Newly established journal. Content in progress. The Journal New Trends in Computer Sciences publishes original research papers that provide insights into computer sciences and applied computing issues.
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Distinguishing lexical deviation from semantic resilience in LLM-based round-trip translation
Round-trip translation (RTT) is widely used for data augmentation and paraphrasing, yet its impact on meaning preservation remains poorly understood. We evaluate RTT systematically across two phases totaling 80,000 translation operations: 200 synthetic English passages are routed through single- and multi-hop pipelines spanning Bulgarian, Russian, and Chinese, translated by four systems ranging from a classical web translator (googletrans) to recent large language models (4o-mini, Claude Haiku 4.5, GPT-5-nano), with every passage undergoing ten RTT passes scored by BLEU, COMET-Kiwi, and xCOMET, joined by SBERT in the second phase. The results show that RTT outputs remain fluent while rapidly losing meaning: even the best configuration reaches only 0.646 xCOMET, and the four systems follow three distinct degradation regimes (front-loaded damage, continuous drift, and deterministic fixedpoint convergence). High surface overlap masks this loss, creating a surface fidelity trap, and genre-level analysis suggests that creative and news texts are most vulnerable. Sample-level inspection further reveals that xCOMET over-penalizes legitimate paraphrastic variation while under-penalizing domain-critical substitutions in legal text, exposing a mismatch between translation-oriented metrics and paraphrase evaluation. These findings argue against RTT in meaning-sensitive workflows and call for paraphrase-specific evaluation metrics.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
A secure restful deployment pattern for course recommendation microservices using proof-of-work blockchain authentication
Deploying machine learning models across distributed environments requires robust architectural patterns that ensure both cross-platform interoperability and secure access control. While Representational State Transfer (REST) APIs are widely adopted for service exposure, existing implementations in cross-machine scenarios remain vulnerable to token spoofing and post-hoc administrative tampering of centralized authorization logs. Therefore, this study presents a secure, deployable architectural pattern that integrates localized machine learning workflows with a decentralized authentication boundary. An online course recommendation system using text preprocessing, Term Frequency–Inverse Document Frequency (TF–IDF) vectorization, and cosine similarity is implemented and utilized explicitly as a functional demonstration case to validate the framework. The core machine learning engine is exposed via a Flask-implemented RESTful service layer, while secure access control is enforced by embedding a Proof-of-Work (PoW) blockchain token authentication mechanism at the API boundary. The empirical results demonstrate that access to protected endpoints is granted only upon successful validation of user tokens against immutable on-chain ledger records, structurally rejecting unauthorized requests. Performance benchmarking indicates that while the decentralized ledger operations introduce an operational latency trade-off (a 1113 ms mining overhead and 2.45-second end-to-end authorized query response), the proposed pattern provides an effective, tamper-evident firewall for delivering secure microservices in distributed client–server environments.
