Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.

Abstract: Techniques relate to efficiently encoding data. A data set in a first specific format is received. The data set is transformed into data-string values in a second specific format, where the data-string values includes a set of data-string values, wherein each of the set of data-string values is represented by an intensity for each of one or more channels. A digitally encoded image is generated by transforming the data-string data into an image comprising a pixel value for each of a set of pixels, wherein the pixel values for the set of pixels have at least three different intensities and/or at least three different colors relative to each other. The transformation may include generating the data set or a representation thereof in a hexadecimal format. The digitally encoded image is output.

Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.

Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.

Abstract: Methods, systems, and computer-readable media are disclosed herein for an application that optimizes the design element and workflow configuration of a target computer program. Generally, the application automatically displays preview images of graphical user interface in response to a user-input answer in a questionnaire, where the preview image accounts for design element and/or workflow impacts to the graphical user interface of the target computer program. The application also predicts performance indicators for target computer programs that account for design elements and/or workflow steps directly or indirectly selected through the user-input answers.

Abstract: Methods, systems, and computer-readable media are disclosed herein for an application that optimizes the design element and workflow configuration of a target computer program. Generally, the application automatically displays preview images of graphical user interface in response to a user-input answer in a questionnaire, where the preview image accounts for design element and/or workflow impacts to the graphical user interface of the target computer program. The application also predicts performance indicators for target computer program that account for design element and/or workflow steps directly or indirectly selected through the user-input answers.

Abstract: Methods, systems, and computer-readable media are disclosed herein for an application that optimizes the design element and workflow configuration of a target computer program. Generally, the application automatically displays preview images of graphical user interface in response to a user-input answer in a questionnaire, where the preview image accounts for design element and/or workflow impacts to the graphical user interface of the target computer program. The application also predicts performance indicators for target computer program that account for design element and/or workflow steps directly or indirectly selected through the user-input answers.

Abstract: Methods, systems, and computer-readable media are disclosed herein for an application that optimizes the design element and workflow configuration of a target computer program. Generally, the application automatically displays preview images of graphical user interface in response to a user-input answer in a questionnaire, where the preview image accounts for design element and/or workflow impacts to the graphical user interface of the target computer program. The application also predicts performance indicators for target computer program that account for design element and/or workflow steps directly or indirectly selected through the user-input answers.

Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.