Sum Of Squared Errors Research Articles

Influence of organic acids on the viscosity and rheological behavior of guar gum solution

The present study aims to determine the influence of four edible organic acids (ascorbic, citric, malic, and tartaric) at two concentrations (0.5, and 1 %) on the viscosity and rheological behavior of guar gum dispersion (0.2 %, w/v). The finding of this study revealed that the apparent viscosity of guar gum dispersions reduced when the shear rate increased. Additionally, the apparent viscosity of the guar gum dispersions reduced as the organic acids concentration increased from 0 to 1 %. The highest decrease in viscosity was related to 1 % ascorbic acid and the lowest was related to 0.5 % malic acid. The rheological behavior of dispersions were successfully modeled using Power law, Bingham, Herschel-Bulkley, and Casson models, and the Power law model was the best one for describing the behavior of guar gum dispersions containing organic acids. The Power law model showed good performance with the maximum r-value (mean = 0.9988) and least sum of squared error (SSE) values (mean = 0.0021) and root mean square error (RMSE) values (mean = 0.0119) for all samples. The consistency coefficient (k-value) values of the samples (Power law and Herschel-Bulkley models) reduced as the acid percent was increased. The sample containing 1 % tartaric acid had the lowest k-value and the sample containing 0.5 % ascorbic acid had the highest k-value. The flow behavior index (n-value) (Power law and Herschel-Bulkley models) of the samples increased when the acid percent was increased. In summary, the use of guar gum in food products containing high concentrations of tartaric and ascorbic acids is not recommended.

Parametric Optimization of Proton Exchange Membrane Fuel Cell Using Chaotic Swarm Intelligence Technique

AbstractThe proton exchange membrane fuel cell (PEMFC) converts chemical energy into electricity without pollution or noise. PEMFC has a stable electrolyte, reliable, fast start‐up, and lightweight portability. Optimal PEMFC parameter prediction is difficult. Due to inaccurate estimation, many meta‐heuristic models are inaccurate. Particle swarm, Ant colony, and grey wolf algorithms are tested for fuel cell parameter estimation. These traditional algorithms benefit from chaotic maps for exploitation and exploration. Analyzing V–I and P–I polarization curves, hybrid approaches estimate unknown parameters. Results are compared with others to support the identifying strategy. This project examined SR‐12 and NedSstack PS‐6 PEMFC stacks. The objective function was shown by the sum of square errors between the experiments, and once an appropriate map is identified, the single chaotic map inclusion method can perform better than the multiple chaotic map scheme, and the “Iterative” approach in chaotic swarm is the most promising for multidimensional challenges.

Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

Central Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2 °C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.

MATHEMATICAL APPROACH TO FORECAST THE ELECTRICITY CRISES IN PAKISTAN AND ITS MITIGATION

Electricity is widely recognized as the backbone of economic success and progress, playing a vital role in driving socioeconomic development. This comprehensive study conducted an in-depth analysis of Pakistan's electricity consumption and aimed to develop a suitable model for forecasting the country's future electricity consumption (E.C.). Due to the strong positive correlation between E.C. of different sectors with Population and Gross Domestic Product (GDP), multiple linear regressions were employed for the analysis of electricity consumption. To predict population and GDP different mathematical model such as linear, Exponential, Polynomial and logarithmic models were applied on population and GDP data. In order to get the best fitted model a number of goodness-of-fit tests Coefficient of Determination, Sum of Square Errors, Sums of Square Regression, Mean Square Error, Root Mean Square Error, And F- Statistics (Adj-R2, SSE, SSR, MSE, RMSE and F Statistics) applied and got the polynomial model to be the most effective one for population and GDP. According to the goodness-of-fit test-based models, Pakistan's population is on track for steady growth, with projections reaching approximately 244428310 in 2025 and further increasing to 319825879 in 2040. This indicates significant demographic changes on the horizon. At the same time, Pakistan's GDP per capita is expected to rise, starting at 1958.3 $ in 2025 and potentially reaching 3,210.9 $ in 2040, signifying a positive trajectory towards economic development and prosperity in the country. Based on the selected models suggested by goodness of fit tests, the study throws light on the expected future electricity consumption in Pakistan. According to these projections, the expected electricity consumption of Pakistan may reach 121,121.1 GWh in 2025, 138,861.8 GWh in 2030, 157,716.8 GWh in 2035, and 177,686.3 GWh in 2040 respectively.

