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: 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 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: 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.

Abstract: Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

Abstract: Aspects of the disclosure relate to annotated video content. A computing platform may train a model using video content, which may configure the model to classify topics and identify speakers of future video content using STT and NLP. The computing platform may input new video content into the model, which may output an annotated version of the new video content. The model may be configured to generate the annotated version of the new video content by: 1) using one or more STT techniques to produce STT results, 2) classifying topics based on the STT results, 3) identifying speakers based on analysis of voice information, and 4) generating an index indicating a speaker and/or a topic corresponding to time segments. The computing platform may store the annotated version of the new video content, which may be navigable by a user using the visual index.

Abstract: A system for performing optimization of hyper parameters in machine learning typically includes a classical computer apparatus and a quantum optimizer in communication with the classical computer apparatus. The classical computer apparatus is configured for gathering data sets associated with an application, identifying parameters associated with the application, constructing a machine learning model using the data sets and the parameters, determining conditions associated with optimizing the machine learning model, transmitting the machine learning model, the data sets, and the conditions to the quantum optimizer. The quantum optimizer computing a set of optimal hyperparameters for the machine learning model based on the data sets and the conditions and transmitting the set of optimal hyperparameters to the classical computer apparatus.

Abstract: Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.

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: Aspects of the disclosure relate to transliteration of machine interpretable languages. The 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 parameters from the query to create a query key; 2) storing the non-essential parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key; 4) based on identifying that the query library includes portions of the query key rather than the query key, recursively identify the translated query by nesting the portions of the query key; and 5) input the non-essential parameters into the translated query to create an output query. The computing platform may execute the output query on the second database.

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: 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: 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: 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: A model correction tool automatically detects and corrects model drift in a model for a machine learning application. To detect drift, the tool continuously monitors input data, outputs, and/or technical resources (e.g., processor, memory, network, and input/output resources) used to generate outputs. The tool analyzes changes to input data, outputs, and/or resource usage to determine when drift has occurred. When drift is determined to be occurring, the tool retrains a model for a machine learning application.

Abstract: A system retrains a natural language understanding (NLU) model by regularly analyzing electronic documents including web publications such as online newspapers, blogs, social media posts, etc. to understand how word and phrase usage is evolving. Generally, the system determines the frequency of words and phrases in the electronic documents and updates an NLU dictionary depending on whether certain words or phrases are being used more frequently or less frequently. This dictionary is then used to retrain the NLU model, which is then applied to predict the meaning of text or speech communicated by a people group. By analyzing electronic documents such as web publications, the system is able to stay up-to-date on the vocabulary of the people group and make correct predictions as the vocabulary changes (e.g., due to natural disaster). In this manner, the safety of the people is improved.