[
Army, U.S. (2006). Field Manual 4-02.51: Combat and Operational Stress Control, Department of the Army, Washington DC.
]Search in Google Scholar
[
Aylwin-Foster, N.R.F (2005). Changing the army for counterinsurgency operations, Military Review: 27–40.
]Search in Google Scholar
[
Benesty, J., Chen, J., Huang, Y. and Cohen, I. (2009). Pearson correlation coefficient, in J. Cohen et al. (Eds), Noise Reduction in Speech Processing, Springer, Berlin/Heidelberg, pp. 1–4.10.1007/978-3-642-00296-0_5
]Search in Google Scholar
[
Bongers, A. and Torres, J.L. (2019). A bottleneck combat model: An application to the Battle of Thermopylae, Operational Research 21: 2859–2877, DOI: 10.1007/s12351-019-00513-0.10.1007/s12351-019-00513-0
]Search in Google Scholar
[
Chabir, K., Rhouma, T., Keller, J.Y. and Sauter, D. (2018). State filtering for networked control systems subject to switching disturbances, International Journal of Applied Mathematics and Computer Science 28(3): 473–482, DOI: 10.2478/amcs-2018-0036.10.2478/amcs-2018-0036
]Search in Google Scholar
[
Chai, T. and Draxler, R.R. (2014). Root mean square error (RMSE) or mean absolute error (MAE), Geoscientific Model Development Discussions 7(1): 1525–1534.10.5194/gmdd-7-1525-2014
]Search in Google Scholar
[
Chmielewski, M., Kukie, M., Fr, D., Kukiełka, M., Frąszczak, D. and Bugajewski, D. (2018). Military and crisis management decision support tools for situation awareness development using sensor data fusion, in J.Świ ˛atek et al. (Eds), Information Systems Architecture and Technology: Proceedings of the 38th International Conference on Information Systems Architecture and Technology, ISAT 2017, Springer, Cham, pp. 189–199.10.1007/978-3-319-67229-8_17
]Search in Google Scholar
[
Coulson, S.G. (2018). Lanchester modelling of intelligence in combat, IMA Journal of Management Mathematics (2): 149–164.10.1093/imaman/dpx014
]Search in Google Scholar
[
Deitchman, S.J. (1962). A Lanchester model of guerrilla warfare, Operations Research 10(6): 818–827.10.1287/opre.10.6.818
]Search in Google Scholar
[
El-Douh, A.A.-R., Lu, S.F., Elkouny, A.A. and Amein, A. (2022). Hybrid cryptography with a one-time stamp to secure contact tracing for COVID-19 infection, International Journal of Applied Mathematics and Computer Science 32(1): 139–146, DOI: 10.34768/amcs-2022-0011.
]Search in Google Scholar
[
Gambo, A. (2020). Mathematical modeling of dynamics behavior of terrorism and control, Caspian Journal of Mathematical Sciences 9(1): 68–85.
]Search in Google Scholar
[
Hu, X., Lai, F., Chen, G., Zou, R. and Feng, Q. (2019). Quantitative research on global terrorist attacks and terrorist attack classification, Sustainability 11(5): 1487.10.3390/su11051487
]Search in Google Scholar
[
Janis, I.L. and Mann, L. (1977). Emergency decision making: A theoretical analysis of responses to disaster warnings, Journal of Human Stress 3(2): 35–48.10.1080/0097840X.1977.9936085864252
]Search in Google Scholar
[
Junosza-Szaniawski, K., Nogalski, D. and Rzążewski, P. (2022). Exact and approximation algorithms for sensor placement against DDoS attacks, International Journal of Applied Mathematics and Computer Science 32(1): 35–49, DOI: 10.34768/amcs-2022-0004.
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Belhadj, M. and Ben Said, L. (2020a). A multi-agent model for countering terrorism, in H. Fujita et al. (Eds), Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT_20), IOS Press, Amsterdam, p. 260.10.3233/FAIA200571
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Belhadj, M. and Bensaid, L. (2020b). A multi-agent model for countering terrorism, Proceedings of the 33rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE_20), Kitakyushu, Japan, pp. 1–8.10.3233/FAIA200571
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Belhaj, M., Ben Said, L. and Akrout, K. (2020c). A multi-agent architecture for modeling organizational planning against terrorist attacks in urban areas, 2020 International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA), Tunis, Tunisia, pp. 1–8.10.1109/OCTA49274.2020.9151843
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Belhaj, M., Said, L.B. and Akrout, K. (2020d). MAMCTA—Multi-agent model for counter terrorism actions, Revue de l’Information Scientifique et Technique 25(1): 76–90.
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Rejeb, L. and Said, L.B. (2022). Simulating actors’ behaviors within terrorist attacks scenarios based on a multi-agent system, Proceedings of the 12th International Defence and Homeland Security Simulation Worskhop (DHSS 2022), Rome, Italy, pp. 12–20.
