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, 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, 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, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Single exponential smoothing method and neural network in one method for time series prediction(IEEE, 2004-12-01); The purpose of this paper is to present a new method that combines statistical techniques and neural networks in one method for the better time series prediction. In this paper- we presented single exponential smoothing method (statistical technique) merged with feed forward back propagation neurat network in one method named as Smart Single Exponential Smoothing Method (SSESM). The basic idea of the new method is to learn from the mistakes. More specifically, our neural network learns from the mistakes made by the statistical techniques. The mistakes are made by the smoothing parameter, which is constant. In our method, the smoothing parameter is a variable. It is changed according to the prediction of the neural network. Experimental results show that the prediction with a variable smoothing parameter is better than with a constant smoothing parameter. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed data processing in wireless sensor networks based on artificial neural-networks algorithms(IEEE, 2005-06-27); Most of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, several algorithms developed within the artificial neuralnetworks 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 auto-classification of sensor readings. Lower communication costs and energy savings can be obtained as a consequence of the dimensionality reduction achieved by the neural-networks clustering algorithms, In this paper we will present three possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of deliberately made faulty sensors show the data robustness of these architectures. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of wavelet neural-networks in wireless sensor networks(IEEE, 2005-05-23); ; Trajkovski, GoranMost of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, 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 auto-classification 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 this paper we will present two different data aggregation architectures with algorithms using artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of purposefully faulty sensors show the data robustness of these architectures. These architectures are further developed adding one pre-processing level which will use wavelets for initial data-processing of the sensory inputs at different resolutions and later introduced into the artificial neural-networks. The effects of this additional wavelet pre-processing are given for the two above mentioned architectures.
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