Fuzzy Linear Regression Research Articles

Clustering-Based Hybrid Model for Predicting Symptoms for Colorectal Cancer: A Fuzzy Machine Learning Approach

Colorectal cancer (CRC) is a form of cancer that originates in the colon (large intestine) or rectum, which are components of the digestive system. It generally begins as small, benign growths known as polyps that can form on the inner lining of the colon or rectum. In Malaysia, colorectal cancer ranks among the most prevalent cancers, especially within the Chinese and Malay communities. A study released by the Malaysian National Cancer Registry indicates that colorectal cancer is the second most common cancer type following breast cancer, impacting both genders. The occurrence of colorectal cancer in Malaysia has been consistently increasing, with approximately 15–20% of cancer cases in the nation being colorectal cancer. This type of cancer arises when cells in the body start to multiply excessively, leading to various symptoms. In this research, a novel hybrid fuzzy linear regression with symmetric parameter clustering combined with a support vector machine (FLRWSPCSVM) model is employed to predict the high-risk symptoms associated with the onset of colorectal cancer in Malaysia. The study analyzed secondary data from 180 patients diagnosed with colorectal cancer and treated in a general hospital in Kuala Lumpur, considering twenty-five independent variables with various combinations of variable types. Furthermore, the model included parameters, errors, and explanations, along with two statistical measurement errors. The findings revealed that FLRWSPCSVM identified ovarian symptoms and a history of cancer symptoms as high-risk indicators for the development of colorectal cancer, with the lowest mean square error (MSE) recorded at 100.605 and a root mean square error (RMSE) of 10.030.

Adjustment of the Height Triangular Fuzzy Regression as Early Awareness of Breast Cancer

Breast cancer is the most popular malignancy among Malaysian women. In Malaysia, approximately one in every 19 women is at risk. Breast cancer is the most common cancer among Malaysian women (32.9%), followed by colorectal (11.9%) and ovarian cancers (7.2%). Many cases are found to be advanced, with considerable tumour development or metastasis to untreatable locations and unaware of the factors. This study aims to determine the best height triangular fuzzy regression model by adjustment between 0 – 1 and measure the value of statistical error using mean square error (MSE) and root mean square error (RMSE). Secondary data was used where 569 patients having BREAST cancer and receiving treatment in hospitals was recorded by nurses and doctors. The patient data for breast cancer were analysed using MATLAB, SPSS, and Microsoft Excel. Based on the results, H = 0 of fuzzy linear regression is the best model to predict the breast cancer awareness with lowest value of MSE and RMSE by 1.455 and 1.206 respectively. Malaysians must be aware of the warning factors of breast cancer to increase survival and minimise death rates.

Predictive Analytics of Comparison Fuzzy Modelling Towards Symptoms of Colorectal Cancer in Malaysia

Colorectal cancer (CRC) is a cancer that begins in the colon and rectum of the human body and is the third leading cause of death among cancer patients. Colorectal cancer develops when cells in the body begin to proliferate uncontrollably with such symptoms. However, the high-risk symptoms of CRC in Malaysia are still ambiguous and unclear. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory’s concept can deal with data not to a precise point value (uncertainty data), this study uses comparison fuzzy modelling as predictive analytics to predict the high-risk symptoms that contribute to the development of colorectal cancer in Malaysia. Secondary data of 180 colorectal cancer patients who received treatment in a general hospital with seventeen independent variables with different combinations of variable types were considered. Other than that, the parameter, error, and explanation for the model were included using two measurement statistical errors. Fuzzy linear regression with symmetric parameters found ovarian symptom is the high-risk symptom to develop colorectal cancer. The weightage value is 25.73, with the results of the least value of the mean square error (MSE) value being 98.212 and the root mean square error (RMSE) value being 9.910.

