Trivodaliev, Kire
Preferred name
Trivodaliev, Kire
Official Name
Trivodaliev, Kire
Main Affiliation
Email
kire.trivodaliev@finki.ukim.mk
53 results
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Item type:Publication, Q-FiberMapper-A framework for tractography and tractometry of clinical data(2022) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart city solution for early flood detection(IEEE, 2022-11-15); Risteska Stojkoska, BiljanaMost of the world population is expected to live in urban areas in the future, therefore, there is an evident need for general smart city solutions, especially for big cities. The aim of this paper is: (i) to present an architecture for general smart city solution that collects different environmental parameters from different sites in a big city; (ii) to present implementation details of a separate module for flood detection that works on the top of the general solution; and (iii) to present a smart city application that monitors flood related parameters and informs residents if there is a flooded underpass, or increased water levels on a particular river or lake. The system collects environmental measurements using The Things Network, through LoRaWAN base stations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning-supported MRI Radiomics to predict the volumetric response in meningiomas after Gamma Knife radiosurgery.(2022-01-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Survey of Graph Neural Network Architectures in Ligand Binding Affinity Prediction Models(IEEE, 2024-05-20); Ligand affinity prediction plays a pivotal role in drug discovery, influencing the efficiency and success of drug development processes. Traditional methods struggle in accurately capturing the complex interactions within molecular structures, prompting the exploration of advanced techniques such as Graph Neural Networks (GNNs). This paper provides an analysis of GNNs in the context of ligand affinity prediction, exploring their architecture, applications, and potential impact on revolutionizing drug discovery. Our findings suggest that GNNs can offer improvements over traditional computational methods, particularly in handling the dynamic and complex nature of molecular interactions. We highlight innovative GNN architectures that have shown notable success in predicting ligand binding affinities, such as heterogeneous graph representation and attention mechanisms. The implications of these advancements suggest a paradigm shift in drug discovery, where GNNs can lead to more accurate predictions and accelerate the identification of potential drug candidates. This study underscores the transformative potential of GNNs in enhancing predictive accuracy and efficiency in drug development. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, O-076 Genomic and morphokinetic blueprint of mosaic embryos is evidence supporting their distinct clinical category(Oxford University Press, 2022-07-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Complete Air Pollution Monitoring and Prediction Framework(Institute of Electrical and Electronics Engineers (IEEE), 2023); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Exploration of Autism Spectrum Disorder Classification from Structural and Functional MRI Images(Springer Nature Switzerland, 2022-09-29); ; ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Is there an association between paternal age and aneuploidy? Evidence from young donor oocyte-derived embryos: a systematic review and individual patient data meta-analysis(Oxford University Press (OUP), 2021-05) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Check for updates Multiplex Collaboration Network of the Faculty of Computer Science and Engineering in Skopje(Springer Nature, 2024); ; Multiplex collaboration networks facilitate intricate connections among individuals, enabling multidimensional collaborations across various domains and fostering synergistic knowledge exchange. This study focuses on the construction and basic analysis of a multiplex collaboration network among employees at the Faculty of Computer Science and Engineering (FCSE), Ss. Cyril and Methodius University in Skopje. The multiplex network is built with three layers based on: scientific collaborations resulting from joint project participations by FCSE employees, joint employees participations in the FCSE graduation thesis committees, and scientific FCSE employees collaborations defined by co-authorships in Google Scholar papers. The network's structure plays a vital role in determining the information accessibility and cooperative opportunities for individuals within FCSE institution. The aim here is to investigate the FCSE multiplex collaboration network's internal structure for discovering its latent knowledge and understand its implications. We perform identification of key individuals within the network, by computing various centrality and hubs detection network metrics. Additionally, we employ a community detection algorithm to reveal the underlying modular structure of the network. By comprehensively analyzing the acquired multiplex collaboration network model, we contribute to a better understanding of the collaboration patterns among FCSE employees. The findings can potentially inform decision-making processes and foster strategic planning aimed at enhancing collaboration and knowledge sharing within the institution. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation issues of different cryptography algorithms in wireless sensor networks(NATO-ARW, 2006-09-04);