New Trends in Computer Sciences
type: channel
description: 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.
publisher: Vilnius Gediminas Technical University
language: en
publication name: New Trends in Computer Sciences
issn: 2783-6851
copyright: Authors who publish with this journal agree to the following terms that this article contains no violation of any existing copyright or other third party right or any material of a libelous, confidential, or otherwise unlawful nature, and that I will indemnify and keep indemnified the Editor and THE PUBLISHER against all claims and expenses (including legal costs and expenses) arising from any breach of this warranty and the other warranties on my behalf in this agreement; that I have obtained permission for and acknowledged the source of any illustrations, diagrams or other material included in the article of which I am not the copyright owner. on behalf of any co-authors, I agree to this work being published in New Trends in Computer Sciences as Open Access, and licenced under a Creative Commons Licence, 4.0 https://creativecommons.org/licenses/by/4.0/legalcode. This licence allows for the fullest distribution and re-use of the work for the benefit of scholarly information. For authors that are not copyright owners in the work (for example government employees), please contact VILNIUS TECH to make alternative agreements.
items: Enhancing prediction of ride-hailing fares using advanced deep learning techniques
items: Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder
items: Framework for deep reinforcement learning in Webots virtual environments
items: Enhancing land use classification with hybrid machine learning and satellite imagery
items: Trends and challenges in personalizing learning content for students using artificial intelligence
items: Dynamic multi-scale simulation for evaluating combat effectiveness against aerial threats
items: Prediction of simulated factory layout throughput using artificial intelligence
items: Just-In-Time Software Defect Prediction using a deep learning-based model
items: 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
items: Introducing rapid web application development with Oracle APEX to students of higher education
items: Identification of software quality attributes from code defect prediction: a systematic literature review
items: The efficiency of machine learning algorithms in classifying non-functional requirements
items: Leveraging generative adversarial networks to improve training image dataset
items: Educational data mining and learning analytics: text generators usage effect on students’ grades
items: Review and experimental comparison of generative adversarial networks for synthetic image generation
items: Extending simulation-based assembly planning to include human learning and previous experience: a simulation study
items: Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents
items: Forging connections: the social dynamics of “Death Stranding” as a paradigm shift in gaming
items: Evolution of regulatory models for public health data ecosystems from a linked democracy perspective
items: Classification of raisin grains variety using some machine learning methods
items: Multi-purpose password dataset generation and its application in decision making for password cracking through machine learning
items: Generation of a learning path in e-learning environments: literature review
items: Explaining XGBoost predictions with SHAP value: a comprehensive guide to interpreting decision tree-based models
items: Current applications and challenges of the Internet of Things

Enhancing prediction of ride-hailing fares using advanced deep learning techniques
type: item
description: 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.
subject: Articles
  topic: section
subject: fare prediction
  topic: keywords
subject: ride-hailing
  topic: keywords
subject: LSTM
  topic: keywords
subject: BiLSTM
  topic: keywords
subject: Attention mechanism
  topic: keywords
creator: Tuyet Ngoc Thi Huynh
creator: Huu Dat Bui
creator: Tuyet Nam Thi Nguyen
creator: Tan Dat Trinh
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 64
ending page: 82
doi: 10.3846/ntcs.2025.23932

Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder
type: item
description: 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.
subject: Articles
  topic: section
subject: autoencoder
  topic: keywords
subject: milk analysis
  topic: keywords
subject: deep learning
  topic: keywords
subject: near-infrared spectroscopy
  topic: keywords
subject: noise removal
  topic: keywords
subject: PLSR
  topic: keywords
creator: Özcan Çataltaş
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-06-19
publication date: 2025-06-19
starting page: 38
ending page: 48
doi: 10.3846/ntcs.2025.24139

Framework for deep reinforcement learning in Webots virtual environments
type: item
description: 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.
subject: Articles
  topic: section
subject: reinforcement learning
  topic: keywords
subject: virtual environments
  topic: keywords
subject: robot testing
  topic: keywords
subject: Webots
  topic: keywords
subject: Stable-Baselines3
  topic: keywords
subject: simulation tools
  topic: keywords
subject: digital twin
  topic: keywords
subject: robotic training
  topic: keywords
creator: Andrej Šareiko
creator: Dalius Mažeika
creator: Algirdas Laukaitis
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 49
ending page: 63
doi: 10.3846/ntcs.2025.23668

Enhancing land use classification with hybrid machine learning and satellite imagery
type: item
description: 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.
subject: Articles
  topic: section
subject: Sentinel-2
  topic: keywords
subject: land use classification
  topic: keywords
subject: image recognition
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: machine learning
  topic: keywords
subject: cloud interpolation
  topic: keywords
creator: Julius Jancevičius
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-05-26
publication date: 2025-05-26
starting page: 1
ending page: 17
doi: 10.3846/ntcs.2025.23701

