(IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 3, 2010 

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$\left(\begin{array}{c}\frac{x-a}{b-a}\\ \frac{x-b}{b-a}\end{array}\right)$.  $\left(\begin{array}{c}\frac{x-a}{b-a}\\ \frac{x-b}{b-a}\end{array}\right)$.  

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$x\leq a$.$\mu$m.$x\leq a$  $a\leq x\leq a$  $a+b$  $a+b$  $b\leq x\leq b$  $c\leq x\leq c+d$  $2$  $c+d$  $2$  $\leq x\leq d$
In (5) to (12) shows the MFs of the fuzzy sets for all variables in this research. The value of membership degree is usually in the range [0, 1] where "1" means full membership and "0" means no membership. For fuzzy variables, there are place, *the status of victim* and *the status of terrorist*. These inputs are considered for each of their properties, and are performed the membership degree computing. Table II shows the structure of three fuzzy variables namely place, the status of victim and *the status of terrorist* fuzzy variable with three linguistic terms, such as low, *medium* and *high.* Table III shows the *tactic* class labels that have four linguistic terms namely *very low, low, medium* and *high,* respectively. Table IV domain experts define the minimum, mean and maximum of bound for all fuzzy variables and fuzzy values set as follow: 
1) *place* variable; 2) the status of *victim* variable; 3) the status of terrorist variable; and 4) *Tactic* variable (class labels). First three fuzzy variables have fuzzy value sets namely low, medium and *high*, and last fuzzy variable has also *very low* value set. All fuzzy values of MF are not the same because they are computed using the different MFs and different parameters. The fuzzy value depends on definition of the bound fuzzy variable that is defined by domain experts. 

Layer 3 Knowledge Base Unit: the fuzzy rules are represented. The basic idea is to perform precondition matching of fuzzy logic rules with their links, so the outputs are linked with associated linguistic term in the next layer. The algebraic product operation is used to compute the precondition matching degree as shown in (13). 

Table II. Fuzzy values of fuzzy variables defined by domain expert 

| Fuzzy   | Fuzzy      |                          |                |                   |
|---------|------------|--------------------------|----------------|-------------------|
| Values  | Values     | Meaning                  | The status     |                   |
| Place   | of victim  | The status  of terrorist |                |                   |
| Stand   | Abbreviate |                          |                |                   |
| low     | L          | Lowly important place    | No victim died | No terrorist died |
| medium  | M          | Important place          | Victim injured | Terrorist injured |
| high    | H          | Very important place     | Victim died    | Terrorist died    |

Table III. Class labels of *tactics* defined by domain expert 

| Class  Stand   | Class label   |                                             |                                                                                                           |
|----------------|---------------|---------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| label          | Abbreviate    | Tactic Stand                                | Tactic Meaning                                                                                            |
| very  low      | VL            | Unsuccessful attack or no  important damage | No victim died and damage occurred at  lowly important place                                              |
| low            | L             | Demolition                                  | local properties destroyed                                                                                |
| medium         | M             | Assassination                               | Officials or populace killed                                                                              |
| high           | H             | Suicide attack                              | local properties, destroyed officials or  populace killed, focusing on the success  over terrorists' life |

In this paper, domain experts define the rules as shown in Table V. 

min ( 1 _ _ 1 _ _ _ _ 1 _ _ _ _ i place low i status of victim low i status of terr , , orist low ) ij *O t t* = t (13) 
Layer 4 Decision-Making Unit: the multiplier receives the incoming signals and sends the product out as a circle node. 

Each node output represents the firing strength of a rule. Tnorm operators perform in this layer (AND, OR, NOT). The fuzzy values of outputs are defined by domain experts, which is four linguistic terms namely *very low, low, medium*, and high as shows in Table V. respectively. 

Layer5 Output Defuzzification interface: performing the defuzzification process to get the *tactic* of the term pair. The center of area method [8] is adopted to carry out the defuzzified process. The term pair of event is computed for the tactic values using (14) as below. 

$$O_{ij}=\frac{\sum\limits_{p=1}^{r}\sum\limits_{q=1}^{c}O_{pq}V_{pq}}{\sum\limits_{p=1}^{r}\sum\limits_{q=1}^{c}O_{pq}}\tag{14}$$

| No.     | Fuzzy Variable               | Fuzzy Values   | Left  Bound   | Mean  Bound   | Right  Bound   |
|---------|------------------------------|----------------|---------------|---------------|----------------|
| 1       | Place (P)                    | P_low          | 0             | 1.0           | 9.0            |
|         | Range [ 0-25]                | P_medium       | 6.0           | 13            | 20.0           |
| P_high  | 16                           | 25             | 25            |               |                |
| 2       | The status of  victim (V)    | V_low          | 0             | 0             | 1.2            |
|         | Range [ 1-4]                 | V_medium       | 0.3           | 1.5           | 2.7            |
| V_high  | 1.8                          | 3              | 4.0           |               |                |
| 3       | The status of  terrorist (T) | T_low          | 0             | 0             | 1.2            |
|         | Range [ 1-4]                 | T_medium       | 0.3           | 1.5           | 2.7            |
| T_high  | 1.8                          | 3              | 4.0           |               |                |
| 4       | Tactic (TT)                  | Tt_verylow     | 1             | 1             | 2.2            |
|         | (class label)                | Tt_low         | 1.3           | 2.5           | 3.7            |
|         | Range [ 1-4]                 | Tt_medium      | 1             | 2.5           | 4              |
| Tt_high | 2.8                          | 4              | 4             |               |                |

 1 1
where r is the number of rule nodes, c is the number of linguistic terms of output fuzzy variable, and Vpq is the gravity of qth output linguistic term associated with the pth rule node. 

This research defined r and c to be 27 and 4, respectively. 

The summation of output class is computed using Eq. (15). 

 
 $\begin{array}{c}{\underset{\underset{i=1}{\overset{n_1}{\sum}}}{o_{ij}}}\\ {\underset{\begin{array}{c}{o_{C}}_{j}{E_1}=\frac{i=1}{n_1}}{o_{1}}\\ {\underset{\begin{array}{c}{n_1}\end{array}}}\end{array}}\end{array}$ (15)  For all $j\text{=}1,\dots,m$
Next is to perform integration the membership degree 
belonging to all news articles represented in this form below. 
$$\{(C_{1};O_{C_{1}}E_{1}\,,\mu_{C_{1}}E_{1}\,,...,\mu_{C_{1}}E_{p}\,)$$ $$....$$ $$(C_{m};O_{Cm}E_{1}\,,\mu_{Cm}E_{1}\,,...,\mu_{Cm}E_{p}\,)\}\tag{16}$$