In an aging population with a changing demographic structure, the government aims to ensure that elderly people receive care. In the concept of lifelong learning, education opportunities are available to senior learners, not just children and young people. The sustainable development for senior learners becomes a very important issue because it promotes a variety of learning activities for senior learners. Many universities have started to offer education for senior learners in Taiwan. Positive experiences for senior learners in senior universities can be fostered by ensuring the sustainable development of senior education. In this paper, a study on sustainable development for senior learners is proposed. This study aims to explore potential tools or approaches in evaluating the sustainable development for senior learners for decision making. In this study, two approaches are applied to analyze the sustainable development for senior learners. The first is a statistical analysis, and the second is the random forest model. The methodology of statistical analysis focuses on three aspects such as social assistance, inspiration, and the learning fulfillment for senior learners in senior universities. The random forest model is used to generate decision rules to support decision making. The random forest in this study obtained 22 decision rules. The results suggest that the items in the questionnaire and the decision rules from random forest could provide useful information that allows decision-makers to analyze the sustainable development of senior learners.

Because of the information age, protecting information is very important to satisfy the three main aspects of information security, namely confidentiality, integrity and availability. In this case, information security has become one of the most important problems in information technology. Information security is a very important activity and risk assessment is the kernel of information security. However, most of the current risk assessment activities are comparatively subjective and the performances are not good enough. To understand this problem, we propose the improved neural network for the risk assessment of information security. Basically, it is processed under back-propagation neural network (BPN). Moreover, particle swarm optimization (PSO) is used for fine parameter optimization of BPN. The experimental results show that the proposed algorithm has the best performance among these compared approaches.