Abstract: An artificial intelligence (AI) automation to improve network quality based on predicted locations is provided. A method can include training, by a first device comprising a processor and according to model configuration parameters received from a second device that is not the first device, a local machine learning model with training data derived from first location data collected by the first device; transmitting, by the first device to the second device, anonymized model features associated with the local machine learning model; in response to the transmitting of the anonymized model features, receiving, by the first device from the second device, an aggregated machine learning model; and estimating, by the first device, a future position of the first device by applying the aggregated machine learning model to second location data collected by the first device.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A method, computer-readable medium, and apparatus for providing customer care for customers are disclosed.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for sentiment analysis tuning. In one example, the method, includes acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words, calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text, acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word, and calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for sentiment analysis tuning. In one example, the method, includes acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words, calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text, acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word, and calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word.

Abstract: Concepts and technologies disclosed herein are directed to systems, methods, and computer-readable storage media for designing, creating, and deploying composite machine learning applications in cloud environments. According to one aspect disclosed herein, a system, including a processor and memory, can present a design studio canvas upon which a user can design a composite machine learning application from at least one of a plurality of building blocks stored in a design studio catalog. The system can receive input to design, on the design studio canvas, a visual representation of the composite machine learning application. The system can save the visual representation of the composite machine learning application, and, in response to saving the visual representation of the composite machine learning application, can generate a composition dump file that includes a graph structure of the composite machine learning application.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for sentiment analysis tuning. In one example, the method, includes acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words, calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text, acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word, and calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise. A network server stores health data that has been gathered from various sources. A user can visualize the data on a client computer networked to the server by downloading from the server user specified sets of health-related data based on a case or set of anomalies describing a subset of the data, and source code for visualizing the data.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: An apparatus and method for qualifying a filter used to filter fluid used in a coating operation associated with photolithography or other semiconductor manufacturing processes, provides a semiconductor manufacturing tool that includes a filter and an acoustic wave generator. The filter may be housed inside a filter housing and the acoustic wave generator may produce ultrasonic, megasonic or other acoustic energy. The acoustic wave generator contacts or is in close proximity with the filter housing and provides acoustic wave energy to the filter through the housing. The acoustic wave energy causes any bubbles in the filter to become disengaged.

Abstract: A rotary turbine cleaning device for cleaning semiconductor fabrication equipment works in conjunction with a clean room vacuum or other vacuum or other air pump. The fluid flow created by the vacuum action causes the rotors of the turbine assembly to rotate, thereby rotating the cleaning head. Attached to the cleaning head are bristles or other cleaning media which may dislodge particles from surfaces. The dislodged particles are drawn into the tube through an opening at the end of the tube and the vacuum action. In some embodiments, a lumen delivers a cleaning fluid to the cleaning head.