公开(公告)号：US11511882B2

公开(公告)日：2022-11-29

申请号：US16898479

申请日：2020-06-11

Applicant: Qatar Foundation for Education, Science and Community Development ,  The Boeing Company

Inventor： Mohamed M. Elshrif ,  Sanjay Chawla ,  Franz D. Betz ,  Dragos D. Margineantu

IPC: G06G7/48 ,  B64D45/00 ,  G06N20/00 ,  G06F9/54 ,  G06F9/30

Abstract: A method for identifying aircraft faults, comprising: receiving a dataset comprising a plurality of low priority messages and a plurality of high priority messages, each low priority message identifying a minor aircraft fault and each high priority message identifying a major aircraft fault; for each low priority message, generating an embedding vector which maps the low priority message in an embedding space; for each high priority message, generating an embedding vector which maps the high priority message in the embedding space; providing, to a machine learning unit, the embedding vector for each low priority message of the plurality of low priority messages and the embedding vector for each high priority message of the plurality of high priority messages; and obtaining, from the machine learning unit, a probability of a target high priority message occurring based on each low priority message of the plurality of low priority messages.

公开(公告)号：US12084205B2

公开(公告)日：2024-09-10

申请号：US16898483

申请日：2020-06-11

Applicant: Qatar Foundation for Education, Science and Community Development ,  The Boeing Company

Inventor： Mohamed M. Elshrif ,  Sanjay Chawla ,  Franz D. Betz ,  Dragos D. Margineantu

IPC: B64F5/60 ,  B64F5/40 ,  B64D45/00

CPC classification number: B64F5/60 ,  B64F5/40 ,  B64D2045/0085

Abstract: A method for identifying aircraft faults, comprising: receiving aircraft health dataset comprising plurality of maintenance identifiers which each identify aircraft fault; storing diagnostics database storing plurality of part identifiers which each identify part of aircraft which is possible cause of generation of at least one maintenance identifier; generating graph of plurality of maintenance identifiers and plurality of edges in which maintenance identifiers are connected to one another by edge if maintenance identifiers are identified by common part identifier in diagnostics database; extracting clique from graph, clique comprising plurality of maintenance identifiers and respective plurality of edges of graph; determining intersection between at least two edges of clique; identifying candidate part identifier which is common to intersecting edges of clique, candidate part identifier identifying part of aircraft which is possible cause of generation of at least some of maintenance identifiers of clique; and outputting candidate part identifier.

公开(公告)号：US11748837B2

公开(公告)日：2023-09-05

申请号：US16866073

申请日：2020-05-04

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Stefano Rizzo ,  Ji Lucas ,  Zoi Kaoudi ,  Jorge-Arnulfo Quiane-Ruiz ,  Sanjay Chawla

IPC: G06Q50/28 ,  G06F16/23 ,  G06N20/00 ,  G06Q10/02 ,  G06N5/04 ,  G06Q10/04 ,  G06Q10/0631 ,  G06Q10/0637

CPC classification number: G06Q50/28 ,  G06F16/2379 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/02 ,  G06Q10/04 ,  G06Q10/06312 ,  G06Q10/06375

Abstract: The present disclosure provides a cargo revenue management system and method that increases the efficiency of cargo revenue management by increasing the prediction accuracy of cargo volumes that customers will tender in order to generate more efficient decisions to accept or reject cargo bookings. The provided system accomplishes this increased efficiency by identifying cargo volumes that customers arbitrarily book when an actual volume is unknown as disguised missing values and deemphasizing such values in the prediction of a cargo volume that will be received. The provided system additionally utilizes machine-learning models trained on a combination of features to predict a cargo volume that will be received for a particular cargo booking. Based on the predicted cargo volume that will be received, the system generates a decision of whether to accept or reject the cargo booking to maximize revenue generation.

公开(公告)号：US20220392567A1

公开(公告)日：2022-12-08

申请号：US17804408

申请日：2022-05-27

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Sanjay Chawla ,  Ehsan Ullah ,  Raghvendra Mall ,  Hossam Almeer ,  Abdurrahman Elbasir

IPC: G16B15/30 ,  G16B40/20 ,  A61K31/438 ,  G06N20/20

Abstract: A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.

公开(公告)号：US11823389B2

公开(公告)日：2023-11-21

申请号：US16723829

申请日：2019-12-20

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Sanjay Chawla

IPC: G06T7/12 ,  G06V20/10 ,  G06V10/82 ,  G06N3/084 ,  G06F16/29 ,  G01C21/00 ,  G06V10/764

CPC classification number: G06T7/12 ,  G01C21/3815 ,  G01C21/3852 ,  G06F16/29 ,  G06N3/084 ,  G06V10/764 ,  G06V10/82 ,  G06V20/182 ,  G06T2207/10032 ,  G06V20/194

Abstract: A method and system of constructing a network map from imagery comprising using an iterative search process guided by a CNN-based decision function to derive a network graph directly from the output of the CNN.

公开(公告)号：US20250111049A1

公开(公告)日：2025-04-03

申请号：US18903354

申请日：2024-10-01

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Issa M. Khalil ,  Ting Yu ,  Dorde Popovic ,  Mohammad Amin Sadeghi ,  Sanjay Chawla

IPC: G06F21/56

Abstract: Example systems, methods, and apparatus are disclosed herein for zero-shot black-box detection of neural Trojans.

公开(公告)号：US11288270B2

公开(公告)日：2022-03-29

申请号：US16407604

申请日：2019-05-09

Applicant: Qatar Foundation for Education, Science and Community Development ,  Hasso-Plattner-Institut fur Digital Engineering gGmbH

Inventor： Jorge Arnulfo Quiane Ruiz ,  Sebastian Kruse ,  Zoi Kaoudi ,  Sanjay Chawla ,  Bertty Contreras ,  Felix Naumann

IPC: G06F16/245 ,  G06F16/2453

Abstract: The present disclosure generally relates to a cost-based optimizer for efficiently processing data through the use of multiple different data processing platforms. The cost-based optimizer may receive an input plan for processing data that includes a number of base operators. The cost-based optimizer may then determine execution operators for each base operator, where each execution operator corresponds to a different data processing platform. From the execution operators, the cost-based optimizer may determine possible subplans for executing the input plan on one or more data processing platforms. The cost-based optimizer may determine the cost of executing each possible subplan and choose the subplan with the lowest cost.