]Search in Google Scholar
[
Kebir, O., Nouaouri, I., Rejeb, L. and Said, L.B. (2021). Conceptual terrorist attacks classification: Pre-processing for artificial intelligence-based classification, Proceedings of the 11th International Defence and Homeland Security Simulation Workshop (DHSS 2021), Kraków, Poland, pp. 16–24.
]Search in Google Scholar
[
Kress, M., Caulkins, J.P., Feichtinger, G., Grass, D. and Seidl, A. (2018). Lanchester model for three-way combat, European Journal of Operational Research 264(1): 46–54, DOI: 10.1016/j.ejor.2017.07.026.10.1016/j.ejor.2017.07.026
]Search in Google Scholar
[
Kress, M. and Szechtman, R. (2009). Why defeating insurgencies is hard: The effect of intelligence in counter-insurgency operations—A best-case scenario, Operations Research 57(3): 578–585.10.1287/opre.1090.0700
]Search in Google Scholar
[
Lanchester, F.W. (1916). Aircraft in Warfare: The Dawn of the Fourth Arm, Constable, London.
]Search in Google Scholar
[
Lee, H.-K. and Zo, H. (2017). Assimilation of military group decision support systems in Korea: The mediating role of structural appropriation, Information Development 33(1): 14–28.10.1177/0266666916628316
]Search in Google Scholar
[
Lucas, T.W. and McGunnigle, J.E. (2003). When is model complexity too much? Illustrating the benefits of simple models with Hughes’ salvo equations, Naval Research Logistics 50(3): 197–217.10.1002/nav.10062
]Search in Google Scholar
[
Maureen, A. (2017). Military mission combat efficiency, Journal of Defense Resources Management 8(1 (14)): 63–76.
]Search in Google Scholar
[
Okoye, C., Collins, O. and Mbah, G. (2020). Mathematical approach to the analysis of terrorism dynamics, Security Journal 33: 427–438, DOI: 10.1057/s41284-020-00235-5.10.1057/s41284-020-00235-5
]Search in Google Scholar
[
Oladejo, M., Udoh, I. and Abam, A. (2020). Optimizing the community’s supports in counter-terrorism operations: A sticks–carrots game theoretic model, Journal of Applied Science and Technology 39(47): 45–67.10.9734/cjast/2020/v39i4731186
]Search in Google Scholar
[
Osoba, O.A. and Kosko, B. (2017). Fuzzy cognitive maps of public support for insurgency and terrorism, Journal of Defense Modeling and Simulation 14(1): 17–32.10.1177/1548512916680779
]Search in Google Scholar
[
Pagán, J.V. (2010). Improving the classification of terrorist attacks: A study on data pre-processing for mining the Global Terrorism Database, ICSTE 2010—2nd International Conference on Software Technology and Engineering, Puerto Rico, USA, Vol. 1, pp. 104–110.
]Search in Google Scholar
[
Pechenkina, A.O. and Bennett, D.S. (2017). Violent and non-violent strategies of counterinsurgency, Journal of Artificial Societies and Social Simulation 20(4): 11.10.18564/jasss.3540
]Search in Google Scholar
[
Saltelli, A. and Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis, Environmental Modelling & Software 25(12): 1508–1517.10.1016/j.envsoft.2010.04.012
]Search in Google Scholar
[
Sandler, T. (2018). Terrorism: What Everyone Needs to Know, Oxford University Press, Oxford.10.1093/wentk/9780190845841.001.0001
]Search in Google Scholar
[
Seehuus, R.A., Rise, Ø.R., Hannay, J.E., Wold, R. and Matlary, P. (2020). Cloud-based decision support system for planning military operations, Technical report, Norwegian Military Academy, Oslo.
]Search in Google Scholar
[
Sumithra, S. and Vadivel, R. (2021). An optimal innovation based adaptive estimation Kalman filter for accurate positioning in a vehicular ad-hoc network, International Journal of Applied Mathematics and Computer Science 31(1): 45–57, DOI: 10.34768/amcs-2021-0004.
]Search in Google Scholar
[
Surdu, J.R. and Kittka, K. (2008). The deep green concept, Proceedings of the 2008 Spring Simulation Multiconference, Ottawa, Canada, pp. 623–631.
]Search in Google Scholar
[
Udoh, I. and Oladejo, M. (2019). Optimal human resources allocation in counter-terrorism (CT) operation: A mathematical deterministic model, International Journal of Advances in Scientific Research and Engineering 5(1): 96–115.10.31695/IJASRE.2019.33008
]Search in Google Scholar
[
Şuşnea, E. (2012). Decision support systems in military actions: Necessity, possibilities and constraints, Journal of Defense Resources Management 3(2): 131–140.
]Search in Google Scholar
[
Vilanova, A., Telea, A., Scheuermann, G. and Möller, T. (2008). Investigative visual analysis of global terrorism, Eurographics, Crete, Greece, Vol. 27, p. 2008.
]Search in Google Scholar
[
Willis, H.H., Morral, A., Kelly, T. and Medby, J. (2005). Estimating Terrorism Risk, RAND Corporation, Santa Monica.10.7249/MG388
]Search in Google Scholar
