Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska" – IAPGOS (“Informatics, Control, Measurement in Economy and Environmental Protection”) is a peer-reviewed, international, multidisciplinary quarterly published in English language by Lublin University of Technology, in an electronic and printed version. The articles presented in this journal concern fields (categories) of automation, biomedical engineering, computer and information sciences, electronic and electrical engineering, information and communication technology, mining and energy. IAPGOS publishes in open access formula under the Creative Commons Attribution licence – CC BY 4.0 Int. (until 2023 inclusive CC BY-SA). Since 2020 the journal's articles are indexed in Scopus database. (more)
Performance evaluation of optimized deep learning model with Multilayered Max-Norm Regularization (MMNR) technique for brain tumour classification in MRI multi-modal images
Brain tumours are aggressive malignant diseases, both in children and adults, representing 86 to 92 percent of all primary and almost half of secondary Central Nervous System (CNS) tumours. For individuals with malignant brain or central nervous system (CNS) tumours, the 5-year survival rate is about 34% for males and 36% for women. Brain tumours can be classified into several types, including benign, malignant, pituitary, etc. This study proposes a new architecture named Multilayered Max-Norm Regularization CNN(MMNR-CNN) and investigates the performance of this model for the classification of brain tumours in multi-modal MRI images. The model incorporates Markov Random Field (MRF) for bias field correction, and Monte Carlo Dropout to quantify prediction uncertainty through stochastic forward passes, enhancing the model's reliability in clinical decision-making. Furthermore, we integrate Explainable AI (XAI) techniques using Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret the regions of MRI scans that contribute most to the classification decisions. We present a complete analysis of the Multilayered Max-Norm Regularization model trained on augmented brain image data and compare the performance on different values of regularization parameters that lead to the automatic selection of spatially important features for the classification task. This increases the generalization and robustness of the training dataset through augmentation. The model is trained using the Br35H database and the Figshare database. Both are used primarily for research in brain tumour detection and classification. The obtained performance metrics are the best in the literature, with a testing accuracy of 99.88% and 100 % precision.
Copyright (c) 2026 Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska https://creativecommons.org/licenses/by/4.0
Stroke detection from brain CT-images and its volume visualization
This study presents a comprehensive methodology for the detection, classification, and volumetric analysis of stroke lesions from computed tomography (CT) images. The approach encompasses several key stages: Skull stripping is performed to remove non-brain tissues, enhancing the accuracy of subsequent analyses. Stroke regions are identified by analysing the symmetry between the two hemispheres of the brain in CT images. Advanced segmentation algorithms are applied to delineate the region of interest (ROI) corresponding to the stroke-affected area. Texture features from the segmented ROI are extracted to capture the characteristics of the stroke lesion. The extracted features are input into classifiers such as Support Vector Machines (SVM) and K-Nearest Neighbors (K-NN) to categorize the type of stroke. For haemorrhagic strokes, the segmented regions from the CT image stacks are used to visualize and quantify the volume of the stroke. The methodology was validated using datasets from five patients, demonstrating its potential in aiding clinical diagnosis and treatment planning for stroke patients.
Adaptive filtering for noise reduction in photoplethysmography signals
This article presents an innovative hybrid filtering approach applied to photoplethysmography (PPG) signals, based on the Pulse-sensor, widely used in physiological monitoring devices. The sensor combines hardware functionality, including conventional analogue filtering based on resistor and capacitor net-works, with the ability to access raw signals via a digital interface. To take full advantage of this capability, we designed a soft filtering system fully implemented in the MATLAB development environment. This digital filtering enables real-time processing of photodiode data, bypassing the limitations inherent in fixed analogue filtering. By combining the advantages of both approaches – hardware robustness and software flexibility – our hybrid system improves PPG signal quality, notably by reducing artefacts linked to motion and ambient noise. The performance of this method has been evaluated through a series of test bench experiments, demonstrating a significant improvement in the accuracy of physiological parameter extraction. This re-search opens up new prospects for the development of intelligent, reliable wearable devices for continuous health monitoring in a variety of clinical and ambulatory settings.
