Search results for: penalized logistic regression

Authors: Swati Srivastava, Ashish Kumar Upadhyay

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Background: According to United Nation Children’s Fund, it has been estimated that worldwide about 165 million children were stunted in 2012 and India alone accounts for 38% of global burden of stunting. In terms of incidence, India is home of more than 60 million stunted children worldwide. Our study aims to examine the effect of pregnancy intention and maternal postnatal depressive symptoms on early childhood stunting in India. We hypothesized that effect of pregnancy intention and postnatal maternal depressive symptoms were mediated by social support. Methods: We used data from first wave of Young Lives Study India. Out of 2011 children recruited in original cohort, 1833 children had complete information on pregnancy intention, maternal depression and other variables. A series of multivariate logistic regression model were used to examine the effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting. Results: Bivariate result indicates that a higher percent of children born after unintended pregnancy (40%) were stunted than children of intended pregnancy (26%). Likewise, proportion of stunted children was also higher among women of high postnatal depressive symptoms (35%) than low level of depression (24%). Results of multivariate logistic regression model indicate that children born after unintended pregnancy were significantly more likely to be stunted than children born after intended pregnancy (Coefficient: 1.70, CI: 1.17, 2.48). Likewise, early childhood stunting was also associated with maternal postnatal depressive symptoms among women (Coefficient: 1.48, CI: 1.16, 1.88). The effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting remains unchanged after controlling for social support and other variables. Conclusions: The findings of this study provide conclusive evidence regarding consequences of pregnancy intention and postnatal depressive symptoms on early childhood stunting in India. Therefore, there is need to identify the women with unintended pregnancy and incorporate the promotion of mental health into their national reproductive and child health programme.

Keywords: pregnancy intention, postnatal depressive symptoms, social support, childhood stunting, young lives study, India

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Authors: Ksenija Dumičić, Anita Čeh Časni, Irena Palić

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This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analysed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analysed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.

Keywords: European union, Internet purchases, multiple linear regression model, outlier

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Authors: Michelle R. Sullivan

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Positive romantic relationships and marriages offer a buffer against a host of physical and emotional difficulties. Conversely, poor relationship quality and marital discord can have deleterious consequences for individuals and families. Research has described non-monogamy, infidelity, and consensual non-monogamy, as both consequential and causal of relationship difficulty, or as a unique way a couple strives to make a relationship work. Much research on consensual non-monogamy has built on feminist theory and critique. To the author’s best knowledge, to date, no studies have examined the predictive relationship between individual and relationship characteristics and expectations of non-monogamy. The current longitudinal study: 1) estimated the prevalence of expectations of partner non-monogamy and 2) evaluated whether gender, sexual identity, age, education, how a couple met, and relationship quality were predictive expectations of partner non-monogamy. This study utilized the publically available longitudinal dataset, How Couples Meet and Stay Together. Adults aged 18- to 98-years old (n=4002) were surveyed by phone over 5 waves from 2009-2014. Demographics and how a couple met were gathered through self-report in Wave 1, and relationship quality and expectations of partner non-monogamy were gathered through self-report in Waves 4 and 5 (n=1047). The prevalence of expectations of partner non-monogamy (encompassing both infidelity and consensual non-monogamy) was 4.8%. Logistic regression models indicated that sexual identity, gender, education, and relationship quality were significantly predictive of expectations of partner non-monogamy. Specifically, male gender, lower education, identifying as lesbian, gay, or bisexual, and a lower relationship quality scores were predictive of expectations of partner non-monogamy. Male gender was not predictive of expectations of partner non-monogamy in the follow up logistic regression model. Age and whether a couple met online were not associated with expectations of partner non-monogamy. Clinical implications include awareness of the increased likelihood of lesbian, gay, and bisexual individuals to have an expectation of non-monogamy and the sequelae of relationship dissatisfaction that may be related. Future research directions could differentiate between non-monogamy subtypes and the person and relationship variables that lead to the likelihood of consensual non-monogamy and infidelity as separate constructs, as well as explore the relationship between predicting partner behavior and actual partner behavioral outcomes.

