Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Agentic AI-Based IoT Precision Agriculture Framework—Our Vision and Challenges(MDPI AG, 2026-04-09); ; ; ; Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Influence of the Yu T-norm on Vaguely Quantified Rough Set Measure Algorithm Accuracy(IEEE, 2022-11-16); ; This study aims to understand the impact of the Yu T-norm on the Vaguely Quantified Rough Set measurement algorithm, which combines the fuzzy and rough set theories. The algorithm uses both theories and concepts such as lower and higher approximations that integrate numerous features like T-norms, fuzzy tolerance relationship metrics, implicators, ambiguous quantifiers etc. to improve the process of real-world datasets to obtain more accurate models. The investigation process focusses on the experimental evaluation of Yu T-norm models obtained on various real-world datasets. The adjusted p-value is obtained using the insights generated by the AUC-ROC metric from the experimental assessment and a two-step approach for estimating the statistical significance. The results show that the k-parameter in Yu T-norm has impact on model performance and that the five fuzzy tolerance metrics that are studied also have impact on the model's accuracy on unseen data for the Yu T-norm. Therefore, we can conclude that a specific configuration of the k-parameter for the Yu T-norm can directly influence the overfitting of the final model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Relationship between Indoor Playrooms and Population in Skopje(IEEE, 2023-05-22); ; Play has a crucial role in childrens’ growth. It teaches them new skills, strengthens their self-confidence, and promotes creativity. Both indoors and outdoors offer play opportunities, but a nearby playground is crucial as it provides a safe and fun place for children to exercise and explore. This can lead to a healthier lifestyle and improved social skills through interacting with other children. In this paper, we analyze the spatial relationship between indoor playrooms and the population in the study area in Skopje. Our study is based on publicly available data on indoor playrooms, including their location, user satisfaction ratings from customers, and data on population density in the area. Our goal is to find potential locations for new indoor playrooms and improve existing indoor playroom offerings through interpolation, hot spot analysis, and spatial data analysis with multiple ring buffers. Our analysis reveals spatial areas of high population density that offer opportunities to improve indoor playroom products and services. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implication of Hamacher T-norm on Two Fuzzy- Rough Rule Induction Algorithms(IEEE, 2022-05-23); ; From the rule induction algorithms we can obtain models in If-Then form that are very easy to be interpreted by humans. To further improve this class of algorithms, in this paper we focus on QuickRules and Vaguely Quantified Rough fuzzy-rough rule induction algorithms, by introducing the novel Hamacher T-norm. It is important to know that T-norms as well as the fuzzy tolerance relationship metrics, implicators and vague quantifiers play an important role in model accuracy because they are used to calculate the lower and upper approximations. For this purpose, in our models’ evaluation, we use five fuzzy tolerance relationship metrics to evaluate the performance of the models that are obtained with the new Hamacher T-norm. The AUC ROC metric was used to evaluate the performance, and later was used to evaluate the statistical significance. The results revealed that fuzzy tolerance relationship metrics have greater influence than the k-parameter from the Hamacher T-norm on models’ performance, and this was also compared to the vaguely quantified algorithm that uses vague quantifiers. For future work, we plan to conduct further investigation of the influence of another T-norms and fuzzy tolerance relationship metrics on this type of algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms(Inderscience Publishers, 2022); ; The fuzzy-rough rule induction algorithms use fuzzy-rough set concepts such as t-norms, implicators and fuzzy tolerance relationship metrics to calculate the upper and lower approximations. In this direction, the paper examines the influence of the novel Webber t-norm on the model performance obtained with the QuickRules and VQRules algorithms over 19 datasets from different research disciplines. The AUC-ROC metric is used to assess model performance as well as the statistical significance compared to the control model with the highest rank. The obtained results revealed that the k-parameter of the Webber t-norm decreases the model descriptive performance as his value increases, but for the predictive performance of the model there was not any influence by this parameter. In both cases, we were able to identify specific algorithm settings, mostly specific metrics for fuzzy tolerance relations that produce models with high accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms(Inderscience Publishers, 2022); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Influence of the Yu T-norm on Vaguely Quantified Rough Set Measure Algorithm Accuracy(IEEE, 2022-11-16); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Novel T-norm for Fuzzy-Rough Rule Induction Algorithm and Its Influence(Springer International Publishing, 2022); ;
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