Generating Aspect-based Extractive Opinion Summary: Drawing Inferences from Social Media Texts

Piryani, Rajesh; Gupta, Vishal; Singh, Vivek Kumar

doi:10.13053/cys-22-1-2784

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…The data 1, 2 available for COVID-19, shows that the disease's incidence and mortality often differ significantly among countries. The variability may be attributed to several factors like race [22], dietary patterns, environment [23], social behaviors [24], genetic differences, and systems of governance. Comprehensive empirical evaluations and trend studies have been conducted that explore the variation and correlation in the COVID-19 fatality rate and BCG vaccination policy status.…”

Section: Visualizationsmentioning

confidence: 99%

Empirical Assessment of Bacillus Calmette-Gu閞in Vaccine to Combat COVID-19

Jain¹,

Gupta²,

Chakraborty³

et al. 2022

Computers, Materials &Amp; Continua

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COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations' governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper's initial research shows that the countries with a longterm compulsory BCG vaccination system are less affected by COVID-19 than those without a BCG vaccination system. This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates. The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data. An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only, such as age groups affected, other diseases, and stringency index. The data of Spain, Portugal, and Germany have been taken for a case study of BCG impact analysis.

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Section: Visualizationsmentioning

confidence: 99%

Empirical Assessment of Bacillus Calmette-Gu閞in Vaccine to Combat COVID-19

Jain¹,

Gupta²,

Chakraborty³

et al. 2022

Computers, Materials &Amp; Continua

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“…The novel coronavirus (COVID-19) depiction started with the discovery of Pneumonia with unseen causes in late December 2019 in Wuhan, Hubei province of China, and has developed into a severe public health problem all over the world [5, 9, 33, ] and has swiftly become a pandemic [11,30,36,46]. The Covid19 widespread was found to cause a virus named severe acute respiratory syndrome coronavirus two, also called SARS-CoV-2 [8,12,28,34]. Coronaviruses (CoV) are a large family of perilous viruses [15] that becomes the source of illnesses ranging from colds to more severe pathologies [8,38].…”

Section: Introductionmentioning

confidence: 99%

Improved COVID-19 detection with chest x-ray images using deep learning

Gupta

Jain

Sachdeva

et al. 2022

Multimed Tools Appl

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The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

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“…Another important aspect is that listening to what others feel about a particular situation helps one understand an entirely different perspective towards different aspects or dimensions of that situation. This is significant while policymaking as every section of the society is considered while making inferences about legislative decisions [26] , [27] , [28] .…”

Section: Introductionmentioning

confidence: 99%