ABSTRACT
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Diagnosis of Computed Tomography (CT), and Chest X-rays (CXR) contains the problem of overfitting, earlier diagnosis, and mode collapse. In this work, we predict the classification of the Corona in CT and CXR images. Initially, the images of the dataset are pre-processed using the function of an adaptive Gaussian filter for de-nosing the image. Once the image is pre-processed it goes to Sigmoid Based Hyper-Parameter Modified DNN(SHMDNN). The hyperparameter modification makes use of the optimization algorithm of adaptive grey wolf optimization (AGWO). Finally, classification takes place and classifies the CT and CXR images into 3 categories namely normal, Pneumonia, and COVID-19 images. Better accuracy of 99.9% is reached when compared to different DNN networks.
ABSTRACT
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Diagnosis of Computed Tomography (CT), and Chest X-rays (CXR) contains the problem of overfitting, earlier diagnosis, and mode collapse. In this work, we predict the classification of the Corona in CT and CXR images. Initially, the images of the dataset are pre-processed using the function of an adaptive Gaussian filter for de-nosing the image. Once the image is pre-processed it goes to Sigmoid Based Hyper-Parameter Modified DNN(SHMDNN). The hyperparameter modification makes use of the optimization algorithm of adaptive grey wolf optimization (AGWO). Finally, classification takes place and classifies the CT and CXR images into 3 categories namely normal, Pneumonia, and COVID-19 images. Better accuracy of 99.9% is reached when compared to different DNN networks.
ABSTRACT
Purpose: The purpose of this paper is to build a better question answering (QA) system that can furnish more improved retrieval of answers related to COVID-19 queries from the COVID-19 open research data set (CORD-19). As CORD-19 has an up-to-date collection of coronavirus literature, text mining approaches can be successfully used to retrieve answers pertaining to all coronavirus-related questions. The existing a lite BERT for self-supervised learning of language representations (ALBERT) model is finetuned for retrieving all COVID relevant information to scientific questions posed by the medical community and to highlight the context related to the COVID-19 query. Design/methodology/approach: This study presents a finetuned ALBERT-based QA system in association with Best Match25 (Okapi BM25) ranking function and its variant BM25L for context retrieval and provided high scores in benchmark data sets such as SQuAD for answers related to COVID-19 questions. In this context, this paper has built a QA system, pre-trained on SQuAD and finetuned it on CORD-19 data to retrieve answers related to COVID-19 questions by extracting semantically relevant information related to the question. Findings: BM25L is found to be more effective in retrieval compared to Okapi BM25. Hence, finetuned ALBERT when extended to the CORD-19 data set provided accurate results. Originality/value: The finetuned ALBERT QA system was developed and tested for the first time on the CORD-19 data set to extract context and highlight the span of the answer for more clarity to the user. © 2020, Emerald Publishing Limited.
ABSTRACT
With the rapid growth of COVID-19 pandemic infectious disease caused by the Corona Virus. It was first identified in Wuhan in December 2019. It expanded its circle all over the world and finally spreading its route to India. The whole world is fighting against the spread of this deadly disease, cases in India also gradually increasing day by day since May after lockdown. This article proposes how to contribute to utilizing the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins. This paper studies the COVID-19 dataset and explore the data by data visualization with different libraries that are available in Python. The paper also discusses the current situation in India while tackling the Covid-19 pandemic and the ongoing development in AI and ML has significantly improved treatment, medication, screening tests, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark. Within this paper, we present Exploratory Data Analysis, Data Preprocessing, Data Cleaning and Manipulations, Machine Learning Algorithms, Pandemic Analyzing Engine GUI, and Deep Learning. We have performed linear regression, Decision Tree, SVM, Random Forest and for forecasting, we performed FBPrompet, ARIMA model to predict the next 15 day’s Pandemic situation. © 2020 Ubiquity Press. All rights reserved.
ABSTRACT
The field of health science accumulates large amounts of data with diverse and correlated attributes. Processing such type of data requires specialized techniques. Data analytics techniques are suitable for that purpose. Statistical evaluation is an important step for understanding health data. In the current scenario of the outbreak of novel Corona Virus (COVID 19), we have utilized the Novel Corona Virus 2019 data collected by John Hopkins University to extract important and interesting insights. A complete analytical study comprising of exploratory analysis, time series analysis and geographical analysis has been attempted so as to assess the impact and spread of the virus in most affected countries like China and also the remaining world. The resulting insights would help the public, governments and the research community to understand the gravity of the situation and take remedial measures. The entire analysis has been carried out in R. © 2020 IJSTR.