scholarly journals O2O Iterative Project-based Teaching Model for Literature Course

2020 ◽  
Vol 15 (16) ◽  
pp. 80
Author(s):  
Yin Dai
Keyword(s):  
Evaluation System ◽  
Teaching Method ◽  
Academic Research ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Space Environment ◽  
Teaching Model ◽  
Implementation Framework ◽  
Teaching Mode ◽  
Cart Algorithm

As a main course of Chinese language and literature major in colleges, Literature Course is one of required courses for students majoring in journalism, hosting and broadcasting, which can cultivate students’ favorable humanistic quality and promote their specialty development. However, some problems exist in traditional teaching process, such as low interest in linguistics, insufficient interaction between teachers and students, and single evaluation system. Starting from Merrill’s chief teaching principle and aiming at teaching deficiencies of Literature Course, this study innovatively introduced O2O iterative project-based teaching model to establish an iterative teaching model based on online and offline integration. In combination of the question-based teaching method, four teaching steps including “construction, guidance, tutoring and introspection” were designed under the cooperation of O2O space environment and collaborative mechanism, and a brand-new implementation framework for linguistics teaching was tentatively constructed. To test the effect of this teaching mode, classification and regression tree (CART) algorithm of data mining technology was applied to compare and evaluate teachers’ teaching ability before and after the implementation of this teaching mode. It is found that, this teaching reform contributes to cultivating students’ critical thinking and innovation ability, and enhancing their academic research spirit and ability of language theory application.

scholarly journals WeChat as a Platform for Problem-Based Learning：towards to exploring new teaching methods for hematological postgraduate (Preprint)

2019 ◽  
Author(s):  
Ping Luo ◽  
Jiancheng Tu ◽  
Fuling Zhou
Keyword(s):  
Teaching Methods ◽  
Teaching Method ◽  
Problem Based Learning ◽  
Learning Objectives ◽  
Teaching Model ◽  
Hard Problem ◽  
Time Saving ◽  
Teaching Mode ◽  
Student Centered ◽  
Time Dispersion

UNSTRUCTURED Hematological medicine is a practical discipline that difficult to study. The cultivation for postgraduate majored in hematology is rather hard. Problem-based learning (PBL) is a student-centered innovating teaching method that students define their own learning objectives from clinically based problem. It has been widely accepted in student-centered medical education. Owing to the geographic and time dispersion of the students, traditional PBL has its own limitations. WeChat, the most popular platform among university students in China, was introduced in hematology teaching. In this study, we combine traditional PBL and WeChat together to explore a new WeChat Problem-Based learning mode for postgraduate majored in hematology. 100 questionnaires were distributed to evaluate how the students and tutors think about the WeChat-PBL teaching mode. The data showed that the WeChat-PBL teaching mode was popular and widely accepted. Our new PBL mode is time saving, convenience and easy to conduct. It emphasizes interoperable, interactive, effective and more participatory. We firmly believe that using this new WeChat-PBL teaching model will certainly help students be more excellent.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

scholarly journals Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382097969
Author(s):  
Kyung Hwan Chang ◽  
Young Hyun Lee ◽  
Byung Hun Park ◽  
Min Cheol Han ◽  
Jihun Kim ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Treatment Planning ◽  
Planning Process ◽  
Helical Tomotherapy ◽  
Regression Tree ◽  
Roc Curves ◽  
Classification And Regression Tree ◽  
Variable Model ◽  
Predicted Probability ◽  
Cart Algorithm

Purpose: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. Methods: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. Results: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. Conclusions: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

scholarly journals On the Teaching Reform of Translation Course Based on the Cultivation of Applied Talents

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Huijia Shen ◽  
Keyword(s):  
Evaluation System ◽  
Teaching Method ◽  
Teaching Mode ◽  
Existing Problems ◽  
Teaching Reform ◽  
Local Characteristics ◽  
Teaching Content ◽  
Social And Economic Development ◽  
Solid Foundation ◽  
Problems Of Translation

This article, taking the translation courses for English majors in Zhejiang University of Finance and Economics Dongfang College as an example, starts from the current teaching problems of translation courses in application-oriented colleges and universities. In order to clarify the teaching system, highlight the local characteristics, and find a way to adapt to the teaching objective of high-quality applied talents, this article establishes the teaching idea of “solid foundation, application-oriented and strong ability” and discusses the teaching reform of translation course by optimizing the teaching syllabus, teaching content, teaching mode, teaching method and evaluation system. The innovation of translation teaching reform mainly lies in the consistency, integration and effectiveness of the “Four-in-One” teaching mode which taking practical ability as the core, integrate the teaching objective, teaching content, teaching methods and teaching evaluation as a whole, so as to solve the existing problems of the translation course and follow the changes of social and economic development.

