Search results for: logistic regression

Authors: Evy Rombaut, Marie-Anne Guerry

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Predicting voluntary turnover of employees is an important topic of study, both in academia and industry. Researchers try to uncover determinants for a broader understanding and possible prevention of turnover. In the current study, we use a data set based approach to reveal determinants for turnover, differing for blue and white collar workers. Our data set based approach made it possible to study actual turnover for more than 500000 employees in 15692 Belgian corporations. We use logistic regression to calculate individual turnover probabilities and test the goodness of our model with the AUC (area under the ROC-curve) method. The results of the study confirm the relationship of known determinants to employee turnover such as age, seniority, pay and work distance. In addition, the study unravels unknown and verifies known differences between blue and white collar workers. It shows opposite relationships to turnover for gender, marital status, the number of children, nationality, and pay.

Keywords: employee turnover, blue collar, white collar, dataset analysis

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Authors: Daphne Ashley Sze, Kenneth Santos, Ariane Gabrielle Lim

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As women are now given more freedom and choice to pursue employment, the world’s over-all fertility has been decreasing mainly due to the shift in time allocation between working and child rearing. As such, we study the case of the Philippines, where there exists a decreasing fertility rate and increasing openness for women labor participation. We focused on the distinction between fertility and fecundity, the former being the manifestation of the latter and aim to trace and compare the effects of both fecundity and fertility to women’s employment status through the estimation of the reproduction function and multinomial logistic function. Findings suggest that the perception of women regarding employment opportunities in the Philippines links the negative relationship observed between fertility, fecundity and women’s employment status. Today, there has been a convergence in the traditional family roles of men and women, as both genders now have identical employment opportunities that continue to shape their preferences.

Keywords: multinomial logistic function, tobit, fertility, women employment status, fecundity

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Authors: Priya Kedia, Kiranmoy Das

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There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost.

Keywords: variable selection, quantile regression, Gibbs sampler, asymmetric Laplace distribution

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Authors: Chander Shekhar, Jeetendra Yadav, Shaziya Allarakha

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Hypertension has been described as an 'iceberg disease' as those who suffered are ignored and hence usually seek healthcare services at a very late stage. It is estimated that more than 2 million Indians are suffering from hypertensive heart disease that contributed to above 0.13 million deaths in 2016. The paper study aims to know the prevalence of Hypertension in India and its variation by socioeconomic backgrounds and to find out risk factors discriminating hypertension with special emphasis on consumption of tobacco and alcohol among men aged 15-54 years in India. The paper uses NFHS (2015-16) data. The paper used binary logistic regression and discriminant analysis to find significant predictors and discriminants of interest. The prevalence of hypertension was 16.5% in the study population. The results suggest that consumption of alcohol and tobacco are significant discriminant characteristics in carrying hypertension irrespective of what socioeconomic background characteristic he possesses.

Keywords: hypertention, alcohol, tobacco, discriminant

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Authors: Ariane C. Lim, Daphne Ashley L. Sze, Kenneth S. Santos

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As women are now given more freedom and choice to pursue employment, the world’s over-all fertility has been decreasing mainly due to the shift in time allocation between working and child-rearing. As such, we study the case of the Philippines, where there exists a decreasing fertility rate and increasing openness for women labor participation. We focused on the distinction between fertility and fecundity, the former being the manifestation of the latter and aim to trace and compare the effects of both fecundity and fertility to women’s employment status through the estimation of the reproduction function and multinomial logistic function. Findings suggest that the perception of women regarding employment opportunities in the Philippines links the negative relationship observed between fertility, fecundity and women’s employment status. Today, there has been a convergence in the traditional family roles of men and women, as both genders now have identical employment opportunities that continue to shape their preferences.

