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2. What can artificial intelligence do for soil health in agriculture?Stefan Schweng, Luca Bernardini, Katharina Keiblinger, Peter Kaul, Iztok Fister, Niko Lukač, Javier Del Ser, Andreas Holzinger, 2025, review article Abstract: 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.
Keywords: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Published in DKUM: 17.10.2025; Views: 0; Downloads: 3
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3. Threshold adaptation for improved wrapper-based evolutionary feature selectionUroš Mlakar, Iztok Fister, Iztok Fister, 2025, original scientific article Abstract: Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets. Evolutionary Feature Selection (EFS) methods employ a threshold parameter � to decide feature inclusion, yet the widely used static setting �=0.5 may not yield optimal results. This paper presents the first large-scale, systematic evaluation of threshold adaptation mechanisms in wrapper-based EFS across a diverse number of benchmark datasets. We examine deterministic, adaptive, and self-adaptive threshold parameter control under a unified framework, which can be used in an arbitrary bio-inspired algorithm. Extensive experiments and statistical analyses of classification accuracy, feature subset size, and convergence properties demonstrate that adaptive mechanisms outperform the static threshold parameter control significantly. In particular, they not only provide superior tradeoffs between accuracy and subset size but also surpass the state-of-the-art feature selection methods on multiple benchmarks. Our findings highlight the critical role of threshold adaptation in EFS and establish practical guidelines for its effective application.
Keywords: feature selection, evolutionary algorithm, feature threshold, evolutionary feature selection
Published in DKUM: 14.10.2025; Views: 0; Downloads: 3
Full text (12,73 MB) |
4. The role of intelligent data analysis in selected endurance sports : a systematic literature reviewAlen Rajšp, Patrik Rek, Peter Kokol, Iztok Fister, 2025, review article Abstract: In endurance sports, athletes and coaches shift increasingly from intuition-based decisionmaking to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes.
Keywords: smart sports training, endurance sports, intelligent data analysis, machine learning, artificial intelligence, computational intelligence, systematic literature review
Published in DKUM: 02.10.2025; Views: 0; Downloads: 6
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5. A novel framework for unification of association rule miningRahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Alexandre Bazin, Syed Attique Shah, Iztok Fister, Sadok Ben Yahia, Dirk Draheim, 2022, original scientific article Abstract: Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.
Keywords: association rule mining, data mining, online analytical processing, statistical reasoning
Published in DKUM: 02.10.2025; Views: 0; Downloads: 2
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6. Micro-location temperature prediction leveraging deep learning approachesAmadej Krepek, Iztok Fister, Iztok Fister, 2025, original scientific article Abstract: Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a longshort term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future.
Keywords: long short-term memory neural networks, air temperature, micro-location, prediction, weather, Internet of Things
Published in DKUM: 25.09.2025; Views: 0; Downloads: 4
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7. Razvoj grafičnega vmesnika za ogrodje NiaAMLAljaž Rant, 2025, undergraduate thesis Abstract: V diplomskem delu predstavljamo razvoj grafičnega uporabniškega vmesnika za ogrodje NiaAML, ki je ogrodje za samodejno strojno učenje, implementirano v programskem jeziku Python, zasnovano na algoritmih po vzorih iz narave. Cilj diplomskega dela je približati uporabo ogrodja tudi uporabnikom brez programerskega znanja ter jim omogočiti preprosto konfiguracijo in zagon optimizacijskih procesov. Delo vključuje teoretični pregled področja, zasnovo in implementacijo vmesnika ter evalvacijo delovanja.
Keywords: samodejno strojno učenje, uporabniški vmesnik, ogrodje NiaAML, Python, klasifikacijski cevovod
Published in DKUM: 23.09.2025; Views: 0; Downloads: 4
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8. Toward explainable time-series numerical association rule mining : a case study in smart-agricultureIztok Fister, Sancho Salcedo-Sanz, Enrique Alexandre-Cortizo, Damijan Novak, Iztok Fister, Vili Podgorelec, Mario Gorenjak, 2025, original scientific article Abstract: This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.
Keywords: association rule mining, explainable artificial intelligence, XAI, numerical association rule mining, optimization algorithms
Published in DKUM: 27.08.2025; Views: 0; Downloads: 2
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9. Sistemski pristop k ponovljivi podatkovni znanosti z upravljalnikom paketov Nix in zabojniki DockerMatic Lozinšek, 2025, undergraduate thesis Abstract: Diplomsko delo preučuje uporabo orodja Docker in upravljalnika paketov Nix za izboljšanje ponovljivosti v podatkovni znanosti. Docker ne glede na operacijski sistem omogoča izolirano izvajanje aplikacij, medtem ko Nix zagotavlja determinističen način upravljanja paketov in konfiguracij, kar omogoča ustvarjanje popolnoma ponovljivih okolij. Analiza se osredotoča na znanstvene projekte s področja podatkovnih disciplin, pri čemer so organizacijski, varnostni in etični vidiki izključeni. Eksperiment, izveden v lokalnem okolju, preučuje tehnične izzive, kot so zastarele knjižnice in manjkajoča dokumentacija. Rezultati eksperimentalnega dela diplomskega dela so pokazali, da Docker izboljšuje prenosljivost in integracijo v obstoječe delovne tokove, medtem ko Nix zagotavlja natančen nadzor nad različicami programske opreme in preprečuje konflikte med paketi.
Keywords: podatkovna znanost, ponovljivost, sistemski pristop, tehnologija, upravljalnik paketov Nix, zabojnik Docker
Published in DKUM: 10.07.2025; Views: 0; Downloads: 24
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10. Primerjava platform za neprekinjeno integracijo in dostavo programske opremeUrška Spevan, 2025, undergraduate thesis Abstract: Neprekinjena integracija, dostava in namestitev bistveno izboljšajo razvoj programskih rešitev, saj naslavljajo problematiko, ki je ročno izvajanje procesov, posledično večje število napak ter podaljšan čas razvoja programske opreme. V diplomskem delu smo primerjali platformi GitHub ter GitLab. V teoriji smo primerjavo platform naredili s podatki iz prebranih študij; v empiričnem delu smo razvili dve preprosti aplikaciji, jih naložili na omenjeni platformi, na njiju vzpostavili cevovod neprekinjene integracije in dostave ter med seboj primerjali postopka in hkrati tudi zahtevnost vzpostavitve cevovoda. Prišli smo do zaključka, da je lažja in bolj praktična za uporabo platforma GitHub.
Keywords: neprekinjena integracija, neprekinjena dostava, GitHub, GitLab
Published in DKUM: 10.07.2025; Views: 0; Downloads: 26
Full text (3,03 MB) |
