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, Learning diatom ecological models with fuzzy order data mining algorithm(IEEE, 2018-05); ; ; The data mining algorithms allow data scientist to extract useful knowledge from raw measured data. The fuzzy data mining algorithms have several advantages over crisp methods, and they have been used more often to obtain ecological knowledge from ecological data. In this paper, we aim to learn suitable habitat models of the ecological conditions where diatoms can exist in lake ecosystems by using a fuzzy data mining algorithm. The algorithm uses several different fuzzy concepts, namely, fuzzy membership functions, similarity metrics and order weighted geometric operator to build predictive ecological model that is able to reveal patterns in ecological data and thus find the suitable diatom ecological conditions. Additionally, we have made experimental evaluation of two similarity metrics that influence the accuracy for both descriptive and predictive models. Later, the results of the models are verified with the known ecological preferences found in the literature. Based on the obtained results, in future we plan to improve the fuzzy operators and similarity metrics and test other membership functions on new ecological data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Experimental Evaluation of Different Membership Functions on Weighted Pattern Trees for Diatom Modelling(IEEE, 2018-07); ; Weighted Pattern Trees (WPT) algorithm is an extension of the pattern tree algorithm, which builds models with different weights and these weights are used for predicting the particular output attribute and they show how much a particular tree model is confident to predict such class. The WPT uses the similarity information between the fuzzy term leaf and the root of the tree model to weight the model. Each fuzzy term is acquired from the input dataset using different types of membership functions (MFs). The shape and mathematical formulation of the MFs plays an important role in the WPT algorithm induction, and thus on the model performance. In this direction, the paper aims to experimentally investigate the influence of three smoothed MFs on real measured ecological dataset using three different type of experiments. The first experiments evaluate the influence of the number of MFs per attribute, the second experiments examine the type of the MFs, and the third experiments investigate the influence of different WPT variants on both descriptive and predictive classification accuracy. The results for the statistical significance with the two-step procedure, showed that models with depth 10 with Sigmoidal +1 MF and high number of MFs per attribute are excellent for building models with high descriptive power. On the other side, the models with low number of MFs with Bell MF and model depth constrained to five have high predictive power. These results encourage us to further investigate the influence of different similarity metrics and fuzzy aggregation operators on the performance of WPT models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of Yager Families of Aggregation Operators in Discovering the Diatoms Indicating Properties(IEEE, 2018-10); ; Fuzzy aggregation operators perform operations between two fuzzy sets that satisfy certain axioms. They play an important role in fuzzy data mining process. As an integral part of many algorithms, the aggregation operators influence on the outcome of model and thus on the experimental evaluation of the models. Both pattern tree (PT) and the weighted pattern tree (WPT) algorithms use the aggregation operators to increase the accuracy of the model by making different operations between the descriptive and target attributes. Selecting the right operator is very important, especially considering generalized families of aggregation operators. Therefore, this paper aims to investigate the influence of the generalized Yager families of aggregation operators and their influence on both (PT and WPT) algorithms accuracy. This is done by modifying the λ parameter. This parameter is not the only parameter that influences the model performance, other factors are also in play, like the shape and the number of the membership functions (MFs), as well as the similarity metric. Our experimental evaluation will evaluate the descriptive and predictive performance of the models as well as the statistical significance of the results. The evaluation results show that the best descriptive and predictive models with both PT and WPT algorithms are obtained when λ is set to 1. For future work, we plan to investigate the influence of this family of aggregation operators with different similarity metrics, as well as other datasets. - 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);