Evaluating knowledge graph enhanced retrieval augmented generation for automated functional requirements extraction
The automation of requirements engineering using large language models offers significant potential for efficiency but struggles with hallucinations and a lack of domain-specific precision in highly regulated fields such as healthcare. While retrieval augmented generation (RAG) addresses some of these issues, standard vector-based retrieval often fails to capture complex semantic relationships required for strict compliance. This research assesses the effectiveness of automated functional requirement extraction by comparing two distinct retrieval architectures: a baseline vector-only RAG versus a knowledge graph-enhanced RAG with three distinct prompt strategies. We implemented an end-to-end automated extraction pipeline applied to a corpus of heterogeneous healthcare documents. The study constructed two separate knowledge bases to perform a comparative analysis. Under the experimental conditions (seven healthcare documents, three security-focused testing areas, and Claude3-Haiku at temperature zero), knowledge base curation was found to be a stronger determinant of extraction quality than retrieval architecture, though the relative impact may vary with corpus scale, domain, and base LLM. With a curated knowledge base, GraphRAG eliminated the systematic relevance collapse observed under standard preprocessing, achieved significant structural traceability validity in independent Neo4j cross-checking, and produced prompt-stable performance across three prompt strategies. Without rigorous preprocessing, graph augmentation amplified rather than mitigated retrieval noise
Fuzzy-based assessment of stakeholder feedback in software requirements
Requirements engineering is essential for aligning software systems with stakeholder needs, yet incorporating stakeholder feedback into existing software requirements remains challenging due to ambiguity, inconsistency, and limited validation mechanisms. This paper proposes an AI-based evaluation approach for assessing the quality of software requirements and the correctness of feedback-driven modifications. The method combines natural language processing, large language models, and a hierarchical fuzzy inference system to support systematic evaluation. A multi-label text classification model is trained to identify common requirement defects, including ambiguity, subjectivity, vagueness, non-verifiability, and negativity. These indicators are aggregated using a fuzzy inference hierarchy to compute requirement quality scores and evaluate the preservation and correctness of changes introduced by stakeholder feedback. The approach is implemented as a web-based software system and evaluated against multiple AI-based methods and human assessments using defined quality criteria and performance metrics. Results indicate that the proposed hybrid method provides consistent and interpretable evaluations and can support more structured assessment of stakeholder feedback implementation in requirements engineering. First published online 3 June 2026
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
AI-enabled software requirements specification for banking chatbot systems
Software Requirements Specification plays an important role in the success of software development projects by providing clear, structured, and consistent documentation of requirements. However, traditional SRS processes are manual, time-consuming, ambiguous, and inconsistent. This paper proposes an AI-enabled software requirements specification framework using Retrieval-Augmented Generation. The method combines Large Language Models with domain-specific knowledge retrieval to generate structured, coherent, and contextual requirement specifications. The proposed approach integrates document management, semantic retrieval, and generative reasoning to improve requirements quality, reduce ambiguity and enhance traceability. Validation was conducted using a banking chatbot case study. Experimental evaluation showed that the proposed approach achieved 0.75 precision, 1.00 recall, 0.857 F1-score and 0.75 accuracy for Functional Requirement identification on a manually labelled 12-sample evaluation subset. However, the system did not correctly identify Non-Functional Requirements in the current evaluation, indicating the need for a refined FR/NFR classification strategy in future work. The results demonstrate that Retrieval-Augmented Generation provides a practical and scalable solution for modern intelligent requirements engineering systems.
Bit error rate characterization of LoRa chirp spread spectrum modulation across spreading factors: a Simulink-based analysis
Low-Power Wide-Area Networks (LPWANs) based on LoRa technology have gained prominence in Internet of Things (IoT) deployments due to their long-range communication capabilities and low power consumption. The physical layer performance of LoRa’s Chirp Spread Spectrum (CSS) modulation under varying signal-to-noise ratio (SNR) conditions is critical for network planning and optimization. This study presents a comprehensive bit error rate (BER) characterization of LoRa CSS modulation across spreading factors SF7 through SF12 using Simulink-based Monte Carlo simulations across multiple channel conditions. The simulation framework implements a LoRa-compatible CSS physical layer model, capturing the essential spreading factor and processing gain characteristics without emulating proprietary chipset-specific implementations. Results demonstrate progressive BER improvement with increasing spreading factors under AWGN, Rayleigh fading, and Rician fading channel conditions, achieving error-free transmission at SNR values ranging from –6 dB (SF7 in AWGN) to –18 dB (SF12 in AWGN). The measured SNR sensitivity gain of approximately 2–4 dB per spreading factor increment confirms theoretical predictions and validates the simulation methodology. Higher spreading factors demonstrate superior resilience under fading conditions, with SF12 maintaining near-zero BER under Rayleigh fading at accessible SNR levels while SF7 does not. These findings provide baseline performance metrics essential for LoRaWAN network deployment planning, link budget calculations, and spreading factor selection in real-world IoT applications with diverse propagation environments. First published online 3 June 2026