Evaluation of informational diagnostic criteria and severity biomarkers using a discrimination model in patients with COVID-19
The paper examines the features of viral pneumonias that in the future may be caused by highly pathogenic viruses (HPCoVs) (SARS-CoV-2, MERS-CoV, SARS-CoV), H5N1, H5N7 and influenza A (H1N1) pdm. Rapidly progressive viral pneumonia that develops in these diseases can lead to a fatal complication – acute respiratory distress syndrome (ARDS). Confirmation and refutation of the diagnosis of ARDS today is a difficult task that requires the development and improvement of diagnostic methods. To compare the diagnostic effectiveness of the methods, the possibility of using criteria and parametric recognition models was considered. The discrimination model was built on the basis of the quadratic normalized Euclidean distance between the vectors of mean values ​​by state of quantities. The perspective of the work is to improve methods for assessing diagnostic criteria and severity biomarkers using a discrimination model based on quadratic notched Euclidean distance, which will allow improving the detection of ARDS – a fatal complication of respiratory system infection with highly pathogenic viruses.
Signal amplifiers in optical communication systems
This work focuses on modelling an optical soliton-based pulse amplitude modulation (PAM) system incorporating linear semiconductor optical amplifiers (LSAs). Simulations were performed to determine the maximum output power for different bit/symbol PAM soliton configurations, with single-soliton propagation analysed over a 200 km optical fibre link. The study also evaluates the peak Q-factor for these PAM schemes at 200 km under varying injection currents supplied to the semiconductor optical amplifiers. In addition, the simulations examine the total electrical power detected at the photodetectors for soliton systems across different PAM formats and SOA injection levels, all over the same fibre length. Results show that increasing the injection current raises the electrical output power of the amplifiers, while the corresponding Q-factor in the soliton transmission tends to decrease.
Analysis of underwater communication systems based on hybrid Li-Fi technology
When humanity reaches a new phase in exploring the ocean floor, one of the most persistent technical challenges will still be maintaining dependable communication beneath the surface. The underwater environment introduces obstacles such as delayed signal transmission, fluctuating water conditions, significant signal loss, and the limited effectiveness of traditional communication methods. To address these issues, this work proposes a combined system that integrates both optical and acoustic channels, leveraging the strengths of each: the extensive range typical of acoustic links, and the fast, low-latency performance made possible by Li-Fi technology. The study compares existing approaches – acoustic, optical, and electromagnetic – with attention to the physical constraints imposed by the marine environment, including energy absorption, light scattering, refraction effects, and multipath propagation. It also considers the role of adaptive, intelligent signal-processing methods designed to maintain link stability as conditions change. From an engineering perspective, the discussion extends to laser wavelength choice, photodetector tuning, and the design of a network framework capable of enabling real-time data exchange between autonomous underwater vehicles and surface-level monitoring systems. The results suggest the potential for building a high-speed, large-scale underwater communication network with substantial capacity. This advancement could significantly accelerate deep-sea research, representing a major step forward in the evolution of marine communications.
Applying Box-Behnken design to research voice control automatic lighting systems
The paper presents the results of an experimental study of a voice control system for smart home elements, implemented on the basis of the WeMos D1 mini microcontroller module using the Google Assistant voice assistant and a mobile application created in the MIT App Inventor environment. The purpose of the study was to determine the influence of the distance to the microphone, the sensitivity of the microphone, and the type of the user's voice on the efficiency of voice command recognition. The experimental planning was performed using the Box-Behnken method, which allowed obtaining statistically significant results with a minimum number of experiments. Analysis of the obtained data showed that the distance to the microphone and its sensitivity have the greatest impact on the accuracy of command recognition, while the type of voice is of secondary importance. The mathematical model built based on the obtained data was recognized as adequate according to the Cochrane, Student, and Fisher criteria. The obtained results can be used to configure and optimize similar IoT systems, as well as for further research in real acoustic environments.
