1. High-performance deployment operational Data analytics of pre-trained multi-label classification architectures with differential-evolution-based hyperparameter optimization (AutoDEHypO)Teo Prica, Aleš Zamuda, 2025, izvirni znanstveni članek Opis: This article presents a high-performance-computing differential-evolution-based hyperparameter optimization automated workflow (AutoDEHypO), which is deployed on a petascale supercomputer and utilizes multiple GPUs to execute a specialized fitness function for machine learning (ML). The workflow is designed for operational analytics of energy efficiency. In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. The workflow analysis considers key factors such as DE strategies and automated use case configurations, such as an ML model architecture and dataset, while monitoring both the achieved accuracy and the utilization of computing resources, such as the elapsed time and consumed energy. While the efficiency of a chosen DE strategy is assessed based on a multi-label supervised ML accuracy, operational data about the consumption of resources of individual completed jobs obtained from a Slurm database are reported. To demonstrate the impact on energy efficiency, using our analysis workflow, we visualize the obtained operational data and aggregate them with statistical tests that compare and group the energy efficiency of the DE strategies applied in the ML models.
Ključne besede: high-performance computing, operational data analytics, energy efficiency, machine learning, AutoML, differential avolution, optimization
Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 2
 Celotno besedilo (1,61 MB) |
2. Comparing algorithms for predictive data analytics : magistrsko deloGoran Kirov, 2024, magistrsko delo Opis: The master’s degree thesis is composed of theoretical and practical parts. The theoretical part describes the basics of predictive data analytics and machine learning algorithms for classification such as Logistic Regression, Decision Tree, Random Forest, SVM, and KNN. We also describe different evaluation metrics such as Recall, Precision, Accuracy, F1 Score, Cohen’s Kappa, Hamming Loss, and Jaccard Index that are used to measure the performance of these algorithms. Additionally, we record the time taken for the training and prediction processes to provide insights into algorithm scalability.
The key part master’s thesis is the practical part that compares these algorithms with a self-implemented tool that shows results for different evaluation metrics on seven datasets. First, we describe the implementation of an application for testing where we measure evaluation metrics scores. We tested these algorithms on all seven datasets using Python libraries such as scikit-learn. Finally, w
Ključne besede: data analytics, machine learning, classification, evaluation metrics
Objavljeno v DKUM: 15.01.2025; Ogledov: 0; Prenosov: 58
Celotno besedilo (2,68 MB) |
3. Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning AnalyticsSaša Brdnik, Vili Podgorelec, Boštjan Šumak, 2023, izvirni znanstveni članek Opis: This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master's students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor's and master's students, with master's students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master's-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
Ključne besede: explainable artificial intelligence, learning analytics, XAI techniques, trust, explanation satisfaction
Objavljeno v DKUM: 12.02.2024; Ogledov: 368; Prenosov: 59
Celotno besedilo (3,24 MB)
Gradivo ima več datotek! Več... |