Keywords: open relationship, polyamory, infidelity, relationship satisfaction

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Authors: Waleerak Sittisom

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The objectives of this research were to analyze transportation route management, to identify potential cost reductions in logistic operation. In-depth interview techniques and small group discussions were utilized with 25 participants from various backgrounds in the areas of logistics. The findings of this research revealed that there were four areas that companies are able to effectively manage a logistic cost reduction: managing the space within the transportation vehicles, managing transportation personnel, managing transportation cost, and managing control of transportation. On the other hand, there were four areas that companies were unable to effectively manage a logistic cost reduction: the working process of transportation, the route planning of transportation, the service point management, and technology management. There are five areas that cost reduction is feasible: personnel management, process of working, map planning, service point planning, and technology implementation. To be able to reduce costs, the transportation companies should suggest that customers use a file system to save truck space. Also, the transportation companies need to adopt new technology to manage their information system so that packages can be reached easy, safe, and fast. Staff needs to be trained regularly to increase knowledge and skills. Teamwork is required to effectively reduce the costs.

Keywords: cost reduction, management, logistics, transportation

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Authors: Ahmed Sami Hammami, Mohamed Jellazi

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Background: The aim of the study is to evaluate the magnitude of different psychological outcomes among Tunisian health care professionals (HCP) during the COVID-19 pandemic and to identify the associated factors. Methods: HCP completed a cross-sectional questionnaire from April 4th to April, 28th 2020. The survey collected demographic information, factors that may interfere with the psychological outcomes, behavior changes and mental health measurements. The latter was assessed through 3 scales; the 7-item questions Insomnia Severity Index, the 2-item Patient Health Questionnaire and the 2-item Generalized Anxiety Disorder. Multivariable logistic regression was conducted to identify factors associated with psychological outcomes. Results: A total of 503 HCP successfully completed the survey; among those, n=493 consented to enroll in the study, 411 [83.4%] were physicians, 323 [64.2%] were women and 271 [55%] had a second-line working position. A significant proportion of HCP had anxiety 35.7%, depression 35.1% and insomnia 23.7%. Females, those with psychiatric history and those using public transport exhibited the highest proportions for overall symptoms compared to other groups e.g., depression among females vs. males: 44,9% vs. 18,2%, P=0.00. Those with a previous medical history and nurses, had more anxiety and insomnia compared to other groups e.g. anxiety among nurses vs. interns/residents vs. attending 45,1% vs 36,1% vs 27,5%; p=0.04. Multivariable logistic regression showed that female gender was a risk factor for all psychological outcomes e.g. female sex increased the odds of anxiety by 2.86; 95% confidence interval [CI], 1, 78-4, 60; P=0.00, whereas having a psychiatric history was a risk factor for both anxiety and insomnia. (e.g. for insomnia OR=2,86; 95% [CI], 1,78-4,60; P=0.00), Having protective equipment was associated with lower risk for depression (OR=0,41; 95% CI, 0,27-0,62; P=0.00) and anxiety. Physical activity was also protective against depression and anxiety (OR=0,41, 95% CI, 0,25-0,67, P=0.00). Conclusion: Psychological symptoms are usually undervalued among HCP, though the COVID-19 pandemic played a major role in exacerbating this burden. Prompt psychological support should be endorsed and simple measures such as physical activity and ensuring the necessary protection are paramount to improve mental health outcomes and the quality of care provided to patients.