Numerical study of Carreau fuzzy nanofluid across a stretching cylinder using a modified version of Buongiorno's nanofluid model

Numerical study of Carreau fuzzy nanofluid across a stretching cylinder using a modified version of Buongiorno's nanofluid model

The Comparison of Fuzzy Regression Approaches with and without Clustering Method in Predicting Manufacturing Income

In the manufacturing area, predicting future income is more important to keep maintain their industry profits. In addition to this, most of the manufacturing company having a bit problem in predicting their manufacturing income, especially in terms of data and method used. Hence, this paper proposed another improvise method of fuzzy regression approach with and without clustering method for uses of predicting manufacturing income. Then, one of the widely uses of statistical analysis are fuzzy regression approach such as fuzzy linear regression (FLR) and fuzzy least squares regression (FLSR). Furthermore, clustering is one of the most common methods for grouping data based on its similarity. Apart from this, fuzzy c-means (FCM) recognised as the best clustering method. This study's model was evaluated by three measurements errors: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Based on numerical calculations, it was determined that the proposed fuzzy least square regression with fuzzy c-means clustering model is superior to others, with RMSE = 59756.78229, MAE = 2948.616554, and MAPE = 13.34916083. Therefore, this model indicates as the robust method and suitable use for prediction analysis, especially in handling uncertain and imprecise data.

Computation of Fuzzy Linear Regression Model using Simulation Data

Regression analysis is a powerful technique for determining the causal influence on population outcome, although it is susceptible to outliers. It also oversimplifies the real-world data and problem, as data is rarely linearly separable. This paper analyses the simulation data of a fuzzy linear regression model (FLRM) model, which can aid in minimizing the interference of unwanted information, hence improving the precision of results. The aim of the fuzzy linear regression model (FLRM) is used to determine the best prediction model with the lowest measurement error. The model provides a fundamental mathematical and statistical framework for acknowledging the data imprecision. This study has applied the fuzzy linear regression model (FLRM) to evaluate the data in 15 rows of respondent models. The study implemented measurement error of cross-validation technique which is mean square error (MSE) and root mean square error (RMSE), to enhance data accuracy. Microsoft Excel and Matlab were applied to obtain the result. The simulation result indicates that comparing models with two measurement errors should be used to determine the optimal results. MSE and RMSE value with six parameters of a degree of fitting H-value have been calculated in the study. It concludes that a degree of fitting H-value of 0.0 is proven as a good model with the lowest MSE and RMSE measurement errors.

Coordinating Qualitative Predictor Variables in an Applied Linear Model: Analysis and Application for Applied Sciences.

Background In applied sciences, statistical models are pivotal for uncovering relationships in complex datasets. The applied linear model establishes associative links between variables. While qualitative predictors are essential, their integration into linear models poses challenges. The dummy variable approach transforms qualitative variables into binary ones for regression analysis. Multilayer Feedforward Neural Networks (MLFFNN) offer validation of regression models, and fuzzy regression offers alternative methods to address the ambiguity of qualitative predictors. This study aims to enhance the integration of qualitative predictors in applied linear models through statistical methodologies. Material and methods This study design involves the transformation of qualitative predictors into dummy variables, the bootstrapping technique to improve the parameter estimates, theMultilayer Feedforward Neural Network, and fuzzy regression. This study uses the programming language Ras an analysis tool. Results The multiple linear regression model demonstrates precision and a significant fit (p<0.05), with an R-squared value of 0.95 and mean square error (MSE) of 9.97. Comparing actual and predicted values, fuzzy regression exhibits superior predictability over linear regression. The MLFFNN yields a reduced MSE net of 0.362, indicating enhanced prediction precision for derived models. Conclusion This study presents a precise methodology for integrating qualitative variables into linear regression, supported by the combination of specific statistical methodologies to enhance predictive modeling. By integrating fuzzy linear regression, MLFF neural networks, and bootstrapping, the proposed technique emerges as the most effective approach for modeling and prediction. These findings underscore the efficacy of this method in seamlessly integrating qualitative variables into linear models, ultimately enhancing accuracy and prediction capabilities.

