Search results for: logistic regression with IV

Authors: Trupti Meher, Harihar Sahoo

Abstract:

Premature menopause refers to the occurrence of menopause before the age of 40 years. Women who experience premature menopause either due to biological or induced reasons have a longer duration of exposure to severe symptoms and adverse health consequences when compared to those who undergo menopause at a later age, despite the fact that premature menopause has a profound effect on the health of women. This study attempted to determine the prevalence and predictors of premature menopause among women aged 25-39 years, using data from the National Family Health Survey (NFHS-4) conducted during 2015–16 in India. Descriptive statistics and multinomial logistic regression were used to carry out the result. The results revealed that the prevalence of premature menopause in India was 3.7 percent. Out of which, 2.1 percent of women had experienced natural premature menopause, whereas 1.7 percent had premature surgical menopause. The prevalence of premature menopause was highest in the southern region of India. Further, results of the multivariate model indicated that rural women, women with higher parity, early age at childbearing and women with smoking habits were at a greater risk of premature menopause. A sizeable proportion of women in India are attaining menopause prematurely. Unless due attention is given to this matter, it will emerge as a major problem in India in the future. The study also emphasized the need for further research to enhance knowledge on the problems of premature menopausal women in different socio-cultural settings in India.

Keywords: India, natural menopause, premature menopause, surgical menopause

Procedia PDF Downloads 207

Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana

Abstract:

Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.

Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker

Procedia PDF Downloads 574

Authors: Shekar Babu, Amalendu Jyothishi

Abstract:

The New Companies Bill-2013 in India has mandated all the companies with a certain profit to spend on Corporate Social Responsibility (CSR). Despite the Corporate Governance (CG) compliances at the strategic level the firms have to engage in social good. For both the Central Public Sector Enterprises (CPSE) and the private companies in India the need for strategic CSR focus through operational efficiency measures are mandated. In this paper the focus is to find out if the Indian companies understand their responsibility towards the society despite government making CSR mandatory. Analyzing both the CPSEs and Private companies the researchers find out which set of companies behave responsibly towards the society. Does any particular industry group(s) impact the society by disclosing their CSR spending activities. The key financial and non-financial parameters that influence CSR spending were identified and through econometric analysis methodologies (logistic regression and OLS models) the results were analyzed. The innovative methods were developed to identify if the firms operate efficiently and at the same time complying with the new CSR laws. An innovative matrix was developed to explain how companies could operate efficiently and be compliant in parallel how some of the companies can strategically realign their spending by operating efficiently.

Keywords: corporate social responsibility(CSR), corporate governance(CG), India, logit function, ordinary least squares (OLS)

Procedia PDF Downloads 355

Authors: Khaoula Azraiel, Mohamed Mehdi Abassi, Amel Sadraoui, Walid Hammami, Azouz Msaddek, Imed Cheikh, Maria Mancebo, Elisabet Perez-Navarro, Antonio Caruz, Henda Triki, Ahlem Djebbi

Abstract:

IL28B rs12979860 genotype is confirmed as an important predictor of response to peginterferon/ribavirin therapy in patients with chronic hepatitis C (CHC). IFNL4 ss469415590 is a newly discovered polymorphism that could also affect the sustained virological response (SVR). The aim of this study was to evaluate the association of IL28B and IFNL4 genotypes with peginterferon/ribavirin treatment response in Tunisians patients with CHC and to determine which of these SNPs, was the stronger marker. A total of 120 patients were genotyped for both rs12979860 and ss469415590 polymorphisms. The association of each genetic marker with SVR was analyzed and comparison between the two SNPs was calculated by logistic regression models. For rs12979860, 69.6% of patients with CC, 41.8% with CT and 42.8% with TT achieved SVR (p = 0.003). Regarding ss469415590, 70.4% of patients with TT/TT genotype achieved SVR compared to 42.8% with TT/ΔG and 37.5% with ΔG /ΔG (p = 0.002). The presence of CC and TT/TT genotypes was independently associated with treatment response with an OR of 3.86 for each. In conclusion, both IL28B rs12979860 and IFNL4 ss469415590 variants were associated with response to pegIFN/RBV in Tunisian patients, without any additional benefit in performance for IFNL4. Our results are different from those detected in Sub-Saharan Africa countries.