Lightweight deep models for video anomaly detection: a comparative study of autoencoders and MobileNetV2 on the avenue dataset
Video anomaly detection aims to identify unusual events in surveillance footage, yet many existing deep learning solutions remain too computationally heavy for real-time deployment on resource-limited hardware. This study presents a systematic comparison of three lightweight deep learning models for frame-level anomaly detection on the Avenue dataset, including a baseline 2D convolutional autoencoder, an enhanced reconstruction-based autoencoder with refined feature representation and decoding strategy, and a MobileNetV2-based supervised classifier fine-tuned for anomaly recognition. The baseline autoencoder achieves moderate detection performance, with an approximately AUC of 0.75. In contrast, the enhanced autoencoder improves reconstruction quality and raises the AUC to approximately 0.84 through more effective feature abstraction rather than increased architectural depth. The strongest results are obtained by the MobileNetV2 classifier, which achieves an AUC close to 0.99, high precision and recall, and a stable confusion matrix. These results demonstrate that lightweight architectures, when combined with appropriate training strategies and careful handling of class imbalance, can outperform more complex models. Overall, the study confirms that architectural efficiency and learning paradigm selection are more critical than model depth alone, making lightweight models well-suited to practical, real-time video anomaly detection scenarios. First published online 02 February 2026
video anomaly detection; lightweight models; convolutional autoencoder; MobileNetV2; frame-level detection;avenue dataset.
A rapid review on ontology- and data-driven business process modelling
In modern organizations, the ability to efficiently manage and adapt business processes is essential. Business process modelling (BPM) is widely used to visualize, analyse, and improve operational processes. As the complexity of business environments increases, the integration of ontological modelling and data-driven approaches becomes increasingly relevant. Ontologies offer a semantic foundation for organizing and structuring process-related information, while data-driven methods support evidence-based decision-making and enable the adaptation of processes to dynamic conditions. Although both approaches show promise, the academic literature still lacks a coherent view of how they are jointly applied within BPM. This research conducts a rapid review of recent scientific publications to investigate how ontological and data-based methods are being used, what challenges are most often identified, and which research directions are emerging. The analysis reveals that the integration of these methods could address issues such as semantic consistency, process automation, and real-time decision-making. The results highlight existing research gaps and provide a clearer understanding of how BPM methodologies can be advanced by combining these two perspectives. This research contributes to the theoretical development of BPM by mapping current practices and offering insights for future researches. First published online 23 January 2026
Candidate assessment in asynchronous video interviewing: a hybrid hyperbolic fuzzy integrated MCDM framework with generative AI expertise
Asynchronous Video Interviewing (AVI) is increasingly used in large-scale recruitment; however, its evaluation remains constrained by opaque predictive models, limited interpretability, and reliance on subjective human judgement. Interview data is often vague and complex. In this study we propose a novel “Multi-Criteria Decision Making (MCDM)” framework designed to navigate this uncertainty by identifying the underlying influence relationships that drive AVI assessment outcomes. The framework integrates “Hyperbolic Fuzzy Sets (HYFS)” with a multi-perspective “Decision Making Trial and Evaluation Laboratory (DEMATEL)” approach to estimate criteria weights and identify directional influence structures while employing state-of-the-art generative AI models as scalable AI-based evaluative agents. To address variability and potential bias in human ratings, a multi-attitudinal variance-based method is used to compute panel participant weights, and interviewees are subsequently ranked using a HYFS-integrated “Weighted Aggregated Sum Product Assessment (WASPAS)” technique. The framework is demonstrated on a real-world AVI dataset comprising 138 interview recordings evaluated across 18 behavioural and non-verbal criteria. The results show that Eye Contact, Smiled, Speaking Rate, Friendly, Paused, and Calm act as dominant drivers, while micro-level speech disfluencies such as fillers exhibit comparatively lower influence once broader behavioural cues are considered. Overall, the proposed framework yields rankings aligned with benchmark scores while offering enhanced interpretability and directional influence insight, supporting fair and trustworthy automated hiring systems.