Construction of Business Model of Unmanned Economy Under Digital Technology

In the ever-evolving landscape of modern business, the integration of advanced technologies is paramount for optimizing operations, ensuring efficiency, and staying competitive. The business model for the unmanned economy comprises the challenges related to supply chain and logistics. This paper proposed an architecture of LM-LSTM (Linear Regression and Long Short-Term Memory) model within the context of the Unmanned business model. The proposed model uses the mandami based fuzzy rule for the computation of the unmanned economy. Within the mandami fuzzy linear regression model is adopted for the computation and estimation of the variables related to the unmanned economy. The objective is to provide a comprehensive analysis of the impact of this predictive modeling system on various dimensions of the business. Through the generated rules the LSTM model is utilized for the classification and computation of the features related to supply chain, forecast demand and other parameters in an unmanned economy. The examination of 10 unmanned products in Chinese products are evaluated. The findings of LM-LSTM stated that Sales forecasting, one of the critical aspects of any business, has seen a remarkable improvement in accuracy, with an average Mean Absolute Error (MAE) of 3.00%. This accuracy ensures that products are produced and stocked according to actual demand, preventing costly overstocking or stockouts. The inventory management process has been streamlined, with tailored strategies for each product category. This adaptation has resulted in reduced stockouts, efficient parts sourcing, and minimal overstock situations. Supply chain optimization has significantly reduced lead times, enhancing customer satisfaction through timely product deliveries. Customer behavior analysis, facilitated by LM-LSTM, has led to a notable increase in sales across the product range, with an average increase of 91%. This enhanced customer engagement is coupled with substantial cost savings, with an overall reduction of 118%. Downtime has been minimized, contributing to smoother operations and improved customer service.

Fuzzy Linear Regression Modelling of Federal Government Budget Performance

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Analysis of Fuzzy Linear Regression Based on Intuitionistic Data

Analysis of Fuzzy Linear Regression Based on Intuitionistic Data

A Novel Approach to Fuzzy Implication Through Fuzzy Linear Regression

A Novel Approach to Fuzzy Implication Through Fuzzy Linear Regression

A new intelligent modelling two-stage of hybrid fuzzy prediction approach by using computation software

With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer.

Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease

Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease

The evaluation of potentially toxic elements using artificial neural networks and fuzzy linear regression analysis methods in cappadocian volcanic ash soils of Turkey

The aim of this study is to determine the relationships between some potentially toxic elements (PTE) (Cu, Mn, Ni, Zn) in human stomach and intestinal tissues and toxic element contents in soil, vegetables and fruits. This study was conducted in the eastern of Erciyes Strato volcano, an area of 2400km2 in Turkey. Tissue samples taken from the stomach and intestines of people living in the study area, soils, vegetables, and fruits were used as material. In the study, tissue samples of 26 people's stomach and intestines, 576 soil samples from 192 points and 3 different depths (0-30cm, 30-60cm, and 60-90cm) and vegetable and fruit samples from 137 sampling points were taken. Cu, Mn, Ni, and Zn contents of human tissue samples, soil samples, vegetable and fruit samples were determined. Artificial Neural Networks method (ANN) and Fuzzy Linear Regression Analysis (FLRA) methods were used to determine the relationships between PTE contents in human tissue samples and soils, vegetables, and fruits. Root Mean squared error (RMSE) and coefficient of determination (R2) indices were used as the test criteria for goodness of fit. When compared with ANN method, it was determined that PTE values in stomach and intestinal tissue estimated by FLRA method had the lowest error and high R2 values. It was found that the most effective variable in estimating the average PTE value in stomach and intestinal tissue is PTE values in soil. It was determined that the FLRA regression analysis method has a better predictive power than the ANN method. Using FLRA and ANN regression methods, it was determined that there is a statistically high relationship between PTE contents in soils and stomach and intestinal tissues. It is recommended to make the study findings more meaningful with effective and reliable service planning by using different regression analysis methods in ecological and clinical studies.

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

The assessment of rocking response is a challenging task due to its high nonlinearity. The present study investigates two methodologies to evaluate finite rocking rotations and overturn of three typical rocking systems. In particular, fuzzy linear regression (FLR) with triangular fuzzy numbers and a hybrid model combining logistic regression and fuzzy logic were adopted. To this end, three typical rocking structures were considered, and nonlinear time history analyses were performed to obtain their maximum response. Eighteen seismic intensity measures (IMs) extracted from recorded seismic accelerograms were considered to predict the responses. In the absence of rocking overturn, the finite rocking rotations and similarity ratios were calculated by adopting the FLR method. Moreover, extensive analysis was performed to evaluate the influence of each IM on the model’s predictions. On the other hand, rocking overturn was evaluated by logistic regression to compute the probability of collapse, followed by the FLR method to estimate the similarity between the different rocking-based structural systems. The root mean square error (RMSE) parameter and the log loss function were determined for every model to assess the predictions that emerged from the two fuzzy methods. As indicated, both methods demonstrated satisfactory results, presenting minimal deviations from the observed values. Finally, in the case of finite rocking rotation predictive models, remarkably high similarity ratios were observed among the various structures, with a median value of 0.96.

