A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

Pandey, Rohan; Gautam, Vaibhav; Pal, Ridam; Bandhey, Harsh; Dhingra, Lovedeep Singh; Sharma, Himanshu; Jain, Chirag; Bhagat, Kanav; Arushi, Arushi; Patel, Lajjaben; Agarwal, Mudit; Agrawal, Samprati; Jalan, Rishabh; Garg, Ayush; Wadhwa, Akshat; Misra, Vihaan; Agrawal, Yashwin; Rana, Bhavika; Kumaraguru, Ponnurangam; Sethi, Tavpritesh

doi:10.2196/preprints.25320

Cited by 28 publications

(18 citation statements)

References 7 publications

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“…There is an extreme lack of awareness and sensitization with the correct information to prevent outbreaks of the COVID-19 pandemic. Therefore, the authors [19] developed an application that uses NLP and Machine Learning that finds accurate information regarding COVID-19 from authentic resources, like WHO daily reports, which deliver content in Hindi by utilizing the text to speech appliances. To prevent COVID-19 spread among people, the automatic finding structure is a fast prediction choice to avoid COVID-19.…”

Section: Role Of Artificial Intelligence (Ai) In Covid-19 Pandemicmentioning

confidence: 99%

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Hussain

Ayoub

et al. 2021

Applied Sciences

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The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

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Section: Role Of Artificial Intelligence (Ai) In Covid-19 Pandemicmentioning

confidence: 99%

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Hussain

Ayoub

et al. 2021

Applied Sciences

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“…As shown in [101,105], and participants recruited by the study [54]. The types of data collected from these data sources were as follows: radiology images (eg, CT and x-ray) 54,[92][93][94][95][96]98,104], biological data (eg, protein and genome sequences) [53,[55][56][57][58][59][60][61][62][63][64][65][67][68][69][70][71][72][73][74][89][90][91], epidemiological data (eg, number of infected and recovered cases) [75][76][77][78][79][80][81][82][83][84][85]87,88,97,102], clinical data (eg, signs, symptoms, physi...…”

Section: Features Of Data Sets Used For Development and Validation Ofmentioning

confidence: 95%

“…AI has also been used for infodemiology [105]. Specifically, AI was used for raising awareness to use water, sanitation, and hygiene through combining authentic sources of information with daily news [105]. Multimedia Appendix 5 presents the purposes or uses of AI techniques in each included study.…”

Section: Purposes or Uses Of Ai Against Covid-19mentioning

confidence: 99%

Artificial Intelligence in the Fight Against COVID-19: Scoping Review

Abd‐Alrazaq¹,

Alajlani²,

Alhuwail³

et al. 2020

J Med Internet Res

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Background In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. Objective This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. Methods A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. Results We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. Conclusions The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

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“…Recently, ML has been interestingly applied in various areas of the IoT and IoMT ( Alimadadi et al, 2020 ; Ardabili et al, 2020 ; Cui et al, 2018 ; Durga, Nag & Daniel, 2019 ; Pannu, 2015 ; Pramanik et al, 2017 ; Xiao et al, 2018 ). ML can be effectively used in many ways to tackle the security issues of the IoMT ( Cui et al, 2018 ; Gupta et al, 2020a ; Pandey et al, 2020 ; Pirbhulal et al, 2019 ), whereas traditional security measures cannot prevent the system from zero-day attacks, and they are resource extensive for the IoMT systems. Advanced ML techniques can learn from massive generated data of the IoMT, thereby finding new trends of attacks ( Hussain et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

Cited by 28 publications

References 7 publications

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Artificial Intelligence in the Fight Against COVID-19: Scoping Review

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

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