Iskanje po katalogu digitalne knjižnice

Opis: In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R2 test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage.
Ključne besede: additive manufacturing, machine learning, XG Boost, magnesium, relative density
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (1,60 MB)
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Opis: This paper investigates the automatic recognition of depression by integrating acoustic, linguistic and emotional features extracted from clinical interviews in the DAIC-WOZ dataset. A total of six classical machine learning classifiers such as Decision Tree, Random Forest, SVM, Gradient Boosting, AdaBoost and XGBoost were systematically evaluated under different class balancing methods (such as SMOTE, SMOTETomek and Random Undersampling) and feature selection strategies. The best model, a decision tree classifier with SMOTE-based balancing and a feature selection technique, achieved a weighted F1 score and accuracy of 0.78 with only eight selected features. These features included all three modalities, demonstrating the added benefit of a multimodal approach. The results suggest that even relatively simple models, when supported by careful preprocessing and dimensionality reduction, can provide accurate and interpretable predictions. This work emphasizes the importance of feature engineering and balancing techniques in clinical machine learning tasks and lays the foundation for future research on scalable and explainable depression detection systems.
Ključne besede: Depression, Classification, Machine Learning, Data Balancing, Feature Selection
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 1
Celotno besedilo (4,28 MB)

Opis: This thesis explores the implementation of a personalized and accurate recommender system for computer components, addressing key challenges within the e-commerce sector. A full-stack solution was developed, featuring a React-based frontend, a Flask backend, and a MongoDB database. The system integrates and evaluates four distinct recommendation algorithms: a basic model, Content-Based Filtering (CBF), Collaborative Filtering (CF), and a hybrid approach. Evaluation metrics revealed that CBF provided the highest accuracy among the individual methods. The hybrid system's performance matched that of the CBF model, primarily due to insufficient user interaction data limiting the effectiveness of the CF component. This outcome underscores the critical need for comprehensive datasets to fully leverage the power of collaborative and hybrid recommendation systems.
Ključne besede: recommender system, computer components, hybrid recommender system, machine learning
Objavljeno v DKUM: 23.10.2025; Ogledov: 0; Prenosov: 9
Celotno besedilo (3,28 MB)

Opis: The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science.
Ključne besede: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Objavljeno v DKUM: 17.10.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (4,22 MB)

Opis: Artificial intelligence is one of the largest technological trends in the 2020s, and many processes have been transformed by it, just like all technological innovations have. It is no longer based exclusively on corporate level, but since its free public launch, AI has become much more accessible that people are even using it for personal needs. Human Resources Management is not immune to the changes and HR leaders are utilising it as well to improve efficiency, data-driven leadership and decision-making. However, there is one concern from general public: how smart and ethical is it to have AI deployed in a department that is about managing humans and its relationships, where we need to have a human to understand the context and emotions? The theoretical part of this thesis explores technological evolution of Human Resources Management, brief fundaments for the AI technology, HRM areas where it can be applied, along with its benefits and detriments, which serves as the basis to gain knowledge and insights through literature review. Furthermore, the empirical part explores the actual usage by interviewing HR leaders that have deployed AI into their technical stack. With rich qualitative data from different perspectives, we are able to see how AI is being used, and HR professionals’ respective opinions, who all agree that AI is a powerful tool that transforms the HRM processes by dealing with repetitive and mostly administrative tasks, leaving plenty of space to focus on more creative and strategic tasks. One of the largest concerns regarding AI is in people; how they train the model and to what extent is AI being relied on.
Ključne besede: human resources management, artificial intelligence, machine learning
Objavljeno v DKUM: 10.09.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (979,04 KB)