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检索条件"机构=Affiliated with the Institute of Data Science and Digital Technologies"

共 214 条记录，以下是1-10 订阅

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Enhancing Arrhythmia Detection Using an Ensemble of Transformer Models for Heartbeat Classification 10th

Enhancing Arrhythmia Detection Using an Ensemble of Transf...

引用

10th International Conference on Machine Learning, Optimization, and data science, LOD 2024

作者： Malūkaitė, Jaunė Treigys, Povilas Bernatavičienė, Jolita Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania

ISBN: (纸本)9783031824838

Cardiovascular diseases stand as the leading cause of death globally. While many irregular heartbeats are not severe, certain rhythm disorders can increase the risk of stroke or heart failure. Therefore, distinguishing between various heartbeats is important for identifying heart disorders. Recognising them accurately from electrocardiograms is challenging even for skilled doctors, and patients may not always understand that they have heart issues. This research aims to identify an ensemble of transformer models for classifying specific arrhythmia heartbeats from electrocardiogram signals since it is difficult to correctly distinguish supra-ventricular premature heartbeats from normal heartbeats. The ensemble incorporates three deep learning models with modified architectures, using different class mappings for dataset transformations. The model’s performance is evaluated by comparing recall metrics with a baseline convolutional neural network model and a transformer model trained with an under-sampled dataset for predicting three heartbeat classes. The analysis shows that the ensemble model achieved higher recall metric values for supra-ventricular premature heartbeats and ventricular premature contractions while reaching high overall and per-class metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Towards Reproducible Research in Machine Learning via Blockchain 4th

Towards Reproducible Research in Machine Learning via Bloc...

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Filatovas, Ernestas Stripinis, Linas Orts, Francisco Paulavičius, Remigijus Vilnius University Institute of Data Science and Digital Technologies Akademijos 4 Vilnius08663 Lithuania

ISBN: (纸本)9783031812460

Artificial Intelligence, particularly in Machine Learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results due to lacking critical details, including the disconnection between publications and the associated codes, data, and parameter settings. Solutions that improve code accessibility, data provenance tracking, research transparency, auditing of obtained results, and trust can significantly accelerate algorithm and model development, validation, and transition into real-world applications. Blockchain technology, with its features of decentralization, data immutability, cryptographic hash functions, and consensus algorithms, provides a promising avenue for developing such solutions. By leveraging the distributed ledger working over a peer-to-peer network, a secure and auditable infrastructure can be established for sharing and controlling data in a trusted manner. Our analysis examines the current state-of-the-art blockchain-based proposals that target reproducibility issues in the Machine Learning domain. Based on the analysis of existing solutions, we propose a high-level architecture and main modules for developing a blockchain-based platform that enhances reproducible research in Machine Learning and can be adapted to other Artificial Intelligence domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Evaluation of Emotions in Artworks using EfficientNet Convolutional Network Integrating the Gram Matrix Modules 4

Evaluation of Emotions in Artworks using EfficientNet Convol...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Motiejauskas, Modestas Dzemyda, Gintautas Vilnius University Institute of Data Science and Digital Technologies VilniusLT-08412 Lithuania

ISBN: (纸本)9798331531225

This paper explores the possibility of an automatic assessment of emotions in artworks. This is a really important question because it can be extended not only to the emotions of artworks but also to the emotions of the environment around us. The EfficientNet convolutional neural network implementation, integrating the Gram matrix modules, was chosen for the problem. In previous studies, such a solution proved effective for analyzing emotions in images of a general nature. Four image datasets with the known emotion of a particular image were used to find the most appropriate one for the problem. The probabilities with which the neural network assigns emotions to the test art images are estimated. We see significant differences between these probability values. The small probability values obtained allow us to determine which emotions are not inherent in a given image. This is only initial research, as art is perceived subjectively, and the human factor is integral to the solution. However, its results are promising. © 2024 IEEE.

