This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas in Kazakhstan and its neighboring regions. By leveraging a comprehensive dataset encompassing significant earthquake data from 1900 to 2023, various machine learning algorithms were employed, including RandomForest, GradientBoosting, Logistic Regression, Support Vector Classification (SVC), K-Nearest Neighbors (KNeighbors), Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier. The primary objective was to analyze and compare the performance of these models in predicting earthquake magnitudes and frequencies. The results reveal that certain algorithms significantly outperformed others in terms of accuracy, underscoring the potential of machine learning techniques to enhance earthquake prediction capabilities. Notably, XGBoost and RandomForest demonstrated the highest predictive accuracy, suggesting their suitability for application in seismic risk assessment. These findings offer valuable insights for governmental agencies engaged in disaster management and prevention planning, highlighting the practical implications of integrating advanced analytical techniques in their strategies. In addition to model performance analysis, a visual heatmap was generated to illustrate the geographical distribution of earthquake occurrences across the studied regions. This visual representation effectively identifies high-risk areas, serving as a crucial tool for local authorities and researchers in making informed decisions regarding safety measures and emergency preparedness. This research contributes to the expanding body of knowledge on earthquake prediction utilizing machine learning, emphasizing the necessity for continuous improvement in predictive models by incorporating additional environmental and geological factors. The implications of these findings extend beyond academic discourse, holding significant potential for enhancing public safety in regions vulnerable to seismic activity. As such, this study advocates for the integration of machine learning methodologies in disaster management frameworks to mitigate risks and enhance preparedness in earthquake-prone regions.

This study investigates the effectiveness of the Whisper model for audio-to-text transcription, specifically targeting the enhancement of accessibility for individuals with hearing impairments. The research focuses on the processing of audio recordings obtained from WhatsApp messenger, which often contain significant background noise that complicates speech recognition. To address this issue, advanced audio processing techniques were employed, including the use of the Librosa library and the Noisereduce package for noise reduction. The spectral gating methods applied in this study effectively diminished wind noise and other ambient sounds, allowing for clearer recognition of spoken content. To ensure the quality of the processed audio, we assessed its clarity using a SimpleRNN model. The training results demonstrated a progressive reduction in loss values across epochs, confirming the successful enhancement of audio quality. Once the audio files were adequately cleaned, we utilized the Whisper model, a sophisticated machine learning tool for speech recognition developed by OpenAI, to transcribe the audio into text. The transcription process yielded accurate Kazakh language output, despite the initial challenges posed by background noise. These findings underscore the critical role of high-quality audio input in achieving reliable transcription results and highlight the potential of machine learning technologies in improving communication access for hearing-impaired individuals. This study concludes with recommendations for future research, including the exploration of additional noise reduction techniques and the application of the Whisper model across various languages and dialects. Such advancements could significantly contribute to creating more inclusive digital environments and enhancing the overall user experience for individuals with hearing impairments.

This study discusses an economical and efficient method for calculating the global optimum of a function of many variables. The proposed algorithm can be attributed to methods based on auxiliary functions. The auxiliary function itself is obtained by converting the objective function using the Lebesgue integral and is a function of one variable. In a previously published paper by one of the authors of this article, this auxiliary function was used to calculate the global minimum of smooth multiextremal functions on convex closed sets. In the same article, an algorithm was proposed for dividing a segment into half to find a global minimum. And in this paper we consider the problem of finding the global minimum of continuous functions defined on bounded closed subsets of an n-dimensional Euclidean space. In addition, curious properties of the auxiliary function are established that are valid for any continuous objective function. For example, its non-negativity, positive homogeneity of some order, uniform continuity, differentiability and strict convexity are proved, and higher-order derivatives are calculated. The optimality criterion is established. The essence of this optimality criterion is that the value of a variable at which the auxiliary function and its derivatives are equal to zero up to a certain order turns out to be equal to the global minimum of the objective function. It follows from this optimality criterion that to calculate the global minimum of the objective function, it is sufficient to find the zero of the auxiliary function or its derivative up to the m-th order. Therefore, Wegstein's algorithm was used as a way to find the root of an equation with one unknown. In addition, the advantage of the Wegstein’s method is that it always converges. And in this situation, it turned out to be more efficient, despite its slow convergence, since it requires almost half the number of calculations of the values of the auxiliary function and that halves the need for numerical calculations of multiple integrals with a large number of variables.

