Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers’ facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness.

The Residue Number System is a widely used non-positional number system. Residue Number System can be effectively used in applications and systems with a predominant proportion of addition, subtraction and multiplication operations, due to the parallel execution of operations and the absence of inter-bit carries. The reverse conversion of a number from Residue Number System to positional notation requires the use of special algorithms. The main focus of this article lies in introducing the new conversion method, which incorporates Chinese Remainder Theorem, Akushsky Core Function and rank of number. The step-by-step procedure of the conversion process is detailed, accompanied by numerical examples. The proof of the relationship between the ranks of positional characteristics using the Chinese Remainder Theorem is presented. Through careful analysis and comparison with existing transformation methods, it is concluded that the presented approach takes on average 8 % less time than the Approximate Method.