Метод аналитических сетей при принятии решений в условиях неопределенности

Т. К. Кравченко

Publications

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Метод аналитических сетей при принятии решений в условиях неопределенности

Экономика и математические методы. 2012. Т. 48. № 4. С. 99–112.

Kravchenko T. K.

Research target: Computer Science

Priority areas: IT and mathematics business informatics

Language: Russian

Full text

Keywords: метод анализа иерархий (МАИ)метод аналитических сетей (МАС)моделирование проблемных ситуаций принятия экономических решений коэффициенты относительной значимости проблемных ситуаций экономико-математические методы и модели

Enhancing Emotion Recognition in Speech Based on Self-Supervised Learning: Cross-Attention Fusion of Acoustic and Semantic Features

Deeb B., Savchenko A., Makarov I., IEEE Access 2026 Vol. 13 P. 56283–56295

Speech Emotion Recognition has gained considerable attention in speech processing and machine learning due to its potential applications in human-computer interaction, mental health monitoring, and customer service. However, state-of-the-art models for speech emotion recognition use many parameters, which leads to computational complexity. In this paper, we introduce a novel deep-learning model to enhance the accuracy ...

Added: June 16, 2026

Automated detection of wolf howls using audio spectrogram transformers

Makarov N., Savchenko A., Zemtsova I. et al., Scientific Reports 2025 Vol. 15 Article 26641

The grey wolf (Canis lupus) is a pivotal species for ecological studies. As a key participant in ecosystem processes, it also serves as a model for investigating social structure formation and ecological adaptation. However, the species’ complex social behavior, spatial dynamics, and expansive habitats make monitoring and population assessments across large areas particularly challenging. In recent years, audio traps ...

Added: June 16, 2026

Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening

Vasilev R., Savchenko A., Blinov P. et al., Frontiers in Medicine 2026 Vol. 13

Automated disease screening systems face challenges when applied to multi-class medical image analysis, particularly under severe class imbalance inherent in clinical datasets. Retinal fundus imaging enables non-invasive screening for multiple ocular and systemic diseases simultaneously, yet current automated systems typically assess risk for only a single pathology or a limited disease range. We developed a ...

Added: June 16, 2026

From Data to Signs: A Foundation Model for Multilingual Sign Language Recognition

Novopoltsev M., Tulenkov A., Murtazin R. et al., IEEE Access 2025 Vol. 13 P. 188170–188181

Video-based Isolated Sign Language Recognition (ISLR) problem presents significant challenges in scaling across diverse languages due to data scarcity and the computational costs associated with training of language-specific models. In this paper, we introduce a novel training pipeline that leverages self-supervised learning on a large-scale sign language dataset. To obtain the foundation model, we utilize ...

Added: June 16, 2026

Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching

Karpukhin I., Savchenko A., Proceedings of the AAAI Conference on Artificial Intelligence 2026 Vol. 40 No. 27 P. 22536–22544

Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of ...

Added: June 16, 2026

B3Emo: Quantifying Affect as a Double-Edged Sword in Strategic LLM Interactions

Stepin A., Mozikov M., Kabanov A. et al., IEEE Access 2026 Vol. 14 P. 48127–48144

The deployment of large language models (LLMs) in interactive roles such as automated negotiators, customer service agents, and strategic partners requires them to handle not only logical tasks but also the socio-emotional dimensions of interaction. In these situations, success often relies on understanding social cues, building trust, and using persuasion effectively. These skills are closely ...

Added: June 16, 2026

ESQA: Event Sequences Question Answering

Abdullaeva I., Karpukhin I., Filatov A. et al., IEEE Access 2026 Vol. 14 P. 59390–59408

Event sequences, a specialized type of tabular data annotated with timestamps, are prevalent across practical domains such as finance, retail, social networks, and healthcare. Despite the importance of event sequence modeling and analysis, there has been little effort to adapt Large Language Models (LLMs) to this domain. In this paper, we propose a novel solution ...

