Meta-Heuristic with Machine Learning-Based Smart e-Health System for Ambient Air Quality Monitoring

Rahi, Pankaj; Sood, Swati; Bajaj, Rohit

doi:10.1007/978-981-16-8892-8_38

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Rahi et al [13] developed a smart e-health AQ monitoring scheme that employs a metaheuristic FA and a CSO technique for powerfully optimizing the selected feature for giving improved outcomes in the feature selection technique. Furthermore, the feature is categorized based on SVM which predicts the index level of AQ and provides improved recall and precision.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System

2023

Global NEST: The International Journal

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<p>Air quality, radiation pollution, and water pollution were thekey featuresthat possess genuine challenges to the environment. Proper monitoring wasneededin such a way that the world couldaccomplish sustainable development, withkeeping a healthy society. Recently, environment monitoring is become a smart environment monitoring (SEM) scheme, with development of modern sensors and the advancements in the internet of things (IoT). IoT devices were mainly used in WSN for pollution control, vehicle marking, temperature control, and effective waste management. Consequently, modern method of environmental monitoring is called as SEM system, owing to usage of wireless sensors, IoT and AI.This paper leverages IoT devices for sustainable air pollution monitoring. The presented model derives an improved red fox optimizer with deep learning based air pollution monitoring system (IRFODL-APMS) using IoT devices. The presented IRFODL-APMS technique makes use of different IoT devices to collect data. Besides, the IRFODL-APMS model performs prediction process using deep learning based on long short term memory (LSTM). At last, IRFO technique is exploited as a hyperparameter tuning process of the LSTM model to accomplish enhanced prediction performance. The presented IRFODL-APMS model is simulated under distinct measures and the outcomes reported the enhanced predictive outcomes of the IRFODL-APMS approach over other existing models.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System

2023

Global NEST: The International Journal

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“…where f is a representation of Fourier transform of an extension of f to L 2 (R d). Convergence rate in the dimension is unaffected by (3). With increasing dimensionality, more derivatives are needed to make anything finite.…”

Section: 𝔼 [∥ ∥𝑥mentioning

confidence: 99%

“…Because clients' data are frequently stored as well as processed in far-away data centers, the use of the cloud poses security risks in contrast to a conventional model. A few of the many factors that may specifically affect cloud solution considerations include virtualization security threats, data storage location trials [3], potentially insecure web techniques, system interoperability problems and regulatory limitations. As a security measure, homomorphic cryptosystems, Service-Oriented Architecture (SOA), Secure Multi-party Computation (SMC), and Secret Share Schemes (SSS) are frequently utilized to guarantee a minimum level of privacy as well as confidentiality for patient health records.…”

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confidence: 99%

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Awasthi

Suchithra

Chakravarty

et al. 2023

Preprint

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Numerous aspects of healthcare have been altered by cloud-based computing. Scalability of required service as well as ability to upscale or downsize data storage, as well as the collaboration between AI and machine learning, are main benefits of cloud computing in healthcare. Current paper looked at a number of different research studies to find out how intelligent techniques can be used in health systems. The main focus was on security and privacy concerns with the current technologies. This study proposes a novel method for cloud service device-to-device communication using feature selection and classification for data analysis in an e-health system. Through a comprehensive requirement analysis as well as user study, the purpose of this research is to investigate viability of incorporating cloud as well as distributed computing into e-healthcare. After that, the smart healthcare system and conventional database-centric healthcare methods will be compared, and a prototype system will be created as well as put into use based on results. Convolutional adversarial neural networks with transfer perceptron are used to analyze the cloud-based e-health data that has been collected. Proposed technique attained training accuracy 98%, validation accuracy 93%, PSNR 66%, MSE 68%, precision 72%, QoS 63%, Latency 58%.

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Optimized Ensembled Model to Predict Diabetes Using Machine Learning

Kamal,

Sharma,

Kumar

2024

Optimized Predictive Models in Healthcare Using Machine Learning

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Meta-Heuristic with Machine Learning-Based Smart e-Health System for Ambient Air Quality Monitoring

Cited by 10 publications

References 34 publications

Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System

Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Optimized Ensembled Model to Predict Diabetes Using Machine Learning

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