Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A work vehicle includes a cutter device for cutting plant in a field, a storage section for storing plant cut by the cutter device, an inclination angle sensor for detecting an inclination angle ((θd)) of the vehicle body, a display device for displaying the inclination angle detected by the inclination angle sensor, and a reporting device for reporting the inclination angle exceeding a permissible inclination angle ((θa)).

The present disclosure relates to a safety management system. The safety management system calculates a real-time data risk score and an incident data risk score based on real-time data received from a wearable device and incident data selected from big data, calculates a total risk score by summing all values obtained by multiplying calculated risk for respective data by weights for respective data, compares the total risk store with a preset threshold score, and transmits a dangerous situation message to a risk recognition subject when it is determined that a user is at risk. The safety management system of the present disclosure may transmit the real-time data, the incident data, and the dangerous situation message using a 5G communication system, and a safety management server for determining whether or not the user is at risk may be implemented using an artificial neural network.

Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

A method and apparatus for processing risk-management feature factors based on user generated content (UGC), an electronic device and a storage medium are disclosed, which relates to the fields of artificial intelligence and cloud computing. An implementation includes generating a feature expression of the UGC based on the UGC; and extracting the risk-management feature factors of the UGC according to a pre-generated risk-management-feature-factor extracting model and the feature expression of the UGC. According to the technology of the present application, the risk-management feature factors of a corresponding user may be extracted based on the UGC without depending on privacy information of the user, such as personal basic attributes, or the like, such that subsequent related processing actions of risk management may be facilitated, an acquiring way and an acquiring mode of the risk-management feature factors may be enriched effectively, and richer information of the risk-management feature factors may be acquired.

The present disclosure describes devices and methods monitoring a technology environment. In particular, a computing device including a processor with computer readable instructions to access a plurality of indicators (e.g., variables) that have corresponding stored historical information. The indicators are then used to calculate a summed weights table of relative risk for each time period in the past. The summed weights table is then correlated to a target variable (e.g., a variable that documents major issues, incidents, or disruptions that occurred in the technology environment in the past). The correlation coefficient between the summed weights table and the target variable is then used to implement a machine learning algorithm in order to better determine current risk levels (e.g., relative values that predict issues, incidents, or disruption).

Using digital unstructured text concerning entities to generate a prediction of a risk of a change in state of one of the entities. One method comprises compiling a training dataset from distinct sets of unstructured digitally stored electronic text documents; training machine learning classifiers using the training dataset, the machine learning classifiers comprising a tree-based random forest model and a generalized linear model corresponding to each of the distinct sets, each of the machine learning classifiers being configured to classify documents based upon digital features and to output a prediction value; obtaining an evaluation dataset comprising other unstructured electronic text documents that are not in the training dataset; executing the machine learning classifiers thereby outputting individual classification outputs, which can be blended to form a final risk index score value; generating user interface presentation instructions to display visualizations of the classification outputs and/or the final risk index score value.

Using digital unstructured text concerning entities to generate a prediction of a risk of a change in state of one of the entities. One method comprises compiling a training dataset from distinct sets of unstructured digitally stored electronic text documents; training machine learning classifiers using the training dataset, the machine learning classifiers comprising a tree-based random forest model and a generalized linear model corresponding to each of the distinct sets, each of the machine learning classifiers being configured to classify documents based upon digital features and to output a prediction value; obtaining an evaluation dataset comprising other unstructured electronic text documents that are not in the training dataset; executing the machine learning classifiers thereby outputting individual classification outputs, which can be blended to form a final risk index score value; generating user interface presentation instructions to display visualizations of the classification outputs and/or the final risk index score value.

Some embodiments of the present disclosure disclose methods and systems for assessing the data risk management capabilities of data processors that receive second-party data as part of an engagement to provide support services. In some embodiments, the transfer of the second-party data to the data processors can be monitored to identify file transfers including unauthorized personally identifiable information (PII) attributes. In some embodiments, the database of the data processor may be scanned to locate any residual second-party data that should be removed after the data processor's engagement to provide the support services have expired.