Cognitive decision-making in smart police industry

The Internet of Things (IoT) has enhanced many industries in terms of smart decision-modeling and performance assessment. Numerous developments in industries such as national security and police forces have been revolutionized to interpret data regarding ubiquitous instances. The current paper introduces an IoT-inspired system for evaluating the competence of police officers' performance. The work presented in this study focuses on evaluating several actions of police officers to determine overall conduct using the Bayesian Classification Model. The probabilistic integrity estimate (PIE) has been quantified using efficient data processing for effective decision-making. Furthermore, the 2-level decision tree model has been presented to evaluate police personnel's efficiency. The presented model is tested on challenging data sets obtained from the online repository for validation purposes. Conspicuously, the presented methodology outperformed state-of-the-art decision methods in terms of classification efficiency, a temporal delay, prediction estimation, stability, and reliability.