Impact of Artificial Intelligence and the Internet of Things in Modern Times and Hereafter: An Investigative Analysis

Gupta, Anshul; Singh, Sunil K.; Chopra, Muskaan

doi:10.1201/9781003369066-14

Cited by 2 publications

(1 citation statement)

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“…These cutting-edge methodologies hold the promise of deciphering the avalanche of real-time data generated by Internet of Things (IoT) devices, ultimately shedding light on the complex dynamics of disease outbreaks. Machine learning models, which form the bedrock of this analytical paradigm, are meticulously deployed to scrutinise and prognosticate disease trajectories, all based on the invaluable data streaming from IoT sources [25].…”

Section: Iot-driven Analytics and Predictive Modellingmentioning

confidence: 99%

SEIR‐driven semantic integration framework: Internet of Things‐enhanced epidemiological surveillance in COVID‐19 outbreaks using recurrent neural networks

Sarin,

Singh,

Kumar

et al. 2024

IET Cyber-Phy Sys Theory & Ap

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With the current COVID‐19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state‐of‐the‐art SEIR‐Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID‐19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real‐time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID‐19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R‐squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R‐squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.

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Section: Iot-driven Analytics and Predictive Modellingmentioning

confidence: 99%