Keywords: Hepatitis C virus, IFNL4, IL28B, Peginterferon/ribavirin, polymorphism

Procedia PDF Downloads 338

Authors: Hyun-E Yeom

Abstract:

Patients’ views about the cause of hypertension may influence their present and proactive behaviors to regulate high blood pressure. This study aimed to examine the internal structure underlying the causal beliefs about hypertension and the influence of causal beliefs on self-care intention and medical compliance in Korean patients with hypertension. The causal beliefs of 145 patients (M age = 57.7) were assessed using the Illness Perception Questionnaire-Revised. An exploratory factor analysis was used to identify the factor structure of the causal beliefs, and the factors’ influence on self-care intention and medication compliance was analyzed using multiple and logistic regression analyses. The four-factor structure including psychological, fate-related, risk and habitual factors was identified and the psychological factor was the most representative component of causal beliefs. The risk and fate-related factors were significant factors affecting lower intention to engage in self-care and poor compliance with medication regimens, respectively. The findings support the critical role of causal beliefs about hypertension in driving patients’ current and future self-care behaviors. This study highlights the importance of educational interventions corresponding to patients’ awareness of hypertension for improving their adherence to a healthy lifestyle and medication regimens.

Keywords: hypertension, self-care, beliefs, medication compliance

Procedia PDF Downloads 351

Authors: Habshah Midi, Mohammed A. Mohammed, Sohel Rana

Abstract:

Multicollinearity occurs when two or more independent variables in a multiple linear regression model are highly correlated. The ridge regression is the commonly used method to rectify this problem. However, the ridge regression cannot handle the problem of multicollinearity which is caused by high leverage collinearity enhancing observation (HLCEO). Since high leverage points (HLPs) are responsible for inducing multicollinearity, the effect of HLPs needs to be reduced by using Generalized M estimator. The existing GM6 estimator is based on the Minimum Volume Ellipsoid (MVE) which tends to swamp some low leverage points. Hence an improvised GM (MGM) estimator is presented to improve the precision of the GM6 estimator. Numerical example and simulation study are presented to show how HLPs can cause multicollinearity. The numerical results show that our MGM estimator is the most efficient method compared to some existing methods.

Keywords: identification, high leverage points, multicollinearity, GM-estimator, DRGP, DFFITS

Procedia PDF Downloads 262

Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

Abstract:

The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

Procedia PDF Downloads 59

Authors: Karl Peltzer, Supa Pengpid

Abstract:

Introduction: The aim of this study was to assess suicidal ideation and associated factors in school-going adolescents in the Association of Southeast Asian Nations (ASEAN) Member States. Methods: The analysis included 30284 school children aged 13-15 years from seven ASEAN that participated in the cross-sectional Global School-based Student Health Survey (GSHS) between 2007 and 2013. Results: The overall prevalence of suicidal ideation across seven ASEAN countries (excluding Brunei) was 12.3%, significantly higher in girls (15.1%) than boys (9.3%). Among eight ASEAN countries with the highest prevalence of suicidal ideation was in the Philippines (17.0%) and Vietnam (16.9%) and the lowest in Myanmar (1.1%) and Indonesia (4.2%). In multivariate logistic regression analysis, female gender, older age (14 or 15 years), living in a low income or lower middle income country, having no friends, loneliness, bullying victimization, having been in a physical fight in the past 12 months, lack of parental or guardian support, tobacco use and having a history of ever got drunk were associated with suicidal ideatiion. Conclusion: Different rates of suicidal ideation were observed in ASEAN member states. Several risk factors for suicidal ideation were identified which can help guide preventive efforts.

