Accenture Global Solutions Limited

General Electric Company

Accenture Global Services Gmbh

Kamal Mannar

Uvaraj Balasundaram

Tau Herng Lim

Senthil Ramani

Andrew Kjell Musselman

Chun Wei Wu

Devanandan Subbarayalu

Fransisca Fortunata

Manik Bhandari

Julian Addison Anthony Samy

Andrea Huanhuan Wang

Yibin Zheng

Aleksandar Vukojevic

Borka Milosevic

Steven J Huff

Vicknesh Manoselvam

Sherly Cendana Koalitas

Douglas E Starasinich

Thanh Hai Le

Boon Siew Seah

Saurabh Mangal

Implementations include receiving flow data representative of communication traffic of the network, determining that at least one blacklisted Internet protocol (IP) address is present in the flow data, and in response: providing a set of high-dimensional flow representations of network traffic by processing historical flow data through a deep learning (DL) model, providing a set of low-dimensional flow representations of the network traffic based on the set of high-dimensional flow representations, and labeling at least a portion of the set of low-dimensional flow representations to provide a sub-set of labeled low-dimensional flow representations and a sub-set of unlabeled low-dimensional flow representations, and identifying a host associated with an unlabeled low-dimensional flow representation as a potentially malicious host, and in response, automatically executing a remedial action with respect to the potentially malicious host.

Artificial intelligence (AI)-based malware detection

In some examples, dark web content analysis and identification may include ascertaining data that includes text and images, and analyzing the data by performing deep learning based text and image processing to extract text embedded in the images, and deep embedded clustering to generate clusters. Clusters that are to be monitored may be ascertained from the generated clusters. A determination may be made as to whether the ascertained data is sufficient for classification. If so, a deep convolutional generative adversarial networks (DCGAN) based detector may be utilized to analyze further data with respect to the ascertained clusters, and alternatively, a convolutional neural network (CNN) based detector may be utilized to analyze the further data with respect to the ascertained clusters. Based on the analysis of the further data, an operation associated with a website related to the further data may be controlled.

Dark web content analysis and identification

In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.

Artificial intelligence based plantable blank spot detection

A system and method to detect fraudulent documents is disclosed. The system uses a generative adversarial network to generate synthetic document data including new fraud patterns. The synthetic document data is used to train a fraud classifier to detect potentially fraudulent documents as part of a document validation workflow. The method includes extracting document features from sample data corresponding to target regions of the documents, such as logo regions and watermark regions. The method may include updating a cost function of the generators to reduce the tendency of the system to generate repeated fraud patterns.

System and method for detecting fraudulent documents

A device may receive one or more images captured by an image capture system. The one or more images may depict one or more objects. The device may process the one or more images using one or more image processing techniques. The device may identify the one or more objects based on processing the one or more images. The device may identify a context of the one or more images based on the one or more objects depicted in the one or more images. The device may determine whether the one or more objects contribute to a value of one or more metrics associated with the context. The device may perform an action based on the value of the one or more metrics.

Processing an image to identify a metric associated with the image and/or to determine a value for the metric

According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Inventory, growth, and risk prediction using image processing

A power distribution system is provided, including at least one capacitor bank with a capacitor bank controller, a transformer, at least one voltage regulating device with a voltage regulating device controller, and a controller. The capacitor bank is selectively connected to the feeder and a capacitor bank controller. The capacitor bank controller controls a switch for selectively connecting the capacitor bank to the feeder. The transformer delivers power to the power distribution system through the feeder. The transformer converts a transmission or a sub-transmission voltage into a distribution voltage. The controller is in communication with the capacitor bank controller, the voltage regulating device, and the transformer. The controller selectively switches the at least one capacitor bank to adjust voltage in the feeder. The controller selectively sends commands to the voltage regulating device to change a source voltage.

Power distribution system

A system for acquiring and analyzing fault conditions for at least one coil of Magnetic Resonance Imaging (MRI) system. Each of the at least one coils is electrically connected through a transmit/receive (T/R) bias circuit to an interface. The system has a central processing unit with a processor configured to execute programmable instructions which when executed by the processor cause the processor to conduct circuit tests at predetermined intervals for the at least one coil, acquire data for the at least one coil to construct a data log with a plurality of input events, and algorithmically filter and analyze the plurality of input events to create an output configured to predict a failure event of the at least one coil. A method for analyzing and acquiring fault conditions is also provided.

Method and system for acquiring and analyzing coil data

A system and method to facilitate device monitoring and servicing is provided. In one embodiment, a system may include a medical device having at least one component, and monitoring circuitry configured to measure operational data of the component. The system can also include a data processing system configured to analyze the operational data and to output a report based on such analysis. The analysis, in turn, may include applying a transform to the operational data and comparing one or more actual coefficient and threshold coefficient characteristics.

Medical equipment monitoring method and system

A system and method to predict a failure of an imaging system that includes a radiation source having an x-ray tube assembly is provided. The system includes a storage medium having a plurality of programmable storage instructions to instruct a processor to perform the steps of acquiring an age of the x-ray tube assembly, calculating a baseline probability of a survivability of the tube assembly for a remaining time period dependent on the age of the tube assembly, acquiring measurement of at least one operating parameter of the x-ray tube assembly, and automatically changing the baseline probability of a survivability of the imaging system for the remaining time period in response to the measurement of the at least one operating parameter of the x-ray tube assembly.

Growing community of inventors

Singapore, Singapore

Kamal Mannar