Paddy fields detection on Sentinel-2 satellite images using EfficientDet model
Agricultural monitoring plays an important role in ensuring food security and sustainable farming practices. This project focuses on the task of paddy field detection on the satellite images collected from the agricultural lands of Andhra Pradesh. Using satellite images of Sentinel-2 and deep learning techniques, our approach aims to improve the accuracy and efficiency of paddy land identification. The project employs a deep learning model, which is the EfficientDet trained on a carefully annotated dataset, to detect the paddy fields in the region. The utilization of remote sensing technology allows for scalable and timely monitoring across vast agricultural lands. The selected model architecture, combined with fine-tuning strategies, ensures adaptability to the unique spatial and seasonal characteristics of South Indian agriculture. Results from the project showcase the capability of the proposed approach in accurately identifying and detecting paddy crops. The integration of advanced technologies for precision agriculture contributes to informed decision-making, resource optimization, and overall sustainability in the farming sector. To collect the ground truth data, we used the AP GIS portal, which is supervised by the Andhra Pradesh agriculture department, which has the data of the percentage of paddy lands in small villages. The places with more than 95 percent of paddy lands are selected for better data samples. The collected samples are cropped and labelled, and trained on the model architecture and verified for accuracy. This project not only advances the field of agricultural monitoring but also holds significant importance for crop management and supporting the livelihoods of farmers of our state. The proposed model achieved an accuracy of 86%, demonstrating its reliability in detecting paddy fields from Sentinel-2 satellite imagery.
Models for assessing accuracy and reliability of fibre-optic gyroscope-based navigation systems
Fibre-optic gyroscope (FOG) navigation systems are widely used in autonomous, aerospace, and terrestrial applications due to their high stability and independence from external navigation signals. While significant progress has been made in improving FOG performance at the sensor level, fewer studies have investigated the system-level impact of residual gyroscope errors, particularly during periods of GNSS unavailability. This study addresses that gap by analysing the accuracy and reliability of navigation in FOG-based inertial navigation systems under various operational modes: GNSS-aided, unaided, and intermittently aided. A mathematical model of navigation is implemented in a MATLAB/Simulink environment to assess error propagation. Performance is evaluated using practical metrics such as heading drift, position error accumulation, and recovery efficiency after GNSS signal restoration. Simulation results show that during unaided navigation, heading errors reach approximately 2–3 degrees over 600–900 seconds, while position errors grow to 40–80 meters during 300–600 seconds of GNSS outage. Upon GNSS reacquisition, error reduction of 80–90% is observed within 30–60 seconds. These results demonstrate that system-level modelling can significantly enhance navigation reliability without requiring modifications to the FOG hardware.
Aggregation of multimodal log and metric streams for neuro-fuzzy anomaly detection in computer systems
Ensuring the stable and reliable operation of modern computer systems is a critical challenge. This is typically achieved through the continuous logging of system events and the monitoring of hardware resource metrics (e.g., CPU, RAM). However, conventional monitoring solutions generally analyse these data streams in isolation. Their direct integration is significantly hindered by fundamental differences in their temporal characteristics and measurement scales. For instance, logs are often processed using OpenSearch, while metrics are monitored via Grafana. Consequently, the correlation context is lost, which impedes the identification of the root causes of system anomalies. To overcome these limitations, this paper proposes a novel method that fuses the multimodal input streams of logs and metrics into a unified feature space, specifically designed for subsequent use by neuro-fuzzy systems for advanced anomaly detection. This study presents a mathematical formalization of the problem domain by introducing a unified system of variables and developing an observation space model. The proposed heterogeneous data aggregation method effectively prepares the input space for neuro-fuzzy classifiers. Temporal synchronization between metrics and events is achieved through a sliding window strategy, while min-max normalization is applied to numerical indicators to eliminate feature dominance. Additionally, log processing is implemented by converting unstructured messages into standardized templates, which are then weighted by their criticality level and further analysed using the entropy of the event stream. The proposed approach generates an informative state space characterized by high spatial separability between normal and anomalous system states, making the resulting feature vector highly suitable for the subsequent training of neuro-fuzzy networks. Experimental results demonstrate that the method successfully captures the synchronous correlation between hardware load spikes and the occurrence of critical errors.
