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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Deep convolutional neural network based Henry gas solubility optimization for disease prediction in data from wireless sensor network

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pp. 2273–2284Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1802XML
Received:
14 Feb 2024
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1802
Pages:
2273–2284

Abstract

In the fight against COVID-19, this study explores clinical image processing and deep learning for effective solutions. Emphasizing collaboration between scientists and policymakers, it addresses data reliability issues and sparse experimentation, critical for accurate COVID-19 identification and mitigation of underreported cases. The proposed Henry Gas Solubility (HGS) optimized Deep Convolutional Neural Network (DCNN) enhances prediction accuracy and computational efficiency, validated through rigorous experiments. The study highlights the importance of integrating diverse datasets and outlines future research directions, highlighting its potential impact on healthcare decision-making and pandemic response strategies.

Keywords

Subject Classifications

68T0768U10

References

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