关键词： Convolutional neural networks

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Halving the Number of Qubits of Quantum Comparators 15th

Halving the Number of Qubits of Quantum Comparators

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Donaire, Laura María Ortega, Gloria Garzón, Ester M. Orts, Francisco Paulavičius, Remigijus Filatovas, Ernestas University of Almería Almería Spain Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania

ISBN: (纸本)9783031856990

Quantum comparators are of significant importance within the realm of various quantum algorithms. In this work we improve the number of qubits needed to perform a comparison of two N-bit strings from 2N+1 qubits to N+2. To achieve this, we resort to an encoding of the bits in which one qubit is not wasted for each bit entered. This encoding is based on the one proposed in the work of Pérez-Salinas et al. (2020), but in our case, we adapt it for use in arithmetic operations. We also use the implementation of the AND operation proposed by Gidney (2018) to reduce the number of T gates (significantly more expensive than the rest of the gates) necessary for comparison. The result is a circuit that equals the T-count of the best comparator for quantum computing currently available while halving the number of qubits required. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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XAI-driven Model Improvements in Interpretable Image Segmentation 2

XAI-driven Model Improvements in Interpretable Image Segment...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Gipiškis, Rokas Vilnius University Institute of Data Science and Digital Technologies 4 Akademijos St Vilnius Lithuania

Semantic image segmentation is the most fine-grained task in computer vision. Its applications range from autonomous vehicles to medical imaging. Despite its deployments in critical areas, interpretable image segmentation remains an underexplored field, especially when compared to explainable AI (XAI) solutions in classification and object detection. Even less attention has been paid to the use of XAI in non-explainability-related scenarios, where XAI methods are applied not for interpretability per se, but rather for other instrumental reasons, such as improving a model’s performance. Such use cases can potentially extend to AI safety, specifically in the case of adversarial attacks, self-supervised learning, neural architecture search (NAS), and continual learning (CL). Most of these areas have never been investigated in the context of interpretable segmentation. This work outlines key developments in the field of interpretable image segmentation, with a particular focus on XAI-driven model improvements. We also consider potential uses of interpretable image segmentation for model compression in the case of NAS, and instance-based memory compression in the case of CL. © 2024 Copyright for this paper by its authors.

关键词： Self-supervised learning

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Comparative Analysis of Various data Balancing Techniques for Propaganda Detection in Lithuanian News Articles 16th

Comparative Analysis of Various Data Balancing Techniques fo...

引用

16th International Baltic Conference on digital Business and Intelligent Systems, Baltic DB and IS 2024

作者： Rizgelienė, Ieva Korvel, Gražina Institute of Data Science and Digital Technologies Faculty of Mathematics and Informatics Vilnius University Vilnius Lithuania

ISBN: (纸本)9783031635427

With the increased use of social networks, spreading propaganda has become more accessible, making developing propaganda detection methods crucial. However, only some datasets are available specifically for automatic propaganda detection, and the situation is particularly dire for low-resource languages such as Lithuanian. In this paper, balancing techniques are proposed as a solution to mitigate this limitation. Four balancing techniques, SMOTE, SMOTE-ENN, SMOTE-TOMEK and ADASYN, were evaluated for classification performance using LR, XGB, SVM, and RF models. The results showed that all data balancing techniques significantly improve the classification performance of machine learning models when the models are trained and tested on the same dataset. At the same time, SMOTE, SMOTE-TOMEK and ADASYN effectively improve performance when tested using new-unseen data, with the ADASYN technique proving to be superior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Enhancing Keystroke Biometric Authentication Using Deep Learning Techniques 18

Enhancing Keystroke Biometric Authentication Using Deep Lear...

