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408 results on '"Gregus, Michal"'

2. Smart Planning, Design, and Optimization of Mobile Networks Ecosystem Using AI-Enhanced Atoll Software

Author

Beshley, Halyna, Gregus, Michal, Urikova, Oksana, Scherm, Ilona, Beshley, Mykola, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Luntovskyy, Andriy, editor, Klymash, Mikhailo, editor, Melnyk, Igor, editor, Beshley, Mykola, editor, and Schill, Alexander, editor

Published
2024
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4. Some remarks on smooth mappings of Hilbert and Banach spaces and their local convexity property

Author

Prykarpatskyy, Yarema A., Pukach, Petro Ya., Vovk, Myroslava I., and Greguš, Michal

Subjects
Mathematics - Functional Analysis, 46B20, 49J50, 52A41, 58C20
Abstract

We analyze smooth nonlinear mappings for Hilbert and Banach spaces that carry small balls to convex sets, provided that the radius of the balls is small enough. Being focused on the study of new and mild sufficient conditions for a nonlinear mapping of Hilbert and Banach spaces to be locally convex, we address a suitably reformulated local convexity problem analyzed within the Leray-Schauder homotopy method approach for Hilbert spaces, and within the Lipscitz smoothness condition both for Hilbert and Banach spaces. Some of the results presented in the work prove to be interesting and novel even for finite-dimensional problems. Open problems related to the local convexity property for nonlinear mapping of Banach spaces are also formulated.

Published
2021

5. A Smartphone-Based Computer Vision Assistance System with Neural Network Depth Estimation for the Visually Impaired

Author

Beshley, Mykola, Volodymyr, Pastukh, Beshley, Halyna, Gregus, Michal, Jr., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2023
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10. New mixture models for decoy-free false discovery rate estimation in mass-spectrometry proteomics

Author

Peng, Yisu, Jain, Shantanu, Li, Yong Fuga, Gregus, Michal, Ivanov, Alexander R., Vitek, Olga, and Radivojac, Predrag

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Motivation: Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target decoy approaches (TDAs) and decoy-free approaches (DFAs), have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. Results: We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs, and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms.

Published
2020

13. I-PNN: An Improved Probabilistic Neural Network for Binary Classification of Imbalanced Medical Data

Author

Izonin, Ivan, Tkachenko, Roman, Greguš, Michal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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14. An Approach Toward Numerical Data Augmentation and Regression Modeling Using Polynomial-Kernel-Based SVR

Author

Izonin, Ivan, Tkachenko, Roman, Horbal, Natalya, Greguš, Michal, Verhun, Volodymyr, Tolstyak, Yaroslav, 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, Saraswat, Mukesh, editor, Roy, Sarbani, editor, Chowdhury, Chandreyee, editor, and Gandomi, Amir H., editor

Published
2022
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15. Development of Highly Sensitive LC-MS and CE-MS Methods for In-Depth Proteomic and Glycomic Profiling of Limited Biological Samples

Author

Gregus, Michal, Zimmerman, Alan, Marie, Anne-Lise, Johnson, Kendall R., and Ivanov, Alexander R.

Subjects
Chemistry
Abstract

Informative and deep proteomic and glycomic characterization of limited availability biological and medical samples has been a significant challenge. Here, we describe our current and recent efforts in advancing sample preparation as well as miniaturized electric field- and pressure-driven separation approaches interfaced with high-end mass spectrometry (MS) to enhance the sensitivity and depth of proteomic and glycomic profiling of several types of limited biological and clinically relevant samples., Mass spectrometry (MS) is one of the most extensively used, sensitive, and powerful techniques to study and characterize metabolomes, lipidomes, proteomes, and glycomes of complex biological systems. Despite many recent [...]

Published
2022

22. Impulse Moments Influence on Single-Frequency Torsional Oscillations of Nonlinear Flexible Bodies

Author

Mariia Sokil, Andriy Andrukhiv, Solomiia Fedushko, Yuriy Syerov, and Gregus Michal Ml

Subjects
Impulse perturbations, nonlinear flexible bodies, Ateb-function, oscillations, mathematical model, resonance, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The method of analytical study of the influence of impulse moments on nonlinear torsional oscillations of a homogeneous constant cross-section of a body under classical boundary conditions of the first, second, and third types have been developed. When the flexible material properties meet the body close to the power law of flexibility, mathematical models of the process have been obtained. It is the boundary value problem for an equation of hyperbolic type with a small parameter at the discrete right-hand side. The latter expresses the effect of pulse momentum on the oscillatory process. Under the effect of periodic pulse momentum on a flexible body, resonant and non-resonant processes are possible. Resonant processes occur when the amplitude of natural oscillations approaches a fixed value. The peculiarities of resonant oscillations are established. The amplitude of passing through the primary resonance is significant for larger values of the nonlinearity parameter and, in the case of the action of pulse momentum, closer to the middle of the body. If the initial perturbation amplitude is less than the amplitude at which the resonance occurs in the presence of only internal forces of viscous friction. So, the external periodic impulse moments of resonance processes have occurred. The extreme case confirms the reliability of the results obtained related to the dynamics of the respective objects under the continuous action of autonomous perturbation.

Published
2022
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29. Crowdfunding – An Innovative Corporate Finance Method and Its Decision-Making Steps

Author

Busse, Valerie, Gregus, Michal, 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, Barolli, Leonard, editor, Nishino, Hiroaki, editor, and Miwa, Hiroyoshi, editor

Published
2020
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30. Cooperative Decision Making in Crowdfunding – Applying Theory of Behavior and Exemplary Empirical Validation

Author

Busse, Valerie, Strauss, Christine, Gregus, Michal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Luo, Yuhua, editor

Published
2020
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34. An approach toward improvement of ensemble method's accuracy for biomedical data classification.

Author

Izonin, Ivan, Muzyka, Roman, Tkachenko, Roman, Gregus, Michal, Kustra, Natalya, and Mitoulis, Stergios-Aristoteles

Abstract

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcarerelated data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, MLbased ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble. [ABSTRACT FROM AUTHOR]

Published
2024
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