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This chapter offers the improvement of a robust neuro-stimulated algorithm for monitoring and prediction of earthquakes. The designed machine, gaining knowledge of a new algorithm, can successfully filter the critical functions from seismic data and locate numerous seismic activities in actual time. This algorithm is custom-designed for seismic facts analysis and designed for supervised studying. The key feature of this set of rules is its capacity to discover and classify seismograms, which can be new to the model, making it notably predictive. The outcomes of the tracking and prediction show that the advanced neuro-inspired set of rules should successfully discover various seismic activities with a quick latency and low fake positive rate. Furthermore, the proposed algorithm presents an optimal aggregate of binary classification for earthquake monitoring and prediction. This set of rules is a promising tool to enhance …