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18th Iberian Conference on Information Systems and technologies, CISTI 2023

作者： Medvedev, Viktor Budzys, Arnoldas Kurasova, Olga Vilnius University Institute of Data Science and Digital Technologies Vilnius Lithuania

ISBN: (纸本)9789893347928

Ensuring the data security and integrity of data is a critical challenge for organizations. The need for effective intrusion detection and user authentication in critical infrastructure systems has become especially relevant recently. Detecting insiders is challenging because they are difficult to detect and identify, so advanced techniques are required to prevent their activities. This paper discusses an approach that uses a Siamese neural network to authenticate users by transforming their biometric typing data or behavioral characteristics into images. To achieve this goal, the paper considers several methods for transforming numerical (tabular) keystroke data into images. The transformed keystroke data is then used to train Siamese neural networks for image similarity detection to distinguish a real user from a possible insider. The experiments conducted in this study show that transforming time series data into images is a robust and effective approach to improve the accuracy of intrusion detection and user authentication. This paper highlights the importance of comparing different methods for transforming numerical keystroke data into images to improve the accuracy of intrusion detection and user authentication. The proposed methodology can be useful for intrusion detection and user authentication, improving the security and reliability of critical infrastructure systems. © 2023 ITMA.

关键词： Intrusion detection

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Investigation of Machine Learning Methods for Colour Audio Noise Suppression 18

Investigation of Machine Learning Methods for Colour Audio N...

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18th Iberian Conference on Information Systems and technologies, CISTI 2023

作者： Stankevicius, Dominykas Treigys, Povilas Vilnius University Institute of Data Science and Digital Technologies Vilnius Lithuania

ISBN: (纸本)9789893347928

Separating background noise or suppressing it from a given recording to extract clean speech benefits humans with hearing aids and machines, such as Automatic Speech Recognition systems. In recent years, various experiments have been conducted to develop a Speech Enhancement (SE) model or framework that would produce high-quality estimations based on classical algorithms or Deep Neural Network methods. Every research tries to evaluate and compare the new approach with existing ones. Still, a comparison is often biased or insufficient because of the different setup of training data or evaluation metrics used between each trial. The criteria used to corrupt the data with additive noise differ per research, like the evaluation strategy. This work investigates the most promising SE approaches by replicating experiments and using exact training and testing criteria and the same set of evaluation metrics to identify what produces better clean speech signal estimations. © 2023 ITMA.

关键词： Speech enhancement

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Occlusion-Based Approach for Interpretable Semantic Segmentation 18

Occlusion-Based Approach for Interpretable Semantic Segmenta...

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18th Iberian Conference on Information Systems and technologies, CISTI 2023

作者： Gipiskis, Rokas Kurasova, Olga Vilnius University Institute of Data Science and Digital Technologies Vilnius Lithuania

ISBN: (纸本)9789893347928

In this paper, we investigate the application of an occlusion-based approach for the task of interpreting semantic segmentation results. With an increasing deployment of deep learning systems in critical domains, interpretability plays a key role in providing additional information about the model besides the evaluation metric score. An extended modification of occlusion sensitivity allows the generation of saliency maps based on the effect of occlusions on the evaluation metric. Such a perturbation-based post-hoc interpretability method can be used to visualize those image regions that the selected segmentation class is most sensitive to. We observe that, compared to classification cases, the evaluation metric scores for segmentation remain similar to each other even after occlusions. To generate more color intensities in the saliency map, we use normalization and standardization techniques. We also evaluate the results quantitatively using deletion curves. © 2023 ITMA.

关键词： Semantic Segmentation

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Ensemble for quality assessment of eye fundus images involving deep learning methods 18

Ensemble for quality assessment of eye fundus images involvi...

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18th Iberian Conference on Information Systems and technologies, CISTI 2023

作者： Ruminaviciute, Gabriele Bernataviciene, Jolita Vilnius University Institute of Data Science and Digital Technologies Vilnius Lithuania

ISBN: (纸本)9789893347928

Accurate diagnosis in telemedicine relies on high-quality eye fundus images. However, the increasing adoption of imaging technology and the increasing volume of images taken per patient have the potential to result in a decrease in image quality. This paper aims to create a reliable method of automated image quality assessment by creating an ensemble of methods to evaluate deep features of the images as well as additional indicators such as blood vessel presence and clarity, optic disc appearance, and image blurriness. The ensemble was tested on two digital fundus image datasets and achieved high accuracy rates of 0.9146 and 0.9845, demonstrating the potential for machine learning algorithms to be used for reliable automated image quality assessment in telemedicine, helping to ensure accurate diagnoses and better patient care. © 2023 ITMA.

关键词： Telemedicine

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