Abstract: Systems, methods, and computer program products are provided for facilitating high frequency processing using stored models. A method for facilitating high frequency processing using stored models is provided. The method includes receiving a set of code relating to a machine learning model configured to process data. The method also includes generating a model executable file from the set of code relating to the machine learning model. The model executable file is configured to process inputted data using the machine learning model upon execution. The method still further includes storing the model executable file on an in-memory of a local device used to process inputted data.

Abstract: A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

Abstract: Customized DL anomaly detection models and generated and deployed on disparate edge devices. Configuration-related information is fetched from the edge devices and, based on the configuration/capabilities of the edge device, at least one primary deep learning-based anomaly detection model is selected, which are customized based on the configuration/capabilities of the edge device. Customization involves limiting the volume of the predictors/variables and optimizing the iterations used to determine anomalies and/or make predictions. The customized models are subsequently packaged in edge device-specific formats, such as a customized set of binaries in C language or the like. The resulting customized DL anomaly detection application is subsequently deployed to the edge device where it is executable without the need for specialized hardware or communication with network entities, such as cloud nodes or servers.

Abstract: Customized DL anomaly detection models and generated and deployed on disparate edge devices. Configuration-related information is fetched from the edge devices and, based on the configuration/capabilities of the edge device, at least one master deep learning-based anomaly detection model is selected, which are customized based on the configuration/capabilities of the edge device. Customization involves limiting the volume of the predictors/variables and optimizing the iterations used to determine anomalies and/or make predictions. The customized models are subsequently packaged in edge device-specific formats, such as a customized set of binaries in C language or the like. The resulting customized DL anomaly detection application is subsequently deployed to the edge device where it is executable without the need for specialized hardware or communication with network entities, such as cloud nodes or servers.

Abstract: A system for querying web pages to validate entity names is disclosed. The disclosed system receives a request for validating an entity name. The system identifies a search web link based on the information in the request. The system converts the request to a format appropriate for a search web page corresponding to the search web link. Then, the system sends the formatted request to the search web page via the search web link. The system receives a result web page and extracts a set of result entity names. The system then computes a hash value for the entity name in the request and each of the result entity names. Next, the system compares the hash value computed for the entity name in the request with hash values computed for each of the result entity names and determines a matching score for each of the result entity names based on the comparison of the hash values.

Abstract: A system for querying web pages to validate entity names is disclosed. The disclosed system receives a request for validating an entity name. The system identifies a search web link based on the information in the request. The system converts the request to a format appropriate for a search web page corresponding to the search web link. Then, the system sends the formatted request to the search web page via the search web link. The system receives a result web page and extracts a set of result entity names. The system then computes a hash value for the entity name in the request and each of the result entity names. Next, the system compares the hash value computed for the entity name in the request with hash values computed for each of the result entity names and determines a matching score for each of the result entity names based on the comparison of the hash values.

Abstract: A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.