Abstract: Various aspects of the disclosure relate to bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system may include bi-directional hopping to support multi language transpilation, automatic conversion of a mapping into a transformation specification, a hybrid feedback mechanism to update the transformation mappings, automatic scaling and/or creation of enterprise wide mapping and token (e.g., grammar) vocabulary, and/or a self-healing and/or corrective translation capability to perform automatic correction of any partial transpilations over time from a learned mapping.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a request to perform a data migration from a first database configured in a first format to a second database configured in a second format. The computing platform may receive, from the client application and at an abstraction layer, a query. Based on identifying that the query is formatted for execution at the second database, the computing platform may route the query to the second database for execution. Based on identifying that the query is not formatted for execution at the second database, the computing platform may: 1) translate the query from the first format to the second format by using pre-verified query keys to convert the query from the first format into the second format, and 2) route the translated query to the second database for execution.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform receive a source query formatted in a first format for execution on a source database. The computing platform may execute the source query on the source database to produce a first data result. The computing platform may input the first data result into a reversal logic engine to produce a target query formatted in a second format corresponding to a target database. The computing platform may execute the target query on the target database to produce a second data result. Based on identifying that the second data result matches the first data result, the computing platform may validate the target query. Based on identifying that the second data result does not match the first data result, the computing platform may adjust the reversal logic engine based on the discrepancy.

Abstract: A computing platform may train, using historical telemetry state images, an image comparison model to identify matches between telemetry state images. The computing platform may generate a plurality of system alerts corresponding to a period of time. The computing platform may access telemetry data corresponding to the period of time. The computing platform may generate, based on the telemetry data and for a time corresponding to each of the plurality of system alerts, a telemetry state image. The computing platform may input, into the image comparison model, the telemetry state images to identify whether or not any of the plurality of telemetry state images match. Based on detecting a match, the computing platform may consolidate system alerts corresponding to the matching telemetry state images, which may produce a single system alert and may send, to a user device, the single system alert.

Abstract: A computing platform may train a hybrid deep learning model, including a CNN and RNN, to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data, and may generate an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate an additional telemetry state image. The computing platform may classify, using the CNN and based on historical telemetry state images, the initial telemetry state image and the additional telemetry state image. The computing platform may identify, using the RNN and based on the classified telemetry state images and transitions between the classified telemetry state images, a matching pattern. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may cause modification of operations at the system to prevent a predicted failure.

Abstract: A computing platform may configure a rules-based state machine to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data. The computing platform may generate, based on the initial telemetry data, an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate, based on the additional telemetry data, an additional telemetry state image. The computing platform may compare a pattern, corresponding to the initial telemetry state image, the additional telemetry state image, and a corresponding transition, to historical patterns to identify a match. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may send, based on the likelihood of failure for the system, preemptive resolution commands causing modification of operations at the system to prevent a predicted failure.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may train a machine learning model using source syntax trees and target dialect syntax trees, which may configure the model to output source dialect keys and their corresponding target dialect queries. The computing platform may execute the corresponding target dialect queries to identify whether they are valid. For a valid target dialect query, the computing platform may store the valid target dialect query and first source dialect keys corresponding to the valid target dialect query in a lookup table. For an invalid target dialect query resulting in error, the computing platform may: 1) identify a cause of the error; 2) generate a transliteration rule to correct the error; and 3) store, in the lookup table, the invalid target dialect query, second source dialect keys corresponding to the invalid target dialect query, and the transliteration rule.

Abstract: Various aspects of the disclosure relate to bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system may include bi-directional hopping to support multi language transpilation, automatic conversion of a mapping into a transformation specification, a hybrid feedback mechanism to update the transformation mappings, automatic scaling and/or creation of enterprise wide mapping and token (e.g., grammar) vocabulary, and/or a self-healing and/or corrective translation capability to perform automatic correction of any partial transpilations over time from a learned mapping.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may configure a client application to use a custom driver when communicating with an enterprise database. The computing platform may receive a database query formatted in a first database format corresponding to a first database. The computing platform may translate, using a query translation library, the database query from the first database format into a second database format corresponding to a second database, which may cause the custom driver to execute a transliteration process using pre-verified query keys stored in the query translation library to convert the database query from the first database format into the second database format. The computing platform may execute the translated database query on the second database to obtain a query result, and may send the query result to the client application.

Abstract: Apparatus and methods for an opt-in distributed ledger consortium to detect and prevent fraudulent transactions are provided. Two or more entities may opt into a private distributed ledger. A program may receive financial transaction information. The program may record the transaction on the distributed ledger. The program may activate a smart application on the ledger. The smart application may analyze each transaction for indicators of illegal activity. When indicators of illegal activity are found within a transaction, the program may generate a report. The program may transmit the report to each entity that has opted into the private distributed ledger. The program may also record the report on the private distributed ledger.

