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269 results on '"Kurasova, Olga"'

1. Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey

Author

Gipiškis, Rokas, Tsai, Chun-Wei, and Kurasova, Olga

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions., Comment: 35 pages, 9 figures, 2 tables

Published
2024

3. Optimal Cut-Off Points for Pancreatic Cancer Detection Using Deep Learning Techniques

Author

Dzemyda, Gintautas, Kurasova, Olga, Medvedev, Viktor, Šubonienė, Aušra, Gulla, Aistė, Samuilis, Artūras, Jagminas, Džiugas, Strupas, Kȩstutis, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Alvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Colla, Valentina, editor

Published
2024
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6. Techniques Involved in the Development of the New Dataset for Anomaly Detection in Computer Networks

Author

Vaišnoras, Žydrūnas, Kurasova, Olga, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Alvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, and Moreira, Fernando, editor

Published
2022
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13. Comparative Analysis of Spectral and Cepstral Feature Extraction Techniques for Phoneme Modelling

Author

Korvel, Grazina, Kurasova, Olga, Kostek, Bozena, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Choroś, Kazimierz, editor, Kopel, Marek, editor, Kukla, Elżbieta, editor, and Siemiński, Andrzej, editor

Published
2019
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15. A Decision Support System for Solving Multiple Criteria Optimization Problems

Author

Filatovas, Ernestas and Kurasova, Olga

Abstract

In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria and solving single-criterion optimization problems (SOPs). However, the WS approach does not guarantee either finding evenly distributed solutions of a Pareto front, or finding Pareto optimal solutions in non-convex regions. In order to solve the problems, some methods have been proposed. In this paper, an adaptive weighted-sum (AWS) approach has been used. We suggest presenting the solutions, obtained not only by the weighted-sum method, but also by the adaptive weighted-sum approach, to a decision maker (DM). Thus the decision making will be facilitated.

Published
2011

17. Exploring Multidimensional Embeddings for Decision Support Using Advanced Visualization Techniques.

Author

Kurasova, Olga, Budžys, Arnoldas, and Medvedev, Viktor

Subjects
DEEP learning, DECISION support systems, DATA visualization, ARTIFICIAL intelligence, DATA structures
Abstract

As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Quality Prediction of Compressed Images via Classification

Author

Tichonov, Jevgenij, Kurasova, Olga, Filatovas, Ernestas, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2017
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22. Parallel Computing for Dimensionality Reduction

Author

Zubova, Jelena, Liutvinavicius, Marius, Kurasova, Olga, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Dregvaite, Giedre, editor, and Damasevicius, Robertas, editor

Published
2016
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28. Applications of Visualization

Author

Dzemyda, Gintautas, Kurasova, Olga, Žilinskas, Julius, Pardalos, Panos, Series editor, Dzemyda, Gintautas, Kurasova, Olga, and Žilinskas, Julius

Published
2013
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33. Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps

Author

Stefanovič, Pavel, Kurasova, Olga, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Laaksonen, Jorma, editor, and Honkela, Timo, editor

Published
2011
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34. Occlusion-Based Approach for Interpretable Semantic Segmentation.

Author

Gipiškis, Rokas and Kurasova, Olga

Subjects
SEMANTICS, DATA modeling, DEEP learning, ARTIFICIAL neural networks, DATA visualization
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2023

35. Enhancing Keystroke Biometric Authentication Using Deep Learning Techniques.

Author

Medvedev, Viktor, Budžys, Arnoldas, and Kurasova, Olga

Subjects
BIOMETRIC identification, DEEP learning, TIME series analysis, COMPUTER access control, ARTIFICIAL neural networks
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2023

36. Semi-Supervised Learning with Pseudo-Labeling for Pancreatic Cancer Detection on CT Scans.

Author

Kurasova, Olga, Medvedev, Viktor, Šubonienė, Aušra, Dzemyda, Gintautas, Gulla, Aistė, Samuilis, Artūras, Jagminas, Džiugas, and Strupas, Kęstutis

Subjects
PANCREATIC cancer diagnosis, MEDICAL screening, DEEP learning, CONVOLUTIONAL neural networks, COMPUTED tomography
Abstract

Deep learning techniques have recently gained increasing attention not only among computer science researchers but are also being applied in a wide range of fields. However, deep learning models demand huge amounts of data. Furthermore, fully supervised learning requires labeled data to solve classification, recognition, and segmentation problems. Data labeling and annotation in the medical domain are time-consuming and labor-intensive. Semi-supervised learning has demonstrated the ability to improve deep learning performance when labeled data is scarce. However, it is still an open and challenging question on how to leverage not only labeled data but also the huge amount of unlabeled data. In this paper, the problem of pancreatic cancer detection on CT scans is addressed by a semi-supervised learning approach based on pseudolabeling. Preliminary results are promising and show the potential of semi-supervised deep learning to detect pancreatic cancer at an early stage with a limited amount of labeled data. [ABSTRACT FROM AUTHOR]

Published
2023

37. Combination of Vector Quantization and Visualization

Author

Kurasova, Olga, Molytė, Alma, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Perner, Petra, editor

Published
2009
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39. Parameter System for Human Physiological Data Representation and Analysis

Author

Kurasova, Olga, Dzemyda, Gintautas, Vainoras, Alfonsas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martí, Joan, editor, Benedí, José Miguel, editor, Mendonça, Ana Maria, editor, and Serrat, Joan, editor

Published
2007
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40. Dimensionality Problem in the Visualization of Correlation-Based Data

Author

Dzemyda, Gintautas, Kurasova, Olga, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Beliczynski, Bartlomiej, editor, Dzielinski, Andrzej, editor, Iwanowski, Marcin, editor, and Ribeiro, Bernardete, editor

Published
2007
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43. Parallelization of the SOM-Based Integrated Mapping

Author

Dzemyda, Gintautas, Kurasova, Olga, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Rutkowski, Leszek, editor, Siekmann, Jörg H., editor, Tadeusiewicz, Ryszard, editor, and Zadeh, Lotfi A., editor

Published
2004
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45. AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery.

Author

Gudzius, Povilas, Kurasova, Olga, Darulis, Vytenis, and Filatovas, Ernestas

Subjects
*REMOTE-sensing images, *OBJECT recognition (Computer vision), *REMOTE sensing, *ARTIFICIAL satellite launching
Abstract

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field. [ABSTRACT FROM AUTHOR]

Published
2023
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