Abstract: One embodiment provides a method for clustering documents based upon a structure of each of the documents, including: receiving, at a device utilizing the machine-learning model, at least one document, each including a plurality of characters and having a structure; converting, for each of the at least one document, each of the plurality of characters to one of a plurality of character representations, wherein the converting includes identifying an attribute of a character and selecting a character representation corresponding to the attribute; producing at least one array for each of the one or more documents, wherein the at least one array includes the plurality of characters converted to the character representations; and clustering the at least one document into document clusters having similar structures by grouping the at least one arrays into groups of arrays having similarities, wherein each document cluster include documents corresponding to the arrays within one of the groups of arrays.

Abstract: One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Abstract: One embodiment provides a method, including: obtaining, using a processor, voice data; obtaining, using a processor, geographic location data; identifying, based on the geographic location data, a language model; and generating, using the language model, a textual representation of the voice data. Other aspects are described and claimed.