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2. Reinforcement learning for robot manipulation tasks in human-robot collaboration using the CQL/SAC algorithmsA. Husaković, Lejla Banjanović-Mehmedović, A. Gurdić-Ribić, Naser Prljača, Isak Karabegović, 2025, original scientific article Abstract: The integration of human-robot collaboration (HRC) into industrial and service environments demands efficient and adaptive robotic systems capable of executing diverse tasks, including pick-and-place operations. This paper investigates the application of Soft Actor-Critic (SAC) and Conservative Q-Learning (CQL)—two deep reinforcement learning (DRL) algorithms—for the learning and optimization of pick-and-place actions within HRC scenarios. By leveraging SAC’s capability to balance exploration and exploitation, the robot autonomously learns to perform pick-and-place tasks while adapting to dynamic environments and human interactions. Moreover, the integration of CQL ensures more stable learning by mitigating Q-value overestimation, which proves particularly advantageous in offline and suboptimal data scenarios. The combined use of CQL and SAC enhances policy robustness, facilitating safer and more efficient decision-making in continually evolving environments. The proposed framework combines simulation-based training with transfer learning techniques, enabling seamless deployment in real-world environments. The critical challenge of trajectory completion is addressed through a meticulously designed reward function that promotes efficiency, precision, and safety. Experimental validation demonstrates a 100 % success rate in simulation and an 80 % success rate on real hardware, confirming the practical viability of the proposed model. This work underscores the pivotal role of DRL in enhancing the functionality of collaborative robotic systems, illustrating its applicability across a range of industrial environments.
Keywords: human-robot collaboration, robot learning, deep reinforcement learning, soft actor-critic algorithm, Conservative Q-learning, robot manipulation tasks
Published in DKUM: 16.01.2026; Views: 0; Downloads: 0
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3. Perspective chapter: recognition of activities of daily living for elderly people in the era of digital healthMirjam Sepesy Maučec, Gregor Donaj, 2024, independent scientific component part or a chapter in a monograph Abstract: People around the world are living longer. The question arises of how to help
elderly people to live longer independently and feel safe in their homes. Activity of
Daily Living (ADL) recognition systems automatically recognize the daily activities of
residents in smart homes. Automated monitoring of the daily routine of older individuals, detecting behavior patterns, and identifying deviations can help to identify
the need for assistance. Such systems must ensure the confidentiality, privacy, and
autonomy of residents. In this chapter, we review research and development in the
field of ADL recognition. Breakthrough advancements have been evident in recent
years with advances in sensor technology, the Internet of Things (IoT), machine
learning, and artificial intelligence. We examine the main steps in the development of
an ADL recognition system, introduce metrics for system evaluation, and present the
latest trends in knowledge transfer and detection of behavior changes. The literature
overview shows that deep learning approaches currently provide promising results.
Such systems will soon mature for more diverse practical uses as transfer learning
enables their fast deployment in new environments.
Keywords: digital health, elderly, activities of daily living, recognition of activities, sensors, machine learning
Published in DKUM: 15.01.2026; Views: 0; Downloads: 0
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5. Enhancing learning performance in primary education : the roles of problem solving and creative thinking challengesPham Ngoc Thien Nguyen, Khanh-Trinh Tran, Giam Buu Le, 2025, original scientific article Abstract: This study investigated the roles of problem-solving and creative thinking activities in primary science education. Participants included 64 third-grade and 62 fifth-grade students in Southern Vietnam, with half assigned to experimental groups and the others to control groups. The experimental groups, which received structured lessons, outperformed the control groups, which received traditional instruction. Results highlight the positive impact of integrating these activities on academic performance, supporting the effectiveness of structured support in enhancing learning outcomes.
Keywords: problem-solving, creativity, learning performance, primary education, vietnamese students
Published in DKUM: 08.01.2026; Views: 0; Downloads: 0
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6. A study of classroom potential based on elementary school students’ seating position preferencesKukuh Rizki Satriaji, Imam Santosa, Achmad Syarief, Andriyanto Wibisono, 2025, original scientific article Abstract: Previous research has shown a link between seating position and student interest in and motivation for learning. This study explores classroom potential based on seating preferences and reasons behind student choices. Teachers rarely allow students to select their own seats, despite the benefits of comfort and engagement. Conducted in 5th and 6th elementary grades, this research used a participatory approach involving students and teachers. The study found that students’ seating preferences were influenced by spatial characteristics, opportunities for social engagement, and academic motivations. Understanding these preferences can help create a more conducive learning environment, enhancing student comfort, participation, and overall academic motivation.
