Prediction of COVID-19 epidemic curve of India using supervised learning approach

Mongia, Shweta; Sharma, Swati; Jaisankar, N.; Kumar, Manoj; Arora, Vasudha; Stephan, Thompson; Shankar, Achyut; Gupta, Pragya; Kachhawaha, Raghav

doi:10.1504/ijcat.2021.120469

Cited by 2 publications

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, the authors forecast a monthly percentage increase in the number of COVID-19 cases from May 1st to December 1st, 2020. This analysis would enable the relevant authorities to implement effective preventive measures during the decision-making process ( 16 ). According to the study conducted in India, France, China, and Nepal to predict the coronavirus outbreak, the number of COVID-19 cases will increase gradually and the epidemic will continue, but the number of active cases has been drastically reduced in three of these countries with France being the exception ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

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Compliance level toward COVID-19 preventive measures and associated factors among the Ambo University community, 2021

Roga

Bekele²,

Gonfa³

2022

Front. Public Health

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BackgroundThe coronavirus illness is a highly contagious viral infection with serious health consequences that has occurred all around the world. Application of COVID-19 prevention precautions and social interventions by the general public are critical to successfully combating the epidemic. Therefore, this study aimed to determine the level of compliance and associated factors with COVID-19 prevention strategies.MethodsA facility based cross-sectional study was conducted from June 01 to August 30, 2021 on a sample of 380 randomly selected Ambo University community members. A self-administered structuralized questionnaire was used to collect the data. The determining factors with the level of compliance were finally identified using a multivariate logistic regression model. The association was reported using odds ratios with a 95% CI, and significance was declared using a P < 0.05.ResultsA total of 378 respondents participated in the study with a response rate of 98.9%. Most of the respondents, 75.7%, 57.9%, 47.4%, 61.9% had good knowledge, favorable attitude, good practice and good compliance toward COVID-19 preventive measures, respectively. In participants aged between 18 and 30 [AOR = AOR = 2.23, 95% CI: (1.13, 4.41)], good knowledge [AOR = 2.64, 95% CI: (1.46, 4.78)], favorable attitude [AOR = 4.5, 95% CI: (2.63, 7.71)], and good practice [AOR = 2.98, 95% CI: (1.82, 4.89)] were significantly associated with good compliance toward COVID-19 preventive measures.Conclusion and recommendationDespite the fact that COVID-19 is a global and national priority, the preventive measures were not sufficiently followed. Therefore, it is essential to continue working on the community's knowledge, practices, and attitude about COVID-19 preventive measures through media campaigns, which will ultimately increase compliance. Additionally, the concerned stakeholders should consider the required interventions for the strongly associated factors that have been discovered in this current study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compliance level toward COVID-19 preventive measures and associated factors among the Ambo University community, 2021

Roga

Bekele²,

Gonfa³

2022

Front. Public Health

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CLSA-CapsNet: Dependency based concept level sentiment analysis for text

Mahendhiran¹,

Kannimuthu

2022

IFS

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The refining of information from the immense amount of unstructured data on the internet can be a critical issue in identifying public opinion. It is difficult to extract relevant concepts from huge amounts of data. Concept level semantic parsing is improved over word-based investigation as it conserves the semantical data relevant to many-word articulations. The semantic proposals offer a superior comprehension of textual data and serve to altogether precision the exactness of numerous mining operations in text assignments. The extraction of concepts from textual data is a significant step forward in content analysis at the concept stage. We present a CLSA-CapsNet method that extracts concepts from natural language text. Then the extracted concepts are applied in Capsule networks (CapsNet). Moreover, the integration of Concept Level Sentiment Analysis (CLSA) and Capsule Networks (CapsNet) has not yet been implemented on the hotel review dataset. This is the first attempt, which we researched and embraced by the capsule network, to develop classification models for hotel reviews. The developed results demonstrated excellent performance with a prediction accuracy of 86.6% for CLSA-CapsNet models, respectively. Various similarities have also been made across our techniques and they are implemented by some other deep learning algorithms, such as rnn-lstm. Overall, the outstanding success obtained by CLSA-CapsNet in this investigation highlights its ability in the hotel review dataset. We likewise show exploratory outcomes, in which the proposed system outpaced the state-of-the-art CLSA-CapsNet model.

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Prediction of COVID-19 epidemic curve of India using supervised learning approach

Cited by 2 publications

References 0 publications

Compliance level toward COVID-19 preventive measures and associated factors among the Ambo University community, 2021

Compliance level toward COVID-19 preventive measures and associated factors among the Ambo University community, 2021

CLSA-CapsNet: Dependency based concept level sentiment analysis for text

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