Added: June 16, 2026

Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Association for Computational Linguistics, 2026.

Added: June 14, 2026

Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)

Strube M., Braud C., Hardmeier C. et al., Suzhou: Association for Computational Linguistics, 2025.

Added: June 11, 2026

TreeDQN: Sample-efficient off-policy reinforcement learning for combinatorial optimization

Sorokin D., Kostin A., Savchenko L. et al., Knowledge-Based Systems 2026 Vol. 348 Article 116258

A convenient approach to optimally solving combinatorial optimization tasks is the Branch-and-Bound method. Its branching heuristic can be learned to solve a large set of similar tasks. The promising results here are achieved by the recently appeared on-policy reinforcement learning method based on the tree Markov Decision Process. To overcome its main disadvantages, namely, very large training time ...

Added: June 10, 2026

Microbial diversity and production of milk spirit using traditional Buryat fermentation and distillation technologies

Namsaraev Z., Nanzatov B., Kozlova A. et al., Scientific Reports 2026 Vol. 16 No. 1 Article 17769

Distilled fermented milk beverages are rare in food technology, despite the global prevalence of plant-based spirits. Currently, the production of distilled strong alcoholic beverages from fermented milk using traditional technologies is known only among Mongolic-speaking peoples and their Siberian neighbors. This study provides the first interdisciplinary analysis of darasun, a traditional Buryat spirit made from fermented ...

Added: June 10, 2026

Artificial intelligence and digital twins for failure prediction in data center cooling systems: a comprehensive literature review (2018–2026)

Butorova A., Bobakov V., Sergeev A. et al., European Physical Journal: Special Topics 2026 P. 1–19

This paper presents a review of artificial intelligence (AI) methods for failure prediction in data center cooling systems, with a focus on the integration of digital twins (DTs), physics-informed learning, and graph-based models. Positioned within complex network science, this review addresses a limitation of conventional graph approaches—their reliance on pairwise connectivity—whereas real-world failures often arise ...

Added: June 10, 2026

Innovations in Information and Decision Sciences. Proceedings of the 13th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2025), Volume 4

Springer, 2026.

The book presents the proceedings of the 13th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6–7, 2025. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with ...

Added: June 8, 2026

ML-based Fast Simulation of FARICH Responses

Shipilov F., Barnyakov A., Ivanov A. et al., / Series Physics "arxiv.org". 2026.

A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation software but are computationally expensive. We present a machine-learning-based approach to fast simulation of the Focusing Aerogel Ring Imaging Cherenkov (FARICH) detector response. Given a particle track and momentum, ...

Added: May 19, 2026

Natural hazard database from Internet publications: text mining with a large language model

Derkacheva A., Sakirkina M., Kraev G. et al., /. 2026.

Comprehensive data on natural hazards and their consequences are crucial for effective for risk assessment, adaptation planning, and emergency response. However, many countries face challenges with fragmented, inconsistent, and inaccessible data, particularly regarding local-scale events. To address this data gap in Russia, we developed an end-to-end processing pipeline that scrapes news from various online sources, ...

Added: April 28, 2026

Algorithmic overlaps as thermodynamic variables: from local to cluster Monte Carlo dynamics in critical phenomena

Pilé I., Deng Y., Shchur L., / Series arXiv "math". 2026. No. 2604.10254.

We investigate the spatial overlap of successive spin configurations in Markov chain Monte Carlo simulations using the local Metropolis algorithm and the Svendsen-Wang and Wolff cluster algorithms. We examine the dynamics of these algorithms for two models in different universality classes: the Ising model and the Potts model with three components. The overlap of two ...

Added: April 20, 2026

Using predefined vector systems to speed up neural network multimillion class classification

Gabdullin N., Androsov I., / Series Computer Science "arxiv.org". 2026.

