Search results for: multilevel regression

Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

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Authors: Han Longxiang

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In the process of treating industrial oil wastewater with complex components, the traditional treatment methods (flotation, coagulation, microwave heating, etc.) often produce high operating costs, secondary pollution, and other problems. In order to solve these problems, the materials with high flux and stability applied to surfactant-stabilized emulsions separation have gained huge attention in the treatment of oily wastewater. Nevertheless, four stable oil-in-water emulsions can be formed due to different surfactants (surfactant-free, anionic surfactant, cationic surfactant, and non-ionic surfactant), and the previous advanced materials can only separate one or several of them, cannot effectively separate in one step. Herein, a facile synthesis method of graphene-based multilevel filter materials (GMFM) can efficiently separate the oil-in-water emulsions stabilized with different surfactants only through its gravity. The prepared materials with high stability of 20 cycles show a high flux of ~ 5000 L m-2 h-1 with a high separation efficiency of > 99.9 %. GMFM can effectively separate the emulsion stabilized by mixed surfactants and oily wastewater from factories. The results indicate that the GMFM has a wide range of applications in oil-in-water emulsions separation in industry and environmental science.

Keywords: emulsion, filtration, graphene, one-step

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Without a doubt, soil shear strength is the most important property of the soil. The majority of fatal and catastrophic geological accidents are related to shear strength failure of the soil. Therefore, its prediction is a matter of high importance. However, acquiring the shear strength is usually a cumbersome task that might need complicated laboratory testing. Therefore, prediction of it based on common and easy to get soil properties can simplify the projects substantially. In this paper, A hybrid model based on the classification and regression tree algorithm and logistic regression is proposed where each leaf of the tree is an independent regression model. A database of 189 points for clay soil, including Moisture content, liquid limit, plastic limit, clay content, and shear strength, is collected. The performance of the developed model compared to the existing models and equations using root mean squared error and coefficient of correlation.

Keywords: model tree, CART, logistic regression, soil shear strength

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Authors: Deepak Raj Bhat, Ryuichi Yatabe

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In this study, a residual-state creep failure model was developed based on the residual-state creep test results of clayey soils. To develop the proposed model, the regression analyses were done by using the R. The model results of the failure time (tf) and critical displacement (δc) were compared with experimental results and found in close agreements to each others. It is expected that the proposed regression model for residual-state creep failure will be more useful for the prediction of displacement of different clayey soils in the future.

Keywords: regression model, residual-state creep failure, displacement prediction, clayey soils

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Authors: O. Poleshchuk, E. Komarov

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This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

Keywords: interval type-2 fuzzy sets, fuzzy regression, weighted interval

Procedia PDF Downloads 336

Authors: Han Longxiang

Abstract:

In the process of treating industrial oily wastewater with complex components, the traditional treatment methods (flotation, coagulation, microwave heating, etc.) often produce high operating costs, secondary pollution, and other problems. In order to solve these problems, the materials with high flux and stability applied to surfactant-stabilized emulsions separation have gained huge attention in the treatment of oily wastewater. Nevertheless, four stable oil-in-water emulsions can be formed due to different surfactants (surfactant-free, anionic surfactant, cationic surfactant, and non-ionic surfactant), and the previous advanced materials can only separate one or several of them, cannot effectively separate in one step. Herein, a facile synthesis method of graphene-based multilevel filter materials (GMFM) which can efficiently separate the oil-in-water emulsions stabilized with different surfactants only through its gravity. The prepared materials with high stability of 20 cycles show a high flux of ~ 5000 L m-2 h-1 with a high separation efficiency of > 99.9 %. GMFM can effectively separate the emulsion stabilized by mixed surfactants and oily wastewater from factories. The results indicate that the GMFM have a wide range of applications in oil-in-water emulsions separation in industry and environmental science.

Keywords: emulsion, filtration, graphene, one-step

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Authors: Shih-Pin Chen, Shih-Syuan You

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This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria.

Keywords: dissemblance index, forecasting, fuzzy sets, linear regression

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Authors: Zikiou Nadia, Lahdir Mourad, Ameur Soltane

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In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement.