Trends and challenges in personalizing learning content for students using artificial intelligence
type: item
description: 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
subject: Articles
  topic: section
subject: personalized learning
  topic: keywords
subject: educational content
  topic: keywords
subject: students
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: NLP
  topic: keywords
subject: LLMs
  topic: keywords
creator: Laura Blaževičiūtė
creator: Natalija Matyjaškoit
creator: Robert Leščinskij
creator: Pavel Stefanovič
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 117
ending page: 127
doi: 10.3846/ntcs.2024.23699

Dynamic multi-scale simulation for evaluating combat effectiveness against aerial threats
type: item
description: 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.
subject: Articles
  topic: section
subject: Discrete Event Simulations, Julia, Monte Carlo, Uncertainty Quantification, Error budget, multi-scale simulations
  topic: keywords
creator: Irene Ndindabahizi
creator: Tom Vancaeyzeele
creator: Ben Lauwens
creator: Johan Gallant
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-06-18
publication date: 2025-06-18
starting page: 18
ending page: 37
doi: 10.3846/ntcs.2025.23627

Prediction of simulated factory layout throughput using artificial intelligence
type: item
description: 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
subject: Articles
  topic: section
subject: discrete simulation
  topic: keywords
subject: AI-supported simulation
  topic: keywords
subject: neural network
  topic: keywords
subject: transportation
  topic: keywords
subject: decision support systems
  topic: keywords
creator: Patrick Eschemann
creator: Astrid Nieße
creator: Jürgen Sauer
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 101
ending page: 116
doi: 10.3846/ntcs.2024.22160

Just-In-Time Software Defect Prediction using a deep learning-based model
type: item
description: 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
subject: Articles
  topic: section
subject: software defect prediction
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
creator: Rodrigo Alexandre Dos Santos
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 91
ending page: 100
doi: 10.3846/ntcs.2024.22274

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
type: item
description: 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.
subject: Articles
  topic: section
subject: rapid application development
  topic: keywords
subject: low code
  topic: keywords
subject: knowledge evaluation
  topic: keywords
subject: Kirkpatrick's model
  topic: keywords
subject: higher education
  topic: keywords
subject: digital transformation
  topic: keywords
creator: Urtė Radvilaitė
creator: Diana Kalibatienė
creator: Jelena Stankevič
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 81
ending page: 90
doi: 10.3846/ntcs.2024.22062

Introducing rapid web application development with Oracle APEX to students of higher education
type: item
description: 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.
subject: Articles
  topic: section
subject: rapid application development
  topic: keywords
subject: low-code
  topic: keywords
subject: Oracle APEX
  topic: keywords
subject: higher education
  topic: keywords
subject: web applications
  topic: keywords
subject: databases
  topic: keywords
creator: Tarmo Robal
creator: Uljana Reinsalu
creator: Lembit Jürimägi
creator: Risto Heinsar
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-09-16
publication date: 2024-09-16
starting page: 69
ending page: 80
doi: 10.3846/ntcs.2024.21227

Identification of software quality attributes from code defect prediction: a systematic literature review
type: item
description: 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.
subject: Articles
  topic: section
subject: code smell
  topic: keywords
subject: machine learning
  topic: keywords
subject: software quality attribute
  topic: keywords
subject: systematic literature review
  topic: keywords
creator: Lukas Rumbutis
creator: Asta Slotkienė
creator: Birutė Pliuskuvienė
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-06-27
publication date: 2024-06-27
starting page: 57
ending page: 68
doi: 10.3846/ntcs.2024.21305

The efficiency of machine learning algorithms in classifying non-functional requirements
type: item
description: 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.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: non-functional requirements
  topic: keywords
subject: classification
  topic: keywords
subject: support vector machine
  topic: keywords
subject: ensemble models
  topic: keywords
subject: K-Fold cross validation
  topic: keywords
creator: Milda Maciejauskaitė
creator: Jolanta Miliauskaitė
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-06-19
publication date: 2024-06-19
starting page: 46
ending page: 56
doi: 10.3846/ntcs.2024.21574

Leveraging generative adversarial networks to improve training image dataset
type: item
description: 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.
subject: Articles
  topic: section
subject: computer vision
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: deep learning
  topic: keywords
subject: generative adversarial networks
  topic: keywords
subject: image classification
  topic: keywords
subject: image synthesis
  topic: keywords
creator: Henrikas Giedra
creator: Gabriela Vdoviak
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-06-05
publication date: 2024-06-05
starting page: 31
ending page: 45
doi: 10.3846/ntcs.2024.20515

