IoT in Combating COVID-19 Pandemics: Lessons for Developing Countries

Peter, Oyekola; Swain, Suchismita; Muduli, Kamalakanta; Ramasamy, Adimuthu

doi:10.1007/978-3-030-79753-9_7

Cited by 11 publications

(2 citation statements)

References 55 publications

(41 reference statements)

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“…However, concerning the diagnosis process, the authors claimed that the proposed design of the smart helmet took less time compared to the normal screening process. The authors of [ 107 ] discussed how digital technologies such as IoT, AI, and cloud computing, which have a wider range of applications during this unfortunate crisis, can work together to lessen the impact of the outbreak.…”

Section: Artificial Intelligence and Machine Learning (Ml)mentioning

confidence: 99%

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Butt

Malik

Qamar

et al. 2023

Sensors

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Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

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Section: Artificial Intelligence and Machine Learning (Ml)mentioning

confidence: 99%

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Butt

Malik

Qamar

et al. 2023

Sensors

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“…Because of the vast amount of data created by the use of these devices, standard data analysis techniques can overload the system, necessitating the use of statistical tools for data interpretation. IOT and AI are currently being used in digital healthcare devices like phones and point-of-care platforms all around the globe, thanks to recent advancements (Peter et al, 2022). Currently, AI-enabled technologies use supervised, unstructured, or reinforced learning techniques.…”

Section: Advances In Intellectual Technologies and Its Barriers To Mh...mentioning

confidence: 99%

Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

Swain

Muduli

Luthra³

2023

IJIEOM

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PurposeThe goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.Design/methodology/approachPotential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.FindingsThe study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.Practical implicationsPromoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.Originality/valueAt this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

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