Keywords: image compression, 2D discrete wavelet transform (DWT-2D), support vector regression (SVR), SVM Kernels, run-length, arithmetic coding

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Authors: Ali Hesari, Arezoo Esmaeeli

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Regarding to dramatic changes of fertility and higher order births during recent decades in Iran, access to knowledge about affecting factors on different birth orders has crucial importance. In this study, According to hierarchical structure of many of social sciences data and the effect of variables of different levels of social phenomena that determine different birth orders in 365 days ending to 1390 census have been explored by multilevel approach. In this paper, 2% individual row data for 1390 census is analyzed by HLM software. Three different hierarchical linear regression models are estimated for data analysis of the first and second, third, fourth and more birth order. Research results displays different outcomes for three models. Individual level variables entered in equation are; region of residence (rural/urban), age, educational level and labor participation status and province level variable is GDP per capita. Results show that individual level variables have different effects in these three models and in second level we have different random and fixed effects in these models.

Keywords: fertility, birth order, hierarchical approach, fixe effects, random effects

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Authors: Adriano Z. Zambom, Preethi Ravikumar

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One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: additive model, nonparametric regression, variable selection, Akaike Information Criteria

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Authors: Masood Beheshtirad

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Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed.

Keywords: landslide, mapping, multiple model, regression

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Authors: Arun Goel

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Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper, attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly and Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly and Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicates the improvement in the performance of SVM (Poly and Rbf) in comparison to dimensional form of scour.

Keywords: modeling, pier scour, regression, prediction, SVM (Poly and Rbf kernels)

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Authors: Voodi Kalandhar, Veera Reddy, Yuva Tejasree

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Researchers have become interested in multilevel inverters (MLI) because of their potential for medium- and high-power applications. MLIs are becoming more popular as a result of their ability to generate higher voltage levels, minimal power losses, small size, and low price. These inverters used in high voltage and high-power applications because the stress on the switch is low. Even though many traditional topologies, such as the cascaded H-bridge MLI, the flying capacitor MLI, and the diode clamped MLI, exist, they all have some drawbacks. A complicated control system is needed for the flying capacitor MLI to balance the voltage across the capacitor and diode clamped MLI requires more no of diodes when no of levels increases. Even though the cascaded H-Bridge MLI is popular in terms of modularity and simple control, it requires more no of isolated DC source. Therefore, a topology with fewer devices has always been necessary for greater efficiency and reliability. A new single-phase MLI topology has been introduced to minimize the required switch count in the circuit and increase output levels. With 3 dc voltage sources, 8 switches, and 13 levels at the output, this new single- phase MLI topology was developed. To demonstrate the proposed converter's superiority over the other MLI topologies currently in use, a thorough analysis of the proposed topology will be conducted.

Keywords: DC-AC converter, multi-level inverter (MLI), diodes, H-bridge inverter, switches

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Authors: Abdullah A. AlShaher

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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Xianpei Li, Yanmin Yuan, Tracy Lin Huan

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Researchers are doing enchanting research into the inherited features of Alzheimer’s disease and probable consistent therapies. In Alzheimer’s, memories are extinct in reverse order; memories formed lately are more transitory than those from formerly. Reminiscence therapy includes the conversation of past actions, trials and knowledges with another individual or set of people, frequently with the help of perceptible reminders such as photos, household and other acquainted matters from the past, music and collection of tapes. In this manuscript, the competence of reminiscence therapy for Alzheimer’s disease is measured using logistic regression based linear bootstrap aggregating. Logistic regression is used to envisage the experiential features of the patient’s memory through various therapies. Linear bootstrap aggregating shows better stability and accuracy of reminiscence therapy used in statistical classification and regression of memories related to validation therapy, supportive psychotherapy, sensory integration and simulated presence therapy.

Keywords: Alzheimer’s disease, linear bootstrap aggregating, logistic regression, reminiscence therapy

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Authors: Chinyama Lukama, Million Phiri, Namuunda Mutombo

