Kitanovski, Ivan

Preferred name

Kitanovski, Ivan

Official Name

Kitanovski, Ivan

Main Affiliation

ivan.kitanovski@finki.ukim.mk

Now showing 1 - 10 of 38

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Local Hybrid SVMDT Classifier
(IEEE, 2011-11)
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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 additionally focuses on reducing the number of samples, which need to be classified by the support vector machines in order to reach the final decision about a sample class. In this paper we propose a novel architecture that integrates decision trees and local SVM classifiers for binary classification. Results show that there is a significant improvement in speed with little or no compromise to classification accuracy.
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Crop Type Prediction Across Countries and Years: Slovenia, Denmark and the Netherlands
(IEEE, 2022-07-17)
Merdjanovska, Elena
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Kokalj, Žiga
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Kocev, Dragi
Crop type prediction is a very relevant and a very challenging task. The increasing availability of high-quality satellite imagery and machine learning have enabled the development of automatic crop type classification methods. In this paper, we present a crop type prediction data suite that consists of crop type information from three countries (Denmark, the Netherlands, and Slovenia) across three years (2017, 2018 and 2019). By considering the complex challenges contained by this data suite, we investigate the robustness of 7 deep learning methods used for crop type prediction (TempCNN, MSResNet, InceptionTime, OmniscaleCNN, LSTM, StarRNN, and Transformer networks). The comprehensive experiments reveal that the recurrence-based methods perform the best (with LSTM being the best performing). The methods can achieve very good predictive performance - up to a weighted F1 score of 0.8432.
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Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification
(Elsevier, 2023-03-01)
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Kocev, Dragi
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Simidjievski, Nikola
We present AiTLAS: Benchmark Arena – an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena.
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Longitudinal Brain MRI Retrieval for Alzheimer’s Disease Using Different Temporal Information
(Institute of Electrical and Electronics Engineers (IEEE), 2018)
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In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
(Institute of Electrical and Electronics Engineers (IEEE), 2024)
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Simidjievski, Nikola
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Kocev, Dragi
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Image Retrieval for Alzheimer’s Disease Based on Brain Atrophy Pattern
(Springer International Publishing, 2017)
Trojacanec, Katarina
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null, null
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Crop Type Prediction Across Countries and Years: Slovenia, Denmark and the Netherlands
(IEEE, 2022-07-17)
Merdjanovska, Elena
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Kokalj, Žiga
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Kocev, Dragi
Crop type prediction is a very relevant and a very challenging task. The increasing availability of high-quality satellite imagery and machine learning have enabled the development of automatic crop type classification methods. In this paper, we present a crop type prediction data suite that consists of crop type information from three countries (Denmark, the Netherlands, and Slovenia) across three years (2017, 2018 and 2019). By considering the complex challenges contained by this data suite, we investigate the robustness of 7 deep learning methods used for crop type prediction (TempCNN, MSResNet, InceptionTime, OmniscaleCNN, LSTM, StarRNN, and Transformer networks). The comprehensive experiments reveal that the recurrence-based methods perform the best (with LSTM being the best performing). The methods can achieve very good predictive performance - up to a weighted F1 score of 0.8432.
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Aitlas: Artificial intelligence toolbox for earth observation
(MDPI, 2023-04-28)
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Panov, Panče
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Kostovska, Ana
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Simidjievski, Nikola
We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models.
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Content based retrieval of MRI based on brain structure changes in Alzheimer’s disease
(SCITEPRESS, 2015-01-12)
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Loshkovska, Suzana
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The aim of the paper is to present Content Based Retrieval of MRI based on the brain structure changes characteristic for Alzheimer’s Disease (AD). The approach used in this paper aims to improve the retrieval performance while using smaller number of features in comparison to the descriptor dimensionality generated by the traditional feature extraction techniques. The feature vector consists of the measurements of cortical and subcortical brain structures, including volumes of the brain structures and cortical thickness. Two main stages are required to obtain these features: segmentation and calculation of the quantitative measurements. The feature subset selection is additionally applied using Correlation-based Feature Selection (CFS) method. Euclidean distance is used as a similarity measurement. The retrieval performance is evaluated using MRIs provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Experimental results show that the strategy used in this research outperforms the traditional one despite its simplicity and small number of features used for representation.
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AiTLAS: Artificial Intelligence Toolbox for Earth Observation
(2022-01-21)
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Panov, Panche
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Simidjievski, Nikola
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Kocev, Dragi
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-theart machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which further allows benchmarking of various existing and novel AI methods tailored for EO data.

Kitanovski, Ivan

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