Improving AHP consistency through cognitive collaboration with large language models
The Analytic Hierarchy Process (AHP) is a human-centered method designed to structure complex problems and extract the authentic, consistent opinions of decision-makers. However, its practical application is often limited by inconsistency in human judgments, often caused by the respondent’s insufficient understanding of the task rather than simple mathematical error. The main goal of the article is to explore the possibilities of integration of innovative Artificial Intelligence (AI) tools for improving the AHP method. In order to improve respondent understanding and facilitate more intuitive and transparent consistency adjustments, this study also analyzes how to reduce the occurrence of inconsistency in pairwise comparison matrices and improve the Consistency Ratio (CR) by using advanced capabilities of large language models. The initial stage of study included a literature review, identifying typical problems in this area, reviewing the tools and methods used for obtaining better results, and presenting areas for improvement. At the second stage, the possibilities for improving consistency by increasing the influence of humans as decision makers, moving from the use of powerful mathematical optimization mechanisms to the application of human-centered explanatory AI techniques were analyzed. Based on the study results, the description of approaches for improvement of consistency in AHP was presented. First published online 27 January 2026
Enhancing prediction of ride-hailing fares using advanced deep learning techniques
Fare prediction is a critical component of online ride-hailing services, as it significantly influences consumer decision-making and enhances operational efficiency for service providers. Reliable fare prediction is especially important in dynamic pricing environments, where fares are affected by factors such as demand fluctuations, traffic conditions, and weather patterns. This study aims to enhance fare prediction in ride-hailing services by utilizing advanced deep learning models. Using a comprehensive dataset of Uber and Lyft fare data collected in Boston during the winter of 2018, we evaluated three deep learning architectures: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and BiLSTM with an attention mechanism (BiLSTM + Attention). The results showed that the BiLSTM + Attention model achieved the highest prediction accuracy, making it the most effective approach for fare prediction. However, its longer training time poses limitations for time-sensitive applications. Conversely, the LSTM model provided a strong balance between predictive accuracy and computational efficiency, making it a suitable alternative for scenarios that require faster model deployment. Additionally, our analysis identified key factors influencing fare variability – such as trip distance, time of day, and weather conditions – highlighting the importance of feature selection in enhancing model performance. By improving fare prediction accuracy, this study offers valuable insights for optimizing dynamic pricing strategies, enhancing consumer satisfaction, and helping ride-hailing platforms better manage supply–demand imbalances. These findings provide a foundation for future research exploring hybrid models and real-time data integration to further improve predictive capabilities in ride-hailing services.
Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder
In this study, a deep learning-based denoising autoencoder approach is proposed to increase the robustness of near-infrared spectroscopy data to random noise and improve quantitative modeling accuracy. Artificial Gaussian noise at four different levels (10, 15, 20, and 25 dB) was added to the near-infrared spectra obtained from milk samples to mimic the real measurement conditions. The noisy spectra were denoised by processing with an autoencoder architecture consisting of fully connected layers. The noise removal performance is quantitatively evaluated with both theoretical and measured signal-to-noise ratio values. The results show that the AE model significantly improves the spectral signal quality at all signal-to-noise ratio levels. In particular, at the lowest signal-to-noise ratio level (10 dB), the signal-to-noise ratio value nearly tripled to 29.6 dB with the autoencoder. At all other levels, an average increase of 18-20 dB was observed in the signal-to-noise ratio of the denoised spectra. In the second stage of the study, Partial Least Squares Regression models were built using both the noisy and cleaned spectra and evaluated on the test set with root mean square error and coefficient of determination. The Partial Least Squares Regression models built with the denoised spectra achieved lower root mean square error and higher coefficient of determination values at all signal-to-noise ratio levels. Especially at the 10 dB signal-to-noise ratio level, the coefficient of determination value of the model increased from 0.44 to 0.71, while the root means square error decreased from 0.60 to 0.43. The results show that the deep learning-based AE architecture can effectively reduce random noise in near-infrared spectral data and significantly improve both spectral signal quality and quantitative modeling performance. This approach provides an effective solution to improve model reliability and accuracy in near-infrared spectroscopy analysis.