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.

This paper proposes a prototype-level hierarchical visual–sensor framework for monitoring the Zamioculcas houseplant in complex indoor environments and supporting adaptive care-mode selection. The proposed framework combines a two-level visual pipeline, consisting of YOLO-based target plant detection and MobileViT-S-based leaf-condition classification, with a Plant Health Index (PHI) and a rule-based decision-support module for integrating visual and IoT-derived indicators. For the detection task, YOLOv8, YOLO12, and YOLO26 were compared, with YOLO26 showing the most balanced performance among the evaluated implementations. To improve robustness in real indoor scenes, negative training samples were added; this reduced the image-level false alarm rate on an independent negative-scene test set from 50.7% to 10.0% and increased specificity from 49.3% to 90.0%. For the second visual level, MobileViT-S achieved an accuracy of 0.9857 and an F1-score of 0.9857 on the independent cropped leaf test subset. To reduce the dependence of this result on a single data split, an additional 5-fold cross-validation experiment was conducted on the full cropped leaf dataset of 847 images, resulting in an accuracy of 0.9858 ± 0.0068 and an F1-score of 0.9853 ± 0.0070. To further address plant-level generalization, an additional unseen-plant validation subset of 60 newly collected cropped leaf images was evaluated, and MobileViT-S achieved an accuracy of 0.9500 and an F1-score of 0.9499. These results support the stability of the leaf-condition classifier within the available data, although larger external validation with strict plant-level and session-level separation remains necessary. In addition, an Arduino-based module-level validation was conducted using a capacitive soil-moisture sensor to verify the proposed sensor-based and Vision–IoT decision rules. The experiment demonstrated that the rule-based layer can distinguish dry, normal, and wet soil states and select conservative care actions depending on both soil moisture and visual-condition input. A brief real-time camera–sensor communication test further confirmed that live camera input, Arduino-based soil-moisture sensing, PHI computation, and care-mode selection can be connected within one decision-support pipeline. The proposed PHI and care-mode selection module are therefore presented as a formalized decision-support layer rather than as a fully validated autonomous irrigation system. Further calibration, actuator integration, and closed-loop validation remain necessary before practical autonomous deployment.

The rapid spread of generative artificial intelligence has complicated the interpretation of authorship in educational writing and increased the need for transparent methods that distinguish machine-generated from human-written text without treating automated detection as conclusive evidence of misconduct. This study compares lexical and content-independent stylometric approaches to detecting AI-generated educational essays. The experiments used 44,868 English-language texts from the DAIGT V2 multi-source corpus, including 27,371 human-written and 17,497 AI-generated essays. The lexical representation used TF-IDF unigrams and bigrams with a Linear Support Vector Machine, while the content-independent representation consisted of fourteen structural and stylometric features classified with Logistic Regression. Human and AI texts were additionally compared using the Mann–Whitney U test and effect-size estimates. The TF-IDF model achieved 99.69% accuracy, 99.60% F1-score and 99.98% ROC-AUC on a stratified held-out test set. The stylometry-only model achieved 87.90 ± 0.40% accuracy, 84.34 ± 0.48% F1-score and 95.08 ± 0.17% ROC-AUC under five-fold stratified cross-validation. Average word length showed the strongest selected stylometric separation, while differences were also observed in text length, lexical diversity and punctuation use. The findings show that lexical information provides highly discriminative signals in the analyzed corpus, but substantial predictive information remains even when direct vocabulary is removed. The results support the use of stylometric indicators as an interpretable supplementary signal for educational text analysis, while also emphasizing that detector outputs should not be treated as definitive proof of AI use in individual student work. Robustness checks showed 99.75% accuracy after near-duplicate filtering, 96.46% accuracy for unseen prompts, and 95.39% accuracy when Llama-70B was held out, indicating measurable distribution sensitivity. The contribution is therefore a robustness-oriented comparison of established lexical and interpretable stylometric representations rather than a new classifier.