Internet of Things and Artificial Intelligence in National Security: Applications and Issues

Montasari, Reza

doi:10.1007/978-3-031-21920-7_3

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Thus, with values of x1, x2, x3, x4 equal to 1.0, the value of Ki is 6.0, and with x1, x2, x3, x4 equal to 5.0, the value of Ki is 70.0. The final formula for the region's safety index is given by equation: КS=(((5-1) • (current assessment Кі-1)) / (Кі max-1))+1, (4) where Kicurrent evaluation of Ki, Ki maxmaximum value of Ki.…”

Section: Perform Experimental Verification Of the Proposed Approachesmentioning

confidence: 99%

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Practical Principles of Integrating Artificial Intelligence Into the Technology of Regional Security Predicting

Shefer,

Laktionov,

Pents

et al. 2024

A.I.S.

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Objective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.

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Section: Perform Experimental Verification Of the Proposed Approachesmentioning

confidence: 99%

“…Article [3] discusses the use of artificial intelligence for the environmental safety of the state. Articles [4,5] discuss the use of the Internet of things in national security systems. Articles [6,7] focus on data mining.…”

Section: Introductionmentioning

confidence: 99%