Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids

Zhang, Zhongwei; Al-Bahrani, Mohammed; Ruhani, Behrooz; Heybatian Ghalehsalimi, Hossein; Zandy Ilghani, Nastaran; Maleki, Hamid; Ahmad, Nafis; Nasajpour-Esfahani, Navid; Toghraie, Davood

doi:10.1016/j.cej.2023.144362

Cited by 13 publications

(6 citation statements)

References 119 publications

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“…A greater number of distinct membership functions for an input indicates that the clusters may cover a wider range of that input, suggesting that the input is more influential in estimating DTI measures (or any other output). 64 Theoretically, if an input has only one distinct membership function, it implies that, in the centers of all the data clusters, the input has the same value. Such an input plays an identical role across all fuzzy rules and can be ignored in modeling; however, it does not necessarily diminish its importance as a factor in the disease itself.…”

Section: Methodsmentioning

confidence: 99%

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

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Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

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Section: Methodsmentioning

confidence: 99%

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

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“…There is a lack of understanding and explaining how to model ANFIS and how to determine the number of MF and MF type in the literature. Only Zhang et al [ 12 ] stated a systematic optimization study to precise prediction of thermophysical properties of hybrid nanofluids by applying ANFIS with different types of clustering techniques, including grid partitioning, subtractive clustering, and fuzzy c-means. They indicated that the number of MFs for each input and the type of MF for each input are the structural parameters for grid partitioning ANFIS, and they tested all MFs but only with limited MF numbers (2 and 3) for each inputs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Only Zhang et al [ 12 ] stated a systematic optimization study to precise prediction of thermophysical properties of hybrid nanofluids by applying ANFIS with different types of clustering techniques, including grid partitioning, subtractive clustering, and fuzzy c-means. They indicated that the number of MFs for each input and the type of MF for each input are the structural parameters for grid partitioning ANFIS, and they tested all MFs but only with limited MF numbers (2 and 3) for each inputs [ 12 ]. There are some studies [ [13] , [14] , [15] , [16] , [17] ] related to the prediction of chromium adsorption via ANFIS, however, none of them focused on optimization of ANFIS parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

Duranoğlu,

Sinan Altın,

Küçük

2024

Heliyon

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“…Zhang et al [23] harnessed machine learning to predict crucial thermophysical properties of water-based oxide-MWCNT hybrid nanofluids, offering potential cost and time savings in experimental research. They achieved remarkable precision through rigorous optimisation of their ANFIS models using clustering techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The extensive body of literature has emphasised the enhanced predictive attributes of all the advanced modelling techniques, including ANFIS, ANN, SVM, GA, and LASSO, among others, compared to traditional techniques. Various studies conducted by researchers [20][21][22][23][24][25][26][27][28] have collectively demonstrated the remarkable accuracy and effectiveness of these advanced models in predicting a wide range of thermophysical properties inherent to nanofluids. These properties encompass critical parameters such as λ, µ, density, specific heat capacity, and more.…”

Section: Introductionmentioning

confidence: 99%

Experimental study and ANFIS modelling of the thermophysical properties and efficacy of GNP-Al2O3 hybrid nanofluids of different concentrations and temperatures

Borode,

Tshephe,

Olubambi

et al. 2023

SN Appl. Sci.

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This study delves into an extensive investigation of the thermophysical properties and heat transfer efficacy of a hybrid nanofluid incorporating graphene nanoplatelets and γ-Al2O3 nanoparticles dispersed in deionised water. The nanofluids were characterised for their viscosity (µ), thermal conductivity (λ), and electrical conductivity (σ) over a 15–40 °C temperature range for varying nanoparticle loading (0.1–0.4 vol%). The experimental results revealed notable enhancements in µ, λ, and σ with increasing nanoparticle concentration, while µ decreased at elevated temperatures as λ and σ increased. At the highest concentration (0.4 vol%), µ increased by 21.74%, while λ and σ exhibited peak enhancements of 17.82% and 393.36% at 40 °C. An Adaptive Neuro-fuzzy Inference System (ANFIS) model was devised to enhance predictive precision by meticulously optimising the number of membership functions (MFs) and input MF type. The ANFIS architecture that exhibited the most remarkable agreement with the experimental data for µ, λ, and σ was found to utilise the Product of Sigmas, Difference of Sigmas, and Generalized Bell MFs, respectively, with corresponding input MF numbers being 2–3, 3–2, and 3–2. The optimal ANFIS model for µ, λ, and σ exhibits a higher prediction accuracy with an R2 value of 0.99965, 0.99424 and 0.99995, respectively. The Figure of Merit analysis using Mouromtseff Number identified an optimal nanoparticle concentration range of 0.1–0.2 vol% for enhanced heat transfer performance with a reasonable µ increase. This range guides practitioners in utilising hybrid nanofluids effectively while managing potential drawbacks.

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Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids

Cited by 13 publications

References 119 publications

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

Experimental study and ANFIS modelling of the thermophysical properties and efficacy of GNP-Al2O3 hybrid nanofluids of different concentrations and temperatures

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