Keywords: COVID-19 pandemic, health care professionals, mental health, protective factors, psychological symptoms, risk factors

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Xu Zhang

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This article unveils an indicator of language contact between English and Cantonese in one of the Outer Circle Englishes, Hong Kong (HK) English, through an empirical investigation into 1000 tokens from the Global Web-based English (GloWbE) corpus, employing frequency analysis and logistic regression analysis. It is perceived that Cantonese and general Chinese are contextually marked by an integral underlying thinking pattern. Chinese speakers exhibit a reliance on semantic context over syntactic rules and lexical forms. This linguistic trait carries over to their use of English, affording greater flexibility to formal elements in constructing English sentences. The study focuses on the syntactic positioning of the focusing subjunct ‘also’, a linguistic element used to add new or contrasting prominence to specific sentence constituents. The English language generally allows flexibility in the relative position of 'also’, while there is a preference for close marking relationships. This article shifts attention to Hong Kong, where Cantonese and English converge, and 'also' finds counterparts in Cantonese ‘jaa’ and Mandarin ‘ye’. Employing a corpus-based data-driven method, we investigate the syntactic position of 'also' in both HK and GB English. The study aims to ascertain whether HK English exhibits a greater 'syntactic freedom,' allowing for a more distant marking relationship with 'also' compared to GB English. The analysis involves a random extraction of 500 samples from both HK and GB English from the GloWbE corpus, forming a dataset (N=1000). Exclusions are made for cases where 'also' functions as an additive conjunct or serves as a copulative adverb, as well as sentences lacking sufficient indication that 'also' functions as a focusing particle. The final dataset comprises 820 tokens, with 416 for GB and 404 for HK, annotated according to the focused constituent and the relative position of ‘also’. Frequency analysis reveals significant differences in the relative position of 'also' and marking relationships between HK and GB English. Regression analysis indicates a preference in HK English for a distant marking relationship between 'also' and its focused constituent. Notably, the subject and other constituents emerge as significant predictors of a distant position for 'also.' Together, these findings underscore the nuanced linguistic dynamics in HK English and contribute to our understanding of language contact. It suggests that future pedagogical practice should consider incorporating the syntactic variation within English varieties, facilitating leaners’ effective communication in diverse English-speaking environments and enhancing their intercultural communication competence.

Keywords: also, Cantonese, English, focus marker, frequency analysis, language contact, logistic regression analysis

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Authors: Ketevan Goletiani

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MMultimodal transport is Europe-Asia’s rational decision of the XXI century. Success prerequisite of this form of cargo carriage is not technologic decision, but the comprehensive attitude towards it. Integration of the transport industry must refer to both technical and organizational-economic fields. Support of the multimodal’s must be the priority of the transport policy in different organizations of Europe and Asia. The method of approach to the transport as a unified system has been changed to a certain extent in the market conditions. Nowadays the competition between the different kinds of transport is not to be considered as a competition of one kind of transport towards another one, but is to be considered as a stimulator of the transport development. Basically, transport logistic, as the recent methodology and organization of the rationally flow of cargos at the specialized logistic centres during their procession provides effective rise of such flow of cargos, decreases non-operating expenses and gives the opportunity to the transport companies to come along with the time, to meet market clients’ requirements. It is apparent that the advanced transport-forwarding and logistic firms are being analized.

Keywords: transport systems, multimodal transport, competition, transport logistics

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Authors: Rajinder Singh, Ram Valluru

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Chain Ladder (CL) method, Expected Loss Ratio (ELR) method and Bornhuetter-Ferguson (BF) method, in addition to more complex transition-rate modeling, are commonly used actuarial reserving methods in general insurance. There is limited published research about their relative performance in the context of Mortgage Insurance (MI). In our experience, these traditional techniques pose unique challenges and do not provide stable claim estimates for medium to longer term liabilities. The relative strengths and weaknesses among various alternative approaches revolve around: stability in the recent loss development pattern, sufficiency and reliability of loss development data, and agreement/disagreement between reported losses to date and ultimate loss estimate. CL method results in volatile reserve estimates, especially for accident periods with little development experience. The ELR method breaks down especially when ultimate loss ratios are not stable and predictable. While the BF method provides a good tradeoff between the loss development approach (CL) and ELR, the approach generates claim development and ultimate reserves that are disconnected from the ever-to-date (ETD) development experience for some accident years that have more development experience. Further, BF is based on subjective a priori assumption. The fundamental shortcoming of these methods is their inability to model exogenous factors, like the economy, which impact various cohorts at the same chronological time but at staggered points along their life-time development. This paper proposes an alternative approach of parametrizing the loss development curve and using logistic regression to generate the ultimate loss estimate for each homogeneous group (accident year or delinquency period). The methodology was tested on an actual MI claim development dataset where various cohorts followed a sigmoidal trend, but levels varied substantially depending upon the economic and operational conditions during the development period spanning over many years. The proposed approach provides the ability to indirectly incorporate such exogenous factors and produce more stable loss forecasts for reserving purposes as compared to the traditional CL and BF methods.

