1. Enhanced product defect forecasting using partitioned attributes and ensemble machine learningY. Y. Sun, 2025, original scientific article Abstract: This study addresses a critical challenge in industrial big data analytics for smart manufacturing: conventional machine learning methods often fail to account for data discontinuities caused by scrapped defective intermediates in multi-stage production processes, inadvertently treating non-conforming products as qualified during model training. We propose a novel process-aware data analytics framework specifically designed for process industries, featuring: (1) intelligent attribute partitioning based on information flow discontinuity points, and (2) an ensemble modelling approach combining Random Forest and C5.0 Decision Tree algorithms to generate interpretable prediction rules with quantified feature importance rankings. Validated using real-world production data from a Chinese rail steel manufacturer, our methodology demonstrates superior performance by explicitly incorporating process-specific data correlations. The proposed solution effectively mitigates information distortion caused by scrapped intermediates while maintaining operational interpretability – a crucial requirement for industrial implementation. The research results increased the accuracy rate of the test set of the random forest experiment from 88.39 % to 92.69 %, and the accuracy rate of the test set of the decision tree experiment from 71.89 % to 79.15 %. Additionally, the experimental results verify that, compared with the traditional methods, our framework has better applicability in capturing product quality in the manufacturing industry when process attributes are considered.
Keywords: intelligent manufacturing, process industry, industrial data mining, defect prediction, C5.0 decision tree algorithms, random forest, process-oriented analytics, machine learning
Published in DKUM: 21.01.2026; Views: 0; Downloads: 0
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2. Privacy-preserving AI-based framework for container transportation demand forecasting in sea-rail intermodal systemsL. Huang, D. Y. Jiang, T. Bai, 2025, original scientific article Abstract: In response to the growing demand for accurate freight forecasting in sea-rail intermodal transportation, particularly under the constraints of stringent data protection regulations, we introduce a privacy-preserving, AI-based framework that focuses on the micro-level identification of container transport potential. The framework combines Vertical Federated Learning (VFL) with advanced feature and sample selection techniques. It leverages privacy-preserving methods, such as homomorphic encryption and random noise, enabling secure collaboration between ports and railways while safeguarding commercially sensitive data. Through extensive experiments, our framework demonstrates superior performance in predicting container transport demand, significantly improving the accuracy of resource allocation and scheduling decisions for rail operators. The framework not only ensures compliance with data protection regulations but also provides valuable insights into intermodal transportation planning, optimizing both railway operations and customer service quality. This approach offers a practical solution for improving strategic decision-making in the sea-rail intermodal sector amid increasing privacy demands and complex logistical challenges.
Keywords: freight demand forecasting, container transportation demand forecasting, vertical federated learning, privacy-preserving methods, sample and feature selection, machine learning, homomorphic encryption, resource allocation and scheduling
Published in DKUM: 20.01.2026; Views: 0; Downloads: 0
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4. 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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5. 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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7. 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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8. 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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9. 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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10. 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: 3
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