Abstract: Embodiments for intelligent design structure selection in an Internet of Things (IoT) computing environment by a processor. Levels of satisfaction and behavior patterns of one or more users having similar user profiles influencing the behavior patterns may be learned and evaluated. One or more design structure solutions may be cognitively suggested according to the levels of satisfaction and the behavior patterns.

Abstract: Embodiments for intelligent scheduling of events by a processor. One or more events may be cognitively scheduled and/or assigned to one or more members from a plurality of group members according to learned relationships and responsibilities associated with the one or more events and the plurality of group members.

Abstract: Automatically managing ameliorative process that controls undesired code use in an application based on predicted valuation and risk vectors. A multidimensional risk vectors may be received in relation to current and future use of undesired code. Amelioration actions may be classifies by using a custom trained machine learning model. An optimal amelioration action may be selected based on at least one optimization objective function. The selected amelioration action may be taken to control undesired code use in application. Examples of an amelioration action may include changing of the characteristics of a user interface and stopping an application or a component of the application from running.

Abstract: Embodiments for facilitating ride scheduling by a processor. An occurrence of an event associated with a user may be predicted based on user data. One or more ride scheduling parameters relating to the event may be determined. One or more ride scheduling models may be determined satisfying the ride scheduling parameters. Facilitate scheduling a vehicle for the user according to the ride scheduling models.