Keywords: adolesents, ASEAN, correlates, suicidal behaviour

Procedia PDF Downloads 269

Authors: Anat Achiron

Abstract:

Background: Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system in young adults. It involves the immune system attacking the protective covering of nerve fibers (myelin), leading to inflammation and damage. MS can result in various neurological symptoms, such as muscle weakness, coordination problems, and sensory disturbances. Seizures are not common in MS, and the frequency is estimated between 0.4 to 6.4% over the disease course. Objective: Investigate the frequency of seizures in individuals with multiple sclerosis and to identify associated risk factors. Methods: We evaluated the frequency of seizures in a large cohort of 5686 MS patients followed at the Sheba Multiple Sclerosis Center and studied associated risk factors and comorbidities. Our research was based on data collection using a cohort study design. We applied logistic regression analysis to assess the strength of associations. Results: We found that younger age at onset, longer disease duration, and prolonged time to immunomodulatory treatment initiation were associated with increased risk for seizures. Conclusions: Our findings suggest that seizures in people with MS are directly related to the demyelination process and not associated with other factors like medication side effects or comorbid conditions. Therefore, initiating immunomodulatory treatment early in the disease course could reduce not only disease activity but also decrease seizure risk.

Keywords: epilepsy, seizures, multiple sclerosis, white matter, age

Procedia PDF Downloads 71

Authors: Humeyra Bolakar Tosun

Abstract:

The transport sector has an undisputed place in human life. People need transport access to continuous increase day by day with growing population. The number of rail network, urban transport planning, infrastructure improvements, transportation management and other related areas is a key factor affecting our country made it quite necessary to improve the work of transportation. In this context, it plays an important role in domestic rail freight demand planning. Alternatives that the increase in the transportation field and has made it mandatory requirements such as the demand for improving transport quality. In this study generally is known and used in studies by the definition, rail freight transport, railway line length, population, energy consumption. In this study, Iron Road Load Net Demand was modeled by multiple regression and ANN methods. In this study, model dependent variable (Output) is Iron Road Load Net demand and 6 entries variable was determined. These outcome values extracted from the model using ANN and regression model results. In the regression model, some parameters are considered as determinative parameters, and the coefficients of the determinants give meaningful results. As a result, ANN model has been shown to be more successful than traditional regression model.

Keywords: railway load carrying, neural network, modelling transport, transportation

Procedia PDF Downloads 143

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

Abstract:

The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 111

Authors: Terri Cole, Fay Sweeting

Abstract:

Abuse of position for sexual gain by police is defined as behavior involving individuals taking advantage of their role to pursue a sexual or improper relationship. Previous research has considered whether it involves ‘bad apples’ - individuals with poor moral ethos or ‘bad barrels’ – broader organizational flaws which may unconsciously allow, minimize, or do not effectively deal with such behavior. Low level sexual misconduct (e.g., consensual sex on duty) is more common than more serious offences (e.g., rape), yet the impact of such behavior can have severe implications not only for those involved but can also negatively undermine public confidence in the police. This ongoing, collaborative research project has identified variables from 514 historic case files from 35 UK police forces in order to identify potential risk indicators which may lead to such behavior. Quantitative analysis using logistic regression and the Cox proportion hazard model has resulted in the identification of specific risk factors of significance in prediction. Factors relating to both perpetrator background such as a history of intimate partner violence, debt, and substance misuse coupled with in work behavior such as misusing police systems increase the risk. Findings are able to provide pragmatic recommendations for those tasked with identifying potential or investigating suspected perpetrators of misconduct.

Keywords: abuse of position, forensic psychology, misconduct, sexual abuse

Procedia PDF Downloads 194

Authors: Brahim Boukabcha, Amar Rebbouh

Abstract:

The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

Procedia PDF Downloads 380

Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

Abstract:

This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian process regression, ensemble kernels, bayesian optimization, pharmaceutical sales analysis, time series forecasting, data analysis

Procedia PDF Downloads 71

Authors: Cristina Giosuè, Federica Biondo, Sergio Vitale

Abstract:

In the last years, the consumers' awareness for environmental concerns has been increasing, and seafood eco-labels are considered as a possible instrument to improve both seafood markets and sustainable fishing management. In this direction, the aim of this study was to carry out a meta-analysis on consumers’ willingness to pay (WTP) for eco-labeled wild seafood, by a meta-regression. Therefore, only papers published on ISI journals were searched on “Web of Knowledge” and “SciVerse Scopus” platforms, using the combinations of the following key words: seafood, ecolabel, eco-label, willingness, WTP and premium. The dataset was built considering: paper’s and survey’s codes, year of publication, first author’s nationality, species’ taxa and family, sample size, survey’s continent and country, data collection (where and how), gender and age of consumers, brand and ΔWTP. From analysis the interest on eco labeled seafood emerged clearly, in particular in developed countries. In general, consumers declared greater willingness to pay than that actually applied for eco-label products, with difference related to taxa and brand.

