Application of nature-inspired computing and implementation of algorithm for earthquake detection

Improve learning techniques and to prepare reference entropy which measures from the field of information theory, building upon entropy  generally calculating the difference between two probability distributions. Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks. The performance of the proposed neural network with respect to cross entropy is presented in this research.  The performance can be improved by including more data and optimization. The proposed research work will be used for time series data of events detection and prediction such as seismic event’s (Earthquake).The point of the present work is to tune the suitable algorithms for meaningful detection of the disastrous earthquake events and to generate the proper timely warning to the public.