Emotion classification through text with proposed hybrid learning emotion model

Natural language processing (NLP) and artificial intelligence (AI) are important to enrich human-computer communication. The NLP is widely applied in various domains such as e-commerce, health, social media sentiment, etc. There has been an increasing need to deliver classes online since the COVID-19 pandemic, and various efforts and tools have been explored to improve students' online studies. Students usually communicate with instructors and ask questions through text messages in online classes;Hence, NLP could be used to identify students' emotions to improve the online learning experience. The current emotion classification works focused on the seven (7) universal emotions: anger, contempt, disgust, enjoyment, fear, sadness, and surprise. There is a lack of studies specializing in learning emotion classification. This research proposes a hybrid learning emotion model to predict students' emotions through text messages. Emotions can affect the learner at different stages of the learning process. Understanding the student's emotions is important because it will impact their attention, motivation, and self-regulated learning ability. The proposed hybrid learning emotion model is designed to classify four types of learning emotions: engagement, confusion, boredom, and hopefulness. In this research, the text messages from the student were collected based on the proposed hybrid learning emotion models, and the multinomial Naïve Bayes approach was used to predict the learning emotion. © VDE VERLAG GMBH.

COVID-19 Monitoring and Response for Military Bases in Singapore-Perspectives and Lessons From January to June 2020

Introduction Military forces around the world face an increased risk of the spread of communicable diseases, due to the close living quarters and congregated nature of the military camps. The Singapore Armed Forces (SAF) implemented a multi-pronged surveillance and containment strategy to reduce the risk of a coronavirus disease 2019 (COVID-19) outbreak within the local military camps. This paper details the epidemiological investigations of the COVID-19 cases in the SAF and highlights the strategies and public health measures undertaken, aligned with the national COVID-19 control strategy, to reduce the risk of COVID-19 transmission in the military camps. Materials and Methods Medical data of our military personnel who were infected with COVID-19 during the first 180 days of the pandemic were extracted from the military electronic health records. Contact tracing and activity mapping results were obtained from unit-level epidemiological data. A review of the organization's response plans, instructions, and orders was conducted to collate the measures implemented across the same time period. Results Prompt contact tracing and activity mapping was done for each of the 24 SAF military personnel diagnosed with COVID-19 between February 2020 and June 2020, with possible links among the cases identified and investigated. Conclusion A combination of strategies in the formulation of public health measures based on key principles of early warning and surveillance, prompt diagnosis, and early containment were successful in preventing the formation of COVID-19 clusters within the SAF. This will provide a framework for the management of future pandemics within the military setting, driven by strong governance and leadership, to meet the military's need to maintain operational readiness in a safe manner.

Redesigning Patient Flow in Paediatric Eye Clinic for Pandemic Using Simulation

This study proposes a systematic approach to the construction of a simulation model to support decision-making concerning the capacity limit and staffing configurations at the paediatric eye clinic in Singapore under the COVID-19 pandemic situation. During the pandemic, the clinic must ensure that the operations are aligned to the safe-distancing regulations put in place by the Ministry of Health while coping with the demand. We developed simulation models to examine the 'as-is' process and proposed numerous 'to-be' processes for new clinic configurations to operate under the pandemic conditions. We combined scenario-thinking and simulation optimization to determine the additional manpower and physical resource requirements to enable the decision-makers at the clinic to better plan the necessary for continuous care to the young patients in a populated city, while coping with the healthcare demands during the pandemic. © 2021 IEEE.