1. Advancing sustainable mobility: artificial intelligence approaches for autonomous vehicle trajectories in roundaboutsSalvatore Leonardi, Natalia Distefano, Chiara Gruden, 2025, original scientific article Abstract: This study develops and evaluates advanced predictive models for the trajectory planning of autonomous vehicles (AVs) in roundabouts, with the aim of significantly contributing to sustainable urban mobility. Starting from the “MRoundabout” speed model, several Artificial Intelligence (AI) and Machine Learning (ML) techniques, including Linear Regression (LR), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Neural Networks (NNs), were applied to accurately emulate human driving behavior and optimize AV trajectories. The results indicate that neural networks achieved the best predictive performance, with R2 values of up to 0.88 for speed prediction, 0.98 for acceleration, and 0.94 for differential distance, significantly outperforming traditional models. GBR and SVR provided moderate improvements over LR but encountered difficulties predicting acceleration and distance variables. AI-driven tools, such as ChatGPT-4, facilitated data pre-processing, model tuning, and interpretation, reducing computational time and enhancing workflow efficiency. A key contribution of this research lies in demonstrating the potential of AI-based trajectory planning to enhance AV navigation, fostering smoother, safer, and more sustainable mobility. The proposed approaches contribute to reduced energy consumption, lower emissions, and decreased traffic congestion, effectively addressing challenges related to urban sustainability. Future research will incorporate real traffic interactions to further refine the adaptability and robustness of the model.
Keywords: sustainable mobility, autonomous vehicles, machine learning, roundabouts, artificial intelligence, ChatGPT
Published in DKUM: 04.04.2025; Views: 0; Downloads: 1
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2. Statistically significant differences in AI support levels for project management between SMEs and large enterprisesPolona Tominc, Dijana Oreški, Vesna Čančer, Maja Rožman, 2024, original scientific article Abstract: Background: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing a sample of 473 SMEs and large Slovenian enterprises.
Methods: To validate the observed differences, statistical analysis, specifically the Mann–Whitney U test, was employed.
Results: The results confirm the presence of statistically significant differences between SMEs and large enterprises across multiple dimensions of AI support in project management. Large enterprises exhibit on average a higher level of AI adoption across all five AI utilization dimensions. Specifically, large enterprises scored significantly higher (p < 0.05) in AI adopting strategies and in adopting AI technologies for project tasks and team creation. This study’s findings also underscored the significant differences (p < 0.05) between SMEs and large enterprises in their adoption and utilization of AI technologies for project management purposes. While large enterprises scored above 4 for several dimensions, with the highest average score assessed (mean value 4.46 on 1 to 5 scale) for the usage of predictive Analytics Tools to improve the work on the project, SMEs’ average levels, on the other hand, were all below 4. SMEs in particular may lag in incorporating AI into various project activities due to several factors such as resource constraints, limited access to AI expertise, or risk aversion.
Conclusions: The results underscore the need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company’s competitiveness.
Keywords: enterprises, project management, leadership, artificial intelligence
Published in DKUM: 04.04.2025; Views: 0; Downloads: 2
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3. Sensors and artificial intelligence methods and algorithms for human - computer intelligent interaction: a systematic mapping studyBoštjan Šumak, Saša Brdnik, Maja Pušnik, 2022, original scientific article Abstract: To equip computers with human communication skills and to enable natural interaction
between the computer and a human, intelligent solutions are required based on artificial intelligence
(AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing
the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer
intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence,
and identify potential directions for future research. We conduct a systematic mapping study of the
HCII body of research. Four hundred fifty-four studies published in various journals and conferences
between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have
primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using
sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and
others. Researchers most often apply deep-learning and instance-based AI methods and algorithms.
The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition,
primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN)
is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture
recognition solutions.
Keywords: human–computer intelligent interaction, intelligent user interfaces, IUI, sensors, artificial intelligence
Published in DKUM: 31.03.2025; Views: 0; Downloads: 1
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4. Predicting corn moisture content in continuous drying systems using LSTM neural networksMarko Simonič, Mirko Ficko, Simon Klančnik, 2025, original scientific article Abstract: As we move toward Agriculture 4.0, there is increasing attention and pressure on the productivity of food production and processing. Optimizing efficiency in critical food processes such as corn drying is essential for long-term storage and economic viability. By using innovative technologies such as machine learning, neural networks, and LSTM modeling, a predictive model was implemented for past data that include various drying parameters and weather conditions. As the data collection of 3826 samples was not originally intended as a dataset for predictive models, various imputation techniques were used to ensure integrity. The model was implemented on the imputed data using a multilayer neural network consisting of an LSTM layer and three dense layers. Its performance was evaluated using four objective metrics and achieved an RMSE of 0.645, an MSE of 0.416, an MAE of 0.352, and a MAPE of 2.555, demonstrating high predictive accuracy. Based on the results and visualization, it was concluded that the proposed model could be a useful tool for predicting the moisture content at the outlets of continuous drying systems. The research results contribute to the further development of sustainable continuous drying techniques and demonstrate the potential of a data-driven approach to improve process efficiency. This method focuses on reducing energy consumption, improving product quality, and increasing the economic profitability of food processing
Keywords: drying, moisture prediction, big data, artificial intelligence, LSTM
Published in DKUM: 21.03.2025; Views: 0; Downloads: 10
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5. From chaos to the absurd : existentialism for the 21st centuryBoris Aberšek, 2024, original scientific article Abstract: As Sartre pointed out, philosophical questions are questions that each generation must ask themselves because only this promotes the feeling of being alive, which is especially true for existential questions closely related to time-space, the moment, and our society. Sartre placed his philosophy of existentialism in wartime and the social conditions of the time at the beginning of the 20th century. We can equate these conditions with today's conditions; we are once again facing threats of war, and once again, we are facing chaotic conditions that increasingly lead to absurdity but are also entirely different. Today, at the beginning of the 21st century, the clarity and disambiguation of the 20th century no longer exist, as the relationships between beings and the world have drastically changed. We can observe that (1) the world is not one; there are two worlds, the physical and the cyber world and (2) being is not one; there are two beings (entities), human and AI-based forms of artificial life (ALF), between which there is a permanent tension. We advocate the thesis that in the society of the future, man must still play a master role; he must still be the being who will guide this society. Also, as Sartre claimed, each era must create its philosophy and consider real time–space. Responses to changes in this time–space also relate to existentialism in the 21st century. In this context, it is necessary to redefine the view of the future and the guidelines for the development of future society.