A New Approach to Computationally- Successful Linear and Polynomial Regression Analytics of Large Data in Medicine

In the realm of healthcare, predictive modeling stands as a pivotal tool for deciphering patient outcomes and refining medical decision-making processes. However, the accuracy of machine learning algorithms, which underpin these predictive models, often falls short, leading to erroneous predictions. This study offers a new approach to optimize linear and polynomial regression models for healthcare analytics, which aims to tackle this challenge. In contrast to earlier efforts, this method focuses on using a scaled-down data transformation to improve linear regression model performance. The main goal of this study is to reduce the sum of squared errors (SSE) and improve the predictive power of linear regression models by using a data transformation function to reduce the size of all variables. In a series of experiments, we used non-Bayesian statistics in SPSS and Matlab to generate 40 trials of linear regression models, with 1,000 observations in each trial. In addition, we used SPSS for regression analysis, Excel for data manipulation, Wilcoxon signed-rank tests, and Cronbach’s alpha statistics for optimization model performance evaluation.Our findings show that the suggested scale-down transformation method is effective, since the sum of squared errors is significantly reduced (absolute Z-score=5.511, effect size=0.779, p-value<0.001, Wilcoxon signed-rank test). Furthermore, the optimized model's robust internal consistency was confirmed by inter-item reliability testing (Cronbach's alpha=0.993)

A New Approach to Computationally- Successful Linear and Polynomial Regression Analytics of Large Data in Medicine

In the realm of healthcare, predictive modeling stands as a pivotal tool for deciphering patient outcomes and refining medical decision-making processes. However, the accuracy of machine learning algorithms, which underpin these predictive models, often falls short, leading to erroneous predictions. This study offers a new approach to optimize linear and polynomial regression models for healthcare analytics, which aims to tackle this challenge. In contrast to earlier efforts, this method focuses on using a scaled-down data transformation to improve linear regression model performance. The main goal of this study is to reduce the sum of squared errors (SSE) and improve the predictive power of linear regression models by using a data transformation function to reduce the size of all variables. In a series of experiments, we used non-Bayesian statistics in SPSS and Matlab to generate 40 trials of linear regression models, with 1,000 observations in each trial. In addition, we used SPSS for regression analysis, Excel for data manipulation, Wilcoxon signed-rank tests, and Cronbach’s alpha statistics for optimization model performance evaluation.Our findings show that the suggested scale-down transformation method is effective, since the sum of squared errors is significantly reduced (absolute Z-score=5.511, effect size=0.779, p-value<0.001, Wilcoxon signed-rank test). Furthermore, the optimized model's robust internal consistency was confirmed by inter-item reliability testing (Cronbach's alpha=0.993).

