Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: An identification and management system for log entries may filter historical data and generate closed log entries as a reference dataset. A dynamic learning engine may perform statistical modelling using the reference dataset to assign predetermined categories to each of a number of open log entries. An automation index may be generated for each of the open log entries. The automation index is indicative of accuracy of the assigned categories. Some of the open log entries may be identified as priority log entries which are representative of a group of the open log entries. The assigned category of the priority log entries may be analyzed for accuracy and the results of the analysis may be used to train the statistical model so that the open log entries may be iteratively assigned and re-assigned a category until the category of each open log entry reaches a desired accuracy.

Abstract: Implementations are directed to providing categorization of transactional data, and include actions of providing a plurality of word embeddings based on domain-relevant text data, clustering word embeddings of the plurality of word embeddings into a plurality of clusters, receiving, in real-time, transactional data representative of a transaction, providing a category that is to be assigned to the transaction based on the transactional data, and the plurality of clusters, processing the category, the transactional data, the text data, and the plurality of clusters using a semantic search to provide reason text data, the reason text data representing a reason for selection of the category assigned to the transaction, and storing the transaction data, the category, and the reason text data in a transaction database.

Abstract: Implementations are directed to providing categorization of transactional data, and include actions of providing a plurality of word embeddings based on domain-relevant text data, clustering word embeddings of the plurality of word embeddings into a plurality of clusters, receiving, in real-time, transactional data representative of a transaction, providing a category that is to be assigned to the transaction based on the transactional data, and the plurality of clusters, processing the category, the transactional data, the text data, and the plurality of clusters using a semantic search to provide reason text data, the reason text data representing a reason for selection of the category assigned to the transaction, and storing the transaction data, the category, and the reason text data in a transaction database.

Abstract: An identification and management system for log entries may filter historical data and generate closed log entries as a reference dataset. A dynamic learning engine may perform statistical modelling using the reference dataset to assign predetermined categories to each of a number of open log entries. An automation index may be generated for each of the open log entries. The automation index is indicative of accuracy of the assigned categories. Some of the open log entries may be identified as priority log entries which are representative of a group of the open log entries. The assigned category of the priority log entries may be analyzed for accuracy and the results of the analysis may be used to train the statistical model so that the open log entries may be iteratively assigned and re-assigned a category until the category of each open log entry reaches a desired accuracy.

Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.

Abstract: A method of determining an analyte parameter is described for an analyte in a detection region having at least one analyte binding zone. The method includes detecting presence of an analyte element bound at the binding zone, incrementing or decrementing a count and preventing recount of the element to obtain absolute quantification.