The coral reefs are important ecosystems to protect underwater life and coastal areas. It is also a natural attraction that attracts many tourists to eco-tourism under the sea. However, the impact of climate change has led to coral reef bleaching and elevated mortality rates. Thus, this paper modeled and predicted coral reef bleaching under climate change by using machine learning techniques to provide the data to support coral reefs protection. Supervised machine learning was used to predict the level of coral damage based on previous information, while unsupervised machine learning was applied to model the coral reef bleaching area and discovery knowledge of the relationship among bleaching factors. In supervised machine learning, three widely used algorithms were included: Naïve Bayes, support vector machine (SVM), and decision tree. The accuracy of classifying coral reef bleaching under climate change was compared between these three models. Unsupervised machine learning based on a clustering technique was used to group similar characteristics of coral reef bleaching. Then, the correlation between bleaching conditions and characteristics was examined. We used a 5-year dataset obtained from the Department of Marine and Coastal Resources, Thailand, during 2013–2018. The results showed that SVM was the most effective classification model with 88.85% accuracy, followed by decision tree and Naïve Bayes that achieved 80.25% and 71.34% accuracy, respectively. In unsupervised machine learning, coral reef characteristics were clustered into six groups, and we found that seawater pH and sea surface temperature correlated with coral reef bleaching.

This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments.

Risk assessment system to reduce the risk of personnel to staff were not aware that their work can also be a risk prevention and behavior change work habits to avoid the risk of working in the hospital. personnel Able to work properly so that the workplace / department that is likely threats in question would pose a danger not much to consider to take any action to solve the problem Such as radiation, heat, chemical accidents. lifting too heavy Sitting or standing too long exposure to various viruses. those risks appropriately developed a risk assessment system in the work of hospital personnel by keeping the risk of things at a critical level and the level of opportunity that will occur to the management at the risk department. To work the procedure of personnel steps to find a way to prevent and solve problems with the work of personnel to reduce the risk of personnel work.