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AI foundations of the international business planning and the AI consciousness model

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  • Laskai András

Abstract

With the continuous development of data mining instruments we acquire increasingly accurate and deep knowledge regarding the processes and network correlations related to business planning that can be considered one of the cornerstones of the world of business. During data mining activity patterns can be derived that provide assistance in the analysis of basic correlations. The most widely accepted analysis methods are hybrid data mining models and instruments, and within those neural networks that model the functioning of the human brain, which provide an avenue for the discovery and understanding of deeper correlations. By the application of data mining a consciousness model can be formulated, which evaluates and analyzes company movements, operations and decision making mechanisms. Data mining is one of the most important current paradigms of advanced business analyses and decision making instruments. It is a multidisciplinary approach that applies various techniques in the areas of statistics, machine learning and databases. One of the special branches of data mining inspects the correlations of financial balance sheets and reports as well as their derived indicators. In the case of this method, data mining provides the identification of data patterns in an innovative and ultimately interpretable manner. Data mining makes it possible for organizations to identify the statistical correlations between performance indicators more easily. In connection with this, by using hybrid data mining instruments the conscious operation of a specific company unit can be defined. By mapping conscious operations research may get closer to the mapping of the Unified Field.

Suggested Citation

  • Laskai András, 2019. "AI foundations of the international business planning and the AI consciousness model," International Journal of Science and Business, IJSAB International, vol. 3(1), pages 17-28.
    • Handle: RePEc:aif:journl:v:3:y:2019:i:1:p:17-28
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