Different Solution Strategies for Solving Epidemic Model in Imprecise Environment

Mahata, Animesh; Mondal, Sankar Prasad; Ahmadian, Ali; Ismail, Fudziah; Alam, Shariful; Salahshour, Soheil

doi:10.1155/2018/4902142

Cited by 14 publications

(5 citation statements)

References 77 publications

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“…In recent years, there has been a great interest in research with fuzzy difference equations and stability of fuzzy difference equations. [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Fuzzy Set and Fuzzy Difference Equationsmentioning

confidence: 99%

A Mathematical Model of Susceptible Diabetes Complication (SDC) Model in Discrete Time Fuzzy and Crisp Environment

Merdan

Şişman

2022

Cumhuriyet Science Journal

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In this study, we examined the mathematical model of the discrete-time equation system with susceptible diabetes complication (SDC), which is known to be caused by environmental and genetic factors in a fuzzy environment. From the diabetes complication (DC) model, the susceptible diabetes complication (SDC) model is being developed. It was obtained using definitions of how the behavior of this model changes in a fuzzy environment. A nonlinear differential equation system transforms the sensitive diabetes complication (SDC) model into a discrete time equation system. Stability analysis of the model with jury criterion was examined. In addition, numerical solutions and graphics of the analysis of the discrete model in fuzzy environment are obtained by using the MATLAB package program.

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Section: Fuzzy Set and Fuzzy Difference Equationsmentioning

confidence: 99%

A Mathematical Model of Susceptible Diabetes Complication (SDC) Model in Discrete Time Fuzzy and Crisp Environment

Merdan

Şişman

2022

Cumhuriyet Science Journal

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“…Indeed, the applications of FDEs are not limited to the theory of control, but they have been proposed in studying biomathematics [192], diabetes mellitus [193], [194], cerebrospinal fluid pressure [126], hydrologic process [195], water movement in a horizontal column [196], epidemic model [197], population growth model [82], [98], Newton's law of cooling [72], electric circuits [72], [198], and the brain tumor [199]. Moreover, some of the other proposed applications of FDEs can be found in [200] for a predatorprey model, in [201] for production inventory model, in [72] for an economical investment, and in [202] for oil palm frond.…”

Section: Applications Of Fdesmentioning

confidence: 99%

A Review on Fuzzy Differential Equations

Mazandarani¹,

Li²

2021

IEEE Access

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Since the term "Fuzzy differential equations" (FDEs) emerged in the literature in 1978, prevailing research effort has been dedicated not only to the development of the concepts concerning the topic, but also to its potential applications. This paper presents a chronological survey on fuzzy differential equations of integer and fractional orders. Attention is concentrated on the FDEs in which a definition of fuzzy derivative of a fuzzy number-valued function has been taken into account. The chronological rationale behind considering FDEs under each concept of fuzzy derivative is highlighted. The pros and cons of each approach dealing with FDEs are also discussed. Moreover, some of the proposed FDEs applications and methods for solving them are investigated. Finally, some of the future perspectives and challenges of fuzzy differential equations are discussed based on our personal view point.

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“…Also, various investigators have applied FST to obtain good results in the field of science and engineering. Such as the Fuzzy HIV model 47 , Predator–prey model 48 , Growth model 49 , Population dynamics model 50 , Application of fuzzy Laplace transform 51 , Fuzzy Integro-differential equation 52 , Giving up smoking model 53 , Fuzzy chemostat model 54 , Fuzzy dengue virus model 55 , fuzzy fractional PDEs 56 , Fuzzy epidemic model 57 , Uncertain conjugate heat transfer 58 and Transient heat transfer problem 59 .…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of a second-grade fuzzy hybrid nanofluid flow and heat transfer over a permeable stretching/shrinking sheet

Nadeem

Siddique

Awrejcewicz

et al. 2022

Sci Rep

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In this work, the heat transfer features and stagnation point flow of Magnetohydrodynamics (MHD) hybrid second-grade nanofluid through a convectively heated permeable shrinking/stretching sheet is reported. The purpose of the present investigation is to consider hybrid nanofluids comprising of Alumina $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} } \right)$$ Al 2 O 3 and Copper $$\left( {{\text{Cu}}} \right)$$ Cu nanoparticles within the Sodium Alginate (SA) as a host fluid for boosting the heat transfer rate. Also, the effects of free convection, viscous dissipation, heat source/sink, and nonlinear thermal radiation are considered. The converted nonlinear coupled fuzzy differential equations (FDEs) with the help of triangular fuzzy numbers (TFNs) are solved using the numerical scheme bvp4c. The numerical results are acquired for various engineering parameters to study the Nusselt number, skin friction coefficient, velocity, and temperature distribution through figures and tables. For the validation, the current numerical results were found to be good as compared to existing results in limiting cases. It is also inspected by this work that with the enhancement of the volume fraction of nanoparticles, the heat transfer rate also increases. So, it may be taken as a fuzzy parameter for a better understanding of fuzzy variables. For the comparison, the volume fraction of nanofluids and hybrid nanofluid are said to be TFN [0, 0.1, 0.2]. In the end, we can see that fuzzy triangular membership functions (MFs) have not only helped to overcome the computational cost but also given better accuracy than the existent results. Finding from fuzzy MFs, the performance of hybrid nanofluids is better than nanofluids.

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Different Solution Strategies for Solving Epidemic Model in Imprecise Environment

Cited by 14 publications

References 77 publications

A Mathematical Model of Susceptible Diabetes Complication (SDC) Model in Discrete Time Fuzzy and Crisp Environment

A Mathematical Model of Susceptible Diabetes Complication (SDC) Model in Discrete Time Fuzzy and Crisp Environment

A Review on Fuzzy Differential Equations

Numerical analysis of a second-grade fuzzy hybrid nanofluid flow and heat transfer over a permeable stretching/shrinking sheet

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