
(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 7, No. 3, 2010 

Table V. Fuzzy inference rules defined by domain experts 

Fuzzy variables Class labels 

Rules Place 

(P) **the status of victim** 

(V) **the status of terrorist** 

(T) Tactic (TT) 

1 H H H H 2 H H M M 

3 H H L M 4 H M H H 

5 H M M L 6 H M L L 

7 H L H H 8 H L M L 9 H L L L 

10 M H H H 

11 M H M M 12 M H L M 

13 M M H H 14 M M M M 

15 M M L M 16 M L H H 17 M L M L 18 M L L L 19 L H H H 20 L H M M 21 L H L M 22 L M H H 

23 L M M M 

24 L M L M 25 L L H VL 26 L L M VL 

27 L L L VL 

Moreover, adaptive neuro-fuzzy inference system 
(ANFIS) is an extension of the TSK fuzzy model. In this paper, ANFIS toolbox is available in MATLAB program. 

ANFIS used neural network to identify parameters. Neural network in this research adapts back-propagation (BP) 
learning algorithm and hybrid algorithm that combine the gradient method and the least square method estimation 
(LSM) for identification parameters. It adjusts the shape parameters of membership function (MF) in FIS by learning from a set of given input and output data. In general ANFIS is more complicate than FIS; the design of itself considers the features specification as follow: 
1) Learning and reasoning: ANFIS algorithm provides a leaning method of neural network (NN) which can obtain corresponding fuzzy rules from data for fuzzy modeling. The learning method can effectively calculate the best parameter for the MF and best simulate the expected or actual relations between inputs and outputs based on given data. 

2) Structure and parameters: The ANFIS structure similar to the structure of neural network (NN) which the input space are mapped into output space with their MFs and parameters. 

For the parameters, the ANFIS function estimates MFs using NN, namely back-propagation (BP) or hybrid algorithm that combine the least square method (LSM) estimation with the gradient method. The parameters can adjust and change the shape of MFs to reduce error between the desired output 
(target) and the expected system (FIS output) through a learning procedure. *Parameter* in each MF is investigated, such as the training epochs, the number of MFs for each input, the optimization method, the type of output MFs, and the training data and testing data. 

3) Model and data set: The modeling procedure starts to connect input variables, the MFs of input variables, fuzzy rules, output variables and the MFs of output variables. Next, the results contained a group of pairs of input and output data forms training data to the algorithm of ANFIS according to a certain format. Finally, the process generates the model continuously simulating it to the given training data. 

4) Measure performance: The measurement of the result model measures training data simultaneously. The function chooses suitable results according to the rules resulting in the least error in the training data, which calculates and compares correspondingly. If the system meets a case of no match, the error of the training data becomes less, so that means the system parameters will not match with the training data. 

$$(17)$$

V. RESULTS AND DISCUSSION

## A.Performance Evaluation

The performance measures use root mean squared error
(RMSE) which is the standard deviation of regression, for evaluation the experiment of terrorism information classification as shown in (17) and (18). 

$$R M S E={\sqrt{M S E}}$$
 *RMSE MSE* = (17) 
 
$$\begin{array}{c c c}{{\mathrm{RMSE}=\sqrt{MSE}}}&{{(17)}}\\ {{}}&{{}}&{{}}\\ {{\mathrm{MSE}_{(i)}=\frac{(N-p)MSE-e_{i}\ /\ (1-H_{i i})}{N-p-1}}}&{{}}&{{(18)}}\end{array}$$

These equations are calculated from the data set, where i is the estimated parameter, N is data size, respectively. A value of the root mean squared error closes to 0 indicates a better fit. B.Experimental results The experiment compares FIS settings into two comparisons: first is the comparison between unstructured events (approach 1) and structured events (approach 2) using the same FIS setting; second comparison is the model settings comparing between structured events classified by FIS (approach 2) and ANFIS (approach 3). The eight MFs, such as Triangular, Trapezoidal, Gaussian, Two-Side Gaussian, BellShape, Product of Two Sigmoid, Differences Between Two Sigmoid, and Pi-Shape are tested using training set option. The results in terms of RMSE measure are averaged across all training set experiments. The experiments compare the performance of the different MF. The details of experiment results are summarized in Table VI. The first comparison result shows the classification of FIS resulted from structured events achieves satisfactory accuracy, the classification error of structured event 2.16 %, and is better than the unstructured 