Abstract: Examples of the disclosure provide a system and method for obtaining one or more current candidate resumes and one or more past candidate resumes associated with a role and analyzing full text of the obtained resumes to identify one or more items. Weighted values are determined for the identified items using a dimension reduction technique, and a probability score is calculated for each of the obtained current candidate resumes based on selection data associated with the obtained past candidate resumes. A keyword score is calculated for each of the obtained current candidate resumes based on a presence of one or more keywords associated with the role in the obtained current candidate resumes. A final candidate score is generated for each of the obtained current candidate resumes based on the keyword score and the calculated probability score, and the generated final candidate scores are output in association with the obtained current candidate resumes to a user interface.

Abstract: A feature-based item similarity and forecasting system is provided. An exemplary item forecasting system can include: a processor; and a computer-readable non-transitory storage medium memory storing computer executable instructions, the instructions operable to cause the processor to execute: a shape characteristics based classification module programmed to: acquire a plurality of time series datasets, generate a plurality of first datasets comprising shape and effect features, and generate a plurality of second datasets with shape labels, wherein the items in the second datasets are classified into clusters and wherein each cluster shares a shape label; and a forecasting module programmed to: run a plurality of candidate forecasting models on each shape label, select a best forecasting model with a lowest average forecast error for each shape label, and assign the best forecasting model to each item in the cluster sharing the shape label for an item prediction.

Abstract: Embodiments relate to systems and methods for identifying potential shoplifting incidents. In one embodiment, a system can receive and analyze information related to a number of items removed from a display and a number of items added to a shopping receptacle. These numbers of items can be detected by weight or load changes sensed by a sensor system, and if the counted numbers of items do not match, the system can flag nearby shopping receptacles via their unique identification numbers as potentially involved in shoplifting incidents.

Abstract: Examples of the disclosure provide a system and method for obtaining one or more current candidate resumes and one or more past candidate resumes associated with a role and analyzing full text of the obtained resumes to identify one or more items. Weighted values are determined for the identified items using a dimension reduction technique, and a probability score is calculated for each of the obtained current candidate resumes based on selection data associated with the obtained past candidate resumes. A keyword score is calculated for each of the obtained current candidate resumes based on a presence of one or more keywords associated with the role in the obtained current candidate resumes. A final candidate score is generated for each of the obtained current candidate resumes based on the keyword score and the calculated probability score, and the generated final candidate scores are output in association with the obtained current candidate resumes to a user interface.

Abstract: Embodiments relate to systems and methods for identifying potential shoplifting incidents. In one embodiment, a system can receive and analyze information related to a number of items removed from a display and a number of items added to a shopping receptacle. These numbers of items can be detected by weight or load changes sensed by a sensor system, and if the counted numbers of items do not match, the system can flag nearby shopping receptacles via their unique identification numbers as potentially involved in shoplifting incidents.