Keywords: eco label, meta regression, seafood, willingness to pay

Procedia PDF Downloads 122

Authors: Jay L. Fu

Abstract:

Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

Procedia PDF Downloads 143

Authors: Yassir AbdelRazig, Eren Ozguven, Ren Moses

Abstract:

While there exists a greater understanding of the differences between the driving behaviors of older and younger drivers, there is still a need to further understand how the two groups perform when attempting to perform complex intersection maneuvers. This paper looks to determine if, and to what extent, these differences exist when drivers encounter permissive left-hand turns, pedestrian traffic, two and four-lane intersections, heavy fog, and night conditions. The study will utilize a driving simulator to develop custom drivable scenarios containing one or more of the previously mentioned conditions. 32 younger and 32 older (+65 years) participants perform driving simulation scenarios and have their velocity, time to the nearest oncoming vehicle, accepted and rejected gaps, etc., recorded. The data collected from the simulator is analyzed via Raff’s method and logistic regression in order to determine and compare the critical gaps values of the two cohorts. Out of the parameters considered for this study, only the age of the driver, their experience (if they are a younger driver), the size of a gap, and the presence of pedestrians on the crosswalk proved significant. The results did not support the hypothesis that older drivers would be significantly more conservative in their critical gaps judgment and acceptance.

Keywords: older drivers, simulation, left-turn, human factors

Procedia PDF Downloads 247

Authors: Md Monoarul Haque

Abstract:

Nutritional status is a sensitive indicator of community health and nutrition among preschool children, especially the prevalence of undernutrition that affects all dimensions of human development and leads to growth faltering in early life. The present study is an attempt to assess the food intake pattern and nutritional status of pre-school Chakma tribe children. It was a cross-sectional community based study. The subjects were selected purposively. This study was conducted at Savar Upazilla of Rangamati. Rangamati is located in the Chittagong Division. Anthropometric data height and weight of the study subjects were collected by standard techniques. Nutritional status was measured using Z score according WHO classification. χ2 test, independent t-test, Pearson’s correlation, multiple regression and logistic regression was performed as P<0.05 level of significance. Statistical analyses were performed by appropriate univariate and multivariate techniques using SPSS windows 11.5. Moderate (-3SD to <-2SD) to severe underweight (<-3SD) were 23.8% and 76.2% study subjects had normal weight for their age. Moderate (-3SD to <-2SD) to severe (<-3SD) stunted children were only 25.6% and 74.4% children were normal and moderate to severe wasting were 14.7% whereas normal child was 85.3%. Significant association had been found between child nutritional status and monthly family income, mother education and occupation of father and mother. Age, sex and incomes of the family, education of mother and occupation of father were significantly associated with WAZ and HAZ of the study subjects (P=0.0001, P=0.025, P=0.001 and P=0.0001, P=0.003, P=0.031, P=0.092, P=0.008). Maximum study subjects took local small fish and some traditional tribal food like bashrool, jhijhipoka and pork very much popular food among tribal children. Energy, carbohydrate and fat intake was significantly associated with HAZ, WAZ, BAZ and MUACZ. This study demonstrates that malnutrition among tribal children in Bangladesh is much better than national scenario in Bangladesh. Significant association was found between child nutritional status and family monthly income, mother education and occupation of father and mother. Most of the study subjects took local small fish and some traditional tribal food. Significant association was also found between child nutritional status and dietary intake of energy, carbohydrate and fat.

