September 30 · Article · Scientific Journal of Astana IT University · Artificial Intelligence
This paper investigates the diffusion properties of lightweight encryption algorithms designed for deployment in resource-constrained systems. Modern schemes intended for environments with strict limitations on computational complexity and energy consumption are evaluated. Particular attention is paid to the analysis of isolated round functions ‒ particularly their behavior prior to the application of round keys. This approach enables the assessment of internal structural properties independently of the key scheduling procedures and identifies basic diffusion characteristics determined exclusively by the employed bitwise transformations. Specifically, the impact of flipping a single bit in the input block on the distribution of changes in the output data is analyzed, facilitating an evaluation of the quality of internal transformations and their resistance to cryptanalytic attacks. For quantitative evaluation, classical measures of avalanche behavior – the Avalanche Effect and the Strict Avalanche Criterion (SAC) – are used to assess how rapidly and uniformly information propagates within a block after the application of round transformations. All algorithms are evaluated using a unified round-by-round experimental procedure under identical conditions. This ensures a consistent and objective comparison of their diffusion characteristics across successive encryption rounds. The results reveal distinct advantages and limitations in diffusion speed, stabilization behavior, and uniformity among the evaluated lightweight cipher constructions. The comparison shows that schemes based on cyclic rotations and bitwise operations exhibit high diffusion rates with minimal computational complexity. This analysis demonstrates the effectiveness of approaches relying on simple bitwise transformations. The obtained results provide valuable practical design insights for the selection or design of lightweight cryptographic primitives for microcontrollers, sensor networks, and other devices with limited computational capabilities.
AKArmiyanbek KhaumenOpen access
September 30 · Article · Vestnik of M Kozybayev North Kazakhstan University · Artificial Intelligence
Real-time interpretation of seismic waveform data is essential for monitoring signal dynamics and detecting abnormal activity. However, forecasting seismic amplitudes remains challenging because seismic signals are highly variable, noisy, and non-stationary. This study presents an automated, gap-aware real-time forecasting pipeline for minute-level seismic signal amplitudes built on IRIS FDSN waveform data, together with a rigorous, statistically grounded comparison of seven forecasting models: Naive persistence, ARIMA, Ridge regression, HistGradientBoosting, a standard Gated Recurrent Unit (GRU), an LSTM, and a physics-inspired regularized GRU (PI-GRU) that adds temporal smoothness and amplitude-growth constraints to the training loss. Models were evaluated on 81 independently sampled, non-overlapping forecasting windows drawn from a continuous 91-day observation period across four horizons (5, 15, 30, and 60 minutes), using paired non-parametric significance testing, a quantitative ablation study, and noise- and missing-data robustness testing, and the main findings were additionally verified under an alternative peak-priority signal aggregation. Classical models, particularly Ridge regression and HistGradientBoosting, achieved significantly lower forecasting error than every neural model tested (Friedman and Holm-corrected Wilcoxon tests, p<0.0001) and were robust to synthetic noise and missing data, while the proposed PI-GRU was, contrary to expectation, the weakest-performing model overall and did not produce smoother forecast trajectories than an unregularized baseline GRU. We report this as an honest negative result: as currently configured, the physics-inspired regularization examined here does not demonstrate an accuracy or stability advantage over classical machine learning baselines for this task. The principal contributions of this work are the automated real-time forecasting pipeline itself and a reusable, rigorous evaluation methodology, together with specific, testable hypotheses - concerning hyperparameter tuning, training budget, and calibration of the physics-inspired constraint - for why the neural models underperformed and how this might be addressed in future work.
ZUZhanat UmarovaOpen access
September 30 · Article · Scientific Journal of Astana IT University · Computer Vision and Pattern Recognition
Real-time human movement analysis has become an essential component of modern computer vision applications, including sports performance assessment, rehabilitation monitoring, intelligent surveillance, and human–computer interaction. Although deep learning–based pose estimation methods provide accurate human body landmark detection, executing person detection on every video frame introduces considerable computational overhead, limiting their applicability on resource-constrained CPU-based systems. This paper proposes an efficient YOLO–ROI framework for real-time human movement analysis that combines scheduled YOLO person detection, region-of-interest (ROI) generation, MediaPipe Pose estimation, and lightweight landmark smoothing. Instead of performing object detection on every frame, the proposed framework periodically executes the YOLO detector while reusing the most recently detected ROI between detection cycles. Consequently, MediaPipe Pose continuously estimates body landmarks within the ROI, significantly reducing redundant object detection operations and improving computational efficiency. The proposed framework was evaluated using a self-collected dataset consisting of 200 videos recorded from 10 participants performing five physical exercises under controlled indoor conditions. Computational performance was analyzed under different ROI sizes, detection intervals, and MediaPipe Pose complexity levels using CPU-based execution. Experimental results demonstrate that the proposed optimization strategy achieves a maximum processing speed of 61.32 FPS with an average processing latency of 16.31 ms, enabling stable real-time operation without GPU acceleration. The obtained results further show that scheduled YOLO detection, ROI reuse, and lightweight landmark smoothing effectively reduce computational workload while preserving continuous pose estimation throughout video processing. The proposed framework provides a simple, computationally efficient, and easily deployable solution for real-time human movement analysis and can be readily integrated into sports analytics, rehabilitation systems, intelligent surveillance applications, and edge-based computer vision platforms.
AAArypzhan AbenOpen access
