Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.

The spread of infectious diseases, such as COVID-19, presents a significant problem for public health and healthcare systems. Digital technology plays an important role in achieving access to healthcare by enhancing device connectivity and information sharing. This study aimed to develop, implement, and demonstrate a tracking and surveillance system to enhance monitoring for emerging infectious diseases, focusing on COVID-19 patient profiling. The system integrates IoT-based wearable devices, an artificial intelligence (AI) camera for real-time monitoring, and a MySQL database for data management. The program uses Charts.js for data visualization and Longdo Map API for mapping, leveraging Jetson Nano boards, webcams, and Python (Version 3.9). We employed a classification technique to categorize patients into two groups: those with a positive mood and those with a negative mood. For comparing accuracies, we utilized three types of models: multilayer perceptron (MLP), support vector machine (SVM), and random forest. Model validation and evaluation were conducted using Python programming. The results of this study fall into three parts. The first part involved testing the monitoring and surveillance system. It was found that the system could receive information from the wearable device, display the received data in graph form, and notify the medical staff when examining symptoms to consider whether the patient should be taken to the hospital. The second part focused on testing the device, and it was found that it could measure body temperature, heart rate, and blood oxygen levels (SpO₂) and send those data to the database. The third part involved an AI camera test, and it was found that the most suitable algorithm to analyze the patient’s facial expressions was Random Forest. The results show that the system supports hospitals in managing COVID-19 and similar diseases by enabling timely interventions through facial expression analysis.

A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.

This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

The motivations for automatic grading of image-based multiple choice answer sheets include significant time and cost reductions. The proposed method supports any pencils or pens used on thin papers, as well as low-cost gridded paper that is easy to use in a typical test. Fourteen different scenarios pertaining to 560 answer sheets were evaluated with automatic reporting of the final grading and its summary. The result shows that an average accuracy of 100% for the cases with nearly complete pencil or pen markings. In the cases with incomplete markings, such as small markings, overflow, and deleted or unclean markings, the accuracies were 62.42%, 93.16%, 99.57%, respectively. The proposed system operates 2.5 times faster than the conventional manual method.