Dejan Gjorgjevikj

Dejan Gjorgjevikj
Ss. Cyril and Methodius University in Skopje · Faculty of Computer Science and Engineering

PhD

About

70
Publications
40,204
Reads
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1,318
Citations
Introduction
Dejan Gjorgjevikj currently works at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University. Dejan does research in Machine Learning, Data Mining and Software Engineering. Their current project is 'Use of unobtrusive sensors for human activity recognition'.
Additional affiliations
September 2011 - present
Ss. Cyril and Methodius University in Skopje
Position
  • Professor
January 2009 - December 2011
Ss. Cyril and Methodius University in Skopje
Education
January 1998 - June 2004
Ss. Cyril and Methodius University in Skopje
Field of study
  • Computer Science and Engineering

Publications

Publications (70)
Article
Full-text available
Supervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencod...
Article
Different machine learning approaches have been developed for the fault diagnosis of mechanical systems. To achieve desired diagnosis performance, lots of labeled one-dimensional (1D) signals are required for training machine learning models. however, those signals collected under various working conditions are difficult to be used for both diagnos...
Preprint
Full-text available
Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neura...
Conference Paper
Full-text available
Air pollution in North Macedonia is 20 times over the EU limit. Recently Skopje is mentioned as the most polluted city in Europe. As a result, this is believed to contribute to 2000 annual premature deaths in Skopje, Tetovo and Bitola only. Being able to forecast air pollution levels to take timely precaution could drastically reduce these numbers....
Conference Paper
Full-text available
Unobtrusive human activity monitoring using cheap and widely available sensors are the future for human activity recognition. It will support the extensive penetration of new applications in Ambient Assisted Living (AAL), Smart Homes (SH), Smart Cities (SC) and Health Monitoring (HM). The biggest challenges in these applications are the automatic p...
Chapter
This paper describes an approach to sarcasm and irony detection in English tweets. Accurate sarcasm and irony detection in text is crucial for numerous NLP applications like sentiment analysis, opinion mining and text summarization. The detection of irony and sarcasm in microblogging posts can be even more challenging because of the restricted leng...
Conference Paper
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem whi...
Article
Full-text available
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem whi...
Article
Full-text available
Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between...
Chapter
In this work we tackle the problem of face de-identification in an image. The first step towards a solution to this problem is the design of a successful generic face detection algorithm, which will detect all of the faces in the image or video, regardless of the pose. If the face detection algorithm fails to detect even one face, the effect of the...
Article
This work presents an approach for blocking artifacts removal in highly compressed video sequences using an algorithm based on dictionary learning methods. In this approach only the information from the frame content is used, without any additional information from the coded bit-stream. The proposed algorithm adapts the dictionary to the spatial ac...
Conference Paper
Full-text available
In this paper we analyze the contribution of using agile methodologies and their principles in conducting software development projects. The research is focused on Macedonian IT companies for software development and the goal is to get an insight of the way these companies organize their work and involve agile principles in their way of delivering...
Conference Paper
Full-text available
Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time seri...
Conference Paper
Motivated by an increasing number of new applications, the research community is devoting an increasing amount of attention to the task of multi-label classification (MLC). Many different approaches to solving multi-label classification problems have been recently developed. Recent empirical studies have comprehensively evaluated many of these appr...
Conference Paper
Full-text available
In this paper we compare two shape-based descriptors for plant leaf image classification. The leaves in the dataset are already segmented from the background only the contour detection algorithm is applied to extract the contour points and generate the shape-based descriptors. We propose a reduced size descriptor based on the angles between three p...
Article
Over five decades the scientists attempt to design machine that clearly transcripts the spoken words. Even though satisfactory accuracy is achieved, machines cannot recognize every voice, in any environment, from any speaker. In this paper we tackle the problem of robustness of Automatic Speech Recognition for isolated Macedonian speech in noisy en...
Book
Data is a common ground, a starting point for each ICT system. Data needs processing, use of different technologies and state-of-the-art methods in order to obtain new knowledge, to develop new useful applications that not only ease, but also increase the quality of life. These applications use the exploration of Big Data, High throughput data, Dat...
Conference Paper
Full-text available
Feature selection is important phase in machine learning and in the case of multi-label classification, it can be considerably challenging. In like manner, finding the best subset of good features is involved and difficult when the dataset has significantly large number of features (more than a thousand). In this paper we address the problem of fea...
Conference Paper
Full-text available
Implementing a web-based system for automatic assessment is a big step in the introductionary programming courses. In this paper we study and report the data generated by the usage of the system Code developed at the Faculty of Computer Science and Engineering. The system supports compilation and execution of programming problems in exercises and e...
Conference Paper
Full-text available
Background / Purpose: We aimed to combine structural and functional connectomes in bipolar patients on same brain parcellation; apply graph theory algorithms on connectivity matrices and discuss the results. Main conclusion: Clustering coefficient may be a potential biomarker for bipolar disorder.
Conference Paper
