Acta Scientific Pharmaceutical Sciences (ASPS)(ISSN: 2581-5423)

Review Article Volume 4 Issue 4

Machine Learning Driving Forecasting Paradigm

Bahman Zohuri1* and Farhang Mossavar Rahmani2

1Research Associate Professor, Electrical Engineering and Computer Science Department, University of New Mexico, Albuquerque, New Mexico USA
2Professor of Finance and Director of MBA School of Business and Management, National University, San Diego, California, USA

*Corresponding Author: Bahman Zohuri, Research Associate Professor, Electrical Engineering and Computer Science Department, University of New Mexico, Albuquerque, New Mexico USA.

Received: March 03, 2020; Published: March 10, 2020

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Abstract

  The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the Big Data (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. As necessity of any business to be resilience, one needs Forecasting with a paradigm that fits to that business day-to-day operation using their incoming daily and timely information-driven by those data while comparing them with existing historical data to do Data Analytics (DA) and Data Predictive (DP) which will be derivative the observation of these data. Give the speed of incoming in real-time at sheer volume, leave us no choice but using Artificial Intelligence (AI) and consequently Machine Learning (ML) as its foundation and together with Deep Learning (DL) will enhance our predictive analytic to be to augment a forecasting model into our business to make it more resilience. In this article, we discuss these topics.

Keywords:Artificial Intelligence; Machine Learning; Deep Learning; Resilience System; Forecasting and Related Paradigm; Big Data; Fuzzy Logic

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References

  1. B Zohuriand FM Rahmani. “A Model to Forecast Future Paradigm, Knowledge Is Power in Four Dimensions”. Apple Academic Press, a CRC Press, Taylor and Francis Group (2019).
  2. B Zohuri and M Moghaddam. “Neural Network Driven Artificial Intelligence: Decision Making Based on Fuzzy Logic” (Computer Science, Technology and Applications: Mathematics Research Developments), Nova Publisher (2017).
  3. B Zohuri and FM. Mossavar, “Artificial Intelligence Driven Resiliency with Machine Learning and Deep Learning Components”. International Journal of Nanotechnology and Nanomedicine 4.2 (2019): 1-3.
  4. B Zohuri and M Moghaddam. “From Business Intelligence to Artificial Intelligence”. Short Communication in Modern Approaches on Material Science, Lupine Publishers 2.3 (2020).
  5. Anthony Liew Walden. “Understanding Data, Information, Knowledge and Their Inter-Relationships”. Journal of Knowledge Management Practice 8.2 (2007).
  6. Leonardo dos Santos Pinheiro and Mark Dras. “Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading” (2017).
  7. Aurelien Geron. “Hands-On Machine Learning with Scikit-Learn, Keras and Tensor flow, Concepts, Tools, and Techniques to Build Intelligent Systems”. 2nd edition O’Reilly Publication (2019).
  8. B Zohuri and M Moghaddam. “Deep Learning Limitations and Flaws”. Short Communication in Modern Approaches on Material Science, Lupine Publishers 2.3 (2020).
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Citation

Citation: Bahman Zohuri., et al. “Machine Learning Driving Forecasting Paradigm". Acta Scientific Computer Sciences 2.3 (2020): 01-05.



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