Framework for deep reinforcement learning in Webots virtual environments
Reinforcement learning (RL) algorithms, particularly deep reinforcement learning (DRL), have shown transformative potential in robotics by enabling adaptive behaviour in virtual environments. However, a comprehensive framework for efficiently testing, training, and deploying robots in these environments remains underexplored. This study introduces a standardized, open-source framework designed specifically for the Webots simulation environment. Supported by a robust methodology, the framework integrates innovative design patterns and the digital twin (DT) concept with three distinct design patterns for structuring agent-environment interaction, notably including a novel pattern aimed at improving sim-toreal transferability, to enhance RL workflows. The proposed framework is validated through experimental studies on both a model the inverted pendulum and a production-grade Pioneer 3-AT robotic platform. The experiments highlight the framework’s ability to bridge the gap between virtual training and real-world implementation. All resources, including the framework, methodology, and experimental configurations, are openly accessible on GitHub.
Enhancing land use classification with hybrid machine learning and satellite imagery
The growing accessibility of satellite imagery and the rapid evolution of machine learning (ML) techniques have significantly advanced land use classification for environmental monitoring. However, challenges such as cloud coverage, varying image resolutions, and seasonal changes continue to hinder classification accuracy and consistency. This study aims to improve land use classification by proposing an integrated cloud interpolation, vegetation indices and ML based approach for classification of Sentinel-2 (S2) satellite data across the Baltic States. Specifically, a spatiotemporal interpolation module is introduced that reconstructs cloud-obscured pixels using multi-temporal coherence and derives optimized vegetation-index composites to enhance class separability under varying seasonal conditions. In order to achieve this aim and to choose the best ML algorithm for land use classification, we compare the performance of three classification algorithms, i.e., Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and evaluate their effectiveness in handling noisy and incomplete data. Our experimental results show that all three methods achieve strong classification accuracy, with RF exceeding 90%, while KNN and SVM also demonstrate competitive results. These methodological enhancements have been demonstrated to reduce cloud-induced misclassification and provide a scalable, transferable framework for operational land-use mapping in challenging atmospheric and seasonal contexts. These findings highlight the robustness of the proposed approach and provide valuable insights for future applications of ML in land use classification and environmental analysis.
Trends and challenges in personalizing learning content for students using artificial intelligence
Artificial intelligence-based solutions are widely used in different areas. With the advent of chatGPT, education has faced many challenges, such as student cheating by generating text in various practical and assessment tasks. As the number of large language models increases, it becomes difficult to control their use, and their capabilities increase over time as well. However, large language models do not only provide a negative aspect, but when used properly, they can be applied to useful and meaningful solutions. One of these is the personalization of learning, which would help to direct learning to the right needs without much human intervention, for example, when there are certain difficulties, knowledge gaps or lack of motivation. This manuscript, using a systematic analysis of the scientific literature, reviews technological solutions that are already currently used in personalizing learning. It also reviews the latest trends and challenges that would allow this area to be raised to a higher level. First published online 09 May 2025
Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Dynamic multi-scale simulation for evaluating combat effectiveness against aerial threats
The development and assessment of modern weapon systems require efficient and flexible simulation tools. This paper introduces a multi-scale discrete-event simulation framework designed to evaluate the dynamic combat effectiveness of weapon systems. The framework combines high-resolution and low-resolution models to address the complexities of real-world engagements while maintaining computational efficiency. Physical processes are encapsulated as modular state transition functions, allowing seamless integration of a multi complexity level modeling approach. The framework’s versatility is demonstrated through a case study analyzing the effectiveness of a tank weapon system against a fleet of drones. Non-deterministic methods such as Monte Carlo simulations for uncertainty quantification are used to evaluate probabilistic key metrics, such as projectile accuracy and lethality, providing insights into engagement dynamics and optimization of firing strategies. By leveraging a hybrid continuous/discrete approach and modular design, the framework enables comprehensive assessments of weapon effectiveness during an engagement, bridging gaps in traditional deterministic methodologies for both static and dynamic targets. Future enhancements will focus on optimizing sampling techniques for broader applicability of high-resolution stochastic simulations in modern combat scenarios.