Keywords: multinomial logistic function, tobit, fertility, women employment status, fecundity

Procedia PDF Downloads 592

Authors: K. Assis, B. Bonaventure, A. Abdul Rahim, H. Affendy, A. Mohammad Amizi

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Oil palm smallholders are considered as a very important producer of oil palm in Malaysia. They are categorized into two, which are organized smallholder and independent smallholder. In this study, there were 1000 oil palms smallholders have been interviewed by using a structured questionnaire. The main objective of the survey is to identify the relationship between socioeconomic characteristics of smallholders with their knowledge, attitude, and practices toward Ganoderma disease. The locations of study include Peninsular Malaysia and Sabah. There were three important aspects studied, namely knowledge of Ganoderma disease, attitude towards the disease as well as the practices in managing the disease. Cluster analysis, factor analysis, and binary logistic regression were used to analyze the data collected. The findings of the study should provide a baseline data which can be used by the relevant agencies to conduct programs or to formulate a suitable development plan to improve the knowledge, attitude and practices of oil palm smallholders in managing Ganoderma disease.

Keywords: attitude, Ganoderma, knowledge, oil palm, practices, smallholders

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

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Authors: Paul Bigala, Samuel Oladipo, Steven Adebowale

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This study examines factors influencing knowledge of HIV/AIDS Prevention Practices and Transmission (KHAPPT) among young undergraduate students (15-24 years). Knowledge composite index was computed for 820 randomly selected students. Chi-square, ANOVA, and multinomial logistic regression were used for the analyses (α=.05). The overall mean knowledge score was 16.5±3.4 out of a possible score of 28. About 83% of the students have undergone HIV test, 21.0% have high KHAPPT, 18% said there is cure for the disease, 23% believed that asking for condom is embarrassing and 11.7% said it is safe to share unsterilized sharp objects with friends or family members. The likelihood of high KHAPPT was higher among students who have had HIV test (OR=3.314; C.I=1.787-6.145, p<0.001) even when other variables were used as control. The identified predictors of high KHAPPT were; ever had HIV test, faculty, and ever used any HIV/AIDS prevention services. North-West University Mafikeng should intensify efforts on the HIV/AIDS awareness program on the campus.

Keywords: HIV/AIDS knowledge, undergraduate students, HIV testing, Mafikeng

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Authors: Joonas Pääkkönen

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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

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Authors: Tahani Salman Alangari

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Student success can be impacted by internal factors such as their emotional well-being and external factors such as organizational support and instructional support in the classroom. This study is to identify at least one factor that forecasts student engagement. It is a cross-sectional, conducted on 6206 teachers and encompassed three years of data collection and observations of math instruction in approximately 50 schools and 300 classrooms. A multiple linear regression revealed that a model predicting student engagement from emotional support, classroom organization, and instructional support was significant. Four linear regression models were tested using hierarchical regression to examine the effects of independent variables: emotional support was the highest predictor of student engagement while instructional support was the lowest.

Keywords: student engagement, emotional support, organizational support, instructional support, well-being

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Authors: Seydou Sinde

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The aims of this paper are to formulate mathematical expressions that can be used to estimate the standpipe pressure (SPP). The developed formulas take into account the main factors that, directly or indirectly, affect the behavior of SPP values. Fluid rheology and well hydraulics are some of these essential factors. Mud Plastic viscosity, yield point, flow power, consistency index, flow rate, drillstring, and annular geometries are represented by the frictional pressure (Pf), which is one of the input independent parameters and is calculated, in this paper, using Herschel-Bulkley rheological model. Other input independent parameters include the rate of penetration (ROP), applied load or weight on the bit (WOB), bit revolutions per minute (RPM), bit torque (TRQ), and hole inclination and direction coupled in the hole curvature or dogleg (DL). The technique of repeating parameters and Buckingham PI theorem are used to reduce the number of the input independent parameters into the dimensionless revolutions per minute (RPMd), the dimensionless torque (TRQd), and the dogleg, which is already in the dimensionless form of radians. Multivariable linear and polynomial regression technique using PTC Mathcad Prime 4.0 is used to analyze and determine the exact relationships between the dependent parameter, which is SPP, and the remaining three dimensionless groups. Three models proved sufficiently satisfactory to estimate the standpipe pressure: multivariable linear regression model 1 containing three regression coefficients for vertical wells; multivariable linear regression model 2 containing four regression coefficients for deviated wells; and multivariable polynomial quadratic regression model containing six regression coefficients for both vertical and deviated wells. Although that the linear regression model 2 (with four coefficients) is relatively more complex and contains an additional term over the linear regression model 1 (with three coefficients), the former did not really add significant improvements to the later except for some minor values. Thus, the effect of the hole curvature or dogleg is insignificant and can be omitted from the input independent parameters without significant losses of accuracy. The polynomial quadratic regression model is considered the most accurate model due to its relatively higher accuracy for most of the cases. Data of nine wells from the Middle East were used to run the developed models with satisfactory results provided by all of them, even if the multivariable polynomial quadratic regression model gave the best and most accurate results. Development of these models is useful not only to monitor and predict, with accuracy, the values of SPP but also to early control and check for the integrity of the well hydraulics as well as to take the corrective actions should any unexpected problems appear, such as pipe washouts, jet plugging, excessive mud losses, fluid gains, kicks, etc.

