Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus

Bilgehan, Bülent; Özyapıcı, Ali; Hammouch, Zakia; Gürefe, Yusuf

doi:10.1007/s00500-022-06996-y

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…This research served as a baseline for the work presented in this paper. Several studies have been conducted on data analysis for COVID-19 using different approaches such as time-series prediction [9], machine learning methods [10,11], and deep learning models [10,11]. These studies investigate factors that significantly affect the spread of the virus and make predictions about future trends.…”

Section: -1-current Approachmentioning

confidence: 99%

A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

Riasetiawan

Ashari

2023

Emerg Sci J

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The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PDF

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Section: -1-current Approachmentioning

confidence: 99%

A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

Riasetiawan

Ashari

2023

Emerg Sci J

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“…Example application areas are biomedical image analysis [5], economics [6], time-scale theory [7], chemical engineering [8], processing of real-world signals [9], fractional dynamical systems [10], and digital image interpolation [11]. A recent study developed an analytical model to predict COVID-19 spread by incorporating multiplicative calculus into machine learning [12]. The proposed prediction model generates measurements to identify the diffusion of COVID-19 in either precaution conditions or careless circumstances.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Solution of Multiplicative Differential Equations through Laguerre Polynomials

Kosunalp,

Bas,

Kosunalp

2024

Symmetry

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The field of multiplicative analysis has recently garnered significant attention, particularly in the context of solving multiplicative differential equations (MDEs). The symmetry concept in MDEs facilitates the determination of invariant solutions and the reduction of these equations by leveraging their intrinsic symmetrical properties. This study is motivated by the need for efficient methods to address MDEs, which are critical in various applications. Our novel contribution involves leveraging the fundamental properties of orthogonal polynomials, specifically Laguerre polynomials, to derive new solutions for MDEs. We introduce the definitions of Laguerre multiplicative differential equations and multiplicative Laguerre polynomials. By applying the power series method, we construct these multiplicative Laguerre polynomials and rigorously prove their basic properties. The effectiveness of our proposed solution is validated through illustrative examples, demonstrating its practical applicability and potential for advancing the field of multiplicative analysis.

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A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

Singh,

Khanna,

Garg

et al. 2024

Multimed Tools Appl

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Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus

Cited by 5 publications

References 25 publications

A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

An Efficient Solution of Multiplicative Differential Equations through Laguerre Polynomials

A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

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