Discrete Event Simulations, Julia, Monte Carlo, Uncertainty Quantification, Error budget, multi-scale simulations
Prediction of simulated factory layout throughput using artificial intelligence
The use of artificial neural networks for the optimisation of factory layouts is not a common practice, primarily due to the challenge of collecting sufficient layout data to form datasets for artificial intelligence (AI) model training. This paper presents a supervised learning method derived from a PhD thesis that employs neural networks to assess factory layouts. The training data is generated using a random layout algorithm, which is capable of producing numerous layouts. These layouts are then labeled through a discrete event simulation. The combination of layouts and simulation metrics serves as the training basis for the neural network. The AI framework integrates a convolutional neural network with a multilayer perceptron, which is capable of handling both tabular and image data. Ultimately, this allows us to calculate of the simulated throughput. First published online 20 January 2025
Just-In-Time Software Defect Prediction using a deep learning-based model
The increase in software complexity, driven by technological developments and user demands, has created major challenges for companies in Software Quality Assurance. Companies seek efficient ways to identify and mitigate defects, recognizing that they cause high financial costs and other problems with negative impacts on business. Among defect prediction approaches, Just-In-Time Software Defect Prediction has received increased attention from software industry professionals in recent years. This technique aims to identify and treat defects early, to improve the quality of the software development cycle. This study proposes a Deep Learning-based approach for Just-In-Time Software Defect Prediction using a large dataset of historical data from several popular software projects. The Deep Learning model was trained to identify defects by analyzing the software metrics provided by the dataset. The model achieved an accuracy of 82.08% in its predictions, and it was possible to determine the most relevant metrics for its conclusions through interpretability techniques. The results obtained demonstrate the potential of Just-In-Time Software Defect Prediction as a tool for improving software quality and encouraging the development of new studies and improvements in this area of research. First published online 17 January 2025
Implementing a rapid application development course in higher education and measuring its impact using Kirkpatrick’s model: a case study at Vilnius Gediminas Technical University
Nowadays, technological development and improvement in business is happening rapidly, so higher education (HE), and not only, studies should constantly provide and develop new up-to-date knowledge and skills to students, in order to train competitive specialists, address digital transformation by developing digital readiness of higher institutions, and increase employment opportunities of students. Consequently, this paper discusses the implementation of the newly developed courses for teaching Rapid Application Development (RAD) on the Oracle Application Express platform into the studies at Vilnius Gediminas Technical University (VILNIUS TECH) and presents the effectiveness of the implementation of this course measured using Kirkpatrick’s model. The obtained results show that students’ knowledge of RAD increased after attending the course. In addition, a 76% agreed that this course increased their knowledge of the subject matter.
Introducing rapid web application development with Oracle APEX to students of higher education
The rapid development of information and communication technologies (ICT), and enforcement of digital transformation have caused the need to develop and deliver software in even shorter time than previously, and has become crucial to businesses. Yet, this new need has caused a challenge of finding expert IT workforce able to deliver software applications due expected time. Moreover, in a rapidly changing business world, the traditional software development can no longer keep pace and provide solutions to businesses fast enough. Low-code development and inclusion of so-called citizen developers are seen as a solution for the problem. This paper explores the introduction of rapid application development courses on Oracle APEX technology to students of a European technological university. The paper discusses the implementation of the course and its design, and looks how the developed basic-level course was received by students. It is shown that the courses have been well-received by students, who based on provided feedback are satisfied with the online set up for self-regulated learning of rapid application development skills.
