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Сравнение эффективности ядер SVM-классификатора для различения пола на основе структурных коннектом
С. 1–13.
Додонова Ю., Петров Д., Zhukov L. E.
Comparison of the kernel effectiveness of SVM classifier to distinguish gender based on the structural connectome
In book
St. Petersburg: Институт проблем передачи информации им. А.А. Харкевича РАН, 2015.
Behzadidoost R., Neurocomputing 2025 Vol. 665 P. 1–21
While earlier research has focused on detecting misinformation content, identifying the users who spread it, referred to in this paper as fake information spreaders, remains a relatively new challenge. These users deliberately mix true and false information, making detection more difficult. This paper proposes a textual fingerprint learning model to detect fake information spreaders. The ...
Added: March 12, 2026
Suyuncheva A., Saada D., Gavrilenko Y. et al., Advances in Intelligent Systems and Computing 2021 Vol. 1358 P. 319–328
In this article, the process of internal pronunciation (covert speech) is associated with the internal speech through an intellectual process such as silent reading. The objective of the research is to compare the EP of visual and auditory perception and internal pronunciation of phonemes and syllables; to classify phonemes, words and syllables from covert speech, ...
Added: October 2, 2025
Белокопытов А. С., Makarova M., Саламатин М. И. et al., Известия высших учебных заведений. Прикладная нелинейная динамика 2024 Т. 32 № 2 С. 223–238
. The purpose of this study is to develop a classifier capable of detecting typical absence seizures in realtime using electroencephalogram (EEG) data and a Support Vector Machine (SVM) model. Methods. Sections of the EEG, previously identified by a specialist as containing typical absences, were used to train the SVM model. Key features for classification ...
Added: December 27, 2024
Белокопытов А. С., Макарова М. М., Саламатин М. И. et al., Известия высших учебных заведений. Прикладная нелинейная динамика 2024 Т. 32 № 2 С. 223–228
. The purpose of this study is to develop a classifier capable of detecting typical absence seizures in real-time using electroencephalogram (EEG) data and a Support Vector Machine (SVM) model. Methods. Sections of the EEG, previously identified by a specialist as containing typical absences, were used to train the SVM model. Key features for classification ...
Added: October 23, 2024
Vukovic D., Romanyuk K., Ivashchenko S. et al., Expert Systems with Applications 2022 Vol. 194 No. May 2022 Article 116553
This paper investigates the forecasting performance for credit default swap (CDS) spreads by Support Vector
Machines (SVM), Group Method of Data Handling (GMDH), Long Short-Term Memory (LSTM) and Markov
switching autoregression (MSA) for daily CDS spreads of the 513 leading US companies, in the period
2009–2020. The goal of this study is to test the forecasting performance of ...
Added: February 4, 2022
Muratova A., Mitrofanova E., Islam R., , in: Intelligent Information and Database Systems: 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7–10, 2021, Proceedings.: Springer, 2021. P. 630–642.
Added: April 6, 2021
Lin F., Lee H., Kuo W. et al., Frontiers in Psychology 2021 Vol. 11 Article 547353
While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, ...
Added: March 10, 2021
Meunier D., Pascarella A., Altukhov D. et al., Neuroimage 2020 Vol. 219 No. october P. 1–13
Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, ...
Added: November 12, 2020
Pomorina M., Ефимов Д. А., Лысцев С. С., Управленческий учет и финансы 2019 № 1 С. 2–9
Predicting the impact of news events on changes in the price of financial assets can be used to manage the value of the company. The work demonstrates the possibility of using the method of content analysis to identify the degree of influence of non-financial risks on the market value of a public company. Using four ...
Added: October 31, 2019
CEUR Workshop Proceedings, 2019.
Workshop concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments will be compared with noisy industrial datasets from the web e.g. Insights from behavioral sciences will help data scientists. Behavior scientists will see new inspirations to ...
Added: October 18, 2019
Cham: Springer, 2019.
This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data ...
Added: October 17, 2019
Springer, 2019.
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019.
The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named:
Part I: best ranked papers; machine learning; pattern recognition; ...
Added: September 23, 2019
Berlin: Springer, 2018.
This book constitutes the proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, held in Moscow, Russia, in July 2018.
The 29 full papers were carefully reviewed and selected from 107 submissions (of which 26 papers were rejected without being reviewed). The papers are organized in topical sections on ...
Added: September 5, 2018
Kurmukov A., Dodonova Y., Burova M. et al., , in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & StatisticsVol. 247.: Springer, 2018. P. 299–308.
Human brain networks show modular organization: cortical regions tend to form densely connected modules with only weak inter-modular connections. However, little is known on whether modular structure of brain networks is reliable in terms of test-retest reproducibility and, most importantly, to what extent these topological modules are anatomically embedded. To address these questions, we use ...
Added: December 15, 2017
Petrov D., Dodonova Y., Zhukov L. E., , in: "Информационные технологии и системы 2015".: St. Petersburg: Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1–15.
We study di erences in structural connectomes between typically developing and autism spectrum disorders individuals with machine learning techniques using connection weights and network metrics as features. We build linear SVM classi er with accuracy score 0:64 and report 16 features (seven connection weights and nine network node centralities) best distinguishing these two groups. ...
Added: March 5, 2017
Petrov D., Dodonova Y., Zhukov L. E. et al., , in: PRNI 2016. The 6th International Workshop on Pattern Recognition in Neuroimaging. Trento, Italy, June 22nd – 24th, 2016.: NY: IEEE, 2016. P. 1–4.
The structural connectome classification is a challenging task due to a small sample size and high dimensionality of feature space. In this paper, we propose a new data prepossessing method that combines geometric and topological connectome normalization and significantly improves classification results. We validate this approach by performing classification between autism spectrum disorder and normal ...
Added: March 5, 2017
Lozinskaia A. M., Жемчужников В. А., Perm University Herald. Economy 2017 Vol. 12 No. 1 P. 49–60
The ability to predict the dynamics of financial instruments is an important topic for financial market players. In the context of large and heterogeneous information, there is a need to use effective methods to data processing for management decision-processing. In particular, machine learning techniques are becoming very popular in financial modeling. The aim of this ...
Added: December 24, 2016