An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak

Bebortta, Sujit; Dalabehera, Aditya Ranjan; Pati, Bibudhendu; Panigrahi, Chhabi Rani; Nanda, Gyana Ranjan; Sahu, Biswajit; Senapati, Dilip

doi:10.1016/j.smhl.2022.100308

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…The concept of the Internet of Medical Things (IoMT), the next-label invention, has played a tremendous role in the real-time diagnosis of COVID-19. 1,2 Cloud computing (CC) has recently spread widely. It is a terrific approach to managing enormous amounts of data and providing stakeholders with resources on demand using IoT sensors in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

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IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

et al. 2023

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Performing a coronary disease diagnosis remotely is challenging now‐a‐days. COVID‐19 is a worldwide pandemic, and methods for detecting COVID‐19 are hampered by insufficient data and a lack of validation testing. Internet of Things (IoT) applications that rely on cloud computing (CC) are being studied in an effort to improve e‐Healthcare systems, even though CC presents substantial latency, bandwidth, energy consumption, security and privacy issues and so forth. The extension to CC, fog computing (FC), can overcome these said limitations. This study aims to diagnose COVID‐19 patients to fight the outbreak remotely. This study proposes IFCnCov, which enables remote users to diagnose COVID‐19 disease in real‐time using integrated IoT, FC, and CC principles, as well as ensemble learning (EL) and deep learning (DL). The proposed system is a two‐layered architecture, trained with DL approaches to two different datasets: a symptom‐based dataset and a chest x‐rays imaging dataset obtained from the Kaggle repository employing several evaluative measures. From various experiments, this proposed IFCnCov achieves comparatively enhanced accuracies of 97.71% and 98.64%, precision of 97.38% and 98.52%, sensitivity of 98.19% and 98.92%, specificity of 97.21% and 98.34%, and F1‐scores of 97.79% and 98.72% in the first and second stages respectively, which also outperforms some other considered state‐of‐the‐art works. Additionally, this work is validated in terms of several network parameters, including scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.

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Section: Introductionmentioning

confidence: 99%

“…Increasing the GDP depends on the population's recovery; thus, it is a top priority. The concept of the Internet of Medical Things (IoMT), the next‐label invention, has played a tremendous role in the real‐time diagnosis of COVID‐19 1,2 . Cloud computing (CC) has recently spread widely.…”

Section: Introductionmentioning

confidence: 99%