Terrorism Event Classification Using Fuzzy Inference Systems
  Terrorism has led to many problems in Thai societies, not only property
damage but also civilian casualties. Predicting terrorism activities in advance
can help prepare and manage risk from sabotage by these activities. This paper
proposes a framework focusing on event classification in terrorism domain using
fuzzy inference systems (FISs). Each FIS is a decision-making model combining
fuzzy logic and approximate reasoning. It is generated in five main parts: the
input interface, the fuzzification interface, knowledge base unit, decision
making unit and output defuzzification interface. Adaptive neuro-fuzzy
inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic
and neural network. The ANFIS utilizes automatic identification of fuzzy logic
rules and adjustment of membership function (MF). Moreover, neural network can
directly learn from data set to construct fuzzy logic rules and MF implemented
in various applications. FIS settings are evaluated based on two comparisons.
The first evaluation is the comparison between unstructured and structured
events using the same FIS setting. The second comparison is the model settings
between FIS and ANFIS for classifying structured events. The data set consists
of news articles related to terrorism events in three southern provinces of
Thailand. The experimental results show that the classification performance of
the FIS resulting from structured events achieves satisfactory accuracy and is
better than the unstructured events. In addition, the classification of
structured events using ANFIS gives higher performance than the events using
only FIS in the prediction of terrorism events.