NA-ALGINATE BEADS OF CALCIUM / IRON-LAYERED DOUBLE HYDROXIDE FOR TREATING WATER CONTAMINATED WITH AMOXICILLIN ANTIBIOTIC

The objective of this study was to prepare an adsorbent material from eggshells of chicken banished to the ambient as wastes to satisfy the ecological requirements of sustainable. The preparation process based on the extraction of calcium ions from eggshells and these ions must be reacted with iron to form nanoparticles of (Ca/Fe)-layered double hydroxides (LDHs) which immobilized as Na-alginate beads. Molar ration of calcium to iron, pH and dosage of LDH nanoparticles must be equal to 1, 12 and 5 g/100 mL to ensure that the prepared beads have highest ability to remove of Amoxicillin (AMOX) antibiotic with removal efficiency equal to 32% for operational conditions of Co=100 mg/L, beads dosage=0.5 g/50 mL, speed=200 rpm, pH=7 for 3 hrs. To increase this efficiency to ≥ 90%, best conditions must be time 90 min, pH 7, and beads mass 1.2 g/ 50 mL for Co 100 mg/L at 200 rpm in the batch mode. The Pseudo second order has high capability in the description of such tests with coefficient of determination (R2) ≥ 0.9924 and sum of squared error (SSE) ≤ 0.1287. Hence, the sorption of AMOX onto beads is governed by the chemisorption process. The reflections of XRD analysis proved the presence of (Ca/Fe)-LDH nanoparticles with size of 13.49 nm, calcium hydroxide (Ca(OH)2) and calcium carbonate (CaCO3)

Migration time prediction and assessment of toxic fumes under forced ventilation in underground mines

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines. To reduce numerical simulation time and optimize ventilation design, several back propagation neural network (BPNN) models optimized by honey badger algorithm (HBA) with four chaos mapping (CM) functions (i.e., Chebyshev (Che) map, Circle (Cir) map, Logistic (Log) map, and Piecewise (Pie) map) are developed to predict the migration time. 125 simulations by the computational fluid dynamics (CFD) method are used to train and test the developed models. The determination coefficient (R2), the variance accounted for (VAF), the Willmott’s index (WI), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the sum of squares error (SSE) are utilized to evaluate the model performance. The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2 (0.9945), WI (0.9986), VAF (99.4811%), and the lowest values of RMSE (15.7600), MAPE (0.0343) and SSE (6209.4), respectively. The wind velocity in roadway (Wv) is the most important feature for predicting the migration time of toxic fumes. Furthermore, the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.

A Final Cost Estimating Model for Building Renovation Projects

The construction sector in Greece has been developing radically in the field of building renovations. The foremost problem for projects in the building construction industry is producing an accurate and reliable cost estimate at the onset of construction. The artificial neural network (AΝΝ) approach, using data available at the early stages of the project, can help resolve or prevent any kind of difficulty that could make the successful completion of a building less likely. ANNs have been highly efficient in gaining results which could prevent the failure of building constructions projects. The ultimate goal is to highlight the usefulness of the adoption of ANNs models to predict the final cost of a building renovation project. Thus, construction companies could avoid financial failure, provided that the gap between cost prediction and final cost for renovation projects is minimized. This paper presents an artificial neural network (ANN) approach for predicting renovation costs in Greek construction projects. The study, based on a comprehensive literature review and real renovation data from construction companies, employs IBM SPSS Statistics software to build, train, and test the ANN model. The model, which uses initial cost, estimated time, and initial demolition cost as inputs, is based on the radial basis function procedure. The model presents high performance with up to 2% sum of squares error and near zero relative error, demonstrating the ANN’s effectiveness in estimating total renovation costs.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

Modeling Maillard reaction kinetics in spray dryers using both lumped‐ and distributed‐parameter modeling approaches

AbstractThis study aimed to develop a simulation model for the kinetics of Maillard reactions during the spray‐drying process of model systems. Two different modeling approaches were used: lumped‐parameter and distributed‐parameter models. The reaction kinetic model was developed by fitting experiment results from iso‐thermal heating experiments with different model systems at various temperatures and moisture contents with Arrhenius‐type equations. The developed reaction kinetic models were coupled with drying kinetics and segregation models adopted from previous studies. The prediction results from the models developed were then compared with those from spray‐drying experiments, and both modeling approaches showed a reasonable agreement with the experimental data. The distributed parameters modeling approach showed a 17.7% improvement in sum of square errors compared to the traditional lumped‐distributed modeling approach.Practical ApplicationsTwo different mathematical models with different approaches were developed and have shown reasonable performance in predicting the kinetics of Maillard reactions in spray drying. The models developed in this study can be used to improve the spray‐drying process of thermally sensitive products where Maillard reaction or other types of thermal degradation may occur.

