Esichaikul, Vatcharaporn
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Gamification to enhance motivation and engagement in blended eLearning for technical and vocational education and training
A collaborative system to improve knowledge sharing in scientific research project
Sentiment analysis of Thai financial news
Due to the big data and FinTech influencers, the novel SentiFine framework was developed to facilitate the financial analysts or specialists who need to understand the financial and economic circumstances from the daily news. The objective of this framework is to analyze the sentiment of Thai financial daily news by integrating the fine-grained sentiment analysis technique with the deep neural network. Based on the proposed SentiFine framework, the prototype of the SentiFine web-based system was developed. With the main feature "View Daily News", the users can view the daily Thai financial news detail, the date and time of the news, the source of news, and its sentiment which is categorized into three tones (positive, neutral, and negative).
Comparison of predictive models for hospital readmission of heart failure patients with cost-sensitive approach
Challenges, trends & opportunities of technical & vocational education & training (Tvet)
A framework to identify factors affecting the performance of third-party B2B e-marketplaces: A seller’s perspective
Predictive modelling for hospital readmission risk in the Philippines
Predictive models have been developed over the years to identify patients at risk of readmission. The goal of this study is to identify the risk factors associated to a patient’s readmission within one year in the cohort study including acute myocardial infarction (AMI), Heart Failure (HF), Chronic Obstructive Pulmonary Disease (COPD) and Pneumonia (PN) in a reputed Philippine hospital. Four predictive models were used and evaluated using performance metrics. The study found Logistic Regression as the most performing model in most of the cohort studies. There are 6 to 8 variables significantly associated with the readmission of high-risk patients.