Abstract: A foreign material exclusion system for positioning within a casing of a turbine to catch any foreign material during maintenance of the turbine. The foreign material exclusion system may include a fabric sleeve with a semicircular shape and a number of poles attached to the sleeve to maintain the sleeve in place within the turbine casing.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: A foreign material exclusion system for positioning within a casing of a turbine to catch any foreign material during maintenance of the turbine. The foreign material exclusion system may include a fabric sleeve with a semicircular shape and a number of poles attached to the sleeve to maintain the sleeve in place within the turbine casing.

Abstract: Methods and systems for rule-based machine learning for precise fraud detection. One system includes an electronic processor configured to determine, via a decision tree, a first subset of datasets of an aggregate dataset collection generated using a rule-based model. The electronic processor is also configured to select a third collection of datasets, each dataset included in the third collection of datasets associated with a user characteristic associated with fraud. The electronic processor is also configured to determine, via the decision tree, a second subset of datasets of the third collection of datasets, each dataset included in the second subset of datasets associated with a second set of user characteristics associated with fraud. The electronic processor is also configured to, in response to determining that an accuracy score associated with the second set of user characteristics satisfies a threshold, generate and transmit a report for display.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: Methods and systems for rule-based machine learning for precise fraud detection. One system includes an electronic processor configured to determine, via a decision tree, a first subset of datasets of an aggregate dataset collection generated using a rule-based model. The electronic processor is also configured to select a third collection of datasets, each dataset included in the third collection of datasets associated with a user characteristic associated with fraud. The electronic processor is also configured to determine, via the decision tree, a second subset of datasets of the third collection of datasets, each dataset included in the second subset of datasets associated with a second set of user characteristics associated with fraud. The electronic processor is also configured to, in response to determining that an accuracy score associated with the second set of user characteristics satisfies a threshold, generate and transmit a report for display.

Abstract: A system, method and computer-readable medium for recommending signals to monitor in a fraud prevention application, including a server including an electronic processor. The electronic processor is configured to calculate a similarity score for one or more clients, and, for one or more signals, determine, for each of the one or more clients, a signal weight associated with the signal. The electronic processor is also configured to multiply the signal weight by the similarity score associated with the client to generate a weighted similarity score and generate a total similarity score and, for each signal, a total weighted similarity score. The electronic processor is configured to, for each signal, generate a recommendation score based on the total weighted similarity score for the signal and the total similarity score for the signal and, based on the recommendation scores, generate one or more recommended signals for a first client.

Abstract: A system, method and computer-readable medium for recommending signals to monitor in a fraud prevention application, including a server including an electronic processor. The electronic processor is configured to calculate a similarity score for one or more clients, and, for one or more signals, determine, for each of the one or more clients, a signal weight associated with the signal. The electronic processor is also configured to multiply the signal weight by the similarity score associated with the client to generate a weighted similarity score and generate a total similarity score and, for each signal, a total weighted similarity score. The electronic processor is configured to, for each signal, generate a recommendation score based on the total weighted similarity score for the signal and the total similarity score for the signal and, based on the recommendation scores, generate one or more recommended signals for a first client.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.

Abstract: A search engine at a service provider is configured to identify commercial suppliers in response to a keyword specified by a user. Keywords are received and searches are performed on an index database to identify displayable text items. Images are stored in the form of commercially relevant graphical images such as logos. A logo is linked to each item of displayable text and an output list is produced in which each text item includes a displayable graphical image. The system may store different image formats to allow the graphical images to be displayed on a variety of platforms.