scholarly journals Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study

2020 ◽  
Vol 49 (4) ◽  
pp. E13 ◽  
Author(s):  
Tamara Ius ◽  
Teresa Somma ◽  
Roberto Altieri ◽  
Filippo Flavio Angileri ◽  
Giuseppe Maria Barbagallo ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Therapeutic Option ◽  
Regression Tree ◽  
Continuous Variable ◽  
Classification And Regression Tree ◽  
Imaging Data ◽  
Cart Analysis ◽  
Survival Score ◽  
Molecular Features ◽  
Cart Algorithm

OBJECTIVEApproximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients.METHODSClinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score.RESULTSSurvival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell’s c-index of 0.75 (95% CI 0.76–0.81).CONCLUSIONSThe CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly GBM patients with different surgical, radiological, and molecular profiles, thus assisting physicians in daily clinical management. The preliminary model, however, requires validation with future prospective investigations. Practical recommendations for clinicians/surgeons would strengthen the quality of the study; e.g., surgery can be considered as a first therapeutic option in the workflow of elderly patients with GBM, especially when the preoperative estimated EOR is greater than 80%.

scholarly journals Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Cintia Isabel De Campos ◽  
Murilo Castanho Dos Santos ◽  
Cira Souza Pitombo
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Macro Level ◽  
Traffic Fatality ◽  
Level Data ◽  
The Government ◽  
Cart Algorithm

Road traffic accidents occur daily caused by different factors leading to varying degrees of injury severity. Considering this, many studies have been developed to identify and understand these factors to implement preventive actions. A Decision Tree (DT) is one of the techniques that can generate classifications and predictions by detecting a priori unknown patterns. This study aims to identify the characteristics of municipalities with high and very high fatality rates caused by traffic accidents, using a macro level dataset and a DT algorithm (CART - Classification And Regression Tree). Therefore, macro level data from the municipalities of São Paulo state (Brazil) were used, such as demographic and socioeconomic data, fatality rates and other variables related to traffic. The results indicated the Gross Domestic Product (GDP) as the most important variable, and the municipalities were characterized mainly considering the size of the highway network and vehicle fleet (trucks, minibuses, cars, motorcycles). These characteristics could provide support to the government to plan mitigating actions in municipalities with the highest tendency to high traffic fatality rates.

Abstract 12302: Mortality and Readmissions After Heart Failure Hospitalization in a Community Based Cohort: Estimating Risk Using the Acute Decompensated Heart Failure National Registry (ADHERE) Classification and Regression Tree (CART) Algorithm

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Sithu Win ◽  
Imad Hussain ◽  
Virginia Hebl ◽  
Margaret M Redfield
Keyword(s):  
Heart Failure ◽  
Acute Decompensated Heart Failure ◽  
Regression Tree ◽  
Decompensated Heart Failure ◽  
National Registry ◽  
Classification And Regression Tree ◽  
Post Discharge ◽  
Community Based ◽  
Cart Algorithm ◽  
Risk Categories

Background: The Acute Decompensated Heart Failure National Registry (ADHERE) Classification and Regression Tree (CART) algorithm is an accepted method to assess a heart failure (HF) patient’s risk of inpatient mortality based on the patient’s systolic blood pressure (≥115 vs. <115 mmHg), blood urea nitrogen (BUN; ≥43 vs. <43 mg/dL), and creatinine (≥2.75 vs. <2.75 mg/dL) at the time of admission. Whether the ADHERE CART algorithm identifies risk of longer term poor outcomes and is predictive in patients with systolic (EF < 50%) or diastolic (EF ≥ 50%) HF in the community is unclear. Methods: We identified all hospitalizations for a primary diagnosis of HF occurring between 2000-2013 in a community-based cohort living within 40 miles of Rochester, MN. Outcomes including length of stay, in-hospital mortality, 30 and 90 day post-discharge mortality and readmission for any cause at 30 and 90 days were compared across the ADHERE CART risk groups using logistic regression for the entire cohort and separately for systolic (EF < 50%) and diastolic (EF ≥ 50%) HF. Results: See Table. We examined 5,918 heart failure hospitalizations among 3,628 individual patients. Distribution of hospitalizations across the risk categories is shown in table. Length of stay, in-hospital mortality, 30 and 90 day mortality and 30 and 90 day readmission rates all increased with increasing ADHERE risk categories. Similar results were obtained separately for systolic and diastolic HF. Conclusion: The ADHERE CART algorithm is simple, uses data universally available on admission, and identifies groups that differ substantially in their post-discharge adverse outcomes regardless of HF type (systolic vs diastolic) in the community. The ADHERE CART algorithm may inform clinical decision regarding advanced HF treatments, end-of-life planning and care transition services in patients hospitalized for HF in the community.

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