Keywords: standpipe, pressure, hydraulics, nondimensionalization, parameters, regression

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Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

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In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: supervised, functional principal component analysis, functional response, functional linear regression

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: Mashood C., Abdul Vahid K., Ashique C. K.

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The technology is growing beyond human expectation. Internet is one of very sophisticated product of the information technology. It has various advantages like connecting the world, simplifying the difficult tasks done in past etc. Simultaneously it has demerits also; that is lack of authenticity and internet addiction. To find out the problems of internet addiction, a study conducted among the Postgraduate students of Pondicherry University and collected 454 samples. The study strictly focused to identify the internet addiction among students, influence and interdependence of personality on internet addiction among first years and second years. To evaluate this, we used two major analysis, these are Confirmatory Factor Analysis (CFA) to predict the internet addiction with the observed data and Logistic Regression to identify the difference between first years and second years in the case of internet addiction. Before applying to the core analysis, the data applied to some preliminary tests to check the model fit. The empirical findings shows that , the students of Pondicherry University are very much addicted to the internet, But there is no such huge difference between first years and second years in case of internet addiction.

Keywords: internet addiction, students, Pondicherry University, empirical study

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Authors: Kimberly Belfry, Shavon Stafford, Fariha Chowdhury, Jennifer Crawford, Soyeon Kim

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Intervention programs are mostly delivered online during the pandemic. Screen fatigue has become a significant deterrent for virtually-deliveredinterventions, and thus, we aimed to examine factors associated with recruitment and adherence toan online mindfulness program for youths. Our preliminary analysis indicated that 40% of interested youths enrolled in the program. No difference in gender and age was found for those enrolled in the program. Adherence rate was approximately 25%, which warrants further examination. Grounding on the preliminary findings, we will conduct a binary logistic regression analysis to identify elements associated with recruitment and adherence. The model will include predictors such as age, sex, recruiter, mental health status, time of the year. Odds ratios and 95% CI will be reported. Our preliminary analysis showed low recruitment and adherence rate. By identifying elements associated with recruitment and adherence, our study provides transferrable information that can improve recruitment and adherence of online-delivered interventions offered during the pandemic.

Keywords: virtual interventions, recruitment, youth, mindfulness

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Authors: Hanna Zietara

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Gastronomy is one of the oldest forms of commercial activity. It is currently one of the most popular and still dynamically developing branches of business. Socio-economic changes, its widespread occurrence, new techniques, or culinary styles affect the almost unlimited possibilities of its development. Importantly, regardless of the form of business adopted, food service is strongly related to logistics processes, and areas of food service that are closely linked to logistics are of strategic importance. Any inefficiency in logistics processes results in reduced chances for success and achieving competitive advantage by companies belonging to the catering industry. The aim of the paper is to identify the areas of logistic support occurring in the catering business, affecting the scope of the logistic processes implemented. The aim of the paper is realized through a plural homogeneous approach, based on: direct observation, text analysis of current documents, in-depth free targeted interviews.

