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Interpretable Machine Learning in Guided Synthesis of Stable Sols Based on Nanosized Titanium Oxides
P. 1–6.
This report discusses the guided synthesis of sols containing nanosized titanium(IV) oxides for use in biological and medical applications. These sols vary in size (from ∼2 up to 2000nm) and different stability (from 0 up to 90 days). They are synthesized under changing fabrication conditions (temperature, hydrolysis duration, titanium-containing precursors composition and concentration) without surfactants. A specific set of machine learning tools was developed for well interpretable analysis of experimental data. These tools were used in three experimental stages to optimize the three functional characteristics of the synthesized sols changing by five synthesis parameters. The result is a, universal minimalist mechanism for guided synthesis, which allows the relationships between synthesis parameters and functional characteristics to be studied in various experiments.
Kraevskiy A., Sokolovskiy E., Prokhorov A., Emerging Markets Review 2026 No. 74 P. 1–19
Financial markets of emerging economies are vulnerable to extreme and cascading information spillovers, surges, sudden stops and reversals. With this in mind, we develop a new online early warning system (EWS) to detect what is referred to as ‘concept drift’ in machine learning, as a ‘regime shift’ in economics and as a ‘change-point’ in statistics. ...
Added: September 25, 2026
Maghsoohi A., Pavlov V., Rouse P. et al., Health Care Management Science 2026 Vol. 29 Article 37
As the numbers of older people (65 +) rise globally, the pressure on acute hospitals to provide efficient and effective care while addressing resource inequities increases. In this study we introduce Recognising Episodes of Acute Complexity in Health (REACH), a novel Automatic Machine Learning (AutoML)-based predictive model that prioritises older patients and assigns them to complex ...
Added: July 30, 2026
Izyumov P., Ivchenko A., , in: 2024 26th International Conference on Digital Signal Processing and its Applications (DSPA).: IEEE, 2024. P. 1–5.
The paper describes the issues of setting up an experiment, limitations, and features of the platform, primarily focusing on the influence of hardware and software versions of probes, network types, and cross-traffic. The impact of new versions of software and hardware requires analysis and con-sideration in further data work. Additionally, the nature of the global ...
Added: June 28, 2026
NY: Association for Computing Machinery (ACM), 2026.
It is our great pleasure to welcome you to the 35th edition of the Web Conference to be held on June 29 – July 3, 2026, in Dubai, United Arab Emirates.
Following discussions with our partners and key stakeholders, we have taken the decision to postpone the ACM Web Conference 2026, initially planned for April 2026. ...
Added: April 17, 2026
Chieregato Vicentin D., Salati Marcondes de Moraes G. H., Fischer B. et al., Journal of Intellectual Capital 2026 Vol. 27 No. 4 P. 951–974
Purpose
This study investigates the main contextual determinants of entrepreneurial culture (EC) in emerging ecosystems, interpreting EC as an intangible resource that affects the dynamics of Intellectual Capital within entrepreneurial ecosystems. It analyzes how factors based on the pillars of such ecosystems influence perceptions regarding structural conditions associated with creativity and innovation in a given region.
Design/methodology/approach
A ...
Added: March 11, 2026
M.V. Kornilov, Korolev V., Malanchev K. et al., Astronomy and Computing 2025 Vol. 52 Article 100960
We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package ...
Added: October 2, 2025
Ali S., Khizhik A., Ryzhikov A. et al., , in: 2025 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 12-13 May 2025.: IEEE, 2025. P. 357–360.
Three-phase induction motors play a crucial role in industrial applications due to their efficiency, durability, and reliability. However, effective fault detection remains challenging, primarily due to the scarcity of labeled failure data, which limits the performance of traditional machine learning (ML)-based diagnostic models and increases the risk of overfitting and poor generalization. Conventional methods, such ...
Added: July 3, 2025
Saranskaia I., Boris Gutkin, Zakharov D., European Physical Journal: Special Topics 2025 Vol. 234 P. 4159–4177
This review explores the interplay between data representation and machine learning (ML) methods in classifying functional, cognitive, and pathological brain states using magnetoencephalography (MEG). Two primary data representations are considered: sensor-level signals and reconstructed source signals. Sensor signals, when combined with classical ML methods such as linear discriminant analysis (LDA) and support vector machines (SVM), offer computational efficiency and simplified preprocessing, ...
Added: March 15, 2025
Springer, 2024.
Based on the “Sixth International Conference on Dynamics of Disasters” (Piraeus, Greece, July 2023), this volume includes contributions from experts who share their latest discoveries on disasters either caused by natural phenomena or human activities. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response ...