Keywords: existentialism, philosophy of artificial intelligence, philosophy of mind
Published in DKUM: 21.03.2025; Views: 0; Downloads: 2
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6. 44th International Conference on Organizational Science Development : Human Being, Artificial Intelligence and Organization, Conference Proceedings2025, proceedings of peer-reviewed scientific conference contributions (international and foreign conferences) Abstract: The 44th International Scientific Conference on the Development of Organisational Science was focused on developing and advancing knowledge in the organisational sciences, with a focus on the contemporary challenges and opportunities of our time. On the one hand, it is humans who have woven the knowledge of organisations and will continue to enrich the knowledge of organisations in the future. On the other hand, we need to take into account the situational factors and the wider environment that are intrinsic to understanding organisations. The title of this year's conference is: Human being, Artificial Intelligence and the Organisation. The society we live in today is going through a period of great change in various areas of our lives. Although our pace sometimes stops, the forces of the environment do not. The pace of change often no longer surprises us. But the pillars of our action, the achievements of human society, are something of which we can be justly proud. Artificial intelligence is one of the forces that has entered our everyday lives in many places in recent times. Where are the opportunities and where are the dangers of artificial intelligence, where is human intelligence still a significant step ahead of artificial intelligence?
Keywords: organization, human being, artificial intelligence, changes, organizational development
Published in DKUM: 20.03.2025; Views: 0; Downloads: 23
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7. Hybrid reality development - can social responsibility concepts provide guidance?Igor Perko, 2021, original scientific article Abstract: Purpose: This paper aims to define hybrid reality (HyR) as an ongoing process in which artificial intelligence (AI) technology is gradually introduced as an active stakeholder by using reasoning to execute real-life activities. Also, to examine the implications of social responsibility (SR) concepts as featured in the HyR underlying common framework to progress towards the redefinition of global society.
Design/methodology/approach: A combination of systemic tools is used to examine and assess the development of HyR. The research is based on evolutionary and learning concepts, leading to the new meta-system development. It also builds upon the viable system model and AI, invoking SR as a conceptual framework. The research is conducted by using a new approach: using system dynamics based interactions modelling, the following two models have been proposed. The state-of-the-art HyR interactions model, examined using SR concepts; and a SR concept-based HyR model, examined using a smart vehicle case.
Findings: In the HyR model, interaction asymmetry between stakeholders is identified, possibly leading to pathological behaviour and AI technology learning corruption. To resolve these asymmetry issues, an interaction model based on SR concepts is proposed and examined on the example of an autonomous vehicle transport service. The examination results display significant changes in the conceptual understanding of transport services, their utilisation and data-sharing concepts.
Research limitations/implications: As the research proposal is theoretical in nature, the projection may not display a fully holistic perspective and can/should be complemented with empirical research results.
Practical implications: For researchers, HyR provides a new paradigm and can thereby articulate potential research frameworks. HyR designers can recognise projected development paths and the resources required for the implication of SR concepts. Individuals and organisations should be aware of their not necessarily passive role in HyR and can therefore use the necessary social force to activate their status.
Originality/value: For the first time, to the best of the author’s knowledge, the term HyR is openly elaborated and systemically examined by invoking concepts of SR. The proposed model provides an overview of the current and potential states of HyR and examines the gap between them.
Keywords: artificial intelligence, social responsibility, systems thinking, cybernetics, hybrid reality, interactions model
Published in DKUM: 04.02.2025; Views: 0; Downloads: 4
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8. Recent applications of explainable AI (XAI) : a systematic literature reviewMirka Saarela, Vili Podgorelec, 2024, review article Keywords: explainable artificial intelligence, applications, interpretable machine learning, convolutional neural network, deep learning, post-hoc explanations, model-agnostic explanations
Published in DKUM: 31.01.2025; Views: 0; Downloads: 3
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9. Detection of AI-generated synthetic images with a lightweight CNNAdrian Lokner Lađević, Tin Kramberger, Renata Kovačević, Dino Vlahek, 2024, original scientific article Keywords: convolutional neural networks, generative adversarial networks, classification, synthetic images, explanable artificial intelligence
Published in DKUM: 29.01.2025; Views: 0; Downloads: 7
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10. Automatic classification of older electronic texts into the Universal Decimal Classification-UDCMatjaž Kragelj, Mirjana Kljajić Borštnar, 2021, original scientific article Abstract: Purpose:The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.
Design/methodology/approach: The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.
Findings: Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.
Research limitations/implications: The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.
Practical implications: The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.
Social implications: The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.
Originality/value: These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
Keywords: digital library, artificial intelligence, machine learning, text classification, older texts, Universal Decimal Classification
Published in DKUM: 28.01.2025; Views: 0; Downloads: 5
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