Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India DOI Creative Commons
Ankit Kumar Srivastava, Saurabh Mani Tripathi, Sachin Kumar

и другие.

IEEE Access, Год журнала: 2022, Номер 10, С. 95106 - 95124

Опубликована: Янв. 1, 2022

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, M5P techniques have been discussed implemented disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy aforementioned ML approaches, preliminary sample-study has conducted on first wave scenario different countries including United States America (USA), Italy, Australia. Furthermore, contributions study are extended conducting an in-depth scenarios first, second, third waves India. An accurate model proposed, which constructed basis results models findings research highlight LR potential approach outperforms all other tested herein present scenario. Finally, used likely onset fourth

Язык: Английский

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting DOI Creative Commons
Muhammad Usman Tariq,

Shuhaida Binti Ismail,

Muhammad Ali Babar

и другие.

PLoS ONE, Год журнала: 2023, Номер 18(7), С. e0287755 - e0287755

Опубликована: Июль 20, 2023

The pandemic has significantly affected many countries including the USA, UK, Asia, Middle East and Africa region, other countries. Similarly, it substantially Malaysia, making crucial to develop efficient precise forecasting tools for guiding public health policies approaches. Our study is based on advanced deep-learning models predict SARS-CoV-2 cases. We evaluate performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), (RNN). trained these assessed them using a detailed dataset confirmed cases, demographic data, pertinent socio-economic factors. research aims determine most reliable accurate model cases in region. were able test optimize deep learning with each displaying diverse levels accuracy precision. A comprehensive evaluation models’ discloses appropriate architecture Malaysia’s specific situation. This supports ongoing efforts combat by offering valuable insights into application sophisticated timely case predictions. findings hold considerable implications decision-making, empowering authorities create targeted data-driven interventions limit virus’s spread minimize its effects population.

Язык: Английский

Процитировано

11
A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms DOI Open Access
Rocío Aznar-Gimeno, Luis Esteban, Gorka Labata-Lezaun

и другие.

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(16), С. 8677 - 8677

Опубликована: Авг. 17, 2021

The purpose of the study was to build a predictive model for estimating risk ICU admission or mortality among patients hospitalized with COVID-19 and provide user-friendly tool assist clinicians in decision-making process. cohort comprised 3623 confirmed who were SALUD hospital network Aragon (Spain), which includes 23 hospitals, between February 2020 January 2021, period that several pandemic waves. Up 165 variables analysed, including demographics, comorbidity, chronic drugs, vital signs, laboratory data. To models, different techniques machine learning (ML) algorithms explored: multilayer perceptron, random forest, extreme gradient boosting (XGBoost). A reduction dimensionality procedure used minimize features 20, ensuring feasible use practice. Our validated both internally externally. We also assessed its calibration an analysis optimal cut-off points depending on metric be optimized. best performing algorithm XGBoost. final achieved good discrimination external validation set (AUC = 0.821, 95% CI 0.787–0.854) accurate (slope 1, intercept −0.12). 0.4 provides sensitivity specificity 0.71 0.78, respectively. In conclusion, we built prediction from large amount data waves, had ability. created web application can aid rapid clinical

Язык: Английский

Процитировано

21
Physical education movement and comprehensive health quality intervention under the background of artificial intelligence DOI Creative Commons
Bo Zhang, Hao Jin,

Xiaojing Duan

и другие.

Frontiers in Public Health, Год журнала: 2022, Номер 10

Опубликована: Сен. 16, 2022

The application of artificial intelligence has realized the transformation people's production and lifestyle, also promoted progress physical education comprehensive health quality. in current movement is increasing. By utilizing its advanced method virtual simulation technology, purpose this paper to realize interventional research on quality environment intelligence. This proposes use technology Kinect algorithm design sports teaching mode. functional module part where helps experiments, which helpful analyze solve objective system imbalance ecological online teaching. using principles rules Mean Shift image segmentation for reference, investigation students are carried out, so as ecologicalization school. In students, results show that overall these who reached level qualified or unqualified accounting about 30% total number. It worth noting terms scientific cultural quality, only 43.34% all have excellent grades. can be seen important training goal school how reasonable effective methods strategies improve students' level, other scores at same time.

