Applications of artificial intelligence with cloud computing in promoting social distancing to combat COVID-19

Al-Hamiri, Mohammed G.; Abdulsada, Hayder Fadhil; Abdul-Rahaim, Laith Ali

doi:10.11591/ijeecs.v24.i3.pp1550-1556

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…COVID-19 affects global citizens it presents various challenges to humanity. Researchers of various fields are trying to contribute to the fight against this epidemic through new ways by applying technologies such as artificial intelligence, and cloud computing [7]. From the information about the spread of COVID-19 around the world right now, artificial intelligence can be applied to create models to predict the spread of pathogens.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Sengsri

Khunratchasana

2023

IJEECS

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Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period.

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Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Sengsri

Khunratchasana

2023

IJEECS

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“…This system is created by the application of AI algorithms and internet of things, such as face detection, image processing, and Viola-Jones algorithm [28]. To force the social distancing, another research uses cloud computing, they connect their system to the cloud to make it more reliable and contactless [29].…”

Section: Pandemic Managementmentioning

confidence: 99%

Evolution of automated learning techniques for combating COVID-19: an analysis

Mohammed

Hamsheen

2022

IJEECS

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It is now more than two years that the world is battling the tiny invisible virus, COVID-19. Since its appearance, it showered humankind with shock, fear, and death. In small words, this pandemic has paused human life in all its aspects and beauties. Governments, health industry researchers and laboratories have put all their efforts to achieve a universal goal that is, overcoming the crisis and putting an end to the pandemic. However, this goal was never achievable without the smart use of automated learning, artificial intelligence, machine learning and deep learning algorithms. This review paper presents a collection of the experimental research articles tackled using real-time official datasets from hospitals and governments. These datasets are processed using automated learning (AL) algorithms in order to find suitable solutions to most of the COVID-19 related problems. This paper presents the AL applications in a story telling manner, starting from the first phases of COVID-19, when doctors had no experience dealing with the disease and had difficulty in diagnosing it, then moving to the other phases like suggesting a medicine, drug repurposing, facial mask detection, fake news detection, vaccine development, pandemic management, post vaccine statistics and lastly post COVID-19 analysis.

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“…Second, social isolation. The Raspberry Pi and Deep learning have changed the monitoring system [16,17]. There is a need for technology or some other system that can detect social distance and emphasize [18,19] the person in the video if it is not being followed, as stated above.…”

Section: Fig 2 Effect Of Covid In Assorted Countriesmentioning

confidence: 99%

Artificial Intelligence Integrated Social Distancing Analyzer using Deep Neural Nets

Smith

2022

Int. J. Interact. Mob. Technol.

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Corona Virus Disease (COVID) has so far infected millions of individuals, claiming the lives of tens of thousands. Italy and the United States, two major international powers, are particularly hard hit, with millions of people dead per day. For nations like as India, France, Germany and Spain, Corona has wreaked havoc on the global economy. Throughout the globe, this devastation has been inflicted by this catastrophic virus. After the lockdown limitations have been relaxed, it is necessary to guarantee that social distance is practised at the locations since no treatment has been identified thus far. After the lockdown restrictions were relaxed in countries like India, where fewer instances were recorded, the nation saw an increase in cases. Implementation of social distancing systems is the topic of this study, which employs sophisticated libraries to keep track of the distance between people in real time and implement the system. Deploying deep learning and Raspberry Pi, we want to change the system of social distance by using a small number of sensors to acquire real-time data. Corona Virus Disease (COVID) has so far infected millions of individuals, claiming the lives of tens of thousands. Italy and the United States, two major international powers, are particularly hard hit, with millions of people dead per day. For nations like India, France, Germany, and Spain, Corona has wreaked havoc on the global economy. Throughout the globe, this devastation has been inflicted by this catastrophic virus. After the lockdown limitations have been relaxed, it is necessary to guarantee that social distance is practiced at the locations since no treatment has been identified thus far. After the lockdown restrictions were relaxed in countries like India, where fewer instances were recorded, the nation saw an increase in cases. Implementation of social distancing systems is the topic of this study, which employs sophisticated libraries to keep track of the distance between people in real-time and implement the system. Deploying deep learning and Raspberry Pi, we want to change the system of social distance by using a small number of sensors to acquire real-time data.

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Applications of artificial intelligence with cloud computing in promoting social distancing to combat COVID-19

Cited by 4 publications

References 30 publications

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Evolution of automated learning techniques for combating COVID-19: an analysis

Artificial Intelligence Integrated Social Distancing Analyzer using Deep Neural Nets

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