1. Prepoznavanje rastlin in njihovih bolezni z mobilno aplikacijoRok Trunkelj, 2025, undergraduate thesis Abstract: Raziskava obravnava prepoznavanje izbranih rastlin in njihovih bolezni s pomočjo
mobilne aplikacije. Na kratko so predstavljena uporabljena orodja: Orange Data mining,
Android Studio, MS Visio, Figma, Flask, Nginx in Gunicorn. Arhitektura rešitve obsega
virtualno okolje v oblaku s strežniki za dostop do modelov za klasifikacijo rastlin in
njihovih bolezni in aplikacijo Android. Opisan je postopek izdelave modelov strojnega
učenja, ki so bili preneseni na strežnik. V nalogi so prikazani pomembni deli kode in
podana razlaga vseh aspektov delovanja aplikacije.
Keywords: umetna inteligenca, razvoj aplikacije, analiza podatkov, podatkovno
rudarjenje, Orange Data Mining.
Published in DKUM: 09.04.2025; Views: 0; Downloads: 5
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4. Abstracts of the 10th Student Computing Research Symposium (SCORES’24)2024, proceedings Abstract: The 2024 Student Computing Research Symposium (SCORES 2024), organized by the Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI) in collaboration with the University of Ljubljana and the University of Primorska, showcases innovative student research in computer science. This year’s symposium highlights advancements in fields such as artificial intelligence, data science, machine learning algorithms, computational problem-solving, and healthcare data analysis. The primary goal of SCORES 2024 is to provide a platform for students to present their research, fostering early engagement in academic inquiry. Beyond research presentations, the symposium seeks to create an environment where students from different institutions can meet, exchange ideas, and build lasting connections. It aims to cultivate friendships and future research collaborations among emerging scholars. Additionally, the conference offers an opportunity for students to interact with senior researchers from institutions beyond their own, promoting mentorship and broader academic networking.
Keywords: student conference, computer and information science, artificial intelligence, data science, data mining
Published in DKUM: 18.09.2024; Views: 0; Downloads: 32
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5. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, original scientific article Abstract: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Keywords: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Published in DKUM: 12.09.2024; Views: 15; Downloads: 19
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7. Variable-length differential evolution for numerical and discrete association rule miningUroš Mlakar, Iztok Fister, Iztok Fister, 2023, original scientific article Abstract: This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Keywords: association rule mining, differential evolution, data mining, variable-lenght solution representation, green AI
Published in DKUM: 18.01.2024; Views: 341; Downloads: 26
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8. Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)2021, proceedings Abstract: The 7th Student Computer Science Research Conference is an answer to the fact that modern PhD and already Master level Computer Science programs foster early research activity among the students. The prime goal of the conference is to become a place for students to present their research work and hence further encourage students for an early research. Besides the conference also wants to establish an environment where students from different institutions meet, let know each other, exchange the ideas, and nonetheless make friends and research colleagues. At last but not least, the conference is also meant to be meeting place for students with senior researchers from institutions others than their own.
Keywords: student conference, computer and information science, artificial intelligence, data science, data mining
Published in DKUM: 13.09.2021; Views: 1360; Downloads: 183
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9. Link prediction on TwitterSanda Martinčić-Ipšić, Edvin Močibob, Matjaž Perc, 2017, original scientific article Abstract: With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.
Keywords: link prediction, data mining, Twitter, network analysis
Published in DKUM: 15.09.2017; Views: 1864; Downloads: 212
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10. Analyzing information seeking and drug-safety alert response by health care professionals as ew methods for surveillanceAlison Callahan, Igor Pernek, Gregor Štiglic, Jurij Leskovec, Howard Strasberg, Nigam Haresh Shah, 2015, original scientific article Abstract: Background: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance.
Objective: To analyze health care professionals% information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource.
Methods: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert.
Results: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert.
Conclusions: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate
Keywords: internet log analysis, data mining, physicians, information-seeking behavior, drug safety surveillance
Published in DKUM: 02.08.2017; Views: 1858; Downloads: 235
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