Gait is a persons manner of walking. It is a biometric that can be used for identifying humans. Gait is an unobtrusive metric that can be obtained from distance, and this is its main strength compared to other biometrics. In this paper we construct and evaluate feature sets with the purpose of finding out the role of different types of features and...
Conference Paper
Full-text available
The paper presents an approach to Optical Character Recognition (OCR) applied on receipts printed in Macedonian language. The OCR engine recognizes the characters of the receipt and extracts some useful information, such as: the name of the market, the names of the products purchased, the prices of the products, the total amount of money spent, and...
Article
Multi-label learning (MLL) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLL are the large scale problem, which have a strong impact on the computational complexity of le...
Conference Paper
Full-text available
This paper shows the current results of development of TTS-MK – a speech synthesizer for Macedonian language. The basic principles for projecting and building of speech synthesizer for Macedonian language, based on concatenation of speech segments, are shown. Every language has its respective and specific speech norms and characteristics that shoul...
Conference Paper
Full-text available
E-Lab is a system developed at Faculty of Computer Science and Engineering for solving and auto-grading programming problems from introduction to programming courses. The main goal is to simplify and improve the organization and the process of solving programming problems from large group of students in dedicated computer labs using centralized ser...
Article
Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We se...
Conference Paper
Multi-label classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the large-scale problem and the high dimensionality of the label space, which have...
Article
A common approach to solving multi-label learning problems is to use problem transformation methods and dichotomizing classifiers as in the pair-wise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in...
Chapter
When an RNA primary sequence is folded back on itself, forming complementary base-pairs, a form called RNA secondary structure is created. The first solution for the RNA secondary structure prediction problem was the Nussinov dynamic programming algorithm developed in 1978 which is still an irreplaceable base that all other approaches rely on. In t...
Article
Full-text available
iKnow is a new university information system that provides electronic services for both university management and students. It is a system enabling complete electronic student services within University management avoiding the need for paper based document processing. The system is web based and implemented using state of the art modular servi...
Conference Paper
Support vector machines are among the most precise classifiers available, but this precision comes at the cost of speed. There have been many ideas and implementations for improving the speed of support vector machines. While most of the existing methods focus on reducing the number of support vectors in order to gain speed, our approach additional...
Conference Paper
Full-text available
In this work we propose a subjective no reference ringing metric using machine learning techniques. For every block in a JPEG compressed image the algorithm outputs a value which corresponds to the annoyance of the ringing artifacts. The extracted feature vector is designed bearing in mind the properties of the HVS (Human Visual System) and the rin...
Conference Paper
A common approach for solving multi-label learning problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learn...
Conference Paper
Full-text available
The main goal of the paper is to explore hierarchical classification. The investigation is performed on the dataset of Magnetic Resonance Images (MRI) which is hierarchically organized. Generalized top-down hierarchical classification architecture is proposed in the paper. Additionally, two specific cases of the generalized architecture are explore...
Conference Paper
A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pairwise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in...
Conference Paper
Full-text available
We have come to a point in time when there is an abundance of database usage in almost all aspects of our lives. However, most of the end users have neither the knowledge nor the need to manage the databases. Even more important, they are unable to generate the ever changing reports they need, based on the data in their databases. Our Applicative S...
Conference Paper
Full-text available
A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in...
Conference Paper
Full-text available
This paper presents the design and implementation of a mobile application along with a web server for geo-tagging favorite and interesting places and sharing them with the community. The design and architecture shows some key aspects and issues concerning this kind of system. The mobile application is implemented in J2ME and tested on GPS enabled N...
Conference Paper
Full-text available
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors...
Conference Paper
Full-text available
Multi-class classification can often be constructed as a generalization of binary classification. The approach that we use for solving this kind of classification problem is SVM based Binary Decision Tree architecture (SVM-BDT). It takes advantage of both the efficient computation of the decision tree architecture and the high classification accura...
Article
Full-text available
Ensemble methods are able to improve the predictive performance of many base classifiers. In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to Binary SVM Decision Tree (SVM-BDT). Binary SVM Decision Tree is a tree based architecture that utilizes support vector machines for solving multiclass pr...
Conference Paper
Full-text available
The problem of extracting meaningful patterns from time changing data streams is of increasing importance for the machine learning and data mining communities. We present an algorithm which is able to learn regression trees from fast and unbounded data streams in the presence of concept drifts. To our best knowledge there is no other algorithm for...
Conference Paper
Full-text available
A novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-DTA) for solving multiclass problems is proposed in this paper. A clustering algorithm was used to determine the hierarchy of binary decision subtasks performed by the SVM binary classifiers. The applied clustering model utilizes Mahalanobis distance meas...
