COVID-19 sentiment analysis via deep learning during the rise of novel cases

PLoS ONE, Год журнала: 2021, Номер 16(8), С. e0255615 - e0255615

Опубликована: Авг. 19, 2021

Social scientists and psychologists take interest in understanding how people express emotions sentiments when dealing with catastrophic events such as natural disasters, political unrest, terrorism. The COVID-19 pandemic is a event that has raised number of psychological issues depression given abrupt social changes lack employment. Advancements deep learning-based language models have been promising for sentiment analysis data from networks Twitter. Given the situation pandemic, different countries had peaks where rise fall new cases affected lock-downs which directly economy During stricter lock-downs, expressing their media. This can provide human psychology during events. In this paper, we present framework employs via long short-term memory (LSTM) recurrent neural novel India. features LSTM model global vector embedding state-of-art BERT model. We review expressed selective months 2020 covers first major peak Our utilises multi-label classification more than one be at once. results indicate majority tweets positive high levels optimism significantly lowered towards peak. predictions generally although optimistic, significant group population annoyed way was handled by authorities.

Язык: Английский

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A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

ACM Computing Surveys, Год журнала: 2021, Номер 54(8), С. 1 - 32

Опубликована: Окт. 4, 2021

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in struggle curb of pandemic. As powerful tool against COVID-19, artificial intelligence (AI) technologies widely used combating this In survey, we investigate main scope contributions AI from aspects disease detection diagnosis, virology pathogenesis, drug vaccine development, epidemic transmission prediction. addition, summarize available data resources that can be for AI-based research. Finally, challenges potential directions fighting discussed. Currently, mainly focuses on medical image inspection, genomics, prediction, thus still great field. This survey presents researchers with comprehensive view existing applications technology goal inspiring continue maximize advantages big fight COVID-19.

Язык: Английский

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Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

Complex & Intelligent Systems, Год журнала: 2020, Номер 7(1), С. 235 - 247

Опубликована: Сен. 22, 2020

Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes new framework cascaded deep learning classifiers enhance performance these CAD highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed constitutes two major advancements as follows. First, complicated multi-label classification images have been simplified series binary each tested case health status. That mimics clinical situation diagnose potential patient. Second, architecture is flexible use different fine-tuned models simultaneously, achieving best confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such Visual Geometry Group Network (VGG) Residual Neural (ResNet). They successfully evaluated on public image dataset normal three diseased The results showed that VGG16, ResNet50V2, Dense (DenseNet169) achieved detection accuracy COVID-19, viral (Non-COVID-19) pneumonia, bacterial images, respectively. Furthermore, our superior other methods previous studies. presents good option be applied routine assist diagnostic procedures infection.

Язык: Английский

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Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Applied Soft Computing, Год журнала: 2020, Номер 96, С. 106626 - 106626

Опубликована: Авг. 13, 2020

Язык: Английский

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Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Personal and Ubiquitous Computing, Год журнала: 2021, Номер 27(3), С. 733 - 750

Опубликована: Янв. 10, 2021

Язык: Английский

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An approach to forecast impact of Covid‐19 using supervised machine learning model

Software Practice and Experience, Год журнала: 2021, Номер 52(4), С. 824 - 840

Опубликована: Апрель 1, 2021

Abstract The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies 21st century and thrown into sharp relief, among other factors, dire need for robust forecasting techniques disease detection, alleviation well prevention. Forecasting been powerful statistical methods employed world over in various disciplines detecting analyzing trends predicting future outcomes based on which timely mitigating actions can be undertaken. To that end, several machine learning have harnessed depending upon analysis desired availability data. Historically speaking, predictions thus arrived at short term country‐specific nature. In this work, multimodel technique is called EAMA related parameters long‐term both within India a global scale proposed. This proposed hybrid model well‐suited to past present For study, two datasets from Ministry Health & Family Welfare Worldometers, respectively, exploited. Using these datasets, data outlined, observed predicted being very similar real‐time values. experiment also conducted statewise countrywise across it included Appendix.

Язык: Английский

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Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

Journal of Biomedical Informatics, Год журнала: 2021, Номер 117, С. 103751 - 103751

Опубликована: Март 24, 2021

Язык: Английский

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Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2021, Номер 13(1), С. 41 - 73

Опубликована: Янв. 15, 2021

Язык: Английский

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A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

IEEE Transactions on Artificial Intelligence, Год журнала: 2022, Номер 4(1), С. 44 - 59

Опубликована: Янв. 11, 2022

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry for other purposes. available traditional methods international epidemic prediction, researchers authorities have given more attention simple statistical epidemiological methodologies. inadequacy absence medical testing diagnosing identifying a solution one key challenges preventing spread COVID-19. A few statistical-based improvements being strengthened answer challenge, resulting partial resolution up certain level. ML advocated wide range intelligence-based approaches, frameworks, equipment cope with issues industry. application inventive structure, such as handling relevant outbreak difficulties, has been investigated article. major goal 1) Examining impact data type nature, well obstacles processing 2) Better grasp importance intelligent approaches like pandemic. 3) development improved types prognosis. 4) effectiveness influence various strategies 5) To target on potential diagnosis order motivate academics innovate expand their knowledge research into additional COVID-19-affected industries.

Язык: Английский

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Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

SN Computer Science, Год журнала: 2022, Номер 3(4)

Опубликована: Май 12, 2022

Язык: Английский

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