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, Automated Structural Classification of Proteins by Using Decision Trees and Structural Protein Features(Springer, Berlin, Heidelberg, 2009-09-28); ; ; ; Pepik, BojanThe protein function is tightly related to classification of proteins in hierarchical levels where proteins share same or similar functions. One of the most relevant protein classification schemes is the structural classification of proteins (SCOP). The SCOP scheme has one negative drawback; due to its manual classification methods, the dynamic of classification of new proteins is much slower than the dynamic of discovering novel protein structures in the protein data bank (PDB). In this work, we propose two approaches for automated protein classification. We extract protein descriptors from the structural coordinates stored in the PDB files. Then we apply C4.5 algorithm to select the most appropriate descriptor features for protein classification based on the SCOP hierarchy. We propose novel classification approach by introducing a bottom-up classification flow, and a multi-level classification approach. The results show that these approaches are much faster than other similar algorithms with comparable accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Project based learning of embedded systems(2016-06-23); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MPEG-4 3D Graphics: from specifications to the screen(2006-07-05); ; Preteux, FrançoiseThis paper presents a novel implementation of a 3D rendering engine able to display 3D graphics MPEG-4 objects. By using the MPEG-4 SDK (Software Developer Kit), the 3D objects are first decoded and the MPEG-4 scene graph structure is filed. We introduce a scene manager able to address in an optimized manner the rendering requirements. It is developed as part of the rendering engine and it enables to create an appropriate form representation of the data resources. The novel concept implemented here is to consider the scene management with respect to the rendering constraints and not to the representation of the data as in a usual MPEG-4 approach. This paper describes the software communication procedures between the MPEG-4 SDK and the rendering scene management in the case of static and animated (skinned) object and some results dealing with the representation of an articulated model illustrate the performances of the developed approach. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Model of Cloud-Based Services for Data Mining Analysis(Canadian Center of Science and Education, 2015-11-01); New cloud-based services are being developed constantly in order to meet the need for faster, reliable and scalable methods for knowledge discovery. The major benefit of the cloud-based services is the efficient execution of heavy computation algorithms in the cloud simply by using Big Data storage and processing platforms. Therefore, we have proposed a model that provides data mining techniques as cloud-based services that are available to users on their demand. The widely known data mining algorithms have been implemented as Map/Reduce jobs that are been executed as services in cloud architecture. The user simply chooses or uploads the dataset to the cloud, makes appropriate settings for the data mining algorithm, executes the job request to be processed and receives the results. The major benefit of this model of cloud-based services is the efficient execution of heavy computation data mining algorithm in the cloud simply by using the Ankus - Open Source Big Data Mining Tool and StarfishHadoop Log Analyzer. The expected outcome of this research is to offer the integration of the cloud-based services for data mining analysis in order to provide researchers with reliable collaborative data mining analysis model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Protein function prediction based on neighborhood profiles(Springer, Berlin, Heidelberg, 2009-09-28); ; The recent advent of high throughput methods has generated large amounts of protein interaction network (PIN) data. A significant number of proteins in such networks remain uncharacterized and predicting their function remains a major challenge. A number of existing techniques assume that proteins with similar functions are topologically close in the network. Our hypothesis is that the simultaneous activity of sometimes functionally diverse functional agents comprises higher level processes in different regions of the PIN. We propose a two-phase approach. First we extract the neighborhood profile of a protein using Random Walks with Restarts. We then employ a “chisquare method”, which assigns k functions to an uncharacterized protein, with the k largest chi-square scores. We applied our method on protein physical interaction data and protein complex data, which showed the later perform better. We performed leave-one-out validation to measure the accuracy of the predictions, revealing significant improvements over previous techniques. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, HCI for m-Learning in Image Processing by Handhelds(Springer, Berlin, Heidelberg, 2007-07-22); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent data aggregation in sensor networks using artificial neural-networks algorithms(2005); Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and will meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and autoclassification of sensor readings. As a result of the dimensionality reduction obtained simply from the outputs of the neural-networks clustering algorithms, lower communication costs and energy savings can also be obtained. In the paper we will propose three different kinds of architectures for incorporating the ART and FuzzyART artificial neural networks into the small Smart-It units’ network. We will also give some results of the classifications of real-world data obtained with a sensor network of 5 Smart-It units, each equipped with 6 different types of sensors. We will also give results from the simulations where we have purposefully made one of the input sensors malfunctioning, giving zero or random signal, in order to show the data robustness of our approach. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed algorithm for a mobile wireless sensor network for optimal coverage of non-stationary signals(2005-04); In this paper we will deal with the problem of optimal coverage of a wireless sensor network for random signals appearing with non-stationary distributions. The wireless sensor network can be either with limited mobility or with large redundancy of the nodes. We will give a distributed algorithm that successfully solves this problem, and we will show its efficiency in simulations in a 2-D environment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent wireless sensor networks using fuzzyart neural-networks(IEEE, 2007-07-01); An adaptation of one popular model of neuralnetworks algorithm (ART model) in the field of wireless sensor networks is demonstrated in this paper. The important advantages of the ART class algorithms such as simple parallel distributed computation, distributed storage, data robustness and autoclassification of sensor readings are confirmed within the proposed architecture consisting of one clusterhead which collects only classified input data from the other units. This architecture provides a high dimensionality reduction and additional communication savings, since only identification numbers of the classified input data are passed to the clusterhead instead of the whole input samples. We have adapted and implemented the FuzzyART neural-network algorithm and used it for initial clustering of the sensor data as a sort of pattern recognition. This adaptation was made specifically for MicaZ sensor motes by solving mainly problems concerning the small memory capacity ofthe motes. At the final clusterhead - server, the data are stored in a database and the results of the data processing are continuously presented in a classification graph.