Analysis of faults fixing system using rough sets: A case study in large-scale telecommunication company (telecom egypt)

Emam, O. E.; Salah, Mona; Abohany, Amr A.

doi:10.1109/icenco.2017.8289822

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Then, reducing the number of them in a set based on the reduced information policy is a separate process known as shorthand, whereby no more attributes can be deleted without losing information in the dataset. As discounts are minimal sub-groups that do not have any irreplaceable features, a reduction should be capable of classifying objects without changing the form of knowledge representation [12,13]. Subset A of group D is called an abbreviation if, and only if, it includes the following characteristics:…”

Section: Reducing Set Of Attributesmentioning

confidence: 99%

Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

El-Shafeiy¹,

Hassanien²,

Sallam³

et al. 2021

Computers, Materials &Amp; Continua

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Currently, COVID-19 is spreading all over the world and profoundly impacting people's lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients' serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COV-ID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN's prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.

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Section: Reducing Set Of Attributesmentioning

confidence: 99%