Static forensic analysis of file carving on SSDs uses NIST and ACPO method
Solid State Drive (SSD) recovery is challenging due to a lower success rate than traditional storage devices. Latest file carving techniques, such as those used in Foremost and Autopsy software, offer a solution for recovering lost files, whether intentionally or unintentionally deleted, on SSDs. The study compares approaches to recovering deleted data from SSDs. NIST method provides accountable reporting for legal proceedings and ACPO framework guides the recovery process through planning, capture, analysis, and presentation stages. In an experiment using a 120 GB Geniune SATA SSD and a 512 GB Eaget mSATA SSD with NTFS, 88 lost files were recovered. Foremost successfully restored 46 files (52.27%), while Autopsy recovered 81 files (92,05%), with success measured by matching MD5 hash values and ensuring files were accessible. Despite not reaching 100% recovery as seen in HDDs, the findings highlight the promise of Autopsy in digital forensics and legal evidence. The recovery process was conducted using Ubuntu and Windows OS, indicating the potential for improved SSD recovery.
Fuzzy logic-based security risk assessment in wireless sensor networks of Industrial IoT
Recent years have seen the widespread deployment of wireless sensor networks (WSN), low-cost sensors, industrial clouds and industrial robots. These technological advancements have facilitated the development of Industrial Internet of Things (IIoT) technologies and fostered the emergence of novel digital applications. Thus, the IIoT is a technological concept involving the communication and interaction of mobile devices over wireless networks. Due to the numerous limitations of wireless sensor networks, security is one of the main issues. In the context of interconnected smart devices, the assumption of control over a single device has the potential to compromise the security of the entire network. The identification of these vulnerabilities can be enhanced by the implementation of remote diagnostics for IIoT devices. Whilst Artificial Intelligence (AI) finds extensive application in the solution of complex scientific, technical and practical issues, the present study investigates its use in wireless sensor networks of IIoT. Fuzzy systems, neural networks and genetic algorithms are three examples of AI techniques that are frequently used in wireless networks to improve their optimization and management. This paper proposes a fuzzy logic-based approach that allows intelligent assessment of the security risk levels of IIoT devices. Thus, a fuzzy inference system (FIS) that evaluates the security risk level of an IIoT device was developed. Its parameters were established, including the input and output variables and their membership functions. The developed FIS was optimized thorough other AI techniques. The efficacy of the FIS was evaluated through the use of a computer simulation in the MATLAB.
Multicriteria optimisation of information protection system configuration based on the NSGA-II algorithm
The article addresses the problem of multi-criteria optimisation of configuration parameters for an information protection system (IPS) within an enterprise or company. The problem lies in the difficulty of allocating a company’s limited resources among the various components of the information security system (ISS) when there are conflicting requirements regarding security and performance. To address this challenge, it is proposed to integrate an analytical risk assessment model with the NSGA-II evolutionary algorithm. This results in the formation of a 3D Pareto front, which will enable the decision-maker to select a security configuration based on a visual analysis of five dimensions: reliability, performance, scalability, cost and compliance. This will eliminate the subjectivity inherent in methods that rely on a priori weighting coefficients. During the computational experiments, the influence of algorithm parameters on the formation of the solution set was examined, and Pareto fronts were visualised using the Plotly library. The trade-offs between the considered criteria were analysed. The results obtained confirmed the effectiveness of the proposed approach for selecting the optimal configuration of the protection system.
Method of structural-block coding of tuple transformant video images
In the modern world, the quality of video information services is important. The greatest difficulties arise in the case of providing remote services using wireless information and communication systems (ICS). This concerns the imbalance between the intensity of the video stream and the speed of data transmission in the ICS. Elimination of the imbalance is possible using compression methods. However, compression of the bit volume of video data is achieved with a loss of integrity. Hence, a relevant scientific and applied problem is the further improvement of coding methods based on the elimination of various types of redundancy. At the same time, most methods are characterized by achieving the desired level of compression by introducing distortions. This necessitates the development of a compression direction based on the controlled elimination of the number of different types of redundancy. Accordingly, to create conditions for increasing the possibilities for detecting characteristic dependencies, the technological apparatus of converting video segments to spectral space is used. Therefore, the research goal of the article is to develop compression methods based on a controlled reduction in the number of different types of redundancy in spectral space. The article outlines the main stages of developing a method for structural block coding of transform tuples. To increase the compression level without loss of integrity, it is proposed to identify characteristic structural dependencies for a set of transforms. Such sets are divided into tuples according to the parametric data of the structural and spectral features of the transforms. Further, such a space will be referred to as the spectral and parametric description of transforms (SPDT). Comparative evaluation of compression methods is carried out according to the system of indicators: compression level - integrity level. Under such conditions, the advantage of the created method over basic analogues in terms of compression level is from 12 to 19%.