Added: March 5, 2025
Vlasov V., Aleksei Y. Kuzin, Florya I. et al., Journal of Energy Storage 2025 Vol. 111 Article 115349
This research presents an advanced method for measuring State of Charge (SoC) in Vanadium Redox Flow
Batteries (VRFB) using Refractive Index (RI) combined with Machine Learning (ML). The study comprised of
three primary phases: ex-situ measurements, in-operando measurements, and ML model training. Initially, tests
were conducted using a hybrid photonic-microfluidic sensor on simulated solutions mimicking specific VRFB
SoCs. Subsequently, ...
Added: February 26, 2025
Vlasenko D., Ушаков В. Г., Zaikin A. et al., Известия высших учебных заведений. Прикладная нелинейная динамика 2025 Т. 33 № 4 С. 557–566
The study of functional brain networks that support cognitive processes is one of the central goals of modern neuroscience. Functional magnetic resonance imaging (fMRI) is widely used to obtain data on brain activity. However, the high dimensionality and dynamic nature of fMRI data makes their processing challenging. Network-based methods of data representation offer a promising approach to describe ...
Added: January 14, 2025
Seyedsaeed M., Ahmad Ostovari Moghaddam, Hadavimoghaddam F. et al., ACS Applied Nano Materials 2024 Vol. 7 No. 5 P. 5513–5524
The development of noble metal-free heterogeneous catalysts holds promise for the solvent-free and selective aerobic oxidation of organic compounds. However, the moderate activity of these catalysts under atmospheric conditions limits their industrial use. In this work, the synthesis of noble metal-free (CoFeMnCuNiCr)3O4 high-entropy oxide (HEO) nanoparticles and their grafting on reduced graphene oxide (rGO) to produce ...
Added: January 12, 2025
Springer, 2024.
This book constitutes the refereed post proceedings of the 18th International Conference on Parallel Computational Technologies, PCT 2024, held in Chelyabinsk, Russia, in April 2024.
The 22 full papers included in this book were carefully reviewed and selected from 62 submissions. These papers have been organized under the following topical sections: High Performance Architectures, Tools and Technologies; Parallel Numerical Algorithms and ...
Added: December 20, 2024
Nazarova A., Malik M. S., Ignatov D. I. et al., Social Network Analysis and Mining 2024 Vol. 14 Article 228
Identifcation of threatening comments on social media platforms has recently gained attention. Prior approaches have addressed
this task in some low-resource languages but the interpretability of results was not studied. In addition, approaches in the English
language are minimal. To support explainable predictive inference, this research proposes an inherently explainable model for threat
comment identifcation on Twitter. The ...
Added: December 11, 2024
Chertenkov V., Shchur L., / Series arXiv "math". 2024. No. 2411.13027.
The main question raised in the letter is the applicability of a neural network trained on a spin lattice model in one universality class to test a model in another universality class. The quantities of interest are the critical phase transition temperature and the correlation length exponent. In other words, the question of transfer learning ...
Added: November 21, 2024
Ali S., Ryzhikov A., Derkach D. et al., Moscow University Physics Bulletin 2024 Vol. 79 No. Suppl. 2 P. S591–S597
In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein ...
Added: November 7, 2024
Kychkin A., Vikentyeva O., Mylnikov L. et al., Computers & Industrial Engineering 2024 Vol. 198 Article 110662
The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, ...
Added: October 28, 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
Sukhoverkhova D., Shchur L., Письма в Журнал экспериментальной и теоретической физики 2024 Т. 120 № 8 С. 644–649
In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the problem of binary classification of snapshots of snapshots of the location of spins of the Ising model on a two-dimensional ...
Added: September 25, 2024
Чудинова О. С., Безбородникова Р. М., Корнейченко Е. Н. et al., Оренбург: Оренбургский государственный университет, 2024.
В учебном пособии рассмотрены методы и алгоритмы решения одной из основных задач машинного обучения – классификации. Внимание сосредоточено на задаче бинарной классификации. Рассмотрены следующие методы классического машинного обучения: логистическая регрессия, метод опорных векторов, метод k ближайших соседей. Описаны реализующие их современные инструментальные средства: платформа KNIME, язык программирования Python.
Учебное пособие предназначено для обучающихся по образовательной программе ...
Added: September 17, 2024
Sergei Koltcov, Surkov A., Filippov V. et al., PeerJ Computer Science 2024 Vol. 10 P. 41
Topic modeling is a widely used instrument for the analysis of large text collections.
In the last few years, neural topic models and models with word embeddings have
been proposed to increase the quality of topic solutions. However, these models
were not extensively tested in terms of stability and interpretability. Moreover, the
question of selecting the number of topics ...
Added: February 16, 2024