Article
Full-text available
In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems is presented. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. For consistency between the clustering model and SVM, the clustering model utilizes distance mea...
Conference Paper
Full-text available
Shot boundary detection is fundamental to video analysis since it segments a video into its basic components. This paper presents a comparison of several shot boundary detection techniques and their variations including color histogram, edge directions histogram and wavelet transformations statistics. The performance and ease of selecting good thre...
Article
Full-text available
Among the applications of computer science in the field of medicine, the processing of medical image data is playing an increasingly important role. With medical imaging techniques such as X-Ray, computer tomography, magnetic resonance imaging, and ultrasound, the amount of digital images that are produced in hospitals is increasing incredibly fast...
Conference Paper
Full-text available
This paper describes the Adaptive Tabu Search algorithm (A-TS), an improved tabu search algorithm for combinatorial optimization. A-TS uses a novel approach for evaluation of the moves, incorporated in a new complex evaluation function. A new decision making mechanism triggers the evaluation function providing means for avoiding possible infinite l...
Article
Full-text available
At present a growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments th...
Article
Full-text available
This paper introduces a novel approach to performance refinement of a heuristic algorithm for combinatorial optimization. The proposed Adaptive Tabu Search (A–TS) algorithm introduces adaptive behavior in the traditional Tabu Search algorithm. The adaptive nature of this algorithm is based on two adaptive coefficients that drive the heuristic. Choo...
Article
Full-text available
This article presents a new algorithm for combinatorial optimization based on the basic Tabu Search scheme named Adaptive Tabu Search (A-TS). The A-TS introduces a new, complex function for evaluation of moves. The new evaluation function incorporates both the aspiration criteria and the long-term memory. A-TS also introduces a new decision making...
Article
In this paper, various cooperation schemes of SVM (Support Vector Machine) classifiers applied on two feature sets for handwritten digit recognition are examined. We start with a feature set composed of structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partiti...
Conference Paper
Full-text available
Recent results in pattern recognition applications have shown that SVMs (Support Vector Machines) often have superior recognition rates in comparison to other classification methods. In this paper, the cooperation of three SVM classifiers for handwritten digit recognition, each using different feature family is examined. We investigate the advantag...
Conference Paper
Full-text available
In this paper, the cooperation of four feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. Although most of presented cooperation schemes are variatio...
Conference Paper
Full-text available
Recent results in pattern recognition have shown that SVM (support vector machine) classifiers often have superior recognition rates in comparison to other classification methods. In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using different feature set is examined. We investigate the advantages and we...
Conference Paper
Full-text available
This work proposes an efficient three-stage classifier for handwritten digit recognition based on NN (neural network) and SVM (support vector machine) classifiers. The classification is performed by 2 NNs and one SVM. The first NN is designed to provide a low misclassification rate using a strong rejection criterion. It is applied on a small set of...
Conference Paper
Full-text available
In this paper, various cooperation schemes of SVM (Support Vector Machine) classifiers applied on two feature sets for handwritten digit recognition are examined. We start with a feature set composed of structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partiti...
Article
Full-text available
In this paper, the cooperation of two feature families for handwritten digit recognition using a committee of Neural Network (NN) classifiers will be examined. Various cooperation schemes will be investigated and corresponding results will be presented. To improve the system reliability,we will upgrade the committee scheme using multistage classifi...
Conference Paper
Full-text available
In this paper, the cooperation of two feature families for handwritten digit recognition using SVM (support vector machine) classifiers is examined. We investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The obtained results show that it is difficult to exceed the recognition rate...
Conference Paper
Full-text available
We investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The cooperation schemes are applied on two SVM (Support Vector Machine) classifiers performing classification tasks on two feature families referenced as structural and statistical features. The obtained results show that it i...
Conference Paper
Full-text available
The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in pattern recognition applications. The cooperation of two feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and...
Article
A subsystem for text-to-speech (TTS) conversion for Macedonian language as a part of a system for support of humans with damaged sight will be presented in this paper. The whole system includes recognition of printed Cyrillic text, archiving, TTS conversion and printing on a Braille printer. None of tested commercial programs for generating human s...
Article
Full-text available
A subsystem for text-to-speech (TTS) conversion for Macedonian language as a part of a system for support of humans with damaged sight will be presented in this paper. The whole system includes recognition of printed Cyrillic text, archiving, TTS conversion and printing on a Braille printer. A subsystem for real-time TTS conversion from unrestricte...
Article
Full-text available
In [2] we have presented a subsystem for text-to-speech (TTS) conversion for macedonian language as a part of a system for support of humans with damaged eyesight [1]. In this paper we present the speech synthesizer which is part of the TTS conversion subsystem. It's based on timedomain syllable concatenation. A novel module for duration and fundam...