Analisis Peramalan Persediaan Bahan Baku dan Penentuan Stok Penyangga (Buffer Stock) Udang Vaname (Littopenaeus Vannamei)

This study examines the analysis of raw material inventory forecasting and buffer stock of vaname shrimp at one of the fishery companies in Makassar Industrial Estate (PT Bogatama Marinusa Makassar). The analysis method used to determine the supply of raw materials needed by the company is the ARIMA Box-Jenkins method. This method is used to forecast raw material inventory on time series data. Determination of buffer stock is done using standard deviation and policy factors. Vaname shrimp (Littopenaeus vannamei) raw material data was obtained from 2020 to 2022 (156 weeks). The results showed that the highest amount of raw material inventory occurred in October week 147 in 2022, amounting to 152,792 tons, while the lowest amount of raw material inventory occurred in May week 122 in 2022, amounting to 13,102 tons. The best vaname shrimp raw material inventory model is the ARIMA (1,1,1) model which has a Sum Square Error (SSE) value of 1.70250, Mean Square Error (MSE) value of 0.0112007. This model is used to forecast raw material inventory for the next 48 weeks. The forecasting results show that there will be a decrease in week 1 to week 6 and a relative increase in raw materials in week 7 to week 48 with a MAPE value of 4.54%. The amount of buffer stock that must be owned by the company is 37.311 tons.

Cryptocurrency assets valuation based on statistics model and machine learning

As a matter of fact, cryptocurrency is an exchange media, which uses cryptographic functions to trade, which achieves decentralisation, transparency, and invariance using blockchain. With the rapid development since the first white book proposed in 2008. The market value of cryptocurrency has been exponentially expanded rapidly in recent years. On this basis, programmatic trading strategy are paid more attention by people in trading cryptocurrency contemporarily. With this in mind, this study provides three models, that is, Exponential Smoothing Model (ESM), Autoregressive Integrated Moving Average (ARIMA) model and Long short-term memory (LSTM) model to forecast the future price of cryptocurrency and use sum of square error to analyse the results of these prediction. According to the analysis, LSTM is the best model to forecast the price of cryptocurrency in this article. Overall, machine learning might be more useful to forecast in this fields than traditional statistical models. Investors and researchers in the future could continue to forecast the price of cryptocurrency combined with LSTM model.

Performance of rime-ice algorithm for estimating the PEM fuel cell parameters

The process of employing optimization techniques to identify the optimum unknown variables appropriate for the creation of a precision fuel-cell performance forecasting model is known as parameter identification of a PEM fuel cell (PEMFC). The manufacturer's datasheet may not always provide these parameters, thus it is necessary to ascertain them to precisely estimate and forecast the fuel cell's performance. six unknown parameters of a PEMFC are computed using six optimization techniques: The Rime-Ice algorithm (RIME), the Grey Wolf Optimizer (GWO), the Moth Flam Optimizer (MFO), the Tunicate Swarm Algorithm (TSA), the Sine Cosine Algorithm (SCA), and the Osprey Optimization Algorithm (OOA). These six parameters serve as choice variables during optimization, and the sum square error (SSE) between the estimated and measured cell voltages is the fitness function that needs to be minimized. Ned Stack PS6, a real-world PEM fuel cell model, is used to verify the functionality of all comparator algorithms, including the suggested RIME method. The RIME algorithm yielded an SSE of 1.945417827, which was followed by MFO, GWO, TSA, SCA, and OOA. In addition, it was concluded that RIME's convergence speed was quicker than that of the other methods examined. The comparative analysis is conducted using the same dataset and the same computation burden for each of the several optimization techniques to provide a fair performance evaluation. The performance of the suggested RIME against the alternative optimization algorithms is further examined using statistical analysis. The results demonstrate a good degree of agreement between the experimental data and the estimated voltage-current curves produced by the suggested RIME.

