[Retracted] A Novel Smart City‐Based Framework on Perspectives for Application of Machine Learning in Combating COVID‐19

Ezugwu, Absalom E.; Hashem, Mohamed; Oyelade, Olaide N.; Almutari, Mubarak S.; Al-Garadi, Mohammed Ali; Abdullahi, Idris Nasir; Otegbeye, Olumuyiwa; Shukla, Amit K.; Chiroma, Haruna

doi:10.1155/2021/5546790

Cited by 21 publications

(12 citation statements)

References 63 publications

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“…This review underscored the pivotal role of forecasting models in shaping various economic and healthcare policies to combat the immediate and long-term challenges of the pandemic. These models have proven to be indispensable not only for immediate responses but also for devising long-term recovery and sustainability strategies [ 1 , 7 , 8 , 21 – 26 , 34 , 35 , 42 , 46 , 50 , 51 , 63 , 69 , 70 , 84 – 86 , 94 , 101 , 128 – 131 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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Section: Literature Reviewmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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“…B. SARS-CoV-2-Infektionen, nicht berücksichtigt werden konnten, welche u. a. für zukünftige vorausschauende Simulationen bzw. weitere Pandemieplanung hilfreich wären [ 2 , 12 ].…”

Section: Diskussionunclassified

Entwicklung konkreter Handlungsoptionen für die sichere Öffnung und den Betrieb von Freizeitdestinationen unter Pandemiebedingungen

et al. 2022

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Zusammenfassung Hintergrund Die SARS-CoV-2-Pandemie führte weltweit zur Schließung von Freizeit- und Erholungseinrichtungen. Im Rahmen einer vom baden-württembergischen Ministerium für Soziales, Gesundheit und Integration geförderten Modellstudie konnte am Beispiel der Öffnung und des Betriebs eines Freizeitparks unter wissenschaftlicher Begleitung gezeigt werden, wie ein Hygiene- und Sicherheitskonzept in die Praxis erfolgreich umsetzbar ist. Ziel Neben der Überprüfung eines möglichen Infektionsgeschehens durch den Besuch des Freizeitparks, zielte das Projekt darauf ab, Handlungsoptionen für die sichere Öffnung und den Betrieb von Freizeiteinrichtungen unter Pandemiebedingungen, die auf andere Destinationen übertragbar sind, zu erarbeiten. Material und Methoden Für dieses Projekt wurden verschiedene Datenquellen genutzt: Expertenrunden multidisziplinärer Teams (Betriebswirtschaft, Versorgungsforschung, Soziologie und Medizin), Aerosolmessdaten, Beobachtungsprotokolle, amtliche Infektionszahlen und Daten aus Besucherbefragungen. Ergebnisse Die in diesem Projekt entstandenen Handlungsoptionen liefern eine Orientierungshilfe für Betreiber von Freizeiteinrichtungen zur Umsetzung von Maßnahmen, durch welche die Sicherheit des Personals und der Gäste erhöht werden und so den Betrieb der Anlagen unter Pandemiebedingungen ermöglichen. Diskussion Diese Studie stellt einen Präzedenzfall am Beispiel eines Freizeitparks in Baden-Württemberg dar, der anderen Einrichtungen als Wegweiser dient. Freizeit- und Tourismusbetriebe sind allerdings einzigartig und Maßnahmen entsprechend schwer direkt übertragbar. Die Handlungsoptionen sollen weiterhin politische Entscheidungsträger in zukünftigen Pandemiesituationen hinsichtlich Maßnahmen zur Schließung, Öffnung und Betrieb solcher Anlagen unterstützen.

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“…It was hence necessary to target this devastating pervasion with state-of-the-art AI tools that could help fight the battle with COVID-19. The computational and precise predictions from AI approaches are quite evident, as can be seen from the results in various real-world applications [ 32 , 33 , 62 , 71 , 74 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

Shukla

Seth

Muhuri

2023

Multimed Tools Appl

Self Cite

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With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

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[Retracted] A Novel Smart City‐Based Framework on Perspectives for Application of Machine Learning in Combating COVID‐19

Cited by 21 publications

References 63 publications

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Entwicklung konkreter Handlungsoptionen für die sichere Öffnung und den Betrieb von Freizeitdestinationen unter Pandemiebedingungen

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

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