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Explainable Glaucoma Screening via Optic Disc Localization and Comparative Class Activation Map-Based Analysis
Machine Learning and Knowledge Extraction. 2026. Vol. 8. No. 7. Article 173.
Ramos-Soto O., Perez-Zarate E., Ramos-Frutos J., Oliva D., Pérez-Cisneros M., Sosa Gómez G., Balderas-Mata S.
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
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
Vasilev R., Savchenko A., Blinov P. et al., Frontiers in Medicine 2026 Vol. 13 Article 1778404
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
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Avdoshin S. M., Pesotskaya E. Y., Business Informatics 2026 Vol. 20 No. 1 P. 7–28
The rapid development of artificial intelligence (AI) is accompanied by increasing computational
complexity and decreasing model transparency, which significantly limits its adoption in critical
domains that require a high level of trust, interpretability, and justification of decisions. Under these
conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it
focuses on approaches and technologies that ...
Added: May 8, 2026
Belov A. V., Fedotov G., , in: Proceedings of the 2025 INTERNATIONAL CONFERENCE "QUALITY MANAGEMENT, DIGITAL SECURITY, INFORMATION TECHNOLOGIES" (2025 QM&DS&IT).: IEEE, 2025. Ch. 1 P. 3–7.
In recent years, significant progress has been observed as content generated using AI technologies. In addition, tools regularly appear with which scammers can create a realistic fake content. Deepfake detection methods are currently actively used in the activities of financial organizations. With their help, a departments within financial organizations responsible for IT Security identify cases ...
Added: April 17, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2026 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2026. P. 842–847.
This article analyzes the evolution of digital image manipulation detection methods over the 2016–2026 decade. It examines the transition from classic passive methods (ELA, metadata analysis, and noise pattern analysis) to deep neural network architectures (VGG16+U-Net, ManTra-Net, SPAN, and RDS-YOLOv5) and then to multimodal systems based on large language models (ForgeryGPT and FakeShield), which provide ...
Added: February 25, 2026
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
Shalileh S., Koptseva A., Shishkovskaya T. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2023 Т. 514 № 2 С. 242–249
This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group ...
Added: February 2, 2024
Kalinin S., Kovalenko A., Valtari A. et al., Journal of Enzyme Inhibition and Medicinal Chemistry 2022 Vol. 37 No. 1 P. 1005–1011
Hydrophilic derivatives of an earlier described series of carbonic anhydrase inhibitors have been designed, prepared and profiled against a panel of carbonic anhydrase isoforms, including the glaucoma-related hCA II. For all hydrophilic derivatives, computational prediction of intraocular permeability routes showed the predominance of conjunctival rather than corneal absorption. The potentially reactive primary or secondary amine periphery ...
Added: January 26, 2024
Krivovicheva V., Bubyrev A., Kalinin S. et al., ChemMedChem 2023 Vol. 18 No. 10 Article e202200607
A newly introduced diazo reagent, 1-diazo-N,N-bis(4-methoxybenzyl)methanesulfonamide, enables access to a range of azole-based primary sulfonamides via [3+2] cycloaddition followed by protecting group removal. Such compounds are representative of the sulfonamide chemical space highly relevant but hitherto not investigated in the context of inhibition of therapeutically relevant isoforms of carbonic anhydrase enzyme. Using this reagent, three ...
Added: January 26, 2024
Baklanova V., Kurkin A., Teplova T., China Finance Review International 2024 Vol. 14 No. 3 P. 522–548
Purpose – The primary objective of this research is to provide a precise interpretation of the constructed
machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype
index was constructed as well as several approaches of ...
Added: December 10, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: FCA4AI 2023 What can FCA do for Artificial Intelligence 2023 Proceedings of the 11th International Workshop "What can FCA do for Artificial Intelligence?" co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Macao, S.A.R. China; August 20, 2023Vol. 3489.: CEUR-WS.org, 2023. P. 69–80.
Rule Learning and Formal Concept Analysis (FCA) are two fields of science that study similar topic yet speak in a very different terms. This paper describes rule-based machine learning models with FCA-based terminology which results in decision quiver model. A decision quiver, discussed in the paper, is a supervised machine learning model that is based ...
Added: October 4, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: 17th International Conference, ICFCA 2023, Kassel, Germany, July 17–21, 2023, Proceedings. Formal Concept Analysis, (LNCS, volume 13934).: Switzerland: Springer, 2023. P. 127–142.
In this paper we introduce and study description quivers as compact representations of concept lattices and respective ensembles of decision trees. Formally, description quivers are directed multigraphs where vertices represent concept intents and (multiple) edges represent generators of intents. We study some properties of description quivers and shed light on their use for describing state-of-the-art symbolic machine ...
Added: October 4, 2023
Parakal E. G., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. Ch. 2 P. 9–22.
Explanations for the predictions made by Machine Learning (ML) models are best framed in terms of
abstract, high-level concepts that are easily comprehensible to human beings. The use of such concepts
constitutes a subfield of interpretability methods known as concept-based explanations. This work uses
concept-based explanations to build an intrinsically interpretable document classifier using a combination
of Formal Concept ...
Added: May 17, 2023
Dudyrev E., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 23–34.
Studies on Explainable Artificial Intelligence show that a model should be small in order to be human understandable. The restriction on the size of a model drastically reduces the space of possible solutions. Many rule learning models still rely on greedy algorithms for generating ensembles of decision trees. This paper discusses FCA-inspired mathematical and engineering ...
Added: November 1, 2022
Suvorova A., , in: Digital Transformation and Global Society. 6th International Conference, DTGS 2021, St. Petersburg, Russia, June 23–25, 2021, Revised Selected Papers.: Springer, 2022. P. 319–331.
The increasing use of intelligent technologies, the development and implementation of machine learning systems in various spheres of life require explaining machine learning-based decisions in such systems. This need for interpretation leads to the increasing development of new methods for interpreting machine learning models and their more intense use in real systems. The paper reviews ...
Added: September 28, 2022
Ignatov D. I., Kwuida L., , in: Ontologies and Concepts in Mind and Machine. 25th International Conference on Conceptual Structures, ICCS 2020.: Springer, 2020. P. 90–102.
Among the family of rule-based classification models, there are classifiers based on conjunctions of binary attributes. For example, the JSM-method of automatic reasoning (named after John Stuart Mill) was formulated as a classification technique in terms of intents of formal concepts as classification hypotheses. These JSM-hypotheses already represent an interpretable model since the respective conjunctions ...
Added: October 30, 2020
Ignatov D. I., Kwuida L., , in: Proceedings of the Fifthteenth International Conference on Concept Lattices and Their ApplicationsVol. 2668.: CEUR-WS.org, 2020. P. 259–271.
We propose the usage of two power indices from cooperative game theory and public choice theory for ranking attributes of closed sets, namely intents of formal concepts (or closed itemsets). The introduced indices are related to extensional concept stability and based on counting generators, especially those that contain a selected attribute. The introduction of such ...
Added: October 30, 2020
Kliuchnikova A., Samokhina N., Ilina I. et al., Proteomics 2016 Vol. 16 No. 13 P. 1938–1946
Twenty-nine human aqueous humor samples from patients with eye diseases such as cataract and glaucoma with and without pseudoexfoliation syndrome were characterized by LC-high resolution MS analysis. In total, 269 protein groups were identified with 1% false discovery rate including 32 groups that were not reported previously for this biological fluid. Since the samples were analyzed individually, but not pooled, 36 proteins were identified ...
Added: March 14, 2018