Analysis of the increase in model forecasting accuracy after data normalization
In modern machine learning, data preprocessing is a crucial step that significantly affects the performance of classification models and the accuracy of prediction. This research investigates the impact of data normalization on the prediction accuracy of two common machine learning algorithms: logistic regression and support vector machine (SVM). Data normalization methods, including logarithmic transformation and scaling methods (such as RobustScaler and MinMaxScaler), were applied to evaluate their impact on model accuracy, balanced accuracy, F1 score, and area under the receiver operating characteristic (AUC). The results showed that the normalization process led to a significant improvement in model performance. In particular, logistic regression showed a moderate increase in all key metrics: accuracy improved by 14.6%, balanced accuracy by 12.3%, F1 score by 11.5%, and AUC by 3.9%. In contrast, SVM showed significant improvements: accuracy increased by 42.5%, balanced accuracy by 81.3%, F1 score by 21.3%, and AUC by 117.9%. These results highlight the importance of preprocessing steps for models sensitive to feature scaling, such as SVM. The ROC curves confirmed the improvement in classification performance, with the AUC of SVM increasing from 0.407 to 0.887 after normalization, indicating a shift from a low-performing model to one that more accurately discriminates between positive and negative classes. This improvement demonstrates the effectiveness of normalization in improving the generalization and robustness of the model, especially when handling unbalanced datasets. The result highlights that data normalization is an important step before training and learning a model. It not only improves the accuracy of the model, but also provides better adaptability and stability in real-world applications. And it is also an important step towards achieving reliable and accurate predictions. The results of the study highlight the importance of data preprocessing in machine learning processes, especially for algorithms that depend on feature scale or geometric methods, such as SVM and logistic regression.
Optimizing parameters for 4D hyperchaotic system using Walrus Optimizer Algorithm
The walrus optimization Algorithm’s (WaOA) crucial significance in improving and creating a hyperchaotic system is the main topic of this study. We have enlarged the six-term, three-dimensional chaotic Liu system to a four-dimensional system with seven terms. In order to create hyperchaotic behaviour with great efficiency, the WaOA algorithm is utilized to optimize the system parameters in order to maximize the biggest Lyapunov exponent. The system’s dynamic features, such as the study of equilibrium points, the Jacobian matrix, Lyapunov exponent, coexistence, and the Lyapunov dimension (Kaplan-Yorke), have all been fully examined. Simulations of the suggested system utilizing NI Multisim (version 14.2) for electrical circuit simulation have shown the algorithm’s efficacy. The work demonstrates the WaOA algorithm’s noteworthy contribution to enhancing hyperchaotic systems performance and broadening their useful applications in a variety of domains.
Iron coagulation optimization during water treatment using artificial intelligence tools
A process of water purification using electrical coagulation and its optimisation using artificial intelligence tools was presented. Experimental data was analysed and correlated. The experimental studies used were developed to optimise iron coagulation during water treatment. A neural network was developed to optimise iron coagulation and machine learning was performed. Neural network tests were conducted and methods for its practical application were proposed.
Optimisation of the generating capacity of droop-based DGs integrated into an isolated AC microgrid using metaheuristic algorithms to minimise power losses
This study proposes three metaheuristic techniques, including differential evolution, moth-flame optimisation, and honey badger algorithm combined with a modified backward-forward sweep load flow approach to identify the optimal droop gains and reference voltage magnitudes of distributed generators (DGs) (i.e., optimal size of droop-based DGs) integrated with renewable sources to meet the electricity demands of an islanded AC microgrid. The suggested mathematical model was tested on a reconfigured 33-bus islanded microgrid under four scenarios with a time-varying load over 24 hours. The findings indicate that Case 3, which applies the honey badger (HB) algorithm to optimise droop coefficients and regulate reference voltage magnitudes within permissible limits, is the most effective in lowering power losses compared to other cases. The final results highlight the HB algorithm’s efficacy in minimising power losses while optimising the size of droop-based DGs in an islanded microgrid integrated with renewable energy sources.