Keywords: actuarial loss reserving techniques, logistic regression, parametric function, volatility

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Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr

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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^rd degree to 1^st degree and suggested valid predictions and stable explanations.

Keywords: design of experiments, regression analysis, SI engine, statistical modeling

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Authors: Anthony J. Kondracki, Bonzo I. Reddick, Jennifer L. Barkin

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We sought to examine the association of maternal Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and treponema pallidum (TP) (syphilis) infections with preterm birth (PTB) (<37 weeks gestation), low birth weight (LBW) (<2500 grams) and prenatal care (PNC) attendance. This cross-sectional study was based on data drawn from the 2020 United States National Center for Health Statistics (NCHS) Natality File. We estimated the prevalence of all births, early/late PTBs, moderately/very LBW, and the distribution of sexually transmitted infections (STIs) according to maternal characteristics in the sample. In multivariable logistic regression models, we examined adjusted odds ratios (aORs) and their corresponding 95% confidence intervals (CIs) of PTB and LBW subcategories in the association with maternal/infant characteristics, PNC status, and maternal CT, NG, and TP infections. In separate logistic regression models, we assessed the risk of these newborn outcomes stratified by PNC status. Adjustments were made for race/ethnicity, age, education, marital status, health insurance, liveborn parity, previous preterm birth, gestational hypertension, gestational diabetes, PNC status, smoking, and infant sex. Additionally, in a sensitivity analysis, we assessed the association with early, full, and late term births and the potential impact of unmeasured confounding using the E-value. CT (1.8%) was most prevalent STI in pregnancy, followed by NG (0.3%), and TP (0.1%). Non-Hispanic Black women, 20-24 years old, with a high school education, and on Medicaid had the highest rate of STIs. Around 96.6% of women reported receiving PNC and about 60.0% initiated PNC early in pregnancy. PTB and LBW were strongly associated with NG infection (12.2% and 12.1%, respectively) and late initiation/no PNC (8.5% and 7.6%, respectively), and ≤10 prenatal visits received (13.1% and 10.3%, respectively). The odds of PTB and LBW were 2.5- to 3-foldhigher for each STI among women who received ≤10 prenatal visits than >10 visits. Adequate prenatal care utilization and timely screening and treatment of maternal STIs can substantially reduce the burden of adverse newborn outcomes.

Keywords: low birthweight, prenatal care, preterm birth, sexually transmitted infections

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Authors: Arindam Chaudhuri

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The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time.

Keywords: regression, epsilon-TSVR, epsilon-FTSVR, epsilon-HFTSVR

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Authors: Braimah Joseph Odunayo, Jiju Gillariose

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This paper is aimed at developing a sampling plan that uses information from precedent and successive lots for lot disposition with a pretention that the life-time of a particular product assumes a Log-logistic distribution. A Two-pronged Truncated Deferred Sampling Plan (TTDSP) for Log-logistic distribution is proposed when the testing is truncated at a precise time. The best possible sample sizes are obtained under a given Maximum Allowable Percent Defective (MAPD), Test Suspension Ratios (TSR), and acceptance numbers (c). A formula for calculating the operating characteristics of the proposed plan is also developed. The operating characteristics and mean-ratio values were used to measure the performance of the plan. The findings of the study show that: Log-logistic distribution has a decreasing failure rate; furthermore, as mean-life ratio increase, the failure rate reduces; the sample size increase as the acceptance number, test suspension ratios and maximum allowable percent defective increases. The study concludes that the minimum sample sizes were smaller, which makes the plan a more economical plan to adopt when cost and time of production are costly and the experiment being destructive.

