These refer to classification approaches in contexts where data is continuously changing or driven by events. Dynamic classification means the model or category assignment can update in real-time as an entity’s attributes change. For example, classifying network traffic as normal or malicious might change dynamically as new packets arrive. Event-based classification triggers categorization upon detecting specific events in data streams – e.g., in surveillance, an event “motion in restricted area” triggers classification of what the object is (person vs. animal). Both concepts often require online learning or stream processing, where incoming data is processed on the fly. They contrast with static classification where a fixed dataset is classified once. Using AI for dynamic and event-based classification allows real-time decisions, like flagging evolving fraud patterns or responding to security incidents as they unfold.