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IJMLC 2019 Vol.9(5): 539-553 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.838

Enhancing Program Management with Predictive Analytics Algorithms (PAAs)

Bongs Lainjo

Abstract—The increase in the amount of big data and the emergence of analytics technologies has created the opportunity for applying algorithm development techniques using machine learning (ML) languages to predict future events. The use of predictive analysis algorithms provides a mechanism for the utilization of organizational databases, files in equipment, videos, images, and other types of data to predict future incidences or events. To conduct inclusive analyses of contemporary literature of existing relevant narratives with a focus on program management themes, including state-of-the art methodologies on current plausible predictive analytics models. The methodology used is the review and applications of programming platforms available that can be applied in the analyses of data to predict future outcomes. Program management requires the utilization of the existing machine learning languages in understanding future events and enabling effective preparations among stakeholders to make strategic decisions that enable the achievement of their goals, objectives, and missions. The use of predictive analytics algorithms has gained thematic significance in automotive industries, energy sector, financial organizations, industrial operations, medical services, governments, and academic institutions. Predictive analytics algorithms are important in promoting the management of future events such as workflow or operational activities in a manner that organizations and institutions can schedule their activities and tasks in order to optimize performance. It also ensures that organizations use existing big data to predict future performance and mitigate risks. The improvements in information technology and data analytics procedures have resulted in the ability of businesses to make effective use of historical data regarding their performances to predict future events. This enables evidence-based planning, mitigating risks, and improvement of operational efficiency. PAA’s models have enabled accurate prediction of performance of companies and planning for increased demand for the products and services they provide.

Index Terms—Models, predictive-analytics-algorithms, program-implications thematic significance.

Bongs Lainjo is with Cybermatic International, Montreal, QC H4W1S8 Canada (e-mail: bsuiru@bell.net).


Cite: Bongs Lainjo, "Enhancing Program Management with Predictive Analytics Algorithms (PAAs)," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 539-553, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net

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