Abstract
Data has tremendously incorporated our lifestyle. With advancements in technology and reduced Internet cost, data usage has increased many folds resulting in generation of huge heaps of unstructured data called as big data. This unstructured big data is difficult to handle using existing database management technology. We observed that genetic information related to coronavirus is tremendously increasing everyday. With implementation of big data analytics, these databases will be easily manageable leading to advancements in COVID-19 research. In this article, we have used HDFS system for efficient data management. In our work, we classified gene classes present in complete sequence so as to quickly detect mutation in no time. To achieve this, we predicted machine learning models to classify gene sequences faster in-class with libraries like matplotlib to construct detailed graph of the data. We choose three different sequences to classify gene sequence using natural language processing technique of Sklearn library and tested our results using logical regression.
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Acknowledgements
We would like to thank Amity Institute of Biotechnology and our family, without their support throughout the process, this paper would have not been accomplished. We would also like to thank Amity University for giving us this great opportunity.
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Mohanty, S., Sharma, R., Saxena, M., Saxena, A. (2021). Heuristic Approach Towards COVID-19: Big Data Analytics and Classification with Natural Language Processing. In: Khanna, A., Gupta, D., Pólkowski, Z., Bhattacharyya, S., Castillo, O. (eds) Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_59
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DOI: https://doi.org/10.1007/978-981-15-8335-3_59
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