Abstract: Mechanisms are provided which generate an action recommendation for a received electronic communication. A machine learning (ML) computer model is trained at least by executing a ML training operation based on electronic communication features extracted from a plurality of historical electronic communications and actions taken by a user in response to each historical electronic communication. The ML computer model is trained to predict an action classification that specifies a predicted action that the user will take in response to receiving electronic communications. A new electronic communication is received and electronic communication features are extracted from the new electronic communication. The extracted features are processed by the trained ML computer model to generate a predicted action classification and an action recommendation output specifying a recommended action to take corresponding to the predicted action classification for the new electronic communication.

Abstract: Examples of techniques for performing a skill gap comparison between a first individual and a second individual (or group) are disclosed. In one example, a computer-implemented method includes receiving first data associated with the first individual and receiving second data associated with the second individual. The method further includes identifying preferences for performing the skill gap comparison and assigning a weight for each of a plurality of elements of the first data and the second data. The method further includes performing the skill gap comparison between the first individual and the second individual by comparing the first data to the second data. The method further includes generating a list of one or more skills for which there is a skill gap between the first individual and the second individual.

Abstract: In one example implementation according to embodiments of the invention, a computer-implemented method includes identifying a first discrete element of the composite object from a first image. The method further includes identifying the composite object based at least in part on the first discrete element of the composite object. The method further includes determining whether a category associated with the composite object is a category of interest. The method further includes, based at least in part on determining that the category associated with the composite object is a category of interest, increasing a discrete element count. The method further includes determining whether the discrete element count meets a threshold. The method further includes, based at least in part on determining that the discrete element count exceeds the threshold, performing a mitigation action.

Abstract: Examples of techniques for performing a skill gap comparison between a first individual and a second individual (or group) are disclosed. In one example, a computer-implemented method includes receiving first data associated with the first individual and receiving second data associated with the second individual. The method further includes identifying preferences for performing the skill gap comparison and assigning a weight for each of a plurality of elements of the first data and the second data. The method further includes performing the skill gap comparison between the first individual and the second individual by comparing the first data to the second data. The method further includes generating a list of one or more skills for which there is a skill gap between the first individual and the second individual.

Abstract: In one example implementation according to embodiments of the invention, a computer-implemented method includes identifying a first discrete element of the composite object from a first image. The method further includes identifying the composite object based at least in part on the first discrete element of the composite object. The method further includes determining whether a category associated with the composite object is a category of interest. The method further includes, based at least in part on determining that the category associated with the composite object is a category of interest, increasing a discrete element count. The method further includes determining whether the discrete element count meets a threshold. The method further includes, based at least in part on determining that the discrete element count exceeds the threshold, performing a mitigation action.