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Item Deep Learning for COVID-19(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Machine Learning Based Data Quality Model for COVID-19 Related Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.V.; Chandrashekar, A.; Chandrasekaran, K.Big Data is being used in various aspects of technology. The quality of the data being used is essential and needs to be accurate, reliable, and free of defects. The difficulty in improving the quality of big data can be overcome by leveraging computing resources and advanced techniques. In this paper, we propose a solution that utilizes a machine learning (ML) model combined with a data quality model to improve the quality of data. An auto encoder neural network that detects the anomalies in the data is used as the Machine Learning model. This is followed by using the data quality model to ensure the data meets appropriate data quality characteristics. The results obtained from our solution show that the quality of data can be improved efficiently and effortlessly which in turn aids researchers to achieve better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Gaining Actionable Insights in COVID-19 Dataset Using Word Embeddings(Springer Science and Business Media Deutschland GmbH, 2022) Jha, R.A.; Ananthanarayana, V.S.The field of unsupervised natural language processing (NLP) is gradually growing in prominence and popularity due to the overwhelming amount of scientific and medical data available as text, such as published journals and papers. To make use of this data, several techniques are used to extract information from these texts. Here, in this paper, we have made use of COVID-19 corpus (https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge ) related to the deadly corona virus, SARS-CoV-2, to extract useful information which can be invaluable in finding the cure of the disease. We make use of two word-embeddings model, Word2Vec and global vector for word representation (GloVe), to efficiently encode all the information available in the corpus. We then follow some simple steps to find the possible cures of the disease. We got useful results using these word-embeddings models, and also, we observed that Word2Vec model performed better than GloVe model on the used dataset. Another point highlighted by this work is that latent information about potential future discoveries are significantly contained in past papers and publications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Role of Genomics in Smart Era and Its Application in COVID-19(Taylor and Francis, 2023) Kumar, S.; Bhowmik, B.Genomics is a rapidly developing field that aims to understand the whole inherited traits of an organism, including its structure, function, and evolution. The purpose of genomics is to gain a detailed understanding of the biological basis for human disease, to explore the genetic variation of several species and humans, and also to enhance rural livelihoods and farming practices. The motivation to completely comprehend the complex biological processes that regulate life on earth and to put this knowledge to enhance people’s lives, improve food security, and safeguard the environment has driven the growth of genomics technologies. The discovery of the genetic roots of human diseases and other complex traits is one of the main goals of genomics, which may lead to the development of treatments and medications. Researchers can find similar genetic pathways and mechanisms to develop drugs and medicines for a broad range of diseases by comparing the genomes of many species. With the introduction of new technologies and advancements in deoxyribonucleic acid sequencing, genomics has evolved into a powerful tool for solving life’s riddles and transforming the lives of people from all over the world. By comparing the genomes of DNA sequencing disorders, researchers can uncover the genes responsible for desirable characteristics such as improved genetics, disease resistance, and better efficiency. This information is essential to develop populations of organisms better adaptable to evolving biological conditions. This chapter provides an overview of genomics, including its background, key attributes, and various types and application areas. The numerous challenges in genomics are also addressed in this chapter, including dealing with large genomes, sequencing and retrieving genetic data, comprehending the features of potential pathogens, and analyzing pathogen sequence trends. The chapter also addresses recent advances in genomics, such as its involvement in the COVID-19 pandemic and the most sophisticated techniques used in the discipline. The development of artificial intelligence in genomics and its usage in COVID-19 research are also discussed in this chapter. Moreover, this chapter provides a comprehensive insight into the evolution, present condition, and future potential of genomics research. Overall, the purpose of the chapter is to understand the problems and accomplishments in genomics and how it may assist healthcare systems. © 2024 Scrivener Publishing LLC.