A fault-tolerant clustering algorithm for processing data from multiple streams

Otero, Abraham; Félix, Paulo; Márquez, David G.; García, Constantino A.; Caffarena, Gabriel

doi:10.1016/j.ins.2021.10.049

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…2 We, therefore, require advanced frameworks for early detection and diagnosis of cardiac irregularities to administer appropriate medical care. The recent advancements in wearable electronics and data transmission infrastructure has led to several devices capable of monitoring human health with the help of wireless sensors [1][2][3]. The human heart is a crucial organ responsible for blood circulation and the epicenter of cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Kumar¹,

Mallik²,

Kumar³

et al. 2023

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Kumar¹,

Mallik²,

Kumar³

et al. 2023

Computers in Biology and Medicine

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“…Large datasets in the distributed cloud environment are reduced by using different clustering algorithms such as fuzzy C‐means. Data grouped in clusters make data retrieval faster and more accurate 21,22 . Keeping in mind the benefits of clustering techniques for the cloud computing environment, a novel technique of clustering is introduced, which is specifically used for cloud migration frameworks.…”

Section: Introductionmentioning

confidence: 99%

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Aslam,

Rahim,

Watada

et al. 2023

Trans Emerging Tel Tech

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Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

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