$GTCH News Out: SAN DIEGO, Aug. 12, 2021 (GLOBE NE
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Posted On: 08/17/2021 10:24:57 AM
$GTCH News Out: SAN DIEGO, Aug. 12, 2021 (GLOBE NEWSWIRE) -- GBT Technologies, Inc. (OTC PINK: GTCH) ("GBT”, or the “Company”), announced that GBT Tokenize (“GBT/Tokenize) is now implementing advanced AI big data analytics methods for Kirilian research results, to find potential correlation with health information.
The Kirlian Electrophotography research techniques produced viable results which the company is now investigating a possibility of potential correlation with health-related data. The result of the Kirlian imaging research includes vast amounts of data with a high complexity, one which requires an intelligent analytics method, such as advanced big data algorithms.
The first stage will be a pattern of identification, including indexing and the creation of data sets. The second stage will focus on rapid data classification and group content identification, including preliminary data insights. Based on this analysis the system will dispose redundant and non-relevant data, concluding a viable, possible correlation pointer. These pointers will go through another stage of consistency and occurrence analysis to reach a high percentage of possible correlation. The AI engine main task will be sifting through Kirlian image huge data, analyzing it, and then cross-reference it with health-related data to match a symptoms or conditions possibility. Machine learning algorithms will be accomplishing this enormous task providing insight for Kirilian images complex data.
The Kirlian Electrophotography research techniques produced viable results which the company is now investigating a possibility of potential correlation with health-related data. The result of the Kirlian imaging research includes vast amounts of data with a high complexity, one which requires an intelligent analytics method, such as advanced big data algorithms.
The first stage will be a pattern of identification, including indexing and the creation of data sets. The second stage will focus on rapid data classification and group content identification, including preliminary data insights. Based on this analysis the system will dispose redundant and non-relevant data, concluding a viable, possible correlation pointer. These pointers will go through another stage of consistency and occurrence analysis to reach a high percentage of possible correlation. The AI engine main task will be sifting through Kirlian image huge data, analyzing it, and then cross-reference it with health-related data to match a symptoms or conditions possibility. Machine learning algorithms will be accomplishing this enormous task providing insight for Kirilian images complex data.
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