In recent years, research has been underway to create working bodies of excavation machines that allow digging soil at higher speeds. One of these new types of machines are excavation and transport machines with an inertial bottom discharge rotor. The paper presents the results of studies of high-speed digging and proposes a method for determining the average values of the energy intensity of soil transportation, and the proposed equations can be recommended for calculating the inertial rotor. The table of costs of specific energy consumption and productivity from the speed of rotation of the rotor at constant chip sizes is given. The analysis of studies showed that the energy intensity increases with an increase in the speed of rotation of the rotor, and the productivity grows according to a linear law. The inertial-type rotor during the test proved to be quite efficient, and the performance of the tested rotor is four times higher than that of the existing gravity-type rotor.

Imbalanced learning (IL) poses a major challenge in fraud recognition especially in financial datasets with extreme class imbalances (IR>20:1), where existing sampling techniques such as oversampling and under- sampling fail to address both inter-class and intra-class imbalances. The practical usefulness of current ensemble approaches is limited by their high processing costs or sub-optimal performance. This paper presents the duple-balanced ensemble (DUBE), a novel framework that integrates adaptive resampling with ensemble learning to mitigate inter- and intra-class imbalances simultaneously. Assessed on a real-world financial dataset (n=8,345 transactions, IR=21:1) using Python-based experiments, DUBE was compared with five benchmark classifiers (multilayer perceptron, k-nearest neighbor, bagging, adaptive boosting, decision tree). It achieved a 12% enhancement in F1-score over baseline models with optimal ensemble size of 10, demonstrating robustness to extreme imbalances without relying on computationally expensive distance-based methods. In spite of its robust performance, DUBE may have its efficacy limited by extreme class sparsity or dynamic fraud patterns due to concept drift. These findings highlight DUBE’s potential as a scalable, efficient solution for fraud detection, with future applications in other high-imbalance domains like medical diagnostics and intrusion detection.

This study aims to develop a responsible and sustainable framework for implementing artificial intelligence (AI) in business process management (BPM), with a focus on aligning technological advancement with strategic economic transformation. It addresses the need for ethical, sector-sensitive AI adoption in emerging economies undergoing digital modernization and diversification. The research integrates enterprise information system considerations, privacy-preserving modular architectures, and national regulatory frameworks related to data localization and cybersecurity. A sectoral analysis is conducted to assess global AI adoption maturity and its implications for economic transformation, using Kazakhstan as a contextual reference point. The results reveal that consumer-facing sectors such as retail and financial services exhibit high near-term adoption potential, while healthcare requires gradual infrastructure and talent development. More significantly, mid-term opportunities in manufacturing, logistics, and transportation sectors present Kazakhstan with a comparative advantage. AI adoption in manufacturing is projected to grow by 83% within three to seven years, underscoring the importance of timely investments in automation, smart technologies, and workforce upskilling. This study contributes a context-aware framework for responsible AI-enabled BPM. It offers actionable insights for policymakers and business leaders in emerging economies, advocating for sectoral prioritization, strategic timing, and capacity-building to ensure sustainable digital transformation.

The analysis of tonality in scientific texts, including citations, is actively advancing, enabling the identification of emotional coloring in references and their impact on scientific discourse. This study focuses on developing and evaluating a hybrid approach that integrates linguistic rules (analysis of parts of speech, syntactic dependencies, and negations) with machine learning algorithms (SVM, RF, NB, J48) to classify citation tonality. Experiments were conducted on the ACL Anthology (8700 sentences) and Clinical Trials (6500 additional sentences) corpora using stratified splitting (70/15/15 for train/val/test) and 5-fold cross-validation. The proposed method achieved 90% macro-F1 and 95% F1-score on the Athar dataset, and 85% macro-F1 on Clinical Trials, showing a 10–15% improvement over baseline models (BERT, LSTM). Ablation studies confirmed the contribution of linguistic rules (F1 increase of 5–7% when excluded). Statistical significance tests (McNemar, p<0.05) validated the robustness of the results. The approach proves effective for automated citation analysis and scientific impact assessment.

While automatic image captioning systems have made notable progress in the past few years, generating captions that fully convey sentiment remains a considerable challenge. Although existing models achieve strong performance in visual recognition and factual description, they often fail to account for the emotional context that is naturally present in human-generated captions. To address this gap, we propose the Sentiment-Driven Caption Generator (SDCG), which combines transformer-based visual and textual processing with multi-level fusion. RoBERTa is used for extracting sentiment from textual input, while visual features are handled by the Vision Transformer (ViT). These features are fused using several fusion approaches, including Concatenation, Attention, Visual-Sentiment Co-Attention (VSCA), and Cross-Attention. Our experiments demonstrate that SDCG significantly outperforms baseline models such as the Generalized Image Transformer (GIT), which achieves 82.01%, and Bootstrapping Language-Image Pre-training (BLIP), which achieves 83.07%, in sentiment accuracy. While SDCG achieves 94.52% sentiment accuracy and improves scores in BLEU and ROUGE-L, the model demonstrates clear advantages. More importantly, the captions are more natural, as they incorporate emotional cues and contextual awareness, making them resemble those written by a human.