Keywords: teaching, school, student, learning process, peer feedback
Published in DKUM: 08.01.2026; Views: 0; Downloads: 1
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7. A personalized approach to understanding food cravings and intake : a study protocolSaša Zorjan, Sašo Karakatič, Marina Horvat, Satja Mulej Bratec, Živa Krajnc, 2025, original scientific article Abstract: Background: Studies on food craving and consumption often overlook the interconnectedness of risk factors, assuming uniform mechanisms that drive individuals to (over)consume food. This project seeks to address this gap by leveraging a precision health framework to explore whether multimodal clustering can predict weight and eating outcomes after six months, providing a more nuanced understanding of individual variability. Methods: The project will include a longitudinal study, encompassing several sub-studies where self-report, electrophysiological, and time series dynamic data will be collected at three time points. At baseline, participants will complete comprehensive assessments, including an electroencephalography (EEG) experiment and a one-week experience sampling study (ESM). Machine learning techniques will be employed to uncover distinct participant clusters, characterized by unique patterns of food consumption and weight changes over six months. Markers that best differentiate these profiles will be identified with explainable AI techniques, which aim to make machine learning model outputs understandable by highlighting the key features or patterns driving predictions, enabling personalized insights into key factors contributing to eating behaviors and weight management. Discussion: By exploring the variability of mechanisms influencing food consumption, eating regulation, and weight gain, we aim to uncover subgroups of individuals who are most affected by specific influences, such as stress, emotion regulation difficulties, or sleep deprivation. This project will advance theoretical understanding by integrating multimodal data and emphasizing idiographic methods to capture individual variability. Findings will provide a foundation for future research on precision approaches to eating behaviors and may offer insights into personalized strategies for prevention and management of both normative and disordered eating patterns.
Keywords: food cue reactivity, EEG, experienxe sampling methodology, personalized medicine, achine learning, explainable artificial inteligence
Published in DKUM: 19.12.2025; Views: 0; Downloads: 0
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8. Statistical methods for analysing logistics dataSanja Bojić, Kristijan Brglez, Maja Fošner, Roman Gumzej, Rebeka Kovačič Lukman, Benjamin Marcen, Marinko Maslarić, Boško Matović, Dejan Mirčetić, 2025, reviewed university, higher education or higher vocational education textbook Keywords: statistics, logistics, supply chains, demand forecasting, simulation modeling, regression analysis, artificial intelligence, machine learning
Published in DKUM: 19.12.2025; Views: 0; Downloads: 2
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9. Augmented reality in biology education : a literature reviewKatja Stanič, Andreja Špernjak, 2025, original scientific article Abstract: This systematic review summarises the latest research on the use of augmented reality (AR) in biology education at primary, secondary and tertiary levels. Searching Web of Science, Scopus and Google Scholar, we found 40 empirical studies published up until early 2024. For each study, we analysed biological content, technical features, learning practices and pedagogical impact. AR is most used in human anatomy, particularly in the circulatory and respiratory systems, but also in genetics, cell biology, virology, botany, ecology and molecular processes. Mobile devices dominate as a mediation platform, with marker-based tracking and either commercial apps or self-developed Unity/Vuforia solutions. Almost all studies embed AR in constructivist or inquiry-based pedagogies, and report improved motivation, engagement and conceptual understanding. Nevertheless, reporting on the technical details is inconsistent and the long-term effects are not yet sufficiently researched. AR should therefore be viewed as a pedagogical tool rather than a technological goal that requires careful instructional design and equitable access to ensure meaningful and sustainable learning.
Keywords: augmented reality (AR), biology teaching, educational technology, learning practice
Published in DKUM: 16.12.2025; Views: 0; Downloads: 1
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10. A multi-task deep learning approach for landslide displacement prediction with applications in early warning systemsDamjan Strnad, Domen Mongus, Štefan Horvat, Ela Šegina, 2025, original scientific article Abstract: Accurate landslide displacement prediction is important for the construction of reliable landslide early warning systems (LEWS). Recently, deep neural networks have become the dominant approach for landslide displacement modeling. However, we show that focusing solely on low prediction residuals is not perfectly aligned with the goals of LEWS, where the emphasis is on precise forecasts near the warning threshold. This can result in poor efficiency of threshold-based warning prediction. We propose a multi-task approach to model training, where auxiliary targets are used to optimize the model towards the performance relevant for LEWS. The methodology is validated using the data from the deep-seated Urbas landslide in north-western Slovenia, which has been monitored by GNSS since 2019. Developing a displacement prediction model for Urbas is a step towards extending the existing wire-based mechanical alarm system. We employ a convolutional neural network for day-ahead displacement prediction using recent landslide activity, hydrometeorological measurements and seismological data. The proposed multi-task model retains a competitive score for warning prediction while achieving a significantly lower mean absolute error compared to the reference models. The proposed methodology is generally applicable and has the potential to improve the efficiency of landslide modeling in the context of LEWS.
Keywords: landslide displacement prediction, neural network, multitask learning, landslide early warning system, remote sensing, GNSS
Published in DKUM: 12.12.2025; Views: 0; Downloads: 2
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