Abstract: Historical data used to train machine learning algorithms can have thousands of records with hundreds of fields, and inevitably includes faulty data that affects the accuracy and utility of a primary model machine learning algorithm. To improve dataset integrity it is segregated into a clean dataset having no invalid data values and a faulty dataset having the invalid data values. The clean dataset is used to produce a secondary model machine learning algorithm trained to generate from plural complete data records a replacement value for a single invalid data value in a data record, and a tertiary model machine learning clustering algorithm trained to generate from plural complete data records replacement values for multiple invalid data values. Substituting the replacement data values for invalid data values in the faulty dataset creates augmented training data which is combined with clean data to train a more accurate and useful primary model.

Abstract: Historical data used to train machine learning algorithms can have thousands of records with hundreds of fields, and inevitably includes faulty data that affects the accuracy and utility of a primary model machine learning algorithm. To improve dataset integrity it is segregated into a clean dataset having no invalid data values and a faulty dataset having the invalid data values. The clean dataset is used to produce a secondary model machine learning algorithm trained to generate from plural complete data records a replacement value for a single invalid data value in a data record, and a tertiary model machine learning clustering algorithm trained to generate from plural complete data records replacement values for multiple invalid data values. Substituting the replacement data values for invalid data values in the faulty dataset creates augmented training data which is combined with clean data to train a more accurate and useful primary model.

Abstract: Historical data used to train machine learning algorithms can have thousands of records with hundreds of fields, and inevitably includes faulty data that affects the accuracy and utility of a primary model machine learning algorithm. To improve dataset integrity it is segregated into a clean dataset having no invalid data values and a faulty dataset having the invalid data values. The clean dataset is used to produce a secondary model machine learning algorithm trained to generate from plural complete data records a replacement value for a single invalid data value in a data record, and a tertiary model machine learning clustering algorithm trained to generate from plural complete data records replacement values for multiple invalid data values. Substituting the replacement data values for invalid data values in the faulty dataset creates augmented training data which is combined with clean data to train a more accurate and useful primary model.

Abstract: A system, apparatus, and method of generating Stokes vectors, a Mueller matrix, and polarized scattering from an aerosol aggregate includes providing an incident infrared laser beam; causing the incident infrared laser beam to be polarization-modulated using variable stress/strain birefringence imposed on a ZnSe crystal; defining a Stokes vector associated with the incident infrared laser beam; scattering the incident infrared laser beam from an aggregate aerosol comprising interferents and analyte particles; producing a scattered-beam reactant Stokes vector by causing the scattered incident infrared laser beam to be polarization-modulated; generating a Mueller matrix by taking a transformation of the Stokes vector; and identifying the analyte using the Mueller matrix. The Mueller matrix may comprise M-elements that are functions of a wavelength of the infrared laser beam, backsattering orientation of the infrared laser beam, and a shape and size of the interferents and analyte particles.