Keywords: food intake pattern, nutritional status, preschool children, Chakma ethnic community

Procedia PDF Downloads 505

Authors: Muller Salah Zaky Toudry

Abstract:

The complexity arises in defining the development great due to its notion, based on inherent market situations and their requirements, the diverse stakeholders itself and their desired output. An quantitative survey based totally approach was adopted in this optimistic examine. A questionnaire-primarily based survey was performed for the assessment of production fine belief and expectations within the context of excellent development technique. The survey feedback of experts of the leading creation corporations/companies of Pakistan production industry have been analyzed. The monetary ability, organizational shape, and production revel in of the construction companies shaped basis for their selection. The great belief become located to be venture-scope-orientated and taken into consideration as an extra cost for a production assignment. Any excellent improvement technique changed into expected to maximize the profit for the employer, via enhancing the productiveness in a creation project. The look at is beneficial for the construction specialists to evaluate the prevailing creation great perception and the expectations from implementation of any pleasant improvement approach in production projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety construction quality, expectation, improvement, perception client loyalty, NPS, pre-construction, schedule reduction

Procedia PDF Downloads 14

Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

Abstract:

The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

Procedia PDF Downloads 336

Authors: Masaki Yamaguchi, Daimei Sasayama, Shinsuke Washizuka

Abstract:

The goal of this study was to examine the possibility of salivary cytokines for the screening of attention deficit hyperactivity disorder (ADHD) in children. We carried out a case-control study, including 19 children with ADHD and 17 healthy children (controls). A multiplex bead array immunoassay was used to conduct a multi-analysis of 27 different salivary cytokines. Six salivary cytokines (interleukin (IL)-1β, IL-8, IL12p70, granulocyte colony-stimulating factor (G-CSF), interferon gamma (IFN-γ), and vascular endothelial growth factor (VEGF)) were significantly associated with the presence of ADHD (p < 0.05). An informative salivary cytokine panel was developed using VEGF by logistic regression analysis (odds ratio: 0.251). Receiver operating characteristic analysis revealed that assessment of a panel using VEGF showed “good” capability for discriminating between ADHD patients and controls (area under the curve: 0.778). ADHD has been hypothesized to be associated with reduced cerebral blood flow in the frontal cortex, due to reduced VEGF levels. Our study highlights the possibility of utilizing differential salivary cytokine levels for point-of-care testing (POCT) of biomarkers in children with ADHD.

Keywords: cytokine, saliva, attention deficit hyperactivity disorder, child

Procedia PDF Downloads 143

Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

Abstract:

Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

Procedia PDF Downloads 126

Authors: Suhani Srivastava

Abstract:

This research delves into the classification of orbital debris through machine learning (ML): it will categorize the intensity of the threat orbital debris poses through multiple ML models to gain an insight into effectively estimating the danger specific orbital debris can pose to future space missions. As the space industry expands, orbital debris becomes a growing concern in Low Earth Orbit (LEO) because it can potentially obfuscate space missions due to the increased orbital debris pollution. Moreover, detecting orbital debris and identifying its characteristics has become a major concern in Space Situational Awareness (SSA), and prior methods of solely utilizing physics can become inconvenient in the face of the growing issue. Thus, this research focuses on approaching orbital debris concerns through machine learning, an efficient and more convenient alternative, in detecting the potential threat certain orbital debris pose. Our findings found that the Logistic regression machine worked the best with a 98% accuracy and this research has provided insight into the accuracies of specific machine learning models when classifying orbital debris. Our work would help provide space shuttle manufacturers with guidelines about mitigating risks, and it would help in providing Aerospace Engineers facilities to identify the kinds of protection that should be incorporated into objects traveling in the LEO through the predictions our models provide.

