Abstract: Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.

Abstract: A method and system for determining a potential for a personal threat to a user based on location. A system receives geolocation data associated with the user, searches for information related to the geolocation data associated with the user, analyzes and correlates the information related to the geolocation data associated with the user and a location to calculate a level of risk to the user by evaluating dangerous situations which are currently in progress in or around the location; and when the level of risk to the user is imminent or latent, sending a personal notification describing the threat to the user.