Abstract: A computerized method is disclosed including operations of receiving a data stream, performing a changepoint detection resulting in a detection of changepoints in the data stream including: maintaining a listing of starting indices for each run within the data stream in a buffer of size L wherein each index of the listing has a run length probability representing a likelihood of being a changepoint, receiving a new data point within the data stream and adding a new index to the buffer resulting in the buffer having size L+1, calculating a posterior run length probability that the new data point is a changepoint, and removing an index from the listing that has a lowest run length probability thereby returning the buffer to size L, and responsive to determining the index removed from the listing does not correspond to the new data point, identifying a changepoint associated with the new data point.

Abstract: A computerized method is disclosed for generating a prioritized listing of alerts based on scoring by a machine learning model and retraining the model based on user feedback. Operations of the method include receiving a plurality of alerts, generating a score for each of the plurality of alerts through evaluation of each of the plurality of alerts by a machine learning model, generating a prioritized listing of the plurality of alerts based on the generated scores, receiving user feedback on the prioritized listing, retraining the machine learning model based on the user feedback by generating a set of labeled alert pairs, wherein a labeled alert pair includes a first alert, a second alert, and an indication as to which of the first alert or the second alert is a higher priority in accordance with the user feedback, and evaluating subsequently received alerts with the retrained machine learning model.

Abstract: The disclosed embodiments illustrate methods and systems for creating a classifier for predicting a personality type of users. The method includes receiving a first tag for messages, from a crowdsourcing platform. The first tag relates to personality type of users. Further, the messages, tagged with first tag are segregated into a training data and a testing data. Further, parameters associated with set of messages in the training data are determined based on type of messages. Further, classifiers are trained for a personality type. Further, a second tag for set of messages in testing data is predicted using trained classifiers for a combination of parameters. A performance of classifiers is determined by comparing the second tag and the first tag associated with set of messages in the testing data. A classifier is selected from classifiers, which is indicative of a best combination of parameters to predict personality type of users.

Abstract: The disclosed embodiments illustrate methods and systems for creating a classifier for predicting a personality type of users. The method includes receiving a first tag for messages, from a crowdsourcing platform. The first tag relates to personality type of users. Further, the messages, tagged with first tag are segregated into a training data and a testing data. Further, parameters associated with set of messages in the training data are determined based on type of messages. Further, classifiers are trained for a personality type. Further, a second tag for set of messages in testing data is predicted using trained classifiers for a combination of parameters. A performance of classifiers is determined by comparing the second tag and the first tag associated with set of messages in the testing data. A classifier is selected from classifiers, which is indicative of a best combination of parameters to predict personality type of users.