$GTCH on radar! The company is researching Probabilistic Neural Network (PNN) algorithms and techniques for Kirlian research.
PNN is a supervised artificial neural network, which is typically used in classification and pattern recognition systems. Kirlian photography contemplates creating a series of techniques to produce colorful images of object’s energy based on its electrical discharge. An image that is produced via Kirlian technique includes vast amount of graphical data.
GBT’s research is aiming to potentially investigate human organs imaging using Kirlian techniques, analyzing the data as a potential of health-related information source through PNN methods and algorithms for image’s density analysis and to identify unique region’s characteristics. The goal is to develop techniques that will potentially identify regions of interest within images, compute distances and train the PNN about the object’s energy traits. The approach aims to achieve fast image’s multilayer perception, deciding if a certain pattern belongs to a specific class, and categorize it as a potential health related topic.
As a PNN method typically is a major time-consuming process, GBT plans to invest in optimization algorithms and memory management to maintain the training performance. GBT will target the PNN to map and vectorize Kirlian images, with the goal of providing classifications according to patterns recurrence and consistencies. These identifiers will be investigated to define classes of potential health related phenomena and symptoms. The use of a PNN goal is to potentially reach a higher level of accuracy and robustness for Kirlian image processing, achieving advanced imaging categorization and conclusions that may provide health related information.
There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.
PNN is a supervised artificial neural network, which is typically used in classification and pattern recognition systems. Kirlian photography contemplates creating a series of techniques to produce colorful images of object’s energy based on its electrical discharge. An image that is produced via Kirlian technique includes vast amount of graphical data.
GBT’s research is aiming to potentially investigate human organs imaging using Kirlian techniques, analyzing the data as a potential of health-related information source through PNN methods and algorithms for image’s density analysis and to identify unique region’s characteristics. The goal is to develop techniques that will potentially identify regions of interest within images, compute distances and train the PNN about the object’s energy traits. The approach aims to achieve fast image’s multilayer perception, deciding if a certain pattern belongs to a specific class, and categorize it as a potential health related topic.
As a PNN method typically is a major time-consuming process, GBT plans to invest in optimization algorithms and memory management to maintain the training performance. GBT will target the PNN to map and vectorize Kirlian images, with the goal of providing classifications according to patterns recurrence and consistencies. These identifiers will be investigated to define classes of potential health related phenomena and symptoms. The use of a PNN goal is to potentially reach a higher level of accuracy and robustness for Kirlian image processing, achieving advanced imaging categorization and conclusions that may provide health related information.
There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.