Язык: Английский

Процитировано

15
Forecasting the Confirmed COVID‐19 Cases Using Modal Regression DOI Open Access
Xin Jing, Jin Seo Cho

Journal of Forecasting, Год журнала: 2025, Номер unknown

Опубликована: Фев. 15, 2025

ABSTRACT This study utilizes modal regression to forecast the cumulative confirmed COVID‐19 cases in Canada, Japan, South Korea, and United States. The objective is improve accuracy of forecasts compared standard mean median regressions. To evaluate performance forecasts, we conduct simulations introduce a metric called coverage quantile function (CQF), which optimized using regression. By applying popular time‐series models for data, provide empirical evidence that generated by outperform those produced regressions terms CQF. finding addresses limitations forecasts.

Язык: Английский

Процитировано

0
Leveraging Artificial Intelligence for Pandemic Management: Case of COVID-19 in the United States DOI
Ehsan Ahmadi, Reza Maihami

Big Data Research, Год журнала: 2025, Номер unknown, С. 100529 - 100529

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics DOI Creative Commons
Foziye Tahmasbi, Esmaeel Toni, Zohreh Javanmard

и другие.

Archives of Public Health, Год журнала: 2025, Номер 83(1)

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0
How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic DOI Open Access
Davide Barbieri, Enrico Giuliani, A. Del Prete

и другие.

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(14), С. 7648 - 7648

Опубликована: Июль 19, 2021

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, adopt innovative solutions control it, is often the case when people are driven by necessity. After 18 months since first case, it time think about pros cons of such technologies, including artificial intelligence—which probably most complex misunderstood non-specialists—in order get out them, suggest future improvements proper adoption. aim this narrative review was select relevant papers that directly address adoption intelligence new technologies in management pandemics communicable diseases SARS-CoV-2: environmental measures; acquisition sharing knowledge general population among clinicians; development drugs vaccines; remote psychological support patients; monitoring, diagnosis, follow-up; maximization rationalization human material resources hospital environment.

Язык: Английский

Процитировано

18
VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants DOI Open Access
Zhifang Liao, Yucheng Song, Shengbing Ren

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2022, Номер 224, С. 106981 - 106981

Опубликована: Июнь 29, 2022

Язык: Английский

Процитировано

12
Deciphering the COVID-19 Health Economic Dilemma (HED): A Scoping Review DOI Open Access
Arielle Kaim,

Tuvia Gering,

Amiram Moshaiov

и другие.

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(18), С. 9555 - 9555

Опубликована: Сен. 10, 2021

Lessons learnt from the initial stages of COVID-19 outbreak indicate need for a more coordinated economic and public health response. While social distancing has been shown to be effective as non-pharmaceutical intervention (NPI) measure mitigate spread COVID-19, costs have substantial. Insights combining epidemiological data provide new theoretical predictions that can used better understand economy tradeoffs. This literature review aims elucidate perspectives assist policy implementation related management ongoing impending outbreaks regarding Health Economic Dilemma (HED). unveiled information-based decision-support systems which will combine pandemic modelling control, with models. It is expected current not only support makers but also researchers on development decision-support-systems comprehensive information various aspects HED.

Язык: Английский

Процитировано

15
On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks DOI Creative Commons
Sandi Baressi Šegota, Ivan Lorencin, Zoran Kovač

и другие.

Biomedicines, Год журнала: 2023, Номер 11(2), С. 284 - 284

Опубликована: Янв. 19, 2023

In the case of pandemics such as COVID-19, rapid development medicines addressing symptoms is necessary to alleviate pressure on medical system. One key steps in medicine evaluation determination pIC50 factor, which a negative logarithmic expression half maximal inhibitory concentration (IC50). Determining this value can be lengthy and complicated process. A tool allowing for quick approximation based molecular makeup could valuable. paper, creation artificial intelligence (AI)-based model performed using publicly available dataset molecules their values. The modeling algorithms used are convolutional neural networks (ANN CNN). Three approaches tested-modeling just properties (MP), encoded SMILES representation molecule, combination both input types. Models evaluated coefficient (R2) mean absolute percentage error (MAPE) five-fold cross-validation scheme assure validity results. obtained models show that highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by large margin, when hybrid network trained with MP SMILES.

Язык: Английский

Процитировано

6