Forecasting Model Selection with Variables Impact to Predict Electricity Demand at Rajshahi City of Bangladesh

The purpose of this study is to forecast electricity demand by using the best-selected method which untangles all the factors that affect electricity demand. Three different methods traditional methods (Multiple Regression Model), modified-traditional methods (ARMA), and soft computing method (Fuzzy Linear Regression Model) are selected for prediction. Environmental parameters like temperature, humidity, and wind speed are included as variables as Rajshahi has very impactful weather. The impact of each variable was calculated from their standardized values to know the effect of environmental parameters. The accuracy of the three forecasting models is compared by different statistical measures of errors. Using Mean Absolute Percentage Error (MAPE), the errors of the Multiple Regression Model, ARMA, and Fuzzy Linear Regression (FLR) Model are 6.85%, 22.24%, and 4.45%. The other three measures of error also give the FLR gives the best results. Finally, the electricity demand of Rajshahi City for the next five years is forecasted using the Fuzzy Linear Regression Model.

The use of fuzzy linear regression for the selection of the most appropriate fuzzy implication in a fly ash-based concrete model

In this research, fuzzy linear regression (FLR) method combined with three well-known fuzzy implications was implemented for evaluating the relation among the amount of fly ash in concrete mixture and the compressive strength of concrete. More specifically, 267 experimental data 40 of which were used for testing the validation of the process were subjected to FLR method for calculating the truth values, which indicated the degree of how the experimental outputs belong to the predicted ones. Also, the degree of fuzziness was calculated for performing the sensitivity analysis of the model. The truth values that emerged were used for applying three basic fuzzy implications such as Lukasiewicz, Reinchenbach, and Kleene-Dienes implication. By evaluating and comparing the results of every fuzzy implication, it was concluded that Lukasiewicz was the most appropriate implication method as it yielded the smallest deviation of truth values (σ = 4.00) in contrast to the theoretical ones (σ = 4.83 in Reinchenbach and σ = 12.31 in Kleene-Dienes fuzzy implication). The accuracy of the FLR method was also validated for calculating the coefficient of the mean absolute percentage error level (MAPE = 5.56%) of the blind prediction process, and the results revealed that the application of fuzzy linear regression method is suitable for evaluating the truth values of experimental data in order to be used in fuzzy implications. Thus, it is a satisfactory procedure for making inferences between concrete parameters.

Evaluation of Pedestrian Level of Service at Signalised Intersections from the Elderly Perspective

The crossing decisions and behaviour of elderly pedestrians are affected by the pedestrian level of service (PLOS). In this paper, an evaluation model was established to analyse the relationship between the traffic environment and the perceived evaluation of elderly pedestrians. Firstly, the characteristic parameters of the selected intersections and the perceived evaluation data of elderly pedestrians at the synchronisation scenery were extracted using manual recording and questionnaire-based truncation methods. The correlation between the perceived evaluation data of elderly pedestrians and the traffic parameters were tested with respect to the dimensions of safety, convenience and efficiency. Then, the significant parameters affecting PLOS were recognised. Based on the traffic characteristic parameters, the PLOS evaluation model from the elderly perspective was established using the fuzzy linear regression method. PLOS classification thresholds were obtained using the fuzzy C-means clustering algorithm. The data from two intersections were used to validate the model. The results show that the difference between the actual and the predicted PLOS values of the two crosswalks were 0.2 and 0.1, respectively. Thus, the proposed PLOS evaluation model in this paper can be used to accurately predict the PLOS from the elderly perspective using the traffic data of signalised intersections.

Fuzzy Bridge Regression Model Estimating via Simulation

The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared using standard mean squares error via simulated experiments and taking different sample sizes (20, 40, 80, and 160). The model's superiority was shown by achieving the least value of the mean squares error (MSE(, which indicated by the fuzzy bridge regression model.&#x0D;

An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income

Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.