Keywords: gastronomy, competitive advantage, logistics, logistics support

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Authors: S. Dey, T. Mukhopadhyay, S. Adhikari

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This paper presents an exhaustive comparative investigation on sampling techniques of polynomial regression model based stochastic natural frequency of composite plates. Both individual and combined variations of input parameters are considered to map the computational time and accuracy of each modelling techniques. The finite element formulation of composites is capable to deal with both correlated and uncorrelated random input variables such as fibre parameters and material properties. The results obtained by Polynomial regression (PR) using different sampling techniques are compared. Depending on the suitability of sampling techniques such as 2k Factorial designs, Central composite design, A-Optimal design, I-Optimal, D-Optimal, Taguchi’s orthogonal array design, Box-Behnken design, Latin hypercube sampling, sobol sequence are illustrated. Statistical analysis of the first three natural frequencies is presented to compare the results and its performance.

Keywords: composite plate, natural frequency, polynomial regression model, sampling technique, uncertainty quantification

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Authors: Yoonsuh Jung, Steven N. MacEachern

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Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows.

Keywords: cross-validation, model selection, quantile regression, tuning parameter selection

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Authors: Himansu, Bedanga Talukdar

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The aim of present study is to investigate the prevalence of various health outcomes and quality of life and analyzes the moderating role of social capital on health outcomes (i.e., self-rated good health (SRH), depression, functional health and quality of life) among elderly in India. Using WHO Study on Global AGEing and adults health (SAGE) data, with sample of 6559 elderly between 50 and above (Mage=61.81, SD=9.00) age were selected for analysis. Multivariate analysis accessed the prevalence of SRH, depression, functional limitation and quality of life among older adults. Logistic regression evaluates the effect of social capital along with other co-founders on SRH, depression, and functional limitation, whereas linear regression evaluates the effect of social capital with other co-founders on quality of life (QoL) among elderly. Empirical results reveal that (74%) of respondents were married, (70%) having low social action, (46%) medium sociability, (45%) low trust-solidarity, (58%) high safety, (65%) medium civic engagement and 37% reported medium psychological resources. The multivariate analysis, explains (SRH) is associated with age, female, having education, higher social action great trust, safety and greater psychological resources. Depression among elderly is greatly related to age, sex, education and higher wealth, higher sociability, having psychological resources. QoL is negatively associated with age, sex, being Muslim, whereas positive associated with higher education, currently married, civic engagement, having wealth, social action, trust and solidarity, safeness, and strong psychological resources.

Keywords: depressive symptom, functional limitation, older adults, quality of life, self rated health, social capital

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Authors: Mohammad Tahir Yousafzai, Rubina Qasim

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We assessed Hepatitis B vaccination and its determinants among health care workers (HCW) in Northwest Pakistan. HCWs from both public and private clinics were interviewed about hepatitis B vaccination, socio-demographic, hepatitis B virus transmission modes, disease threat and benefits of vaccination. Logistic regression was performed. Hepatitis B vaccination was 40% (Qualified Physicians: 86% and non-qualified Dispensers:16%). Being Qualified Physician (Adj. OR 26.6; 95%CI 9.3-73.2), Non-qualified Physician (Adj.OR 1.9; 95%CI 0.8-4.6), qualified Dispensers (Adj. OR 3.6; 95%CI 1.3-9.5) compared to non-qualified Dispensers, working in public clinics (Adj. OR 2.5; 95%CI 1.1-5.7) compared to private, perceived disease threat after exposure to blood and body fluids (Adj. OR 1.1; 95%CI 1.1-1.2) and perceived benefits of vaccination (Adj. OR 1.1; 95%CI 1.1-1.2) were significant predictors of hepatitis B vaccination. Improved perception of disease threat and benefits of vaccination and qualification of HCWs are associated with hepatitis B vaccination.

