Abstract: Methods, apparatus, and processor-readable storage media for a neural network-based message communication framework with summarization and on-demand audio output generation are provided herein.

Abstract: A method comprises receiving logistics operation order data, wherein the logistics operation order data identifies at least one logistics operation to be performed. The logistics operation order data is analyzed using one or more machine learning algorithms. Based at least in part on the analyzing, a logistics provider to perform the at least one logistics operation is predicted.

Abstract: A method comprises receiving event-based data, extracting one or more attributes from the event-based data, and analyzing the one or more attributes to classify whether the one or more attributes comprise personally identifiable information. The analyzing is performed using one or more machine learning models. The event-based data corresponds to one or more events where the one or more attributes are added to at least one of a database and an application.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving touchpoint data of a product and creating a data element based on the retrieved touchpoint data, the data element includes information about a touchpoint of the product by a stakeholder. The method also includes, by the computing device, adding the created data element to a product pedigree list, wherein the product pedigree list is a pedigree list which contains data elements of the product. The method may further include, by the computing device, storing the product pedigree list in a graph database. The method may further include, by the computing device, predicting, using a machine learning (ML) model (e.g., an autoencoder), whether a state of the product pedigree list is normal or anomalous.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device recycling opportunities using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data associated with one or more devices; determining end of life-related information for the one or more devices by processing at least a portion of the obtained data; predicting at least one device recycling opportunity for at least one of the one or more devices by processing at least a portion of the determined end of life-related information using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one predicted device recycling opportunity.

Abstract: Methods, apparatus, and processor-readable storage media for implementing an automated data archival framework using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data associated with one or more storage systems; determining one or more storage-related features within the obtained data by processing at least a portion of the obtained data; predicting at least one data archival class, from a set of multiple predetermined data archival classes, for at least a portion of the obtained data by processing the one or more storage-related features using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one predicted data archival class.

Abstract: A method comprises receiving a request to predict a type and a quantity of respective ones of a plurality of resources for a computing environment. Using a multiple output classification and regression machine learning model, the type and the quantity of the respective ones of the plurality of resources are predicted in response to the request. The machine learning model is trained with a dataset comprising historical resource data corresponding to respective ones of a plurality of users.

Abstract: A method comprises extracting first task data from a first data source corresponding to a first application and second task data from a second data source corresponding to a second application, and comparing the first task data to the second task data using one or more natural language processing techniques. In the method, one or more matching tasks between the first task data and the second task data are identified based at least in part on the comparing. Code of at least one of the first application and the second application is analyzed to determine whether the code of at least one of the first application and the second application implements the one or more matching tasks.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting fail-over of message-oriented middleware systems are provided herein. An example computer-implemented method includes obtaining one or more message-oriented middleware parameter values for at least a portion of multiple message-oriented middleware systems; detecting one or more fail-over-related anomalies associated with at least one of the multiple message-oriented middleware systems by processing at least a portion of the one or more message-oriented middleware parameter values using one or more machine learning techniques; and automatically migrating, based at least in part on the one or more detected fail-over-related anomalies, at least a portion of data associated with the at least one message-oriented middleware system associated with the one or more detected fail-over-related anomalies to at least one of the other of the multiple message-oriented middleware systems.

Abstract: A method comprises receiving work order data, wherein the work order data identifies at least one technical support issue requiring resolution. The work order data is analyzed using one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing, whether the at least one technical support issue will be resolved at one or more respective service locations of a plurality of service locations. Based at least in part on the predicting, a recommendation to respond to the at least one technical support issue at a given service location of the plurality of service locations is generated.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting dispatch-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data, from one or more user channels, pertaining to at least one issue; determining, based at least in part on the obtained data, that at least one dispatch is to be carried out in connection with attempting to resolve the at least one issue; predicting, by processing at least a portion of the obtained data using one or more machine learning techniques, an approval mode associated with the at least one dispatch and at least one outcome associated with the at least one dispatch; and performing one or more automated actions based at least in part on one or more of the predicted approval mode and the at least one predicted outcome.

Abstract: Methods, apparatus, and processor-readable storage media for security-based image classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining one or more images; extracting text from at least a portion of the one or more images by processing the one or more images using at least a first set of one or more artificial intelligence techniques; classifying at least one of the one or more images, into one or more of multiple security-based classification categories, by processing at least a portion of the extracted text using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one of the one or more images.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new sales opportunity from another computing device and determining one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of an opportunity outcome of the new sales opportunity and a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features. The method may also include, by the computing device, sending the first and second predictions to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new lead from another computing device and determining one or more relevant features from the information regarding the new lead, the one or more relevant features influencing prediction of a lead conversion. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features based on the determined one or more relevant features and sending the prediction to the another computing device.

Abstract: Methods, apparatus, and processor-readable storage media for automatically summarizing event-related data using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining text-based data and non-text-based data associated with at least one virtual event comprising one or more participants; generating a content-related summarization of one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a first set of one or more artificial intelligence techniques; generating a participant sentiment-related summarization associated with one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on one or more of the content-related summarization and the participant sentiment-related summarization.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a set of requirements for a database and generating a feature vector representative of the set of requirements for the database. The method also includes, by the computing device, predicting, using a machine learning (ML) model, a database for the set of requirements based on the feature vector and sending information indicative of the predicted database to a client. The predicted database may be a database that is optimal for the received set of requirements. The ML model may be a multiclass classification model.

Abstract: A method comprises receiving product selection data, wherein the product selection data characterizes at least one combination of at least two products. In the method, the product selection data is analyzed using one or more machine learning algorithms. The method further comprises predicting based, at least in part, on the analyzing, whether the at least one combination is anomalous. One or more alerts are generated in response to predicting that the at least one combination is anomalous.

Abstract: Methods, apparatus, and processor-readable storage media for automated database operation classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query related to one or more database operations; obtaining information pertaining to a set of multiple storage resources; classifying the at least one query as associated with at least one of multiple subsets of storage resources among the set of multiple storage resources by processing at least a portion of the query and at least a portion of the information pertaining to the set of multiple storage resources using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one query.