Keywords: aerospace, orbital debris, machine learning, space, space situational awareness, nasa

Procedia PDF Downloads 20

Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

Abstract:

Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

Procedia PDF Downloads 11

Authors: Nam-Seok Joo, Da-Eun Jung, Beom-Hee Choi

Abstract:

Background and Objectives: Recently, the triglyceride/high-density lipoprotine cholesterol (TG/HDL) ratio and pericardial adipose tissue (PAT) has gained attention as an indicator related to metabolic syndrome (MS). To date, there has been no research on the relationship between TG/HDL and PAT, we aimed to investigate the association between the TG/HDL and PAT. Methods: In this cross-sectional study, we investigated 627 patients who underwent coronary multidetector computed tomography and metabolic parameters. We divided subjects into two groups according to the cut-off PAT volume associated with MS, which is 142.2 cm³, and we compared metabolic parameters between those groups. We divided the TG/HDL ratio into tertiles according to Log(TG/HDL) and compared PAT-related parameters by analysis of variance. Finally, we applied logistic regression analysis to obtain the odds ratio of high PAT (PAT volume≥142.2 cm³) in each tertile, and we performed receiver operating characteristic (ROC) analysis to get the cut-off of TG/HDL ratio according to high PAT. Results: The mean TG/ HDL ratio of the high PAT volume group was 3.6, and TG/ HDL ratio had a strong positive correlation with various metabolic parameters. In addition, in the Log (TG/HDL) tertile groups, the higher tertile had more metabolic derangements, including PAT, and showed higher odds ratios of having high PAT (OR=4.10 in the second tertile group and OR=5.06 in their third tertile group, respectively) after age, sex, smoking adjustments. TG/HDL ratio according to the having increased PAT by ROC curve showed 1.918 (p < 0.001). Conclusion: TG/HDL ratio and high PAT volume have a significant positive correlation, and higher TG/HDL ratio showed high PAT. The cut-off value of the TG/HDL ratio was 1.918 to have a high PAT.

Keywords: triglyceride, high-density lipoprotein, pericardial adipose tissue, cut-off value

Procedia PDF Downloads 15

Authors: Raka Aldy Nugraha, Kiwah Andanni, Aditya Indra Pratama, Aswin Guntara

Abstract:

Background: Torus palatinus is a bony protuberance in the hard palate. Sex and race are considered as influencing factors for the development of torus palatinus. Hence, the objective of this study was to determine the prevalence and characteristics of torus palatinus and its correlation with sex and ethnicity among Western Indonesian Population. Methods: We conducted a descriptive and analytical study employing cross-sectional design in 274 new students of Universitas Indonesia. Data were collected by using consecutive sampling method through questionnaire-filling and direct oral examination. Subject with racial background other than indigenous Indonesian Mongol were excluded from this study. Data were statistically analyzed using chi square test for categorical variables whereas logistic regression model was employed to assess the correlation between variables of interest with prevalence of torus palatinus. Results: Torus palatinus were found in 212 subjects (77.4%), mostly small in size (< 3 mm) and single in number, with percentage of 50.5% and 90.6%, respectively. The prevalence of torus palatinus were significantly higher in women (OR 2.88; 95% CI: 1.53-5.39; p = 0.001), dominated by medium-sized and single tori. There was no significant correlation between ethnicity and the occurrence of torus palatinus among Western Indonesian population. Conclusion: Torus palatinus was prevalent among Western Indonesian population. It showed significant positive correlation with sex, but not with ethnicity.

Keywords: characteristic, ethnicity, Indonesia, mongoloid, prevalence, sex, Torus palatinus

Procedia PDF Downloads 268

Authors: Snezana Mugosa, Zoran Todorovic, Zoran Bukumiric, Ivan Radosavljevic, Natasa Djordjevic

Abstract:

Introduction: Various studies have shown that the frequency of clopidogrel resistance ranges from 4-40%. The aim of this study was to provide in depth analysis of genetic and non-genetic factors that influence clopidogrel resistance in cardiology patients. Methods: We have conducted a prospective study in 200 hospitalized patients hospitalized at Cardiology Centre of the Clinical Centre of Montenegro. CYP2C19 genetic testing was conducted, and the PREDICT score was calculated in 102 out of 200 patients treated with clopidogrel in order to determine the influence of genetic and non-genetic factors on outcomes of interest. Adverse cardiovascular events and adverse reactions to clopidogrel were assessed during 12 months follow up period. Results: PREDICT score and CYP2C19 enzymatic activity were found to be statistically significant predictors of expressing lack of therapeutic efficacy of clopidogrel by multivariate logistic regression, without multicollinearity or interaction between the predictors (p = 0.002 and 0.009, respectively). Conclusions: Pharmacogenetics analyses that were done in the Montenegrin population of patients for the first time suggest that these analyses can predict patient response to the certain therapy. Stepwise approach could be used in assessing the clopidogrel resistance in cardiology patients, combining the PREDICT score, platelet aggregation test, and genetic testing for CYP2C19 polymorphism.