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Background: Improving access and utilization to high-quality sexual and reproductive health (SRH) information and services, including family planning (FP) commodities, is central to the global developmental agenda of sub-Saharan Africa (SSA). Despite the importance of family planning use in enhancing maternal health outcomes and fertility reduction, the prevalence of adolescents and young women using modern contraception is generally low in SSA. Zambia is one of the countries in Southern Africa with a high prevalence of teenage pregnancies and fertility rates. Despite many initiatives that have been implemented to improve access and demand for family planning commodities, utilization of FP, especially among adolescents and young women, has generally been low. The objective of this research agenda was to better understand the determinants of modern contraceptive use in adolescents and young women in Zambia. This analysis produced findings that will be critical for informing the strengthening of sexual and reproductive health policy strategies aimed at bolstering the provision and use of maternal health services in order to further improve maternal health outcomes in the country. Method: The study used the recent data from the Demographic and Health Survey of 2018. A sample of 3,513 adolescents and young women (ADYW) were included in the analysis. Multilevel logistic regression models were employed to examine the association of individual and contextual factors with modern contraceptive use among adolescents and young women. Results: The prevalence of modern contraception among sexually active ADYW in Zambia was 38.1% [95% CI, 35.9, 40.4]. ADYW who had secondary or higher level education [aOR = 2.16, 95% CI=1.35–3.47], those with exposure to listening to the radio or watching television [aOR = 1.26, 95% CI=1.01–1.57], and those who had decision-making power at household level [aOR = 2.18, 95% CI=1.71–2.77] were more likely to use modern contraceptives. Conversely, strong neighborhood desire for large family size among ADYW [aOR = 0.65 95% CI = 0.47–0.88] was associated with less likelihood to use modern contraceptives. Community access to family planning information through community health worker visits increased the likelihood [aOR = 1.48, 95% CI=1.16–1.91] of using modern contraception among ADYW. Conclusion: The study found that both individual and community factors were key in influencing modern contraceptive use among adolescents and young women in Zambia. Therefore, when designing family planning interventions, the Government of Zambia, through its policymakers and sexual reproductive health program implementers at the Ministry of Health, in collaboration with stakeholders, should consider the community context. There should also be deliberate actions to encourage family planning education through the media.

Keywords: adolescents, young women, modern contraception use, fertility, family planning

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Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

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Authors: Al Omari Mohammed Ahmed

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This paper focuses on Maximum Likelihood Estimator with Covariate. Covariates are incorporated into the Weibull model. Under this regression model with regards to maximum likelihood estimator, the parameters of the covariate, shape parameter, survival function and hazard rate of the Weibull regression distribution with right censored data are estimated. The mean square error (MSE) and absolute bias are used to compare the performance of Weibull regression distribution. For the simulation comparison, the study used various sample sizes and several specific values of the Weibull shape parameter.

Keywords: weibull regression distribution, maximum likelihood estimator, survival function, hazard rate, right censoring

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Terefe Fuge, George Tsourtos , Emma Miller

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Objectives: Maintaining optimal adherence and viral suppression in people living with HIV (PLWHA) is essential to ensure both preventative and therapeutic benefits of antiretroviral therapy (ART). Prisoners bear a particularly high burden of HIV infection and are highly likely to transmit to others during and after incarceration. However, the level of adherence and viral suppression, as well as its associated factors in incarcerated populations in low-income countries is unknown. This study aimed to determine the prevalence of non-adherence and viral failure, and contributing factors to this amongst prisoners in South Ethiopia. Methods: A prospective cohort study was conducted between June 1, 2019 and July 31, 2020 to compare the level of adherence and viral suppression between incarcerated and non-incarcerated PLWHA. The study involved 74 inmates living with HIV (ILWHA) and 296 non-incarcerated PLWHA. Background information including sociodemographic, socioeconomic, psychosocial, behavioural, and incarceration-related characteristics was collected using a structured questionnaire. Adherence was determined based on participants’ self-report and pharmacy refill records, and plasma viral load measurements which were undertaken within the study period were prospectively extracted to determine viral suppression. Various univariate and multivariate regression models were used to analyse data. Results: Self-reported dose adherence was approximately similar between ILWHA and non-incarcerated PLWHA (81% and 83% respectively), but ILWHA had a significantly higher medication possession ratio (MPR) (89% vs 75%). The prevalence of viral failure (VF) was slightly higher (6%) in ILWHA compared to non-incarcerated PLWHA (4.4%). The overall dose non-adherence (NA) was significantly associated with missing ART appointments, level of satisfaction with ART services, patient’s ability to comply with a specified medication schedule and types of methods used to monitor the schedule. In ILWHA specifically, accessing ART services from a hospital compared to a health centre, an inability to always attend clinic appointments, experience of depression and a lack of social support predicted NA. VF was significantly higher in males, people of age 31-35 years and in those who experienced social stigma, regardless of their incarceration status. Conclusions: This study revealed that HIV-infected prisoners in South Ethiopia were more likely to be non-adherent to doses and so to develop viral failure compared to their non-incarcerated counterparts. A multitude of factors was found to be responsible for this requiring multilevel intervention strategies focusing on the specific needs of prisoners.

