Abstract: An apparatus comprises a processing device configured to monitor for events associated with users interacting with an e-commerce platform, to identify an event type of a given event associated with a given user interacting with the e-commerce platform, and to select, based on the identified event type, at least one of a plurality of machine learning models configured to characterize different types of potentially malicious behavior on the e-commerce platform. The processing device is also configured to determine, utilizing the selected at least one machine learning model, whether the given user is exhibiting at least one of the different types of potentially malicious behavior. The processing device is also configured, responsive to determining that the given user is exhibiting at least one of the different types of potentially malicious behavior, to initiate actions on the e-commerce platform to prevent or mitigate an effect of the potentially malicious behavior.

Abstract: A method, comprising: obtaining an activity signature for a first user of a retail website, the activity signature being based on one or more of navigation data for the first user of the retail website, purchase data for the first user of the retail website, and credit card usage data for the first user of the retail website; detecting whether the first user of the retail website is a reseller by classifying the activity signature for the first user with a neural network; and updating a configuration of a hosting system of the retail website when the first user of the retail website is detected to be a reseller.

Abstract: A management server includes linear regression circuitry to receive the data as input data, based on the input data, the linear regression circuitry generates a linear regression line for the input data. Gradient circuitry analyzes angles between two points of data within the input data. In response to a first angle between a first point of data and a second point of data being greater than a threshold angle, the gradient circuitry determines that an interval of data including the first and second points of data is an abrupt interval, removes first data within the abrupt interval from the input data, and updates the linear regression line accordingly. Support vector machine circuitry receives the input data, classifies outliers in the input data, and removes the outliers. The linear regression line is updated based on the removal of the outliers.

Abstract: A management server includes linear regression circuitry to receive the data as input data, based on the input data, the linear regression circuitry generates a linear regression line for the input data. Gradient circuitry analyzes angles between two points of data within the input data. In response to a first angle between a first point of data and a second point of data being greater than a threshold angle, the gradient circuitry determines that an interval of data including the first and second points of data is an abrupt interval, removes first data within the abrupt interval from the input data, and updates the linear regression line accordingly. Support vector machine circuitry receives the input data, classifies outliers in the input data, and removes the outliers. The linear regression line is updated based on the removal of the outliers.

Abstract: A process for separating a component from a low molecular weight organic mixture by pervaporation. The process uses fluorinated membranes and is particularly useful for treating mixtures containing light organic components, such as methane, propylene or n-butane.