Abstract: Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI.

Abstract: Aspects of the disclosure relate to generating a pseudo-code from a text summarization based on a convolutional neural network. A computing platform may receive, by a computing device, a first document comprising text in a natural language different from English. Subsequently, the computing platform may translate, based on a neural machine translation model, the first document to a second document comprising text in English. Then, the computing platform may generate an attention-based convolutional neural network (CNN) for the second document. Then, the computing platform may extract, by applying the attention-based CNN, an abstractive summary of the second document. Subsequently, the computing platform may generate, based on the abstractive summary, a flowchart. Then, the computing platform may generate, based on the flowchart, a pseudo-code. Subsequently, the computing platform may display, via an interactive graphical user interface, the flowchart, and the pseudo-code.

Abstract: Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI.

Abstract: Aspects of the disclosure relate to generating a pseudo-code from a text summarization based on a convolutional neural network. A computing platform may receive, by a computing device, a first document comprising text in a natural language different from English. Subsequently, the computing platform may translate, based on a neural machine translation model, the first document to a second document comprising text in English. Then, the computing platform may generate an attention-based convolutional neural network (CNN) for the second document. Then, the computing platform may extract, by applying the attention-based CNN, an abstractive summary of the second document. Subsequently, the computing platform may generate, based on the abstractive summary, a flowchart. Then, the computing platform may generate, based on the flowchart, a pseudo-code. Subsequently, the computing platform may display, via an interactive graphical user interface, the flowchart, and the pseudo-code.