Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Stock-return volatility and daily equity trading by investor groups in Korea

Abstract: We examine the short-run relationship between stock-return volatility and daily equity trading by several investor groups in the Korean Stock Exchange. We also investigate whether trade characteristics and trading styles can explain the potential distinct volatility effects of these investor groups. For large stocks, we find that whether a trade is a purchase or a sale and whether it is a contrarian or a momentum trade does not play a role in the relation between volatility and trading. It is the trading of informed institutional investors against non-informed individual investors that drives volatility and produces a negative volatility effect. We further show that net foreign trading has an increasing impact on volatility though it is not always significant. Our results are robust to alternative measures of volatility and obtained after controlling for volatility persistency, total volume and lagged stock returns.

Predicting waiting and treatment times in emergency departments using ordinal logistic regression models.

Abstract: Background Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. Methods Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. Results According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times. Conclusion By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality.

Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems

Abstract: Pedestrian Detection (PD) is one of the most studied issues of driver assistance systems. Although a tremendous effort is already given to create datasets and to develop classifiers for cars, studies about railway systems remain very limited. This article shows that direct application of neither existing advanced object detectors (such as AlexNet, VGG, YOLO etc.), nor specifically created systems for PD (such as Caltech/INRIA trained classifiers), can provide enough performance to overcome railway specific challenges. Fortunately, it is also shown that without waiting the collection of a mature dataset for railways as comprehensively diverse and annotated as the existing ones for cars, a Transfer Learning (TL) approach to fine-tune various successful deep models (pre-trained using both extensive image and pedestrian datasets) to railway PD tasks provides an effective solution. To achieve TL, a new RAilWay PEdestrian Dataset (RAWPED) is collected and annotated. Then, a novel three-stage system is designed. At its first stage, a feature-classifier fusion is created to overcome the localization and adaptation limitations of deep models. At the second stage, the complementarity of the transferred models and diversity of their results are exploited by conducted measurements and analyses. Based on the findings, at the third stage, a novel learning strategy is developed to create an ensemble, which conditionally weights the outputs of individual models and performs consistently better than its components. The proposed system is shown to achieve a log average miss rate of 0.34 and average precision of 0.93, which are significantly better than the performance of compared well-established models.