Elaboration of a Generalized Mixed Model for the wind speed distribution and an assessment of wind energy in Algerian Coastal regions and at the Capes

Understanding and evaluating wind energy involves various steps, including the observed temporal and directional variations in wind speed, choosing a suitable Probability Density Function (PDF) to describe wind regimes, and selecting an appropriate location for building wind farms. The wind speed data archived from nine meteorological stations along the northern coast of Algeria from 2017 to 2021 was used as a dataset to achieve these purposes. Zero-inflated mixed model (ZIMM) has been elaborated to model the wind speed regimes that combine two processes. The first process consists of mixing two PDFs that generate the counts data. The second process generates the residual zero-counts data. The estimation of ZIMM parameters is obtained with the Expectation–Maximization algorithm. ZIMM performances were evaluated at the selected sites using the commonly used goodness of fit (GOF) metrics: coefficient of determination (R2), root mean square error (RMSE), sum of square error (SSE), Kolmogorov–Smirnov (KS), Akaike information criterion (AIK), Bayesian Information Criterion (BIC), and compared with the widespread single PDFs: the two parameters Weibull, generalized extreme value (GEV) and Normal–Weibull mixed model. The datasets are used as input for assessing wind power and the feasibility of wind power plants in 24 Algerian Capes where the data is unavailable, using the Wind Atlas Analyses and Application Program (WAsP) and three different wind turbines, namely Nordex N50, Bonus B54, and Nordex N60. The results show significant variations in diurnal, monthly, and seasonal mean wind speed and power density. According to all GOF, the ZIMM PDF model outperforms conventional PDFs and the mixed model at the nine meteorological stations. The Bonus B54 wind turbine has the best capacity factor (Cp) with the lowest electrical production cost of all Algerian Capes, and the Cp exceeds 0.3 at 15 Capes from the 24 analyzed Capes. The highest Cp, about 0.467 with an annual electric production that reaches 4.09 GWh/year, is obtained at Cape Takouch with the lowest electrical production cost of 0.0414 $/kWh and 2.032 Kton/year of CO2 emission avoided.

Comparison of modeling, optimization, and prediction of important parameters in the adsorption of cefixime onto sol-gel derived carbon aerogel and modified with nickel using ANN, RSM, GA, and SOLVER methods

Today, the main goal of many researchers is the use of high-performance, economically and industrially justified materials, as well as recyclable materials in removing organic and dangerous pollutants. For this purpose, sol-gel derived carbon aerogel modified with nickel (SGCAN) was used to remove Cefixime from aqueous solutions. The influence of important parameters in the cefixime adsorption onto SGCAN was modeled and optimized using artificial neural network (ANN), response surface methodology (RSM), genetic algorithm (GA), and SOLVER methods. R software was applied for this purpose. The design range of the runs for a time was in the range of 5 min–70 min, concentration in the range of 5 mg L−1 to 40 mg L−1, amount of adsorbent in the range of 0.05 g L−1 to 0.15 g L−1, and pH in the range of 2.0–11. The results showed that the ANN model due to lower Mean Squared Error (MSE), Sum of Squared Errors (SSE), and Root Mean Squared Error (RMSE) values and also higher R2 is a superior model than RSM. Also, due to the superiority of ANN over the RSM model, the optimum results were calculated based on GA. Based on GA, the highest Cefixime adsorption onto SGCAN was obtained in pH, 5.98; reaction time, 58.15 min; initial Cefixime concentration, 15.26 mg L−1; and adsorbent dosage, 0.11 g L−1. The maximum adsorption capacity of Cefixime onto SGCAN was determined to be 52 mg g−1. It was found the pseudo-second-order model has a better fit with the presented data.

