Abstract: A computing system configured to generate empathy-based machine-learning outputs, which, for example, can include notifications, automatic service delivery, payments, among others. The system receives as inputs a first set of data sets representative of historical behaviour through tracked interactions, a second set of data sets representative of circumstantial knowledge (e.g., environmental factors, such as weather), and a set of empathy model weights from one or more machine learning models that are configured to model one or more empathy consideration components (e.g., curiosity, preconceptions, inspirations, direct experiences, listened experiences, imagination, among others). Corresponding methods and non-transitory computer readable media are contemplated.

Abstract: A computer-implemented system and method for detecting a boundary in an image are provided. The system includes at least one processor and memory in communication with said at least one processor, wherein the memory stores instructions, when executed at said at least one processor, cause said system to: receive or access a first image comprising a first polygon structure; generate, using a data model representing a neural network, a second image based on the first image by splitting the first polygon structure in the first image, wherein the second image comprises a first portion and a second portion partitioned by a line across the first polygon structure; and generate, based on the second image, a geo-image comprising corresponding spatial-reference information for one or more pixels in the geo-image, the geo-image comprising one of the first portion and the second portion in the second image.

Abstract: A machine learning architecture is proposed that is directed to receive different time-series data sets relating to environmental conditions as well as a target variable for prediction and to transform the time-series data sets for training a plurality of different machine learning models. The trained machine learning models can be utilized to probe various configurations of environmental conditions, and in some embodiments, conduct first and second order co-efficient of variation determinations to generate one or more data values representative of environmental condition sensitivity metrics.

Abstract: Described in various embodiments herein is a technical solution directed to training downstream machine learning models. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve data security during training downstream machine learning models, including decreasing the risk of unauthorized access of training data, decreasing the risk of unauthorized use of training data by authorized users, increasing system systemic speed, and reduced overall computational resource requirements. Training data is manipulated prior to being provided for training machine learning models.