Apriori-based rule generators, which are powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm, are applied to analyze the data sets available in the IEEE BigData 2019 Cup: Suspicious Network Event Recognition. Then, each missing value in the test data set is decided by using the obtained rules. The advantage of our rule-based model is that the obtained rules are very easy to understand in comparison with other ”black-box” machine learning models. Furthermore, two algorithms preserve the logical property ”completeness,” so they generate rules without excess and deficiency. In evaluation, the AUC measure seems unfavorable to our model, so we employed 3-fold cross-validation for the training data set, and we obtained a 94% mean score. This result ensures the validity of our model. We report several meaningful results in this experiment, as well as the estimation of missing values.

Knowledge is presented in various ways such as semantic network. Hierarchical representation is widely used as one of the well-known methods in knowledge representation. Knowledge representation plays a pivotal role in dealing with knowledge, facts, procedures and meanings for solving problems. The knowledge representation is a crucial task in handling problems, and it tends to fail if we do not understand well the problem or situation to model. The aim of this paper is to propose a logical hierarchical structure for knowledge representation in semantic network. We place stress on modeling knowledge from semantic network perspective during analysis phase. This method is known as evidence-based semantic network or ESN. "Scene labeling" is used as an example for the proposed method. The results show the proposed method is easier to understand than original semantic network.

Regression models are well known and widely used as one of the important models in system modeling. In this paper, we extend the concept of regression models in order to handle hybrid data coming from various sources of data quite often exhibiting diverse levels of quality. The major objective of this study is to develop a convex hull method being regarded as a potential vehicle, which helps reduce the computing time, especially in real-time data analysis as well as an overall computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. The method addresses situations when we have to deal with heterogeneous data. In the proposed design setting, we emphasize a pivotal role of convex hull approach which is crucial when alleviating limitations of a linear programming manifesting in system modeling.