Abstract: The present disclosure discloses an intelligent cat litter box, including an outer shell assembly, an inner shell assembly, a driving mechanism, a linkage mechanism, and a feces storage case. The outer shell assembly is a hollow cavity; the inner shell assembly is a hollow cavity and is rotatably arranged in the outer shell assembly; a shovel plate is arranged in the inner shell assembly; a grid area is arranged on the shovel plate; the driving mechanism is arranged on the outer shell assembly, and includes a driving gear and a driving motor connected to the driving gear; the linkage mechanism is arranged on the inner shell assembly, and includes a linkage shaft, an anti-separation member, and a linkage gear.

Abstract: A determination is made at a machine learning service that a training data set comprising a majority category of observation records and one or more minority categories of observation records meets a criterion for automated sampling. A sampling ratio to be used for a particular category of the majority category and the one or more minority categories is identified. A selected sampling methodology is applied to the particular category to obtain a sample in accordance with the sampling ratio. A particular machine learning model is trained using a result of applying at least the selected sampling methodology on the particular category.

Abstract: A merchant profile builder (MPB) is provided. The complete merchant profiling process is split into three pieces: Data Preprocessing, Weekly Summary Calculation, and Merchant Profile Calculation. In the Data Preprocessing stage, authorization data and daily fraud data are processed on a weekly basis to yield: an authorization extract file and fraud authorization data, each of which are sorted by a merchant key. In the Weekly Summary Calculation stage, the files output by the Data Preprocessing stage are used as input to compile basic statistics for each merchant based only on data for one week. There are separate weekly summaries for authorizations and fraud authorizations. In the Merchant Profile Calculation stage, the weekly summaries from the 16 most recent weeks are combined together and averages are calculated and output a set of merchant profiles and a set of default profiles.

Abstract: A merchant profile builder (MPB) is provided. The complete merchant profiling process is split into three pieces: Data Preprocessing, Weekly Summary Calculation, and Merchant Profile Calculation. In the Data Preprocessing stage, authorization data and daily fraud data are processed on a weekly basis to yield: an authorization extract file and fraud authorization data, each of which are sorted by a merchant key. In the Weekly Summary Calculation stage, the files output by the Data Preprocessing stage are used as input to compile basic statistics for each merchant based only on data for one week. There are separate weekly summaries for authorizations and fraud authorizations. In the Merchant Profile Calculation stage, the weekly summaries from the 16 most recent weeks are combined together and averages are calculated and output a set of merchant profiles and a set of default profiles.