Study reports grasshopper-inspired AI for emergency resource forecasting

Researchers reported that an LSTM-GOA system improved prediction accuracy for medical-resource demand by almost 70% in tests on historical emergency data sets. The system combines a long short-term memory network with a grasshopper optimization algorithm that tunes its scheduling decisions. The researchers say it can handle noisy or incomplete data and respond to demand spikes after disasters, health incidents, and major traffic accidents.