Identification of software quality attributes from code defect prediction: a systematic literature review
Identifying and understanding reasons for deriving software development defects is crucial for ensuring software product quality attributes such as maintainability. This paper presents a systematic literature review and the objective is to analyze the suggestions of other authors regarding software code defect prediction using machine learning, deep learning, or other artificial intelligence methods for the identification of software quality. The systemic literature review reveals that many analyzed papers considered multiple software code defects, but they were analyzed individually. However, more is needed to identify software quality attributes. The more profound analysis of code smells indicates the significance when considering multiple detected code smells and their interconnectedness; it helps to identify the software quality sub-attributes of maintainability.
The efficiency of machine learning algorithms in classifying non-functional requirements
Machine learning (ML) algorithms are more and more widely applied in various types of systems, so the research related to them is also increasing. One of the areas of research under consideration is the classification of non-functional requirements (NFRs) using ML algorithms. This area of research is important because the automatic classification of NFRs using high-performance ML algorithms and corresponding features helps requirements engineers classify non-functional requirements more accurately. This paper examines ML algorithms suitable for solving classification problems and their effectiveness in classifying non-functional requirements. Based on the described stages of the research methodology ML algorithms models were compared using the accuracy, precision, recall, and F-score metrics. A majority voting classifier model was created using Support Vector Machine, Naïve Bayes and K Nearest Neighbor Algorithm algorithms. After K-Fold cross validation were obtained these results: accuracy – 0.710 (scale from 0 to 1), precision – 0.845, recall – 0.814 and F-score – 0.815.
Leveraging generative adversarial networks to improve training image dataset
Convolutional neural networks (CNNs) are powerful models of deep learning that are widely used in computer vision classification tasks. The purpose of this study is to investigate the impact of datasets on CNN performance, employing original datasets and expanded datasets with synthetically generated images. The Generative Adversarial Network (GAN) is an unsupervised deep learning method used for synthetic data generation and can address the limitations of image augmentations. In this study, a new GAN architecture is used to synthesize high-resolution images when dealing with limited training data. The StyleGAN2-ADA model is specifically designed to generate high-quality images using limited datasets. Adaptive Discriminator Augmentation (ADA) dynamically adjusts data augmentation, enhancing discriminator efficiency and stability. The findings indicate a reduction in the likelihood of overfitting, enhancement in network generalization, mitigation of class imbalance concerns, and a concurrent increase in the accuracy and stability of network classification.
Educational data mining and learning analytics: text generators usage effect on students’ grades
Today, various types of data are constantly growing, so they can be used for different purposes. In this investigation, educational data has been analyzed to determine the influence of assessment on student knowledge. The newly collected dataset has been prepared and statistically analyzed. The dataset consists of open-question answers collected on one study subject during the midterm exam at Vilnius Gediminas Technical University. The results of the statistical analysis have shown that by using the text generators, students obtained higher grades by paraphrasing the answers to the questions in good quality. Furthermore, research has shown which types of questions are more difficult for students to answer without additional material and using text generation tools. It can be useful for lecturers planning course assessment tasks.
Review and experimental comparison of generative adversarial networks for synthetic image generation
The application of machine learning algorithms has become widespread particularly in fields such as medicine, business, and commerce. However, achieving accurate classification results with these algorithms often relies on large-scale training datasets, making data collection a lengthy and complex process. This paper reviews the current utilization of generative adversarial network (GAN) architectures and discusses recent scientific research on their practical applications. The study emphasizes the significance of addressing data scarcity in the process of training the machine learning algorithms and highlights the potential of advanced GAN architectures, in particular StyleGAN2-ADA, to mitigate this challenge. The findings contribute to ongoing efforts aimed at enhancing the efficiency and applicability of artificial intelligence across diverse domains by presenting a viable solution to the constraint of limited training data for image classification tasks.
