In this paper we propose a predictive model for learning analysis using student teaching data and demonstrate the effectiveness of machine learning methods in predicting retention and dropout. We also examine the impact of online classes due to the COVID-19 pandemic on academic performance, and analyze the correlation between changes in academic performance and prediction of retention and dropout based on recall and precision.

Congestion detection in places where people gather is critical for safety monitoring and disaster management. Conventional methods such as crowd counting, typically estimate the number of people based on still images and it is difficult to determine the overall congestion level in situations when only low quality images are available due to occlusion or weather condition. In this paper we propose a congestion prediction method that employs audio data generated by humans such as footsteps and conversations. By combining the deep learning model for crowd counting with audio features, we can obtain more accurate congestion predictions. Finally, we evaluated the proposed method based on real data.

As the design of knowledge-based systems depends on problem domains, it is often difficult to acquire useful knowledge or to guarantee the consistency of the knowledge-base and the completeness of search. To overcome those defects and construct more effective knowledge-based systems, it is useful to utilize optimization methods of mathematical programming. This requires a model which can integrate both the problem solving process of knowledge-based systems (KBS) and the optimization process of mathematical programming (MP). The authors propose a framework to realize this goal by constructing a domain-oriented structured model for planning-type knowledge-based systems which can include useful optimization techniques of mathematical programming. The proposed model is considered to be useful for evaluation or validation of the completeness and the consistency of knowledge bases.>