer companies”. Journal of Talent Development and Excellence2s (2020): 1814-1820.
  2. Zafary F. “Implementation of business intelligence considering the role of information systems integration and enterprise resource planning”. Journal of Intelligence Studies in Busines1 (2020).
  3. , et al. “Different profiles of decision-making and physiology under varying levels of stress in trained military personnel”. International Journal of Psychophysiology 131 (2018): 73-80.
  4. Naghshvarianjahromi M., et al. “Natural brain-inspired intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory”. Applied Sciences3 (2020). 1150.
  5. Naghshvarianjahromi M., et al. “Brain-inspired cognitive decision- making for Nonlinear and Non-Gaussian Environments”. IEEE Access 7 (2019): 180910-180922.
  6. Naghshvarianjahromi M., et al. “Brain-inspired intelligence for real-time health situation understanding in Smart e-Health Home Applications”. IEEE Access 7 (2019): 180106-180126.
  7. Wu Y., et al. “Brain-inspired global-local hybrid learning towards human-like intelligence”. arXiv (2020).
  8. Trafton A. “How we make complex decisions”. MIT News (2019).
  9. Belwal E. “Decision-making and the factors that affect It”. Leadership and Management 3 (2014): 366-380.
  10. Bose T., et al. “Inhibition and excitation shape activity selection: Effect of oscillations in a decision-making circuit”. Neural Computation5 (2019): 870-896.
  11. Dutt N., et al. “Self-awareness for Autonomous Systems”. Proceedings of the IEEE 7 (2020): 971-975.
  12. Mudra Rakshasa A and Tong M T. “Making “good” choices: Social isolation in mice exacerbates the effects of chronic stress on decision-making”. Frontiers in Behavioral Neuroscience 14 (2020): 81.
  13. Padilla LM., et al. “Decision-making with visualizations: A cognitive framework across disciplines”. Cognitive Research29 (2018).
  14. Poo M., et al. “China Brain Project: Basic neuroscience, brain diseases, and brain-inspired computing”. Neuron 3 (2016): 591-596.
  15. Remmelzwaal L A., et al. “Brain-inspired distributed cognitive architecture”. arXiv (2020).
  16. Zhao F., et al. “A brain-inspired decision-making model based on top-down biasing of prefrontal cortex to basal ganglia and its application in autonomous UAV explorations”. Cognitive Computation2 (2018): 296-306.
  17. Zhao F., et al. “A brain-inspired decision-making spiking neural network and its application in unmanned aerial vehicle”. Frontiers in neurorobotics 12 (2018): 56.
  18. Sardi S., et al. “Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms”. Scientific Reports1 (2020): 1-10.
  19. Byrne K A., et al. “Acute stress improves long-term reward maximization in decision-making under uncertainty”. Brain and cognition 133 (2019): 84-93.
  20. Chen J., et al. “Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent”. Frontiers in Neurorobotics 13 (2019): 40.
  21. Chen S., et al. “Brain-inspired cognitive model with attention for self-driving cars”. IEEE Transactions on Cognitive and Developmental Systems1 (2017): 13-25.
  22. Chen T Y. “A likelihood-based assignment method for multiple criteria decision analysis with interval type-2 fuzzy information”. Neural Computing and Applications12 (2017): 4023-4045.
  23. El Othman., et al. “Personality traits, emotional intelligence, and decision-making styles in Lebanese universities medical students”. BMC Psychology 8 (2020): 1-14.
  24. Friedman A., et al. “Chronic stress alters striosome-circuit dynamics, leading to aberrant decision-making”. Cell 5 (2017): 1191-1205.
  25. Groombridge CJ., et al. “Stress and decision-making in resuscitation: A systematic review”. Resuscitation 144 (2019): 115-122.
  26. McEwen B S. “Brain on stress: How the social environment gets under the skin”. Proceedings of the National Academy of Sciences 109 (2012): 17180-17185.
  27. Durcevic S. “Why data driven business intelligence is your path to success”. Business Intelligence 8 (2019): 1345-1350.
  28. Kurashige H., et al. “Revealing relationships among cognitive functions using Functional Connectivity and a Large-Scale Meta-Analysis Database”. Frontiers in Human Neuroscience 13 (2020): 457.
  29. Liang Q., et al. “Temporal-Sequential Learning with a Brain-Inspired Spiking Neural Network and its application to musical memory”. Frontiers in Computational Neuroscience (2020): 14.
  30. Parve R. “Decision-making and information systems”. Premier University Slide Presentation.
  31. Samsonovich A V. “Socially emotional brain-inspired cognitive architecture framework for artificial intelligence”. Cognitive Systems Research 60 (2020): 57-76.
  32. Zeng T and Si B. “A brain-inspired compact cognitive mapping system”. Cognitive Neurodynamics (2020): 1-11.
  33. Li M., et al. “Ideal time of day for risky decision-making: Evidence from the Balloon Analogue Risk Task”. Nature and Science of Sleep 12 (2020): 477.
  34. Zheng N., et al. “Hybrid-augmented intelligence: Collaboration and cognition”. Frontiers of Information Technology and Electronic Engineering 18 (2017): 153-179.
  35. Doborjeh Z G., et al. “EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes”. In 2018 International Joint Conference on Neural Networks (IJCNN) (2018): 1-7.
  36. Abderrahmane N., et al. “Design space exploration of hardware spiking neurons for Embedded Artificial Intelligence”. Neural Networks 121 (2020): 366-386.
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Citation

Citation: Cheryl Ann Alexander and Lidong Wang. “Brain-inspired Decision-making: A Reduction of the Stress and Anxiety Factor in Business Intelligence”.Acta Scientific Computer Sciences 3.1 (2021): 35-44.




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