Abstract: Apparatus and methods for automatically testing accessibility of programs and hardware are provided. A program may receive configuration settings, a test program, and a set of accessibility rules. The program may automatically create a virtual test environment according to the configuration settings. The program may automatically run the test program within the virtual test environment and analyze the test program against the accessibility rules. When the test program fails to meet one or more of the accessibility rules, the program may determine, create, and test potential fixes until the revised test program meets all of the accessibility rules. The program may generate and transmit a report describing the results of any test.

Abstract: Systems and methods for fraud prevention in a blockchain-based digital transactional system with machine-learning (ML)-powered rule generation are provided. Methods may include creating a distributed ledger in which digital blocks may include foundational transactional parameter rules, and digital blocks may include historical transactional data. Methods may include hosting ML models on the nodes, running each ML model to generate new transactional parameter rules, and adding the new transactional parameter rule as a digital block on the distributed ledger in response to a consensus. Methods may include receiving additional transactional data, running each ML model to generate a score representing a probability that the additional transactional data is associated with fraudulent activity, and triggering an alert for an account associated with the additional transactional data in response to a consensus across the plurality of ML models that the score exceeds a predetermined threshold score.

Abstract: Systems, computer program products, and methods are described herein for analyzing system health of individual electronic components using component relational graphs. The method includes receiving a process request. The process request is a request to execute a process. The method also includes determining one or more components of the system used during the process. The method further includes generating a component knowledge graph for the process. The component knowledge graph includes one or more nodes corresponding to each of one or more components used during the process. The method still further includes determining a component health rating for each of the one or more components used in the process.

Abstract: Embodiments of the present invention provide a system for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies. The system is configured for extracting one or more historical images associated with resiliency status of electronic and software components associated with an entity application, analyzing the one or more historical images to generate a pixel wise average of the one or more historical images, generating similarity scores between the one or more historical images, determining a distribution of the similarity scores, receiving a real-time image associated with a current resiliency status of the electronic and software components associated with the entity application, generating a real-time image similarity score for the real-time image, and comparing the real-time image similarity score with the distribution to detect presence of an anomaly.

Abstract: Systems, computer program products, and methods are described herein for analyzing system health of individual electronic components using image mapping. The method includes receiving a component health image for a component based on an execution of a process. The method also includes comparing the component health image based on the execution of the process to previous component health image(s) for the component based on one or more previous executions of the process The method further includes determining a component health image similarity score based on the comparison of the component health image to the one or more previous component health images for the component. The method still further includes determining a component health action based on the component health image similarity score. The component health action includes causing a transmission of an alert in an instance in which the component health image similarity score is outside of a threshold range.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Abstract: Systems, computer program products, and methods are described herein for performance monitoring using aggregated telemetry. The present disclosure is configured to receive, from the first performance monitoring engine, a first metadata associated with the first resiliency status; receive, from the second performance monitoring engine, a second metadata associated with the second resiliency status; determine, using a machine learning (ML) subsystem, an overall resiliency status of the device based on at least the first metadata, the second metadata, the first resiliency status, and the second resiliency status; determine one or more actions to be executed on the device, wherein the one or more actions are associated with the overall resiliency status; generate a notification indicating the overall resiliency status of the device and the one or more actions associated with the overall resiliency status; and transmit control signals configured to cause a user input device to display the notification.

Abstract: Various aspects of the disclosure relate to bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system may include bi-directional hopping to support multi language transpilation, automatic conversion of a mapping into a transformation specification, a hybrid feedback mechanism to update the transformation mappings, automatic scaling and/or creation of enterprise wide mapping and token (e.g., grammar) vocabulary, and/or a self-healing and/or corrective translation capability to perform automatic correction of any partial transpilations over time from a learned mapping.

Abstract: Systems, computer program products, and methods are described herein for real-time overload detection using image processing analysis. The present disclosure is configured to receive a first set of images associated with a device, wherein the one or more images are associated with a resiliency status of the device; deploy, using a machine learning (ML) subsystem, a trained ML model on the first set of images of the device; determine, using the trained ML model, a change in the resiliency status of the device based on the first set of images; and generate a notification indicating the change in the resiliency status of the device; and transmit control signals configured to cause a first user input device to display the notification.

Abstract: Systems, computer program products, and methods are described herein for providing authentication across a distributed resource network. The method includes receiving an authentication request associated with a user. The authentication request includes one or more potential physical characteristics. The method also includes identifying a non-fungible token associated with the user. The non-fungible token associated with the user includes one or more stored physical characteristic of the user. The method further includes comparing the one or more potential physical characteristics of the authentication request with the one or more stored physical characteristics of the user. The method still further includes generating an access determination for the authentication request based on the comparison of the one or more potential physical characteristics of the authentication request with the one or more stored physical characteristics of the user.