Abstract: Methods, apparatus, and processor-readable storage media for localized anomaly detection in digital documents are provided herein. An example computer-implemented method includes identifying at least one image in a digital document; processing the identified at least one image using at least an image compression algorithm; applying a first machine learning model to the at least one processed image, wherein the first machine learning model is trained to detect whether the at least one image comprises one or more modifications; in response to detecting that the at least one image comprises at least one modification, applying a second machine learning model to identify a location in the at least one image corresponding to the at least one modification; and generating an indication that identifies the location of the at least one modification in the at least one image.

Abstract: Methods, apparatus, and processor-readable storage media for automated request processing using an ensemble machine learning framework are provided herein. An example computer-implemented method includes aggregating interaction data associated with a request; computing a weighted score for the request, wherein the weighted score comprises a first component that is based at least in part on a comparison of the aggregated interaction data to a set of keywords and a second component corresponding to a sentiment predicted by a first machine learning model for at least a portion of the aggregated interaction data; using a second machine learning model to determine whether the request is anomalous based at least in part on the weighted score; and in response to determining that the request is anomalous, initiating one or more automated actions for the request.

Abstract: Techniques described herein relate to a method for performing capacity planning services. The method includes obtaining a current CP state from a client; in response to obtaining the current state: selecting an action based on the current CP state; providing the action to the client, wherein the client performs the action; in response to providing the action: obtaining a new CP state and a headcount associated with the action; calculating a reward based on the headcount and a reward formula; storing the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets; and performing a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic.

Abstract: Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein.

Abstract: Techniques described herein relate to a method for generating address recommendations. The method includes obtaining, by a recommendation system, an address recommendation request associated with an address, wherein the address is associated with a user; in response to obtaining the address recommendation request: generating a context vector associated with the address; generating an address recommendation based on the context vector using a recommendation model; obtaining user feedback associated with the address recommendation; generating a reward based on the user feedback; and updating the recommendation model based on the context vector, the address recommendation and the reward.

Abstract: Techniques described herein relate to a method for performing knowledge extraction and noise removal for prediction models. The method includes obtaining, by a prediction system, live tabular data; in response to obtaining live tabular data: performing data preprocessing on the live tabular data to generate processed live tabular data; generating a knowledge vector based on the processed live tabular data using a dimensionality reduction model and a tabular attention model; generating a prediction using a prediction model and the knowledge vector; and providing the prediction to a client; wherein the client performs prediction processing using the prediction.

Abstract: Techniques described herein relate to a method for performing preprocessing of documents for data extraction. The method includes obtaining a document preprocessing request; in response to obtaining a document preprocessing request: obtaining a document associated with the document preprocessing request; performing data preparation on the document to generate an updated document; generating a document type prediction using the updated document and a document type prediction model; identifying a data extraction service of a plurality of data extraction services associated with the document type prediction; and initiating further processing of the document to perform data extraction using the identified data extraction service.

Abstract: Techniques described herein relate to a method for performing data extraction for documents. The method includes obtaining a data extraction request associated with a document; in response to obtaining the request: generating a data extraction prediction using a prediction model and the document; providing the data extraction prediction to a user; obtaining a user validation associated with the data extraction prediction; making a determination that the user validation indicates that the data extraction prediction is not correct; in response to the determination: generating an updated data extraction prediction based on the user validation; and initiating performance of additional document processing using the document based on the updated data extraction prediction.

Abstract: Techniques described herein relate to a method for identifying anomalies in compensation data. The method includes identifying a compensation data anomaly detection event; in response to identifying the compensation data anomaly detection event: obtaining compensation data associated with the compensation data anomaly detection event; performing preprocessing on the compensation data to generate updated compensation data; performing feature grouping on the updated compensation data to generate grouped compensation data; performing change discovery using the grouped compensation data to identify potential anomalies; generating a comparative anomaly prediction using the potential anomalies; and performing anomaly remediation actions based on the comparative anomaly prediction.

Abstract: Methods, apparatus, and processor-readable storage media for character-based representation learning for information extraction using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for multi-level time series forecasting using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining entity-related features and temporal features from at least a portion of one or more sets of time series data pertaining to at least one entity; creating multiple embeddings by encoding at least a portion of the entity-related features and at least a portion of the temporal features using at least one artificial intelligence-based embedding technique; processing the multiple embeddings using at least one neural network-based attention technique; generating one or more data forecasts across one or more temporal granularity levels by processing at least a portion of results from the processing of the multiple embeddings using at least one artificial intelligence-based categorization technique; and performing one or more automated actions based at least in part on the one or more data forecasts.

Abstract: Methods, apparatus, and processor-readable storage media for automated address data determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes extracting address information from one or more documents using at least one artificial intelligence-based address extraction model; parsing, into one or more address components, at least a portion of the extracted address information using at least one artificial intelligence-based parsing model; comparing at least a portion of the one or more parsed address components to stored address information using at least one artificial intelligence-based scoring model; and performing one or more automated actions based at least in part on results from the comparing of at least a portion of the one or more parsed address components to stored address information.

Abstract: Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automated communication data summarization using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying one or more words relevant to one or more predefined tasks by processing input communication data using a first set of one or more artificial intelligence techniques; extracting, based at least in part on the identifying of the one or more words, multiple portions of the input communication data into individual sub-portions of the input communication data; generating at least one summarization of the input communication data based at least in part on processing the sub-portions of the input communication data using a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one summarization of the input communication data.

Abstract: An information handling system is provided for extracting data associated with a data field of a database from a randomly formatted source document. A processor of the information handling system receives the document, embeds a text-based representation of text of the document into vector data associated with the text, and encodes the vector data through a first neural network into reconstructed text activations. The processor further concatenates the vector data with an image-based representation of the document to provide first concatenated data, and encodes the first concatenated data through a second neural network into extracted visual feature activations. The processor further concatenates the reconstructed text activations with the extracted visual feature activations to provide second concatenated data, decodes the second concatenated data to identify particular data associated with the data field, and updates an entry of the database with the particular data in the data field.

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.

Abstract: Systems and methods for detecting hardware faults in computer-based feedback control systems. Multiple instances of the system control program(s) are run on system processors. System sensor data are input to each instance, and the control commands output by each instance are compared. As instantiations of the same programs receive largely the same sensor data, differences between output commands may indicate the presence of one or more hardware faults.

Abstract: An information handling system is provided for extracting data associated with a data field of a database from a randomly formatted source document. A processor of the information handling system receives the document, embeds a text-based representation of text of the document into vector data associated with the text, and encodes the vector data through a first neural network into reconstructed text activations. The processor further concatenates the vector data with an image-based representation of the document to provide first concatenated data, and encodes the first concatenated data through a second neural network into extracted visual feature activations. The processor further concatenates the reconstructed text activations with the extracted visual feature activations to provide second concatenated data, decodes the second concatenated data to identify particular data associated with the data field, and updates an entry of the database with the particular data in the data field.

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.