Keywords: Adherence , Antiretroviral therapy, Incarceration, South Ethiopia, Viral suppression

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: Atefeh Bagherianziarat, Dana Hamplova

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The inverse social gradient in life satisfaction (LS) is a well-established research finding. To estimate the influence of inequality on LS, most of the studies have explored the effect of the objective aspects of inequality or individuals’ socioeconomic status (SES). However, relatively fewer studies have confirmed recently the significant effect of the subjective aspect of inequality or subjective socioeconomic status (SSS) on life satisfaction over and above SES. In other words, it is confirmed by some studies that individuals’ perception of their unequal status in society or SSS can moderate the impact of their absolute unequal status on their life satisfaction. Nevertheless, this newly confirmed moderating link has not been affirmed to work likewise in societies with different levels of social inequality and also for people who believe in the value of equality, at different levels. In this study, we compared the moderative influence of subjective inequality on the link between objective inequality and life satisfaction. In particular, we focus on differences across welfare state regimes based on Esping-Andersen's theory. Also, we explored the moderative role of believing in the value of equality on the link between objective and subjective inequality on LS in the given societies. Since our studied variables were measured at both individual and country levels, we applied a multilevel analysis to the European Social Survey data (round 9). The results showed that people in deferent regimes reported statistically meaningful different levels of life satisfaction that is explained to different extends by their household income and their perception of their income inequality. The findings of the study supported the previous findings of the moderator influence of perceived inequality on the link between objective inequality and LS. However, this link is different in various welfare state regimes. The results of the multilevel modeling showed that country-level subjective equality is a positive predictor for individuals’ life satisfaction, while the GINI coefficient that was considered as the indicator of absolute inequality has a smaller effect on life satisfaction. Also, country-level subjective equality moderates the confirmed link between individuals’ income and their life satisfaction. It can be concluded that both individual and country-level subjective inequality slightly moderate the effect of individuals’ income on their life satisfaction.

Keywords: individual values, life satisfaction, multilevel analysis, objective inequality, subjective inequality, welfare regimes status

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Authors: Ekele Alih, Enejo Jalija

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Female Genital Mutilation (FGM) is made up of three types namely: Clitoridectomy, Excision and Infibulation. In this study, we examine the factors responsible for FGM in order to identify the causality variables in a logistic regression approach. From the result of the survey conducted by the Public Health Division, Nigeria Institute of Medical Research, Yaba, Lagos State, the tau statistic, τ was used to screen 9 factors that causes FGM in order to select few of the predictors before multiple regression equation is obtained. The need for this may be that the sample size may not be able to sustain having a regression with all the predictors or to avoid multi-collinearity. A total of 300 respondents, comprising 150 adult males and 150 adult females were selected for the household survey based on the multi-stage sampling procedure. The tau statistic,

Keywords: female genital mutilation, logistic regression, tau statistic, African society

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Joshi Manisha, Shivaram, Anand Vinekar, Tanya Susan Mathews, Yeshaswini Nagaraj

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Retinopathy of prematurity (ROP) is abnormal blood vessel development in the retina of the eye in a premature infant. The principal object of the invention is to provide a technique for detecting demarcation line and ridge detection for a given ROP image that facilitates early detection of ROP in stage 1 and stage 2. The demarcation line is an indicator of Stage 1 of the ROP and the ridge is the hallmark of typically Stage 2 ROP. Thirty Retcam images of Asian Indian infants obtained during routine ROP screening have been used for the analysis. A graphical user interface has been developed to detect demarcation line/ridge and to extract ground truth. This novel algorithm uses multilevel vessel enhancement to enhance tubular structures in the digital ROP images. It has been observed that the orientation of the demarcation line/ridge is normal to the direction of the blood vessels, which is used for the identification of the ridge/ demarcation line. Quantitative analysis has been presented based on gold standard images marked by expert ophthalmologist. Image based analysis has been based on the length and the position of the detected ridge. In image based evaluation, average sensitivity and positive predictive value was found to be 92.30% and 85.71% respectively. In pixel based evaluation, average sensitivity, specificity, positive predictive value and negative predictive value achieved were 60.38%, 99.66%, 52.77% and 99.75% respectively.

Keywords: ROP, ridge, multilevel vessel enhancement, biomedical

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Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Tahmidul Islam

Abstract:

Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

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Authors: Frank Kuebler, Rolf Steinhilper

Abstract:

Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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