Keywords: consumers risk, mean life, minimum sample size, operating characteristics, producers risk

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Helena Baffoe

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Context: In the United States, there have been significant advancements in programs aimed at improving the lives of individuals with mental health disorders and substance abuse problems. However, public attitudes and beliefs regarding these issues have not improved correspondingly. This study aims to explore the perceptions and beliefs surrounding mental health disorders and substance abuse in the context of data analytics in the field of social work. Research Aim: The aim of this research is to provide empirical evidence on the beliefs and perceptions regarding mental health disorders and substance abuse. Specifically, the study seeks to answer the question of whether being diagnosed with a mental disorder implies a diagnosis of substance abuse. Additionally, the research aims to analyze the specific roles that social workers can play in addressing individuals with mental disorders. Methodology: This research adopts a data-driven methodology, acquiring comprehensive data from the Substance Abuse and Mental Health Services Administration (SAMHSA). A noteworthy causal connection between mental disorders and substance abuse exists, a relationship that current literature tends to overlook critically. To address this gap, we applied logistic regression with an Instrumental Variable approach, effectively mitigating potential endogeneity issues in the analysis in order to ensure robust and unbiased results. This methodology allows for a rigorous examination of the relationship between mental disorders and substance abuse. Empirical Findings: The analysis of the data reveals that depressive, anxiety, and trauma/stressor mental disorders are the most common in the United States. However, the study does not find statistically significant evidence to support the notion that being diagnosed with these mental disorders necessarily implies a diagnosis of substance abuse. This suggests that there is a misconception among the public regarding the relationship between mental health disorders and substance abuse. Theoretical Importance: The research contributes to the existing body of literature by providing empirical evidence to challenge prevailing beliefs and perceptions regarding mental health disorders and substance abuse. By using a novel methodological approach and analyzing new US data, the study sheds light on the cultural and social factors that influence these attitudes.

Keywords: mental health disorder, substance abuse, empirical evidence, logistic regression with IV

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Avan Al-Saffar, Eun-Jin Kim

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Both the logistic growth model and the gompertz growth model are used to describe growth processes. Both models driven by perturbations in different cases are investigated using information theory as a useful measure of sustainability and the variability. Specifically, we study the effect of different oscillatory modulations in the system's parameters on the evolution of the system and Probability Density Function (PDF). We show the maintenance of the initial conditions for a long time. We offer Fisher information analysis in positive and/or negative feedback and explain its implications for the sustainability of population dynamics. We also display a finite amplitude solution due to the purely fluctuating growth rate whereas the periodic fluctuations in negative feedback can lead to break down the system's self-regulation with an exponentially growing solution. In the cases tested, the gompertz and logistic systems show similar behaviour in terms of information and sustainability although they develop differently in time.

Keywords: dynamical systems, fisher information, probability density function (pdf), sustainability

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Juraj Camaj, Anna Dolinayova, Jana Lalinska, Miroslav Bariak

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Planning of infrastructure and processes in logistic center within the frame of various kinds of logistic hubs and technological activities in them represent quite complex problem. The main goal is to design appropriate layout, which enables to realize expected operation on the desired levels. The simulation software represents progressive contemporary experimental technique, which can support complex processes of infrastructure planning and all of activities on it. It means that simulation experiments, reflecting various planned infrastructure variants, investigate and verify their eligibilities in relation with corresponding expected operation. The inducted approach enables to make qualified decisions about infrastructure investments or measures, which derive benefit from simulation-based verifications. The paper represents simulation software for simulation infrastructural layout and technological activities in marshalling yard, intermodal terminal, warehouse and combination between them as the parts of logistic center.

