Madzarov, GJorgji
Preferred name
Madzarov, GJorgji
Official Name
Madzarov, GJorgji
Main Affiliation
Email
gjorgji.madjarov@finki.ukim.mk
8 results
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Item type:Publication, Evaluation of Distance Measures for Multi-class Classification in Binary SVM Decision Tree(Springer Berlin Heidelberg, 2010); 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 accuracy of SVMs. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. In this work, we are investigating how different distance measures for the clustering influence the predictive performance of the SVM-BDT. The distance measures that we consider include Euclidian distance, Standardized Euclidean distance and Mahalanobis distance. We use five different datasets to evaluate the performance of the SVM based Binary Decision Tree architecture with different distances. Also, the performance of this architecture is compared with four other SVM based approaches, ensembles of decision trees and neural network. The results from the experiments suggest that the performance of the architecture significantly varies depending of applied distance measure in the clustering process. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data Collection Module for Human Activity Recognition(2018-09); 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 processing and analyzing the large amounts of sensory data as well as building machine learning models for monitoring, detection, recognition and prediction of an activity, movement, state or an event. The aim of this research is to develop a data collection system that will enable detection and monitoring of human activity using very low-cost, unobtrusive passive infrared and microwave radar sensors. Our data collection module is composed of Arduino microcontroller, SD card module and real time clock module and enables connecting several sensors which measurements are to be logged. In our experiments we used a modified microwave radar sensor RCWL-0516 and a modified passive infrared sensor HC-SR501. Both are extremely low cost, easily accessible sensors usually used for general purpose applications like motion detection for light switching. Both sensors were modified in a way to make the raw analog output of the sensor available for logging by the microcontroller. The data collection module enables collecting measurements of up to 4 analog (10-bit precision), and up to 8 digital sensor inputs with sampling rates of up to 200 samples per second. The measurements are logged on a SD card including a precise timestamp that will enable the logs of several modules to be joined together keeping the time alignment of the readings. A separate setup for synchronized initialization of the RTC modules of the separate sensor modules is also presented. A series of experiments in a control environment with volunteers were conducted and the collected data from the sensors are pre-processed and labelled for further analysis and application of machine learning based approaches for automatic recognition and monitoring of human activity. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-class SVM Classifier Utilizing Binary Decision Tree(2009-05); ; 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 measures at the kernel space, rather than at the input space. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVMBDT architecture was designed to provide superior multi-class classification performance. Its performance was measured on samples from MNIST, Pendigit, Optdigit and Statlog databases of handwritten digits and letters. The results of the experiments indicate that while maintaining comparable or offering better accuracy with other SVM based approaches, ensembles of trees (Bagging and Random Forest) and neural network, the training phase of SVM-BDT is faster. During recognition phase, due to its logarithmic complexity, SVM-BDT is much faster than the widely used multi-class SVM methods like “one-against-one” and “one-against-all”, for multiclass problems. Furthermore, the experiments showed that the proposed method becomes more favourable as the number of classes in the recognition problem increases. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamically Configured Stream Processing In Apache Flink - The use case of custom processing rules management and application(Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2021-09-23) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wireless telemedicine services as part of an integrated system for e-medicine(IEEE, 2008-05-05); ; This paper presents an overview of the wireless telemedicine components of an integrated system for e-medicine that we propose and implement in the Republic of Macedonia. The introduction of new wireless broadband technologies enabled creation of telemedicine services previously only possible via cable connections. WiMAX and Wi-Fi are the wireless technologies used to implement our telemedicine functionalities. They are shortly described and a number of proposed and provided services are explained. Advanced web programming technologies have been extensively used in implementation of the services. Guidelines are given for further development and implementation. The experience gained draws conclusions that can be used in areas or countries with similar natural or economical conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of magnetic resonance images(IEEE, 2010-06-21); ; ; 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 are applied to provide feature extraction from the images. The dataset used for classification contains magnetic resonance images classified in 9 classes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Heartbeat tracking application for mobile devices-arrhythmia recognition module(IEEE, 2010-06-21); We want to introduce a lightweight system for arrhythmia recognition. The system is intended for outdoor use and is supposed to be implemented in mobile devices, primarily smart phones and smartwatches. We are only concerned about extracting pulses from an ECG stream and passing them to an Arrhythmia Recognition Module - ARM. These two basic tasks should be completed fast and effectively and give an alert when arrhythmia occurs. The ARM we designed relies on simple algorithm for feature extraction and gives an error of 2.007% with false/positives over false/negatives ratio of 19. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Log Instructions in Log-based Anomaly Detection(IEEE, 2022-07-10);