This paper describes the analyses of the nonlinear vibrations and dynamic stability of an airfoil on hereditary-deformable suspensions. The model is based on two-degree-of-freedom structure in geometrically nonlinear statements. It provides justification for the choice of the weakly singular Abelian type kernel, with rheological parameters. To solve problems of viscoelastic system with weakly singular kernels of relaxation, a numerical method has been used, based on quadrature formulae. With a combination of the Galerkin and the presented method, problems of nonlinear vibrations and dynamic stability in viscoelastic two-degree-of-freedom structure have been solved. A comparison of the results obtained via this method is also presented. In all problems, the convergence of the Galerkin method has been investigated. The implications of material viscoelasticity on vibration and dynamic stability are presented graphically.

Rare event detection in large tabular data remains a computationally challenging problem due to class imbalance, heterogeneous feature distributions, and unstable thresholds. Traditional machine learning approaches based on individual models and fixed thresholds often exhibit limited robustness and reproducibility in such settings. This paper proposes a hybrid ensemble framework for rare event detection that integrates heterogeneous machine learning models through threshold-aware probabilistic aggregation. The framework combines gradient-boosted decision trees, regularized linear models, and neural networks, leveraging their complementary inductive biases. To ensure reproducibility and robust performance evaluation under severe class imbalance, a leaky-controlled evaluation protocol is employed, including rootwise summation, probability calibration, and validation-based threshold optimization. The proposed approach is evaluated on a large tabular dataset containing approximately 50,000 observations. Experimental results demonstrate improved rare event detection and robust generalization performance compared to individual baseline models. Explainability is achieved through Shapley Additive Explanations (SHAP)-based attribution analysis and clustering in the explanation space, enabling transparent analysis of ensemble decision-making behavior. The proposed framework represents a general-purpose computational solution for rare event detection and can be applied to a wide range of data-driven decision-making and anomaly detection problems.

This study proposes an interpretable and high-accuracy ensemble learning framework for predicting aspartate aminotransferase (AST) levels using open-access biomedical datasets. Using a structured pipeline of preprocessing, feature selection, and model ensembling, we evaluated a series of regression algorithms including Random Forest, XGBoost, CatBoost, and three stacking architectures. The best-performing ensemble (Stacking_v2) achieved R² = 0.98 and RMSE = 1.23 on the validation set, surpassing conventional and single-model approaches. Feature importance was assessed using SHAP values, mutual information, and correlation analysis, revealing that gamma-glutamyl transferase, ferritin, and anthropometric markers had the greatest predictive impact. The proposed stacking-based model demonstrates excellent generalization, robust calibration, and high interpretability, and can serve as a benchmark for algorithmic evaluation in medical data modeling. The work highlights the effectiveness of ensemble regression and interpretable AI in real-world clinical prediction tasks using routine biomarkers.

Opinion mining and sentiment analysis are aimed at extracting valuable necessary information from the raw text and determining the polarity of the text. Comparative opinion mining analyzes opinions that compare two or more entities based on a common feature. The purpose of comparative opinions mining is to extract comparative opinions from the text and identify the objects being compared. First step of identification comparative opinions is extraction comparative constructions. There are presently research papers on sentiment analysis and opinion mining in the Kazakh language. But we were unable to find any work on the comparative opinion mining. The aim of the study is to automate the extraction of comparative constructions in the Kazakh language. In this paper, a pattern based approach was implemented to extract comparative constructions in the Kazakh language.

Early detection of autism spectrum disorder (ASD) is essential for enabling timely interventions that significantly impact developmental outcomes. Traditional diagnostic procedures often rely on clinical observations and subjective assessments, which can reduce objectivity and delay diagnosis. This study presents an automated classification approach for detecting ASD in children using gait kinematic data obtained through video-based skeletal tracking. We propose a hybrid deep learning architecture that combines one-dimensional convolutional layers (Conv1D), bidirectional long short-term memory (BiLSTM) networks, and a multi-head attention mechanism to capture complex spatiotemporal patterns in motion data. The dataset includes 100 participants—50 diagnosed with ASD and 50 typically developing (TD) peers. Preprocessing steps included Euclidean norm transformation, logarithmic scaling, Z-score normalization, and sliding window segmentation. The proposed model achieved 94.9% accuracy, 0.91 recall, 0.86 precision, and an area under the curve (AUC) of 0.97, outperforming a baseline long short-term memory (LSTM) architecture. These findings demonstrate the potential of gait-based kinematic features and hybrid neural networks for objective and reproducible ASD screening. The developed system can contribute to AI-assisted decision support tools in clinical and educational environments, enhancing diagnostic accuracy, and supporting inclusive developmental care.