Keywords: marshalling yard, intermodal terminal, warehouse, transport technology, simulation

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Authors: Min Hae Song, Jooyong Park

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Even though assessment using information and communication technology will most likely lead the future of educational assessment, there is little research on this topic. Computerized assessment will not only cut costs but also measure students' performance in ways not possible before. In this context, this study introduces a tool which can overcome the problems of multiple choice tests. Multiple-choice tests (MC) are efficient in automatic grading, however structural problems of multiple-choice tests allow students to find the correct answer from options even though they do not know the answer. A computerized modified multiple-choice testing system (CMMT) was developed using the interactivity of computers, that presents questions first, and options later for a short time when the student requests for them. This study was conducted to find out whether penalizing for wrong answers in CMMT could lower random guessing. In this study, we checked whether students knew the answers by having them respond to the short-answer tests before choosing the given options in CMMT or MC format. Ninety-four students were tested with the directions that they will be penalized for wrong answers, but not for no response. There were 4 experimental conditions: two conditions of high or low percentage of penalizing, each in traditional multiple-choice or CMMT format. In the low penalty condition, the penalty rate was the probability of getting the correct answer by random guessing. In the high penalty condition, students were penalized at twice the percentage of the low penalty condition. The results showed that the number of no response was significantly higher for the CMMT format and the number of random guesses was significantly lower for the CMMT format. There were no significant between the two penalty conditions. This result may be due to the fact that the actual score difference between the two conditions was too small. In the discussion, the possibility of applying CMMT format tests while penalizing wrong answers in actual testing settings was addressed.

Keywords: computerized modified multiple choice test format, multiple-choice test format, penalizing, test format

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Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

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Authors: Ibrahim Halil Korkmaz, Eren Özceylan, Cihan Çetinkaya

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Logistics function has great potential for increasing sustainable competitive advantage, profitability, productivity, customer satisfaction and decreasing costs in all sectors. The performance of logistics sector, which has such great influence on the overall performance of the economy, attracts more attention of both researchers and sector representatives day by day. The purpose of this study is to determine the effects of research and development expenditures which spent by enterprises operating in the transportation and storage sectors on Turkey’s logistic performance index (LPI). To do so, research and development investment expenditure among the years 2009-2015 of Turkish transportation and storage firms data from the Turkish Statistical Institute and Turkeys country points in the World Bank logistics performance index in the same years data were examined. As the result of the parametric evaluation, it is seen that the research and development expenditures made have a positive effect on the logistic performance of Turkey.

Keywords: logistics performance index, R&D investments, transportation, storage, Turkey

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Authors: Mdluli Fezile, Schmidt Stefan, Thamaga-Chitja Joyce

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In view of the frequently reported foodborne disease outbreaks caused by contaminated fresh produce, consumers have a preference for foods that meet requisite hygiene standards to reduce the risk of foodborne illnesses. Producing good quality fresh produce then becomes critical in improving market access and food security, especially for small-scale farmers. Questions of hygiene and subsequent microbiological quality in the rural small-scale farming sector of South Africa are even more crucial, given the policy drive to develop small-scale farming as a measure for reinforcement of household food security and reduction of poverty. Farming practices and methods, throughout the fresh produce value chain, influence the quality of the final product, which in turn determines its success in the market. This study’s aim was to therefore determine the extent to which training on organic farming methods, including modules such as Importance of Production Hygiene, influenced the hygienic farming practices employed by eTholeni small-scale organic farmers in uMbumbulu, KwaZulu-Natal- South Africa. Questionnaires were administered to 73 uncertified organic farmers and analysis showed that a total of 33 farmers were trained and supplied the local Agri-Hub while 40 had not received training. The questionnaire probed respondents’ attitudes, knowledge of hygiene and composting practices. Data analysis included descriptive statistics such as the Chi-square test and a logistic regression model. Descriptive analysis indicated that a majority of the farmers (60%) were female, most of which (73%) were above the age of 40. The logistic regression indicated that factors such as farmer training and prior experience in the farming sector had a significant influence on hygiene practices both at 5% significance levels. These results emphasize the importance of training, education and farming experience in implementing good hygiene practices in small-scale farming. It is therefore recommended that South African policies should advocate for small-scale farmer training, not only for subsistence purposes, but also with an aim of supplying produce markets with high fresh produce.

