Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees

Uppal, Mudita; Gupta, Deepali; Juneja, Sapna; Dhiman, Gaurav; Kautish, Sandeep

doi:10.1155/2021/8106467

Cited by 47 publications

(29 citation statements)

References 42 publications

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“…(a) Social Connect of Individuals. People are socially so deeply connected that they cannot restrict meeting their near and dears, be it in a residential society, a township, a village [45]. (b) Attending the Di erently Abled Ones.…”

Section: Social Bindingsmentioning

confidence: 99%

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IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

Kaur

Elarabawy

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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The COVID-19 infestation has resulted in the loss of the lives of millions of people and the destruction of commercial, communal, and healthcare systems around the world. To control these kinds of pandemics, complete knowledge of the traits and responses to the disease is required, and this knowledge can be gathered using various computational methods. These computational techniques can be very helpful in information building required for taking appropriate action timely and applying preventive mechanisms. Although there are numerous computational approaches that the researchers are applying over the past few months, there is a need to re-evaluate and compare these available techniques for finding out the best technique that can help reduce the extent of COVID-19. Hence, in this paper, we carried out a literature review to emphasize the assistance of various computational approaches around COVID-19. The paper presents a categorization of approaches such as multicriteria decision-making, fuzzy AHP, machine learning, neural network, and data science that have been used to deal with the epidemic. Furthermore, this study discusses various challenges experienced when accessing the COVID-19 data. The conclusions of the paper propose the best computational approach among all to be appraised for future investigations and implementations.

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Section: Social Bindingsmentioning

confidence: 99%

“…ere have been administrative e orts to make people isolated, but on ground, things have not been executed as planned. It has been observed that huge gatherings have taken place irrespective of any number of daily positivity rates in one or the other part of the world [46].…”

Section: Devotional and Religiousmentioning

confidence: 99%

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

Kaur

Elarabawy

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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“…Biological big data: High-throughput sequencing technology is rapidly evolving, increasing the data output capability of life-science-related research organisations, resulting in a variety of gaps across the spectrum of genomics, transcriptomics, proteomics, and metabolomics research, among others [32,33]. The enormous value of these biological data serves to successfully encourage the advancement of biological research, which has applications in agriculture, health, and medicine, among other fields of endeavour [34].…”

Section: Big Data In Health Carementioning

confidence: 99%

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

Juneja¹,

Mohafez

El-Bayoumy³

et al. 2022

Sustainability

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The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

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“…For example, real-time data can be provided by Internet of Things solutions, and large data flows are managed and secured by robust digital infrastructures. In addition, flows and decision-making support are improved by command centers, artificial intelligence, and machine learning [10][11][12]. However, it is through the creation of DTs that much of this can be brought together [8].…”

Section: Introductionmentioning

confidence: 99%

Digital Twins for Managing Health Care Systems: Rapid Literature Review

Elkefi¹,

Asan²

2022

J Med Internet Res

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Background Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk. Objective This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology. Methods We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized. Results The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient. Conclusions The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

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Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees

Cited by 47 publications

References 42 publications

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

Digital Twins for Managing Health Care Systems: Rapid Literature Review

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