Prediction of Future Terrorist Activities Using Deep Neural Networks

Table 1

The attributes in the dataset along with explanation.

S.No.	Feature	Description
1	iyear	This field contains the year in which the incident occurred
2	imonth	This field contains the number of the month in which the incident occurred
3	iday	This field contains the numeric day of the month on which the incident occurred
4	Extended	1 = “Yes,” the duration of an incident extended more than 24 hours; 0 = “No,” the duration of an incident extended less than 24 hours
5	Provstate	Name (at the time of event) of the 1st order subnational administrative region
6	Latitude	The latitude of the city in which the event occurred
7	Longitude	The longitude of the city in which the event occurred
8	Specificity
9	Vicinity	The region in nearby location
10	Crit1
11	Crit2
12	Crit3
13	Doubtterr
14	Multiple
15	Natlty1	The nationality of the target that was attacked
16	Propextent
17	Ishostkid	The hostage of kids
18	Ransom
19	Country	This field identifies the country or location where the incident occurred
20	City	Name of the city, village, or town in which the incident occurred
21	Gname	The name of the group that carried out the attack
22	Individual
23	Nkillus	The number of U.S. citizens who died as a result of the incident
24	Nkillter
25	Nwound	Number of confirmed nonfatal injuries to both perpetrators and victims
26	Nwoundus	The number of confirmed nonfatal injuries to U.S. citizens, both perpetrators and victims
27	Nwoundte
28	Property	The damage to property
29	Targtype1	The general type of target/victim
30	Suicide	1 = “Yes,” the incident was a suicide attack; 0 = “No,” there is no indication that the incident was a suicide attack
31	Success	Success of a terrorist strike
32	Weaptype1	General type of weapon used in the incident
33	Region	This field identifies the region code based on 12 regions
34	Attacktype1	The general method of attack and broad class of tactics used