Keywords: small-scale farmers, leafy salad vegetables, organic produce, food safety, hygienic practices, food security

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Authors: Sahar Elsaleh, Ghada Farhat, Shaikha Al-Derham, Fasih Alam

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Background: Cesarean section (CS) delivery is one of the major concerns both in developing and developed countries. The rate of CS deliveries are on the rise globally, and especially in Qatar. Many socio-economic, demographic, clinical and institutional factors play an important role for cesarean sections. This study aims to investigate factors affecting the prevalence of CS among women in Qatar using the UNICEF’s Multiple Indicator Cluster Survey (MICS) 2012 database. Methods: The study has focused on the women’s questionnaire of the MICS, which was successfully distributed to 5699 participants. Following study inclusion and exclusion criteria, a final sample of 761 women aged 19- 49 years who had at least one delivery of giving birth in their lifetime before the survey were included. A number of socio-economic, demographic, clinical and institutional factors, identified through literature review and available in the data, were considered for the analyses. Bivariate and multivariate logistic regression models, along with a multi-level modeling to investigate clustering effect, were undertaken to identify the factors that affect CS prevalence in Qatar. Results: From the bivariate analyses the study has shown that, a number of categorical factors are statistically significantly associated with the dependent variable (CS). When identifying the factors from a multivariate logistic regression, the study found that only three categorical factors -‘age of women’, ‘place at delivery’ and ‘baby weight’ appeared to be significantly affecting the CS among women in Qatar. Although the MICS dataset is based on a cluster survey, an exploratory multi-level analysis did not show any clustering effect, i.e. no significant variation in results at higher level (households), suggesting that all analyses at lower level (individual respondent) are valid without any significant bias in results. Conclusion: The study found a statistically significant association between the dependent variable (CS delivery) and age of women, frequency of TV watching, assistance at birth and place of birth. These results need to be interpreted cautiously; however, it can be used as evidence-base for further research on cesarean section delivery in Qatar.

Keywords: cesarean section, factors, multiple indicator cluster survey, MICS database, Qatar

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Authors: M. T. Alodat

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In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model.

Keywords: extended skew normal distribution, Gaussian process for regression, predictive distribution, ESGPr model

Procedia PDF Downloads 520

Authors: Kajingulu Malandala, Ranganai Edmore

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The asymmetric Laplace distribution (ADL) is commonly used as the likelihood function of the Bayesian quantile regression, and it offers different families of likelihood method for quantile regression. Notwithstanding their popularity and practicality, ADL is not smooth and thus making it difficult to maximize its likelihood. Furthermore, Bayesian inference is time consuming and the selection of likelihood may mislead the inference, as the Bayes theorem does not automatically establish the posterior inference. Furthermore, ADL does not account for greater skewness and Kurtosis. This paper develops a new aspect of quantile regression approach for count data based on inverse of the cumulative density function of the Poisson, binomial and Delaporte distributions using the integrated nested Laplace Approximations. Our result validates the benefit of using the integrated nested Laplace Approximations and support the approach for count data.

Keywords: quantile regression, Delaporte distribution, count data, integrated nested Laplace approximation

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Authors: Ana Paula Camelo, Keila Sanches

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The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

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Authors: Noora Al Muftah, Reda Rawi, Richard Thompson, Halima Bensmail

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Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers of response to targeted agents. There is an urgent need to identify biomarkers that predict which patients with are most likely to respond to treatment. Systematic efforts to correlate tumor mutational data with biologic dependencies may facilitate the translation of somatic mutation catalogs into meaningful biomarkers for patient stratification. To identify genomic features associated with drug sensitivity and uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we have screened and integrated a panel of several hundred cancer cell lines from different databases, mutation, DNA copy number, and gene expression data for hundreds of cell lines with their responses to targeted and cytotoxic therapies with drugs under clinical and preclinical investigation. We found mutated cancer genes were associated with cellular response to most currently available Glioma cancer drugs and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Keywords: cancer, gene network, Lasso, penalized regression, P-values, unbiased estimator

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

Procedia PDF Downloads 260