Algorithms of pattern recognition for managing logistics processes in Kazakhstan

Abstract

This paper explores the application and development of pattern-recognition algorithms to control logistics processes in Kazakhstan's major transit corridors. The main aim was to develop a model adapted to the context, empirically verify its operational performance, and identify the factors critical to ensuring that the model could be integrated in practice. The implemented approach was a quantitative experimental design: a CNN model was fine-tuned on synthetic local data and tested against a generic benchmark; its effectiveness was evaluated in terms of concurrent accuracy, precision, F1-score, and latency; and stakeholder perceptions were assessed through an Analytic Hierarchy Process survey. Major findings include that the locally adapted model was much more accurate (F1-score: 0.925 vs. 0.855) and met all real-time working requirements. Moreover, survey analysis showed that organizational readiness and regulatory support are viewed as 56% more potent for integration success than technical performance measures. The results suggest that technical adaptation, although critical, is not yet sufficient; to succeed in deployment in Kazakhstan, the data infrastructure would need to be developed in parallel, and favorable policy frameworks should be established. One of the main study limitations is the use of synthetic data, which necessitates field validation of the results.