Educational data mining and learning analytics: text generators usage effect on students’ grades
type: item
description: 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.
subject: Articles
  topic: section
subject: educational data mining
  topic: keywords
subject: learning analytics
  topic: keywords
subject: statistical analysis
  topic: keywords
subject: Lithuanian texts
  topic: keywords
subject: open-questions dataset
  topic: keywords
creator: Birutė Pliuskuvienė
creator: Urtė Radvilaitė
creator: Rasa Juodagalvytė
creator: Simona Ramanauskaitė
creator: Pavel Stefanovič
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-06-04
publication date: 2024-06-04
starting page: 19
ending page: 30
doi: 10.3846/ntcs.2024.21318

Review and experimental comparison of generative adversarial networks for synthetic image generation
type: item
description: 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.
subject: Articles
  topic: section
subject: computer vision
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: deep learning
  topic: keywords
subject: generative adversarial networks
  topic: keywords
subject: image classification
  topic: keywords
subject: image synthesis
  topic: keywords
creator: Gabriela Vdoviak
creator: Henrikas Giedra
rights: Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2024-05-30
publication date: 2024-05-30
starting page: 1
ending page: 18
doi: 10.3846/ntcs.2024.20516

Extending simulation-based assembly planning to include human learning and previous experience: a simulation study
type: item
description: 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
subject: Articles
  topic: section
subject: learning models
  topic: keywords
subject: learning curve
  topic: keywords
subject: assembly
  topic: keywords
subject: discrete event simulation
  topic: keywords
subject: human factors
  topic: keywords
subject: simulation
  topic: keywords
subject: industrial engineering
  topic: keywords
creator: Maximilian Duisberg
creator: Michael Kranz
creator: Verena Nitsch
creator: Susanne Mütze-Niewöhner
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-12-29
publication date: 2023-12-29
starting page: 126
ending page: 142
doi: 10.3846/ntcs.2023.19040

Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents
type: item
description: 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.
subject: Articles
  topic: section
subject: disaster medicine
  topic: keywords
subject: discrete-event simulation
  topic: keywords
subject: victim health state model
  topic: keywords
subject: mass-casualty incidents
  topic: keywords
subject: combined injuries
  topic: keywords
creator: Mehdi Benhassine
creator: Ruben De Rouck
creator: Michel Debacker
creator: Ives Hubloue
creator: Erwin Dhondt
creator: Filip Van Utterbeeck
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-12-28
publication date: 2023-12-28
starting page: 113
ending page: 125
doi: 10.3846/ntcs.2023.19458

Forging connections: the social dynamics of “Death Stranding” as a paradigm shift in gaming
type: item
description: 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.
subject: Articles
  topic: section
subject: video games
  topic: keywords
subject: cooperative games
  topic: keywords
subject: virtual worlds
  topic: keywords
subject: Death Stranding
  topic: keywords
creator: Barbaros Bostan
creator: Sercan Şengün
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-10-05
publication date: 2023-10-05
starting page: 97
ending page: 112
doi: 10.3846/ntcs.2023.19412

Evolution of regulatory models for public health data ecosystems from a linked democracy perspective
type: item
description: 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.
subject: Articles
  topic: section
subject: public health data ecosystems
  topic: keywords
subject: linked democracy governance model
  topic: keywords
subject: privacy
  topic: keywords
subject: data protection
  topic: keywords
subject: meta-rule of law
  topic: keywords
subject: web of data
  topic: keywords
subject: Electronic Health Record (EHR)
  topic: keywords
subject: identity
  topic: keywords
creator: Izabella Lokshina
creator: Cees Lanting
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-09-27
publication date: 2023-09-27
starting page: 70
ending page: 96
doi: 10.3846/ntcs.2023.19166

Classification of raisin grains variety using some machine learning methods
type: item
description: 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.
subject: Articles
  topic: section
subject: machine learning, random forest, decision trees, raisin grains, classification, artificial intelligence
  topic: keywords
creator: Yavuz Unal
creator: Husamettin Kaplan
creator: Yusuf Bektas
creator: Muhammed Bedirhan Caglar
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-05-31
publication date: 2023-05-31
starting page: 62
ending page: 69
doi: 10.3846/ntcs.2023.18015

Multi-purpose password dataset generation and its application in decision making for password cracking through machine learning
type: item
description: This article proposes a method for multi-purpose password dataset generation suitable for use in further machine learning and other research related, directly or indirectly, to passwords. Currently, password datasets are not suitable for machine learning or decision-driven password cracking. Most password datasets are just any old password dictionaries that contain only leaked and common passwords and no other information. Other password datasets are small and include only weak passwords that have previously been leaked. The literature is rich in terms of methods used for password cracking based on password datasets. Those methods are mainly focused on generating more password candidates like the ones included in the training dataset. The proposed method exploits statistical analysis of leaked passwords and randomness to ensure diversity in the dataset. An experiment with the generated dataset has shown significant improvement in time when performing dictionary attack but not when performing brute-force attack.
subject: Articles
  topic: section
subject: passwords
  topic: keywords
subject: password cracking
  topic: keywords
subject: password dataset
  topic: keywords
subject: password strength
  topic: keywords
subject: machine learning
  topic: keywords
creator: Mark Vainer
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-04-11
publication date: 2023-04-11
starting page: 1
ending page: 18
doi: 10.3846/ntcs.2023.17639