Background: National and ethnic mutation frequency databases (NEMDBs) play a crucial role in documenting gene variations across populations, offering invaluable insights for gene mutation research and the advancement of precision medicine. These databases provide an essential resource for understanding genetic diversity and its implications for health and disease across different ethnic groups. Objective: The aim of this study is to systematically evaluate 42 NEMDBs to (1) quantify gaps in standardization (70% nonstandard formats, 50% outdated data), (2) propose artificial intelligence/linked open data solutions for interoperability, and (3) highlight clinical implications for precision medicine across NEMDBs. Methods: A systematic approach was used to assess the databases based on several criteria, including data collection methods, system design, and querying mechanisms. We analyzed the accessibility and user-centric features of each database, noting their ability to integrate with other systems and their role in advancing genetic disorder research. The review also addressed standardization and data quality challenges prevalent in current NEMDBs. Results: The analysis of 42 NEMDBs revealed significant issues, with 70% (29/42) lacking standardized data formats and 60% (25/42) having notable gaps in the cross-comparison of genetic variations, and 50% (21/42) of the databases contained incomplete or outdated data, limiting their clinical utility. However, databases developed on open-source platforms, such as LOVD, showed a 40% increase in usability for researchers, highlighting the benefits of using flexible, open-access systems. Conclusions: We propose cloud-based platforms and linked open data frameworks to address critical gaps in standardization (70% of databases) and outdated data (50%) alongside artificial intelligence–driven models for improved interoperability. These solutions prioritize user-centric design to effectively serve clinicians, researchers, and public stakeholders.

The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics.

The presented paper is devoted to the development of a method for identifying and correcting spelling errors in Kazakh texts. In this paper, the study object is methods for more accurate correction of spelling errors in Kazakh texts. The aim of the study is to develop an augmented version of the Damerau-Levenshtein method for correcting spelling errors in Kazakh language texts. Automatic detection and correction of spelling errors have become a default feature in modern text editors for working with text data, in text messaging applications such as chatbots, messengers, etc. However, although this task is well solved in geographically widespread languages, it has not been fully solved in languages with a small audience, such as the Kazakh language. The methods developed so far cannot correct all spelling errors found in Kazakh texts. Therefore, the development of a method with specific algorithms for spelling error correction in Kazakh texts is considered. As a result of the research work, algorithms for correcting errors found in Kazakh language texts were developed, and the developed algorithms were included in the Damerau-Levenshtein method. The experimental testing results of the augmented Damerau-Levenshtein method showed 97.2 % accuracy in correcting specific errors found only in Kazakh words and 92.8 % accuracy in correcting common errors from letter symbols. The standard Damerau-Levenshtein method testing results showed 76.4 % accuracy in correcting specific errors found only in Kazakh words. The results of the tests in correcting common errors from letter symbols with the standard Damerau-Levenshtein were approximately the same with the augmented Damerau-Levenshtein method, the accuracy is 92.2 %. The extent and conditions of practical application of the results are implemented by including them in text editors, messengers, e-mails and similar applications that work with text data.

A hardware-software system has been implemented to monitor the environmental state (EnvState) at the site of railway (RY) accidents and disasters. The proposed hardware-software system consists of several main components. The first software component, based on the queueing theory (QT), simulates the workload of emergency response units at the RY accident site. It also interacts with a central data processing server and information collection devices. A transmitter for these devices was built on the ATmega328 microcontroller. The hardware part of the environmental monitoring system at the RY accident site is also based on the ATmega328 microcontroller. In the hardware-software system for monitoring the EnvState at the RY accident site, the data processing server receives information via the MQTT protocol from all devices about the state of each sensor and the device's location at the RY accident or disaster site, accompanied by EnvState contamination. All data is periodically recorded in a database on the server in the appropriate format with timestamps. The obtained information can then be used by specialists from the emergency response headquarters. © 2023 Polish Academy of Sciences. All rights reserved.

This paper presents a certificateless group signature scheme designed specifically for Unmanned Aerial Vehicle (UAV) communications in resource-constrained environments. The scheme leverages Physical Unclonable Functions (PUFs) and elliptic curve cryptography (ECC) to provide a lightweight security solution while maintaining essential security properties including anonymity, unforgeability, traceability, and unlikability. We describe the cryptographic protocols for system setup, key generation, signing, verification, and revocation mechanisms. The implementation shows promising results for UAV applications where computational resources are limited, while still providing robust security guarantees for group communications. Our approach eliminates the need for computationally expensive certificate management while ensuring that only legitimate group members can create signatures that cannot be linked to their identities except by authorized group managers.