Bank of America Corporation

MadhuSudhanan Krishnamoorthy

Ramaswamy M

Prasanna Soppimath

Sheirly Stephen Soans

Dimple Kapadia

Bhaswati Mitra

Oliver Sampson

Sreekanth Raghuveer Maringanti

Manmohan Singh

Nagar P Varadarajan

Sudhir Kumar Biswal

Clara Paul

Bhargavi Ponnapalli

Gayathri Jawahar

Sowjanya Kanthamneni

Deepika Sehgal

Embodiments of the invention are directed to a system, method, or computer program product for providing an intelligent quality accelerator with root mapping system. The system provides a business language model and process automation that stratifies sampling of resource exchanges from products using a machine learning loop and provides an end to end simulation for root cause analysis. In this way, the system provides two layers, a robust sampling and root cause analysis.

Intelligent quality accelerator with root mapping overlay

An Automated Teller Machine (ATM) for detecting security vulnerabilities by removing noise artifacts from documents receives a transaction request when a document is inserted into the ATM, where the document contains a noise artifact at least partially obstructing a portion of the document. The ATM generates an image of the document, where the image displays at least one data item comprising a sender's name, a receiver's name, and a number representing an amount. The ATM determines whether the noise artifact obstructs at least partially one data item. In response to determining that the noise artifact obstructs at least partially one data item, the ATM generates a test clean image of the document by removing the noise artifact from the image. In response to determining that the noise artifact is removed, the ATM approves the transaction request.

Automated teller machine for detecting security vulnerabilities based on document noise removal

An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

Hosted virtual desktop slicing using federated edge intelligence

A system for adapting an image noise removal model based on a device processing capability receives, from a computing device, a request to adapt an image noise removal module for the computing device. The system compares a processing capability of the computing device with a threshold processing capability. The system determines whether the processing capability is greater or smaller than the threshold processing capability. In response to determining that the processing capability is greater than the threshold processing capability, the system sends a version of the image noise removal module that is adapted for computing devices with processing capabilities less than the threshold processing capability, where the version of the image noise removal module is adapted to have a number of neural network layers less than a threshold number of neural network layers.

Adapting image noise removal model based on device capabilities

A system for removing a noise artifact from an image of a document extracts a first set of features from the image, where the first set of features represents items on the image. The system identifies noise artifact features from the first set of features representing pixel values of the noise artifact. The system generates a second set of features by removing the noise artifact features from the first set of features. The system generates a test clean image of the document based on the second set of features as an input. The system determines whether a portion of the test clean image that previously displayed the noise artifact corresponds to a counterpart portion of the training clean image. If it is determined that the portion of the test clean image corresponds to the counterpart portion of the training clean image, the system outputs the test clean image.

Image processing system and method for image noise removal

A system for implementing an application programming interface (API) service modification generates API sample requests based on content and metadata associated with historical API requests. The content associated with a historical API request may comprise data that is requested in the historical API request. The metadata may comprise a data format associated with the content. The system sends the API sample requests to an API hosting server and receives API responses. The system compares an API response with an expected API response, where the API response and the expected API response are associated with a particular task. The system determines whether the API response corresponds with the expected API response. If it is determined that the API response does not correspond with the expected API response, the system identifies a modification made to the API response and modifies future API requests associated with the particular task according to the modification.

System and method for an application programming interface (API) service modification

Embodiments of the invention are directed to systems, methods, and computer program products for integration of multiple technical architecture diagrams into a single diagram. Architectural diagrams are commonly used in program level impact analysis and may depict relationships, constraints, and boundaries between components of an individual software program as well as the relationships between multiple software programs, hardware components, and users. The present invention automatically identifies touchpoints, or connections between multiple architecture diagrams and applies a series of image-processing algorithms, generating a single integrated diagram for organizational use. Embodiments of the invention also provide for multiple rounds of image processing, allowing multiple integrated diagrams to be combined into single diagrams.

System for cognitive technical architecture integration

Systems, computer program products, and methods are described herein for testing application resiliency via context aware auditing equilibrium. The present invention is configured to receive an indication to test application resiliency of an application; initiate a resiliency test engine on the application; determine, using the resiliency test engine, one or more artifacts associated with the application to be tested; determine a first subset of the one or more test scripts is able to capture a behavior of a first subset of the one or more artifacts; initiate a context-based classification engine on the first subset of the one or more test scripts and the first subset of the one or more artifacts; classify the first subset of the one or more test scripts into a supporting behavior class and an opposing behavior class; generate a graphical representation of the classification; and display the graphical representation of the classification.

Application resiliency via context aware scriptless auditing equilibrium

An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to generate, based on a given role of a plurality of roles within an enterprise, a policy for a new user assigned to the given role. The policy includes recommendations of virtual desktop resources of an enterprise system to provide to the new user. The machine learning algorithm was trained using information identifying virtual desktop resources used by a set of existing users of the system, each of whom is assigned to a role of the plurality of roles within the enterprise. The processor receives a request to provide a new user with access to the enterprise system. In response, the processor implements the machine learning algorithm to generate the policy for the new user, and subsequently provisions the new user with the one or more virtual desktop resources recommended by the policy.

System and method for provisioning hosted virtual desktop resources to remote users

A device for removing a noise artifact from a document receives a scan of the document, where the document contains a noise artifact at least partially obstructing a portion of the document. The device generates an image of the document, and extracts a first set of features from the image. The device identifies noise artifact features from the first set of features, and generates a second set of features by removing the noise artifact features. The device generates a test clean image of the document based on the second set of features. The device determines whether a portion of the test clean image that previously displayed the noise artifact corresponds to a counterpart portion of the training clean image. If it is determined that the portion of the test clean image corresponds to the counterpart portion of the training clean image, the device outputs the test clean image.

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Tamil Nadu, India

MadhuSudhanan Krishnamoorthy