Corrigendum to “A hybrid deep learning-based framework for future terrorist activities modeling and prediction” [Egypt. Inform. J. 23(3) 2022, 437–446] (Egyptian Informatics Journal (2022) 23(3) (437–446), (S1110866522000263), (10.1016/j.eij.2022.04.001))

The authors regret that the original version missed two tables namely Table 1 and Table 3. Table 1. Class of attack type [Formula presented] Table 1 is cited in the Subsection A. Data Analysis “7) Type of Attack: In the data sets, nine different labels are used for the type of attacks used. The labels are Execution, Car hijacking, Bombings/explosions, abducting captives, Attack is armed, kidnapping (kidnapping), Unsafe attacks, Attack on institution/infrastructure, others (see Table 1). The data set's dimensions are (95, 7242 34). Eighty percent of the information (861,517 cases) is used for learning, and 20% is used for testing (95,725 points). There are 88,255 instances of each class”. The table 1 in the online version of the paper became Table 2. [Formula presented] The new citation in the text: “In Table 2 we compared the proposed algorithm with the benchmark scheme”. Table 3. Success rate with attack type [Formula presented] The citation of Table 3 is correct in the online version of the paper The authors would like to apologise for any inconvenience caused.