In light of the ongoing occurrence of epidemics, the general populace frequently makes the decision to curtail their nomadic lifestyle in order to protect both their health and their safety. This has resulted in a number of issues, the most notable of which are the drop in the people's living happiness index and the poor business that the tourism industry has been experiencing as a result. Therefore, the idea of "cloud tourism" is undoubtedly the first candidate for the tourism industry, and in order to meet the requirements of cloud tourism, it is necessary to have an entirely new system to serve this, of which the scenic guide robot is an important part. At the same time, the quickening development of 5G technology offers solutions that may be put into practice for the multifurther IoT's expansion in smart cities. People will be able to experience the real outdoors without having to leave their homes, which will improve the people's well-being and alleviate the chilly status quo in the tourism industry. This is the plan, and it will be accomplished through the use of innovative guide robots that will make the experience more convenient and reliable.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.