Chemical composition, structural and electrical properties of CdZnTeSe thick polycrystalline films
Thick polycrystalline Cd₁₋ₓZnₓTe₁₋ᵧSeᵧ (CZTS) films were synthesized via the close-spaced vacuum sublimation (CSVS) technique. Comprehensive investigations of their structural, compositional, and electrical properties were performed to assess their suitability as base layers for X-ray detector applications. The films were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). Morphological and structural analyses revealed uniform composition, dense microstructure, and high crystalline quality of the films deposited on glass substrates. Electrical and photosensitivity measurements demonstrated that detector prototypes incorporating CZTS films exhibit pronounced photoresponse under light illumination, confirming their potential for use in next-generation X-ray detection devices.
Substantiation of a new method for separation of bulk materials on a vibro-friction separator
The processes of separating bulk materials are an important component of processing, food, chemical, pharmaceutical industries, and agricultural production. This ensures the separation of components based on various characteristics, which is crucial for operations such as cleaning, sorting, or calibrating bulk materials. A new method for separating components of bulk materials based on a set of characteristics has been proposed and comprehensively substantiated. An analytical-experimental methodology was used for the research. A mathematical model of plane-parallel vibrational non-detached movement of components on an inclined non-perforated frictional vibrating plane was developed. In this model, the cross-section of particles is approximated by a figure representing a point mass, from which rays of finite length emanate uniformly in all directions. Based on the conducted studies of the vibrational movement of components, using the example of a bulk material of sugar beet seed mixture, the fundamental possibility of their separation was established. Significant factors of the process of separating bulk materials on a vibro-friction separator were analytically and experimentally determined: amplitude and frequency of oscillations, direction angle of oscillations, longitudinal and transverse tilt angles of the working body. The result of the research was the rational ranges of these separator parameters for the studied material, which produced high-quality fractions of bulk material. The efficiency of the proposed separator was: for seed material – an increase in conditioned seeds with high sowing properties. The proposed technical solution, developed calculation methodology, and rational parameters will allow intensifying the processes of separating bulk materials and increasing the efficiency of various technologies.
Software-based performance evaluation and forecasting of web applications using machine learning models
This research aims to develop and evaluate a software-based methodology for performance assessment and forecasting of web applications using machine learning models. The proposed approach integrates automated data collection, preprocessing, analysis, and real-time visualization within a unified software framework. The experimental evaluation was conducted on a dataset comprising over 10,000 real-world performance records, including response time, CPU and memory usage, network throughput, and JavaScript execution metrics collected via browser DevTools. Linear and polynomial regression models, a decision tree, and a neural network were applied to identify performance patterns and predict key metrics. The results demonstrate that linear regression achieves the highest overall accuracy (MAE = 187.25, R² = 0.9363), while neural networks provide comparable performance under dynamic workload conditions. The developed software framework enables real-time monitoring and reporting through WebSocket-based visualization and supports the identification of performance bottlenecks. The findings confirm that integrating machine learning into automated performance evaluation improves prediction accuracy and accelerates the detection of performance degradation compared to traditional manual monitoring approaches.
Comparative analysis of Java unit and integration testing tools: JUnit, TestNG and Spock
The purpose of the study is to compare popular Java testing tools: JUnit, TestNg and Spock. The evaluation was con-ducted based using four criteria: performance, popularity, ease of learning, and code readability. A quantitative analysis was performed based on an experiment with predefined research scenarios, which included assertion tests, database operations, and computational tasks. Execution times of the scenarios were measured in various configurations of single-threaded and multi-threaded tests. The comparative analysis showed that no single tool is universally optimal. Spock demonstrates superior performance in terms of code readability and clarity. TestNG, by contrast, offers high execution performance and requires relatively little effort to learn and use effectively. JUnit stands out particularly in its support for multithreaded data access. The results therefore challenge the widely held belief in the JUnit framework's universality.
Application of UML in the development process of computer games
The paper explores the use of UML diagrams in the process of developing computer games as an effective tool for organizing team interaction and planning project architecture. Particular attention is paid to the use of class, use case, sequence, and activity diagrams for modelling game mechanics, code structure, and user interaction scenarios with the game. The paper examines how UML diagrams assist development teams – including programmers, designers, testers, and managers – in aligning on requirements, identifying logical inconsistencies at early stages, distributing tasks effectively, and maintaining development transparency. The results presented demonstrate the importance of using UML to improve game quality, speed up the development cycle, and reduce development costs.
