A Novel Cellular Automata Classifier for COVID-19 Prediction

Introduction: China has witnessed a new virus Corona,which is named COVID-19. It has become the world’s most concern as this virus has spread over the worldat a higher speed;the world has witnessed more than one lakh cases and one thousand deaths in a span of few days. Methods: We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the datasets from Kaggle and other standard websites. Results: The proposed classifier, hybrid non-linear cellular automata (HNLCA), was trained with 23,078 datasets and tested with 6785 datasets. HNLCA is compared with conventional methods of long short-term memory, AdaBoost, support vector machine, regression, and SVR and has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc.

A Novel Cellular Automata Classifier for COVID-19 Prediction

China has witnessed a new virus Corona, which is named COVID-19. It has become the world's most concern as this virus has spread over the world at a higher speed, the world has witnessed more than one lakh cases and one thousand deaths in a span of a few days. We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the data sets from Kaggle and other standard websites. The proposed classifier, HNLCA (Hybrid Non-Linear Cellular Automata) was trained with 23078 datasets and tested with 6785 data sets. HNLCA is compared with conventional methods LSTM, Adaboost, SVM, Regression, and SVR has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc.

Non-linear Hybrid Cellular Automata as Pseudorandom Pattern Generators for VLSI Systems

The concept of hybrid in space-time Cellular Automata is introduced, for the first time, in this paper, and it is suggested that non-linear hybrid in space-time Cellular Automata can be used as pseudorandom pattern generators for VLSI systems, because they can produce patterns with various densities of “1”, distributed at will in space and time. The cycle lengths of non-linear hybrid Cellular Automata can be estimated using Lyapunov exponents.