Extending simulation-based assembly planning to include human learning and previous experience: a simulation study
When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly. First published online 18 January 2024
Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents
The field of discrete-event simulation for medical disaster management is relatively new. In such simulations, human victims are generated using pre-determined transitions from one health state to the next, based on a set of triggers that correspond to treatment or the clinical progression of untreated injuries or diseases. However, this approach does not account for subtle differences in clinical progression. We propose a parameter-based model to characterize the evolution of symptoms at first for physical and nerve agent chemical injuries. We used a Gompertz function to predict the time of death in trauma based on forensic data. Then we separately considered the effects of the chemical warfare agent sarin (GB) being the origin of the chemical injuries for the purpose of modelling a GB attack in a metro station. We emphasize that our approach can be extended to other CBRN threats pending knowledge of clinical progressions available in the literature for the purpose of casualty estimations. The intent is to provide an estimate of time to death without any treatment and overlay this model with a treatment model, improving the evolution of the health state. A modification for non-life-threatening injuries is included without losing generality. Improvement functions modelling medical treatment are proposed. We argue that the availability of injury scores vs mortality can greatly enhance the validity of the model.
Forging connections: the social dynamics of “Death Stranding” as a paradigm shift in gaming
This paper explores the video game Death Stranding as a shift in digital interactive media, emphasizing altruism, collaboration, and social connection in its gameplay. The close reading of the game focuses on features that diverge from traditional norms, pivoting towards a more empathetic, community-driven model and identifies five domains of analysis: narrative infrastructure; game mechanics; player-to-player interactions; player-to-NPC interactions; and player interactions as a social agent. The findings are discussed with the possibility of defining a new genre, named by the game’s creator as a “strand game” (Kojima, 2019a, 2019b). The analysis reveals Death Stranding’s ability to create a deeply immersive sense of unity among players, demonstrating the game’s potential as a model for future video games that prioritize positive social interactions and mutual support.
Evolution of regulatory models for public health data ecosystems from a linked democracy perspective
Public healthcare is a data-intensive environment that manages ever-increasing volumes of biomedical data resulting from medical data-generating technologies. In this paper, the authors discuss strategies to regulate the collection and use of biomedical data and metadata to build sustainable public health data ecosystems; this can assist citizens to get control of dataflows by defining identity in the public domain and shaping the capacity to use the web of data: get access to healthcare services and receive benefits and appropriate care. The authors suggest that a strategy based on the linked democracy governance model and safeguards, implemented through the meta-rule of law, enables better design of regulatory tools to handle semantically driven data flows. This strategy ties well in with models of deliberative and epistemic democracy, focused on relationships between people, data, and institutions. The authors investigate privacy, security, and data protection issues, applying existing ethical and legal frameworks for public health data and the theory of justice; they discuss the implementation of strategies to articulate the public domain and propose intermediate, anchoring institutions at the meso-level by building ontologies, selecting technical functionalities and algorithms, and embedding protections of the rule of law into specific public health data ecosystems.
Classification of raisin grains variety using some machine learning methods
One of the agricultural crops with considerable nutritional and financial worth is raisins. Every year, the world produces and consumes millions of tons of raisins. In this work, machine learning was used to categorize two different raisin kinds that are grown in our nation. Machine learning techniques Decision Trees and Random Forest were used to classify the 2-class data set with 7 different attributes that were acquired as a ready-made data set. With 020 Random Forest and Decision Trees, classification accuracy was 85.44% and 85.22%, respectively, in the analyses that were conducted.
machine learning, random forest, decision trees, raisin grains, classification, artificial intelligence