Health Insurance Premium Pricing Using Machine Learning Methods

Health insurance is important alongside life insurance products. This product’s subscription is gradually rising and has become one of the public’s main considerations due to their awareness on medical and surgical costs. This study is aimed at (i) identifying whether the independent factors are important in predicting the dependent variable, and (ii) to determine which model (logistic regression model/decision trees/neural networks) is the best model to be utilised. The SAS Enterprise Miner (E-Miner) was used to analyse the data and to select the best model. At the end of this study, the measurements like the Maximum Absolute Error (MAE), Average Squared Error (ASE), Root Average Squared Error (RASE), and Sum Squared Error (SSE) in the decision tree model indicated the lowest errors and led to the selection of the decision tree as the best model to be used in health insurance premium pricing.

An iterative approach for deriving and solving an accurate regression equation

ABSTRACT This paper introduces a method for deriving an accurate regression equation based on a set of any paired data, and a technique for solving the equation. For a practical example, we used five hundred seventy-one pairs of sediment concentration and river flow data to derive an accurate sediment rating equation. The graphs of the measured and predicted sediment concentrations matched each other, and data correlation showed Nash–Sutcliffe efficiency (NSE) of 0.9999860, coefficient of determination ( R 2 ) of 0.99998679, root mean square error (RMSE) of 0.0345, mean average error (MAE) of 0.0067, volume error (VE) of 1, and sum of square error (SSE) of 0.678631. To explain the technique of deriving and solving the accurate regression equation, separate files of video presentation and excel spreadsheet are provided as supplementary materials. In general, the method can be used to model any processes, and any calibration and validation processes can be addressed.

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Proton Exchange Membrane Fuel Cells (PEMFCs) are promising sources of clean and renewable energy, but their performance and efficiency depend on an accurate modeling and identification of their system parameters. However, existing methods for PEMFC modeling suffer from drawbacks, such as slow convergence, high computational cost, and low accuracy. To address these challenges, this research work proposes an enhanced approach that combines a modified version of the SqueezeNet model, a deep learning architecture that reduces the number of parameters and computations, and a new optimization algorithm called the Modified Transient Search Optimization (MTSO) Algorithm, which improves the exploration and exploitation abilities of the search process. The proposed approach is applied to model the output voltage of the PEMFC under different operating conditions, and the results are compared with empirical data and two other state-of-the-art methods: Gated Recurrent Unit and Improved Manta Ray Foraging Optimization (GRU/IMRFO) and Grey Neural Network Model integrated with Particle Swarm Optimization (GNNM/PSO). The comparison shows that the proposed approach achieves the lowest Sum of Squared Errors (SSE) and the highest accuracy, demonstrating its superiority and effectiveness in PEMFC modeling. The proposed approach can facilitate the optimal design, control, and monitoring of PEMFC systems in various applications.

Hydraulic flow unit and rock types of the Asmari Formation, an application of flow zone index and fuzzy C-means clustering methods

Rock types are the reservoir's most essential properties for special facies modeling in a defined range of porosity and permeability. This study used clustering techniques to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units (HFUs) were determined for studied data utilizing histogram analysis, normal probability analysis, and the sum of squared errors (SSE) statistical methods. Then, two flow zone index (FZI) and fuzzy c-means (FCM) clustering methods were used to determine the rock types in the given well according to the results obtained from the HFU continuity index acts in-depth. The FCM method, with a continuity number of 3.12, compared to the FZI, with a continuity number of 2.77, shows more continuity in depth. The relationship between permeability and porosity improved considerably by utilizing HFU techniques. This improvement is achieved using the FZI method study. Generally, all samples increased from 0.55 to 0.81 in the first HFU and finally to 0.94 in the fourth HFU. Similar flow properties in an HFU characterized the samples. In comparison, the correlation coefficients obtained in the FCM method are less than those in the general case of all HFUs. This study aims to determine the flowing fluid in the porous medium of the Asmari reservoir employing the c-mean fuzzy logic. Also, by determining the facies of the rock units, especially the siliceous-clastic facies and log data in the Asmari Formation, the third and fourth flow units have the highest reservoir quality and permeability. Results can be compared to determining HFU in nearby wellbores without cores.