Label prediction in neural networks (NNs) has O(n) complexity proportional to the number of classes. This holds true for classification using fully connected layers and cosine similarity with some set of class prototypes. In this paper we show that if NN latent space (LS) geometry is known and possesses specific properties, label prediction complexity can ...

Added: April 2, 2026

Iterative Ricci-Foster Curvature Flow with GMM-Based Edge Pruning: A Novel Approach to Community Detection

Sorokin K., Beketov M., Онучин А. et al., / arxiv.org. Серия cs.SI "Social and Information Networks ". 2025.

Community detection in complex networks is a fundamental problem, open to new approaches in various scientific settings. We introduce a novel community detection method, based on Ricci flow on graphs. Our technique iteratively updates edge weights (their metric lengths) according to their (combinatorial) Foster version of Ricci curvature computed from effective resistance distance between the ...

Added: January 15, 2026

Implementing Transport Coding in OMNeT++ for Message Delay Reduction

Petrovanov I., Sergeev A., / Series Computer Science "arxiv.org". 2025. No. 2512.18332.

Transport coding reduces message delay in packet-switched networks by introducing controlled redundancy at the transport layer: original packets are encoded into coded packets, and the message is reconstructed after the first successful deliveries, effectively shifting latency from the maximum packet delay to the -th order statistic. We present a concise, reproducible discrete-event implementation of transport coding in OMNeT++, including ...

Added: December 24, 2025

Hessian-based lightweight neural network for brain vessel segmentation on a minimal training dataset

Меньшиков И. А., Бернадотт А. К., Elvimov N. S., / Series arXie "Statistical mechanics". 2025.

Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image ...

Added: December 1, 2025

Determining the boundary of dynamical chaos in the generalized Chirikov map via machine learning

Chernyshov D., Satanin A., Shchur L., / Series arXiv "math". 2025.

We investigate the boundary separating regular and chaotic dynamics in the generalized Chirikov map, an extension of the standard map with phase-shifted secondary kicks. Lyapunov maps were computed across the parameter space (K,K(α, τ)) and used to train a convolutional neural network (ResNet18) for binary classification of dynamical regimes. The model reproduces the known critical ...

Added: November 21, 2025

Эффективный алгоритм торговли на фондовом рынке: ретроспективный анализ, основанный на данных по S&P-500.

Rubchinskiy A., Chubarova D., / Series WP7 "Математические методы анализа решений в экономике, бизнесе и политике". 2025. No. WP7/2025/01.

The article examines one of the most famous examples of socio-economic systems, characterized by significant uncertainty – the S&P-500 stock market, where shares of 500 largest US companies are traded. No assumptions are made about the probabilistic characteristics of the stock market. A flexible algorithm for daily trading has been developed, based on both known fixed data ...

Added: November 9, 2025

Refining the analytic hierarchy process: A statistical and methodological exploration of expert judgments

Lukinskiy V., Lukinsky V., Bazhina D., AEJ - Alexandria Engineering Journal 2025 No. 125 P. 526–536

The research aims to bridge the gap between theoretical assumptions and the actual behavior of experts in real- world scenarios. This gap has significant practical implications, as it affects the validity of the decisions made using AHP. The adjustments proposed are based on a deep statistical and methodological analysis. The analysis of nearly 500 expert ...

Added: September 1, 2025

Prioritization of Alternatives Based on Analytic Hierarchy Process Using Interval Type-2 Fuzzy Sets and Probability-Theoretical Interval Comparison

Konstantin Y. Degtiarev, Borisov M., International Journal of the Analytic Hierarchy Process 2018 Vol. 10 No. 3 P. 447–468

The Analytic Hierarchy Process (AHP) is aimed at enabling decision-makers to prioritize alternatives. However, when expert expresses judgments using natural language statements (e.g. words or phrases), they can be interpreted not precisely due to inherent vagueness of the language constructs. Fuzzy Analytic Hierarchy Process (FAHP) can be viewed in the context of the classical AHP ...

Added: December 20, 2018