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.

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.

Clustering has been widely applied to Information Retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search. Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set .A clustering quality measure is a function that, given a data set and its partition into clusters, returns a non-negative real number representing the quality of that clustering. Moreover, they may behave in a different way depending on the features of the data set and their input parameters values. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. The quality of clustering can be enhanced by using a Cellular Automata Classifier for information retrieval. In this study we take the view that if cellular automata with clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional inverted file search. We conducted a number of experiments using ten document collections and eight hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering with cellular automata is indeed higher and suggest that there is scope for its application to IR

Genes carry the instructions for making proteins that are found in a cell as a specific sequence of nucleotides that are found in DNA molecules. But, the regions of these genes that code for proteins may occupy only a small region of the sequence. Identification of the coding regions plays a vital role in understanding these genes. In this paper we have explored an Artificial Immune System (AIS) that can be used to strengthen and identify the protein coding regions in a genomic DNA system in changing environments and the CA technique for protein structure prediction of small alpha/beta proteins using Rosetta. From an initial round of Rosetta sampling, we learn properties of the energy landscape that guide a subsequent round of sampling toward lower-energy structures. Three different approaches to improve tertiary fold prediction using the genetic algorithm are discussed: refinement of the search strategy; combination of prediction and experiment; inclusion of experimental data as selection criteria into the genetic algorithm. It has been developed using a slight variant of genetic algorithm. Good classifiers can be produced, especially when the number of the antigens is increased. However, an increase in the range of the antigens somehow affects the fitness of the immune system. Experimental results confirm the scalability of the proposed AIS FMACA based classifier to handle large volume of datasets irrespective of the number of classes, tuples and attributes. We note an increase in accuracy of more than 5.2%, over any existing standard algorithms that address this problem. This was the first algorithm to identify protein coding regions in mixed and also non-overlapping exon-intron boundary DNA sequences. The accuracy of prediction of the structure of proteins was also found comparable.

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