Kalajdziski, Slobodan

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Kalajdziski, Slobodan

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slobodan.kalajdziski@finki.ukim.mk

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Attention Models for PM2.5 Prediction
(IEEE, 2020-12)
Kalajdjieski, Jovan
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A Complete Air Pollution Monitoring and Prediction Framework
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Kalajdjieski, Jovan
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Mirceva, Georgina
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An Exploration of Autism Spectrum Disorder Classification from Structural and Functional MRI Images
(Springer Nature Switzerland, 2022-09-29)
Krajevski, Jovan
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There are strong indications that structural and functional magnetic resonance imaging (MRI) may help identify biologically relevant phenotypes of neurodevelopmental disorders such as Autism spectrum disorder (ASD). Extracting patterns from MRI data is challenging due to the high dimensionality, limited cardinality and data heterogeneity. In this paper, we explore structural and resting state functional MRI (rs-fMRI) for ASD classification using available ABIDE II dataset, using several standard machine learning (ML) models and convolutional neural networks (CNNs). To overcome the high dimensionality problem, we propose a simple data transformation method based on histograms calculation for the standard ML models and a simple 3D-to-2D and 4D-to-3D data transformation method for the CNNs in ASD classification. Numerous research has been done for ASD classification using the online available ABIDE I dataset, and several with the ABIDE II dataset, the latter mostly working with single-site classification studies. Here, we take the whole ABIDE II dataset using all structural and functional raw data. Our results show that the proposed methods achive state-of-the art results of high 71.4% accuracy (functional) and 73.4% AUC (structural) compared to the best performing results in literature of 68% accuracy (functional) for ASD classification on all ABIDE dataset.
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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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Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison
(Multidisciplinary Digital Publishing Institute, 2021-06)
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Zanin, Massimiliano
Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.
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Project based learning of embedded systems
(2016-06-23)
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Traditional teaching, usually based on lectures and tutorials fosters the idea of instruction-driven learning model where students are passive listeners. Besides this approach, Project Based Learning (PBL) as a different learning paradigm is standing behind constructivism learning theory, where learning from real-world situations is put on the first place. The purpose of this paper is to present our approach in learning embedded systems at our University. It is based on combination of traditional (face-to-face) learning and PBL. Our PBL represents an interdisciplinary project based on wireless sensor monitoring of real-world environment (greenhouse). The students use UML that was shown as an excellent tool for developing such a projects. From the student perspective, we found that this high level of interdisciplinary is very valuable from the point of view of facing the students with reallife problems.
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Analysis of churn prediction: a case study on telecommunication services in Macedonia
(IEEE, 2016-11-22)
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Petkovski, Aleksandar
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Risteska Stojkoska, Biljana
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— Customer churn is one of the main problems in the telecommunications industry. Several studies have shown that attracting new customers is much more expensive than retaining existing ones. Therefore, companies are focusing on developing accurate and reliable predictive models to identify potential customers that will churn in the near future. The aim of this paper is investigating the main reasons for churn in telecommunication sector in Macedonia. The proposed methodology for analysis of churn prediction covers several phases: understanding the business; selection, analysis and data processing; implementing various algorithms for classification; evaluation of the classifiers and choosing the best one for prediction. The obtained results for the data from a telecommunication company in Macedonia, should be of great value for management and marketing departments of other telecommunication companies in the country and wider.
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Protein Function Prediction Using Semantic Similarity Metrics and Random Walk Algorithm
(Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia, 2012)
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Most protein function prediction methods that have been proposed, are based on sequence or structure protein similarity and do not take into consideration the semantic similarity extracted from protein knowledge databases such as Gene Ontology. In this paper we present an approach for protein function prediction using semantic similarity metrics and the whole network topology of a protein interaction network by using a—semantic driven “random walk with restart. Different semantic similarity metrics are explored and future results should show the relevance of different semantic similarity metrics on protein function prediction using random walk with restart. To achieve the final goal of protein function prediction, the best semantic similarity metric should be used.
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Real time human activity recognition on smartphones using LSTM networks
(IEEE, 2018-05-21)
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Risteska Stojkoska, Biljana
Activity detection is becoming an integral part of many mobile applications. Therefore, the algorithms for this purpose should be lightweight to operate on mobile or other wearable device, but accurate at the same time. In this paper, we develop a new lightweight algorithm for activity detection based on Long Short Term Memory networks, which is able to learn features from raw accelerometer data, completely bypassing the process of generating hand-crafted features. We evaluate our algorithm on data collected in controlled setting, as well as on data collected under field conditions, and we show that our algorithm is robust and performs almost equally good for both scenarios, while outperforming other approaches from the literature.
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Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks
(MDPI AG, 2020-12-18)
Kalajdjieski, Jovan
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Corizzo, Roberto
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<jats:p>Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.</jats:p>

Kalajdziski, Slobodan

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