Design of digital cooking assistant system with modern voice generative AI model
This paper presents the design and implementation of digital cooking assistant that leverages modern generative AI models to enhance accessibility and inclusiveness in everyday cooking, named as "Cooking Master". The proposed system integrates a voice-based assistant capable of real-time dialogue, recipe personalization, and hands-free control of the application interface. By combining LiveKit for low-latency communication, Groq hardware acceleration, and the LLaMA language model, the assistant enables natural interaction through speech recognition and synthesis. Particular emphasis was placed on creating solutions that support disabled users, especially the visually impaired, for whom traditional recipe formats often pose significant barriers. The assistant provides step-by-step instructions, intuitive voice commands, and accessible navigation, allowing independent cooking without the need for touch-based interaction. The multilingual architecture further extends inclusivity, enabling users from diverse linguistic backgrounds to benefit from AI-driven assistance. Experimental results confirmed that the system responds with minimal latency and natural-sounding feedback, making it suitable for real cooking environments. The developed solution demonstrates the potential of generative AI in supporting not only convenience and personalization but also in promoting digital accessibility for users with special needs.
Deep learning architectures for multiclass clothing recognition as the semantic core of automated virtual try-on systems
This article examines and substantiates the choice of deep learning architectures for multiclass clothing classification integrated into virtual try-on (VTO) systems. Systematically compared ResNet-50, EfficientNet-B4, and Vision Transformer (ViT-B/16) on DeepFashion2 and ModaNet datasets. ViT-B/16 achieved the highest accuracy of 92.4% Top-1 on DeepFashion2 and 88.9% on ModaNet, demonstrating an average cross-dataset accuracy drop of 3.9 percentage points, the smallest among evaluated models. Preliminary U2-Net segmentation statistically significantly improved macro-F1 for all architectures (p < 0.001), with an average gain of 3.2 percentage points and reduction of the studio-to-street domain gap from 11 to 6 percentage points. EfficientNet-B4 provided the optimal accuracy-to-latency ratio, achieving 87% Top-1 accuracy at 60 FPS on consumer hardware (RTX 3060), while ViT-B/16 required optimization to maintain 45 FPS. The recommended strategy for industrial VTO systems combines U2-Net segmentation with architecture selection based on target platform capabilities, balancing visual fidelity and computational efficiency.
Knowledge model "Tags about batches and containers" of the ERP system "PlasmIS" with the possibility of self-improvement using local llm models
The main requirements, capabilities and functionality of the "Tags about batch and containers" module of the "PlasmIS" information system are recearched in the article. Mathematical models of the intelligent module including offered database and knowledge base are suggested. The principles of printing actual tags were considered and the method of printing tags from the information system was developed. An approach to automated standardization of technical names of hardware using local large language models (LLM) is considered, which allows to significantly increase the accuracy of processing specialized data. The architecture of interaction of Python scripts with the local Lm studio server is described in detail, including the organization of API requests, the structure of input and output data in JSON format, as well as mechanisms for saving results in the SQLite database.
Paradigms of information technology impact on economic education
Economic education is changing under the influence of information technology, but research on this transformation remains thematically heterogeneous. The article aims to identify the main thematic paradigms and research trends in studies on the impact of information technology on economic education. The study is based on articles and conference proceedings indexed in the Web of Science database for 2015–2024. Publications were selected in the Abstract field using the query "information technology AND economic education". The final dataset included 1,749 documents, whose metadata were processed in VOSviewer. The bibliometric mapping revealed six thematic clusters: economic development, education and information technology; the social impact of IT and digital education; digital technologies, the labour market, and economic models; information literacy and the digital divide; policy, sustainable development, and technology strategies; and online learning, teaching, and educational technologies. The novelty of the study lies in structuring a fragmented research field and interpreting the identified clusters as broader paradigms of IT impact on economic education. The results indicate that economic education is increasingly discussed through digital transformation, human capital development, labour-market change, inclusion, and institutional adaptation. The main limitation is the focused search strategy, which did not include broader terms such as "digital education" or "EdTech" and therefore should be interpreted as a focused bibliometric mapping rather than an exhaustive review of the field.