Accenture Global Solutions Limited

International Business Machines Corporation

Mykhaylo Zayats

Freddy Lecue

Jeremiah Patrick Hayes

Nicholas McCarthy

Qurrat Ul Ain

Chahrazed Bouhini

Benedikt Maximilian Johannes Golla

Sergiy Zhuk

Rahul Nair

Tigran Tchrakian

Luca Costabello

Jonathan P Epperlein

Seshu Tirupathi

Pol MacAonghusa

Jitesh Goyal

Ajitesh Prakash

John Emmett Mannion

Yvonne M Browne

Eilís Delany

Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Three-dimensional delineation of tumor boundaries via supervised machine learning algorithms and augmented reality

Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new surprising or unforeseen facts from relational learning applied to the knowledge graph. This discovery process takes advantage of the knowledge graph structure to improve the computing capabilities of a device executing a discovery calculation by applying both training and inference analysis techniques on the knowledge graph within an embedding space, and generating a scoring strategy for predicting surprising facts that may be discoverable from the knowledge graph.

System to predict surprising links in knowledge graphs

Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Compound discovery via information divergence with knowledge graphs

A device receives a request associated with standardizing organization-specific roles within an organization, where the request includes data that identifies titles for the organization-specific roles. The device converts the data to vectors that represent semantic meanings of the titles. The device sets a configuration of a data model by assigning weighted values to title-class identifiers that are used to associate titles, of a standardized set of titles, to a hierarchy of role classifications. The device uses the data model to determine scores that indicate likelihoods of the titles mapping to the title-class identifiers. The device identifies, based on scores, a subset of title-class identifiers that associate particular titles, of the standardized set of titles, and particular role classifications. The subset of title-class identifiers is stored in association with information relating to the particular titles. The device performs an action based on the information relating to the particular titles.

Identifying correlated roles using a system driven by a neural network

Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Density-based computation for information discovery in knowledge graphs

A device may determine an association between a second set of parameters and a third set of parameters using a pseudoinversion network and a multiple regression procedure. The device may determine semantic embeddings based on a set of semantic descriptions of the second set of parameters. The device may determine a semantic similarity between parameters of the second set of parameters based on the semantic embeddings. The device may determine a consistency error based on the semantic similarity. The device may generate, using a regression-based learning model technique, a matrix representing an association between the second set of parameters and the third set of parameters based on the association and the consistency error. The device may perform an action based on the matrix.

Data set filtering for machine learning

A device may receive organization data defining first capabilities of an organization and industry trend data that is relevant to the organization. The industry trend data may define second capabilities that are relevant to the organization. The device may provide, as input to a capability model, the organization data and the industry trend data. The capability model may have been trained to produce, as output, data specifying recommended changes for the organization. The device may determine, based on the output of the capability model and the industry trend data, a recommendation. The device may perform an action based on the recommendation.

Organization analysis platform for workforce recommendations

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Dublin, Ireland

Mykhaylo Zayats