SDN-Aided Edge Computing-Enabled AI for IoT and Smart Cities

Singh, Rashandeep; Singh, Simranjit; Kumar, Sudhakar; Gill, Shabeg Singh

doi:10.1201/9781003213871-3

Cited by 20 publications

(2 citation statements)

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“…Machine learning (ML) has emerged as a formidable tool in the domain of epidemiological surveillance, lending its prowess to the prediction and intricate analysis of disease outbreaks. ML algorithms, encompassing the realms of deep learning and ensemble methods, are strategically harnessed to model the intricacies of disease dynamics [18,19].…”

Section: Machine Learning In Epidemiological Surveillancementioning

confidence: 99%

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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: Machine Learning In Epidemiological Surveillancementioning

confidence: 99%

“…Robustness in the realm of IoT-enhanced surveillance is intrinsically linked to data integrity, and cryptographic techniques stand as the mathematical guardians of this integrity. Let us delve into the mathematical core of hashing, a cryptographic technique that fortifies data integrity [19].…”

Section: Scalability and Robustness In Iot-enhanced Surveillancementioning

confidence: 99%