Dimitrovski, Ivica

Preferred name

Dimitrovski, Ivica

Official Name

Dimitrovski, Ivica

Main Affiliation

ivica.dimitrovski@finki.ukim.mk

Now showing 1 - 10 of 67

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Web genre classification with methods for structured output prediction
(Elsevier BV, 2019-11)
Madjarov, Gjorgji
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Vidulin, Vedrana
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Kocev, Dragi
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Addressing Item-Cold Start Problem in Recommendation Systems Using Model Based Approach and Deep Learning
(Springer International Publishing, 2017)
Obadić, Ivica
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Madjarov, Gjorgji
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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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Predicting Thermal Power Consumption of the Mars Express Satellite with Data Stream Mining
(Springer International Publishing, 2019)
Stevanoski, Bozhidar
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Kocev, Dragi
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Osojnik, Aljaž
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Džeroski, Sašo
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Crop Type Prediction Across Countries and Years: Slovenia, Denmark and the Netherlands
(IEEE, 2022-07-17)
Merdjanovska, Elena
;
;
Kokalj, Žiga
;
;
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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Data stream mining for predicting the thermal power consumption of the Mars Express spacecraft
(Pergamon, 2023-09-01)
Stevanoski, Bozhidar
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Kocev, Dragi
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Osojnik, Aljaž
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Džeroski, Sasho
The Mars Express (MEX) spacecraft, operated by the European Space Agency (ESA), has been orbiting Mars for the past 18 years. During this period, it has provided unprecedented scientific data about the red planet, but it has also aged, and its batteries have degraded. Thus, MEX needs careful and accurate power modeling to continue its significant contribution without breaking, twisting, deforming, or failure of any equipment. The power consumed by the autonomous thermal subsystem, that keeps all equipment within its operating temperature in a difficult environment, is the only unknown variable in the spacecraft’s power budget. In this pilot study, we address the task of predicting the thermal power consumption (TPC) of MEX on all of its 33 thermal power lines, learning predictive models from the stream of its telemetry data, which is a task of multi-target regression on data streams. To analyze such data streams and to model the MEX power consumption, we consider both local and global approaches, i.e., predicting each target by a separate model and predicting all targets at once by a single model, respectively. Our evaluation of the considered approaches investigates their performance in predicting the MEX power consumption, the influence of the time resolution of the measurements of TPC on this performance, and the success of the methods in detecting and adapting to change.

Dimitrovski, Ivica

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