A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index

Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Findings: In a comparison of the forecasting results of the KSE-100 index over different periods, the least RMSE is found in a linear combination of NNAR and GARCH, followed by NNAR, GARCH, NNAR based GARCH, and GARCH based NNAR models. Conclusion: The study concludes that the hybrid model designed with a linear combination of GARCH and NNAR performs better among all the models in forecasting the volatility of the KSE-100 index

THE INTERCONNECTIONS OF GREEN MOTIVES AND CORPORATE SOCIAL PERFORMANCE: THE MEDIATING ROLE OF GREEN PRACTICES

This research aims to explore the impact of green motives (GM) on Corporate Social Performance (CSP) through green practices (GP) in the hotel industry. Data was collected from the 250 executives and other members of senior management who were involved in the management decision making directly or indirectly. Structural Equation Modeling (SEM) technique was applied through Smart-PLS version 3.2.8. Subsequently, results proved that green motives have a positive association with green practices and CSP. Green practices mediated the relationship between green motive and CSP.  This research isolates itself from the previous ones in this area by integrating the literature of green motives and corporate social performance that how green practices intercede this relationship in the context of the hotel industry. In the hotel industry, owners/managers should focus on green motives and must consider them to keep their stakeholders interested and motivated. This study guides management in practice that how to satisfy their customers timely through the green process and build a strong foundation for CSP. This is quantitative research based on cross-sectional data and has been conducted in Pakistan

Diagnosing isolated hepatosplenic tuberculosis in an immunocompetent patient: A case report

For many years, tuberculosis (TB) has been endemic in Pakistan; many rare and unusual presentations have been reported. There is a myriad of non-specific symptoms which always requires a high index of clinical suspicion for TB. World Health Organization data suggest that Pakistan ranks as the fifth highest country burdened with TB and has the fourth highest prevalence of multi-drug resistant TB globally. With an annual incidence of 277 cases per 100,000, the importance of early diagnosis and treatment is self-evident. We present a case where a strong suspicion of isolated hepatosplenic TB in an immunocompetent patient justified a directed approach

A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index

Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Findings: In a comparison of the forecasting results of the KSE-100 index over different periods, the least RMSE is found in a linear combination of NNAR and GARCH, followed by NNAR, GARCH, NNAR based GARCH, and GARCH based NNAR models. Conclusion: The study concludes that the hybrid model designed with a linear combination of GARCH and NNAR performs better among all the models in forecasting the volatility of the KSE-100 index

Analysis of Growing Tumor on the Flow Velocity of Cerebrospinal Fluid in Human Brain Using Computational Modeling and Fluid-Structure Interaction

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles

Does academic assessment system type affect levels of academic stress in medical students? A cross-sectional study from Pakistan

Introduction Stress among medical students induced by academic pressures is on the rise among the student population in Pakistan and other parts of the world. Our study examined the relationship between two different systems employed to assess academic performance and the levels of stress among students at two different medical schools in Karachi, Pakistan. Methods A sample consisting of 387 medical students enrolled in pre-clinical years was taken from two universities, one employing the semester examination system with grade point average (GPA) scores (a tiered system) and the other employing an annual examination system with only pass/fail grading. A pre-designed, self-administered questionnaire was distributed. Test anxiety levels were assessed by The Westside Test Anxiety Scale (WTAS). Overall stress was evaluated using the Perceived Stress Scale (PSS). Results There were 82 males and 301 females while four did not respond to the gender question. The mean age of the entire cohort was 19.7±1.0 years. A total of 98 participants were from the pass/fail assessment system while 289 were from the GPA system. There was a higher proportion of females in the GPA system (85% vs. 59%; p \u3c 0.01). Students in the pass/fail assessment system had a lower score on the WTAS (2.4±0.8 vs. 2.8±0.7; p=0.01) and the PSS (17.0±6.7 vs. 20.3±6.8; p \u3c 0.01), indicating lower levels of test anxiety and overall stress than in students enrolled in the GPA assessment system. More students in the pass/fail system were satisfied with their performance than those in the GPA system. Conclusion Based on the present study, we suggest governing bodies to revise and employ a uniform assessment system for all the medical colleges to improve student academic performance and at the same time reduce stress levels. Our results indicate that the pass/fail assessment system accomplishes these objectives

Predicting Divorce Prospect Using Ensemble Learning:Support Vector Machine, Linear Model, and Neural Network

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce

Knowledge, attitude and perception survey of doctors regarding antibiotic use and resistance in Karachi, Pakistan

Objective: To establish a better understanding of physicians\u27 knowledge and beliefs, and to compare distinctions in knowledge, attitude and perception of junior and senior doctors regarding rational use of antibiotics.Methods: The cross-sectional study was conducted at a tertiary care hospital in Karachi, from June 1 to July 31, 2016, and comprised senior and junior doctors. A 26-item questionnaire divided in three sections was used to test knowledge, attitude and perception of the subjects regarding rational use of antibiotics. Data was analysed using SPSS 23.Results: Of the 200 subjects, 132(66%) were senior doctors; 68(34%) were junior; 116(58%) were females; 84(42%) were males; and the highest number of respondents were from General Medicine 65(32.5%). While 182(91%) doctors realised that antibiotic resistance was a pressing issue, only 131(65.5%) felt confident about their prescriptions and 94(47%) admitted that they over-prescribed antibiotics. Among young physicians, 13(19.1%) believed that antibiotics did not cause side effects even when prescribed unnecesarily. Also, 47(69.1%) junior doctors felt that patients\u27 demands influenced their prescriptions compared to 66(50%) senior doctors (p=0.01).Conclusion: Although physicians were found to be knowledgeable about rational use of antibiotics, there were gaps in knowledge and perception