• "With an Ultimate Kiosk Hearing Screening Solution we have seen a positive increase in the number of qualified appointments come through our clinic. This unit is a great addition to our marketing vehicles and potential patients seem very interested to try it."
    Michael Ioannidis, Hearing Solutions

Advanced Pseudo-Random Data Generator

Artificially generated data proved to be more cost effective, easier to collect, result in reduced development time and produced better training data for Numeric Knowledge Modules than real life data. Having a domain experts knowledge of  what data samples or variables best represent a specific domain of knowledge and an Advanced Pseudo-Random Data Generator capable of  producing the samples, was the most accurate way to generate data sets for numeric knowledge modeling. Data representation or data sets are defined for this article as set of test results or other data necessary to represent all the variables that are needed to develop the Numeric Knowledge Modules that will ultimately classify accurately patterns within a specific market or domain. The term Pseudo-Random was used as the sample data was restricted to domain specific categories prior to being analyzed by the numeric knowledge module generator. Improving the data sets and not adjusting the mathematical models has proven in one domain, hearing, to have improved accuracy, reduced development time and increased acceptance of this artificially intelligent tool. The Advanced Pseudo-Random Data Generator reduced the domain experts learning curve related to data acquisition, improved statistical accuracy,  simplified data collection and improved acceptance of numerical knowledge modules. This  method of data representation for producing Numeric Knowledge Modules for pattern identification can likely be applied to other knowledge domains. There has been a recent trend in Artificial Intelligence to combine Expert Systems and Numeric Knowledge Modeling as one method to deal with fuzzy logic (gray areas) or pattern overlaps (statistically invalid results). The combining of two AI approaches could be the result of domain experts inability to communicate the complex problems that exist within his domain. In many cases, the Numeric Knowledge Modeling tools tend to be too complicated for the domain expert. It is possible that improved data sets from flexible random generators could  prove to be a more efficient method to deal with complex specific domain knowledge acquisition. 

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