Keywords: clopidogrel, pharmacogenetics, pharmacotherapy, PREDICT score

Procedia PDF Downloads 349

Authors: S. Umar, B. F. Umar

Abstract:

This study was conducted at Giron Masa village, located 30 km from Yauri town. The study determines the socio-economic factors influencing the use of coping strategies among farmers and herders during post-conflict situation. Simple random sampling was employed to select one hundred respondents (50 farmers and 50 herders) from the study area. Logistic regression analysis (LR) was used to ascertain the socioeconomic variables that influenced the use of the coping strategies. The results of the study shows that age, income, family size and farming experience were individually significant and thus influenced the use of POCS by farmers. Annual income and production system influenced the use of POCS by herders. Age, farm size and farming experience were found to be individually significant in influencing the use of EOCS among farmers. Specifically, years of occupation experience among the herders increased the use of emotion oriented coping strategies among herders. The use of SSCS among farmers was influenced by educational level; farm size and farming experience, while the variables are not collectively significant in influencing the use of SSCS among the herders. The research recommends a need to adopt the strategy of community coping to cope with stress.

Keywords: farmers, herders, conflict, coping strategies

Procedia PDF Downloads 374

Authors: Monica Alzate, Marcos Dono, Jose Manuel Sabucedo

Abstract:

As long as collective action and its predictors constitute a big body of work in the field of political psychology, context-dependent studies and moral variables are a relatively new issue. The main goal of this presentation is to examine the differences in the predictors of collective action when taking into account two different types of protest, and also focus on the role of moral obligation as a predictor of collective action. To do so, we sampled both protesters and non-protesters from two mobilizations (N=376; N=563) of different nature (catalan Independence, and an 'indignados' march) and performed a logistic regression and a 2x2 MANOVA analysis. Results showed that the predictive variables that were more discriminative between protesters and non-protesters were identity, injustice, efficacy and moral obligation for the catalan Diada and injustice and moral obligation for the 'indignados'. Also while the catalans scored higher in the identification and efficacy variables, the indignados did so in injustice and moral obligation. Differences are evidenced between two types of collective action that coexist within the same protest cycle. The frames of injustice and moral obligation gain strength in the post-2010 mobilizations, a fact probably associated with the combination of materialist and post-materialist values that distinguish the movement. All of this emphasizes the need of studying protest from a contextual point of view. Besides, moral obligation emerges as key predictor of collective action engagement.

Keywords: collective action, identity, moral obligation, protest

Procedia PDF Downloads 332

Authors: Naragain Phumchusri, Kittitach Pongpairoj

Abstract:

Overbooking is a very useful revenue management technique that could help reduce costs caused by either undersales or oversales. In this paper, we propose an overbooking model for two types of cars that can minimize the total cost for car rental service. With two types of cars, there is an upgrade possibility for lower type to upper type. This makes the model more complex than one type of cars scenario. We have found that convexity can be proved in this case. Sensitivity analysis of the parameters is conducted to observe the effects of relevant parameters on the optimal solution. Model simplification is proposed using multiple linear regression analysis, which can help estimate the optimal overbooking level using appropriate independent variables. The results show that the overbooking level from multiple linear regression model is relatively close to the optimal solution (with the adjusted R-squared value of at least 72.8%). To evaluate the performance of the proposed model, the total cost was compared with the case where the decision maker uses a naïve method for the overbooking level. It was found that the total cost from optimal solution is only 0.5 to 1 percent (on average) lower than the cost from regression model, while it is approximately 67% lower than the cost obtained by the naïve method. It indicates that our proposed simplification method using regression analysis can effectively perform in estimating the overbooking level.

Keywords: overbooking, car rental industry, revenue management, stochastic model

Procedia PDF Downloads 171