Keywords: Hepatitis B vaccine, immunization, healthcare workers, primary health

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Authors: Shady Gamal Aziz Shehata

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Difficulty in defining construction quality arises due to perception based on the nature and requirements of the market, the different partners themselves and the results they want. Quantitative research was used in this constructivist research. A case-based study was conducted to assess the structures of positive attitudes and expectations in the context of quality improvement. A survey based on expert opinions was analyzed among construction organizations/companies operating in the construction industry in Pakistan. The financial strength, management structure and construction experience of the construction companies formed the basis of their selection. A good concept is visible at the project level and is seen as the most valuable part of the construction project. Each quality improvement technique was expected to increase the user's profits by improving the efficiency of the construction project. The Survey is useful for construction professionals to evaluate current construction concepts and expectations for the application of quality improvement techniques in construction projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety construction quality, expectation, improvement, perception

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Authors: Fasanmi Samuel Sunday

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This study investigated the influence of gender and personality traits on emotional distress among awaiting trial inmates in Nigeria. Participants were three hundred and twenty (320) awaiting trial inmates, drawn from three main correctional centres in Northeast Nigeria, namely: Gashua Correctional Centre, Postiskum Correctional Centre, and Bauchi Correctional Centre. Expo facto research design was adopted. Questionnaires such as the Big Five Inventory and the Perceived Emotional Distress Inventory (PEDI) were used to measure the variables of the study. Three hypotheses were tested. Logistic regression was used for data analysis. Results of the analysis indicated that conscientiousness significantly predicted emotional distress among awaiting trial inmates. However, most of the identified personality traits did not significantly predict emotional distress among awaiting trial inmates. There was no significant gender difference in emotional distress among awaiting-trial inmates. The implications of the study were discussed.

Keywords: personality traits, emotional distress, awaiting-trial inmates, gender

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: Jihye Jeon

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This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105.

Keywords: androstane derivatives, chromatography, molecular structure, principal component regression, partial least squares regression

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Mais Khader, Xingjie Wei

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This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

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Authors: V. Rekha, Rama Pal

Abstract:

The study examines the degree and magnitude of wealth-based inequalities in child health and its determinants in India. Despite making strides in economic growth, India has failed to secure a better nutritional status for all the children. The country currently faces the double burden of malnutrition as well as the problems of overweight and obesity. Child malnutrition, obesity, unsafe water, sanitation among others are identified as the risk factors for Non-Communicable Diseases (NCDs). Eliminating malnutrition in all its forms will catalyse improved health and economic outcomes. The assessment of the distributive dimension of child health across various segments of the population is essential for effective policy intervention. The study utilises the fourth round of District Level Health Survey for 2012-13 to analyse the inequalities among children in the age group 0-14 years in Maharashtra, a state in the western region of India with a population of 11.24 crores which constitutes 9.3 percent of the total population of India. The study considers the extent of health inequality by state, districts, sector, age-groups, and gender. The z-scores of four child health outcome variables are computed to assess the nutritional status of pre-school and school children using WHO reference. The descriptive statistics, concentration curves, concentration indices, correlation matrix, logistic regression have been used to analyse the data. The results indicate that magnitude of inequality is higher in Maharashtra and child health inequalities manifest primarily among the weaker sections of society. The concentration curves show that there exists a pro-poor inequality in child malnutrition measured by stunting, wasting, underweight, anaemia and a pro-rich overweight inequality. The inequalities in anaemia are observably lower due to the widespread prevalence. Rural areas exhibit a higher incidence of malnutrition, but greater inequality is observed in the urban areas. Overall, the wealth-based inequalities do not vary significantly between age groups. It appears that there is no gender discrimination at the state level. Further, rural-urban differentials in gender show that boys from the rural area and girls living in the urban region experience higher disparities in health. The relative distribution of undernutrition across districts in Maharashtra reveals that malnutrition is rampant and considerable heterogeneity also exists. A negative correlation is established between malnutrition prevalence and human development indicators. The findings of logistic regression analysis reveal that lower economic status of the household is associated with a higher probability of being malnourished. The study recognises household wealth, education of the parent, child gender, and household size as factors significantly related to malnutrition. The results suggest that among the supply-side variables, child-oriented government programmes might be beneficial in tackling nutrition deficit. In order to bridge the health inequality gap, the government needs to target the schemes better and should expand the coverage of services.

Keywords: child health, inequality, malnutrition, obesity

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