Generation of a learning path in e-learning environments: literature review
type: item
description: Education is moving into an e-learning environment, displacing contact and face-to-face learning. However, current e-learning environments cannot still personalise when creating e-learning paths. Identifying existing solutions’ problems and limitations is critical to generate new, more advanced ideas for creating personalised e-learning paths. The literature analysis, for which 28 articles for 2018–2022 were used, describes the existing solutions used to adapt and optimise e-learning. The article provides an overview of existing research in the field of personalisation of e-learning systems and the creation of e-learning trajectories, proposes the development of a taxonomy of studied methods for recommending and forming individual learning trajectories, analysis of the practices described in the articles to identify the most commonly used of them. Limitations, problems and unresolved issues in previous studies are summarised and provide information for further work on improving the results obtained and for choosing the direction of future research, which is given in the final part of the article.
subject: Articles
  topic: section
subject: adaptive e-learning
  topic: keywords
subject: personalisation
  topic: keywords
subject: e-learning path generation
  topic: keywords
subject: optimisation
  topic: keywords
subject: literature review
  topic: keywords
creator: Olga Ovtšarenko
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-04-11
publication date: 2023-04-11
starting page: 32
ending page: 50
doi: 10.3846/ntcs.2023.18278

Explaining XGBoost predictions with SHAP value: a comprehensive guide to interpreting decision tree-based models
type: item
description: Understanding the factors that affect Key Performance Indicators (KPIs) and how they affect them is frequently important in sectors where data and data science are crucial. Machine learning is utilized to model and predict pertinent KPIs in order to do this. Interpretability is important, nevertheless, in order to fully comprehend how the model generates its predictions. It enables users to pinpoint which traits have aided the model’s ability to learn and comprehend the data. A practical approach for evaluating the contribution of input attributes to model learning has evolved in the form of SHAP (SHapley Additive exPlanations offer an index for evaluating the influence of each feature on the forecasts made by the model. In this paper, it is demonstrated that the contribution of features to model learning may be precisely estimated when utilizing SHAP values with decision tree-based models, which are frequently used to represent tabular data.
subject: Articles
  topic: section
subject: SHAP value
  topic: keywords
subject: machine learning
  topic: keywords
subject: decision tree-based model
  topic: keywords
subject: feature importance
  topic: keywords
creator: Serap Ergün
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-04-11
publication date: 2023-04-11
starting page: 19
ending page: 31
doi: 10.3846/ntcs.2023.17901

Current applications and challenges of the Internet of Things
type: item
description: The concept of the Internet of Things is capable of making a giant leap in the economy, including research in the field of computer science, network technologies, microelectronics and sensor technology. Combined with the technological developments of nanotechnology and robotics, IoT can play a central role in the industrial revolution by creating economic relations between machines and connecting the economy of people and machines, solving a number of problems that humanity is facing. All devices controlled via the Internet are elements of the Internet of things. The IoT has allowed various possibilities for all countries to improve life quality and the technological ideas for efficiency, productivity, security, and profit. An integrated security system is a giant step towards the improved economy. The concept of IoT plays a decisive role in the further development of the infocommunication industry. This is confirmed both by the position of the International Telecommunication Union (ITU) and the European Union on this issue, and by the inclusion of the Internet of Things in the list of breakthrough technologies in the United States, China and other countries. Thus, this article will go over the current state of the IoT as well as describe which devices and industries stand to benefit from the advantages it brings. Additionally, statistical data on the actual trends and investments into IoT across the world are also provided.
subject: Articles
  topic: section
subject: internet of things
  topic: keywords
subject: integration
  topic: keywords
subject: smart devices
  topic: keywords
subject: smart cities
  topic: keywords
subject: remote communication
  topic: keywords
subject: globalization
  topic: keywords
subject: information exchange
  topic: keywords
creator: Rashid Dallaev
creator: Tatiana Pisarenko
creator: Ştefan Ţălu
creator: Dinara Sobola
creator: Jiří Majzner
creator: Nikola Papež
rights: Copyright (c) 2023 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2023-04-11
publication date: 2023-04-11
starting page: 51
ending page: 61
doi: 10.3846/ntcs.2023.17891