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A WSN‐Based WbCNF Algorithm to Enable Cloud Computing and Big Data Analytics in Healthcare.

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Bibliographische Detailangaben
Titel: A WSN‐Based WbCNF Algorithm to Enable Cloud Computing and Big Data Analytics in Healthcare.
Autoren: Ramasamy, S.1 (AUTHOR) ramasamycse85@gmail.com, Vennila, V. Baby2 (AUTHOR) vbabyvennila@gmail.com
Quelle: International Journal of Communication Systems. 11/10/2025, Vol. 38 Issue 16, p1-13. 13p.
Schlagwörter: *CLOUD computing, *BIG data, *HEALTH care industry, *CONVOLUTIONAL neural networks, *MATHEMATICAL optimization, *DISTRIBUTED sensors, *RESOURCE allocation, *OPTIMIZATION algorithms
Abstract: Cloud computing is a technology that enables the internet‐based delivery of computer resources and services, giving customers access to on‐demand infrastructure, platforms, and applications without the need for local hardware or direct control. Cloud computing in healthcare is the use of internet‐based platforms and services for storing, managing, and processing healthcare data. Cloud computing in big data is the use of cloud‐based infrastructure and services to store, manage, and analyze large datasets. This paper proposed a WSN‐based Weighted Boolean Conjunctive Normal Form (WbCNF) method. This approach in cloud computing refers to the use of an optimization technique to handle large‐scale, distributed problems such as resource allocation, scheduling, and decision‐making in cloud environments. Cloud computing environments generate complex and dynamic constraints that can be modeled using weighted CNF formulas to indicate priorities or costs. The work allocation mechanism can be greatly improved, and job execution times can be lowered. This can be performed by employing a revolutionary whale‐based convolutional neural framework technique. The Python framework is used in the proposed approach. The experimental results show that the number of tasks required for the experiment has decreased due to the computing time. [ABSTRACT FROM AUTHOR]
Datenbank: Academic Search Index
Beschreibung
Abstract:Cloud computing is a technology that enables the internet‐based delivery of computer resources and services, giving customers access to on‐demand infrastructure, platforms, and applications without the need for local hardware or direct control. Cloud computing in healthcare is the use of internet‐based platforms and services for storing, managing, and processing healthcare data. Cloud computing in big data is the use of cloud‐based infrastructure and services to store, manage, and analyze large datasets. This paper proposed a WSN‐based Weighted Boolean Conjunctive Normal Form (WbCNF) method. This approach in cloud computing refers to the use of an optimization technique to handle large‐scale, distributed problems such as resource allocation, scheduling, and decision‐making in cloud environments. Cloud computing environments generate complex and dynamic constraints that can be modeled using weighted CNF formulas to indicate priorities or costs. The work allocation mechanism can be greatly improved, and job execution times can be lowered. This can be performed by employing a revolutionary whale‐based convolutional neural framework technique. The Python framework is used in the proposed approach. The experimental results show that the number of tasks required for the experiment has decreased due to the computing time. [ABSTRACT FROM AUTHOR]
ISSN:10745351
DOI:10.1002/dac.70261