A Review of Various Mathematical and Deep Learning based Forecasting Methods for COVID-19 Pandemic

Katarya, Rahul; Anjum,; Gupta, Aakansha; Sachdeva, Shelly; Dhamija, Tashvik; Gupta, Shreyansh; Gupta, Anunay; Kedia, Priyansh; Rai, Varang

doi:10.1109/icaccs51430.2021.9441966

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The significance of these algorithms in predicting and controlling the spread of COVID-19 is reflected in several studies. Some of these studies include predicting the potential impact of the virus, such as the possibility of a third wave in India (Aakansha Gupta & Katarya, 2023 ), analyzing Twitter sentiments related to COVID-19 using machine learning ( Katarya et al, 2022 ), predicting COVID-19 cases based on LSTM (Long Short-Term Memory) and SIR (Susceptible-Infected-Removed) models using social media (Aakansha Gupta & Katarya, 2022 ), conducting a parameter-based literature survey of COVID-19 mortality dynamics using machine learning techniques ( Sewariya & Katarya, 2021 ), reviewing various mathematical and deep learning-based forecasting methods for COVID-19 pandemic ( Katarya et al, 2021 ), and proposing a novel LDA-based framework to forecast COVID-19 trends (Aakansha Gupta & Katarya, 2021b ).…”

Section: Introductionmentioning

confidence: 99%

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Hussein¹,

Mohammed²,

Hassan³

et al. 2023

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Hussein¹,

Mohammed²,

Hassan³

et al. 2023

Expert Systems with Applications

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A deep feature-level fusion model for masked face identity recommendation system

Thaipisutikul

Tatiyamaneekul

Lin

et al. 2022

J Ambient Intell Human Comput

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The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, FIREC , that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by’s cameras, especially during crisis times when people commonly cover their faces with protective masks.

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Data-Driven Modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

Zhang

Guan

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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A Review of Various Mathematical and Deep Learning based Forecasting Methods for COVID-19 Pandemic

Cited by 3 publications

References 10 publications

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

A deep feature-level fusion model for masked face identity recommendation system

Data-Driven Modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

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