| Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai SA Budennyy, VD Lazarev, NN Zakharenko, AN Korovin, OA Plosskaya, ... Doklady mathematics 106 (Suppl 1), S118-S128, 2022 | 261 | 2022 |
| Image processing and machine learning approaches for petrographic thin section analysis S Budennyy, A Pachezhertsev, A Bukharev, A Erofeev, D Mitrushkin, ... SPE Russian petroleum technology conference, D023S014R005, 2017 | 69* | 2017 |
| Anomaly detection in electroluminescence images of heterojunction solar cells A Korovin, A Vasilev, F Egorov, D Saykin, E Terukov, I Shakhray, ... Solar Energy 259, 130-136, 2023 | 47 | 2023 |
| Atomic structure and energy spectrum of Ga (As, P)/GaP heterostructures DS Abramkin, MA Putyato, SA Budennyy, AK Gutakovskii, BR Semyagin, ... Journal of Applied Physics 112 (8), 2012 | 44 | 2012 |
| Hierarchical waste detection with weakly supervised segmentation in images from recycling plants D Yudin, N Zakharenko, A Smetanin, R Filonov, M Kichik, V Kuznetsov, ... Engineering Applications of Artificial Intelligence 128, 107542, 2024 | 35 | 2024 |
| Semi-analytical models for calculating well interference: limitations and applications (Russian) IF Khatmullin, AP Tsanda, AM Andrianova, SA Budenny, AS Margarit, ... Oil Industry Journal 2018 (12), 38-41, 2018 | 29* | 2018 |
| The task of instance segmentation of mineral grains in digital images of rock samples (thin sections) A Bukharev, S Budennyy, O Lokhanova, B Belozerov, E Zhukovskaya 2018 International Conference on Artificial Intelligence Applications and …, 2018 | 21 | 2018 |
| Application of machine learning for oilfield data quality improvement A Andrianova, M Simonov, D Perets, A Margarit, D Serebryakova, ... SPE Russian Petroleum Technology Conference, D023S029R005, 2018 | 20* | 2018 |
| Data-driven stochastic AC-OPF using Gaussian process regression M Mitrovic, A Lukashevich, P Vorobev, V Terzija, S Budennyy, Y Maximov, ... International Journal of Electrical Power & Energy Systems 152, 109249, 2023 | 19 | 2023 |
| Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity H Pham, K Egorov, A Kazakov, S Budennyy Frontiers in Cardiovascular Medicine, 2023 | 18 | 2023 |
| Optimization of multi-stage hydraulic fracturing design in conditions of Bazhenov formation (Russian) AV Bochkarev, SA Budennyy, RN Nikitin, DA Mitrushkin, AA Erofeev, ... Oil Industry Journal 2017 (03), 50-53, 2017 | 16* | 2017 |
| Boosting heterogeneous catalyst discovery by structurally constrained deep learning models AN Korovin, IS Humonen, AI Samtsevich, RA Eremin, AI Vasilev, ... Materials Today Chemistry 30, 101541, 2023 | 15 | 2023 |
| A new approach to clastic rocks pore-scale topology reconstruction based on automatic thin-section images and CT scans analysis V Krutko, B Belozerov, S Budennyy, E Sadikhov, O Kuzmina, D Orlov, ... SPE Annual Technical Conference and Exhibition?, D021S020R005, 2019 | 14 | 2019 |
| Automatic analysis of petrographic thin section images of sandstone AY Bukharev, SA Budennyy, AA Pachezhertsev, BV Belozerov, EA Zhuk ECMOR XVI-16th European Conference on the Mathematics of Oil Recovery 2018 …, 2018 | 14 | 2018 |
| ESGify: Automated classification of environmental, social, and corporate governance risks A Kazakov, S Denisova, I Barsola, E Kalugina, I Molchanova, I Egorov, ... Doklady Mathematics 108 (Suppl 2), S529-S540, 2023 | 13* | 2023 |
| New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements S Budennyy, A Kazakov, E Kovtun, L Zhukov Scientific Reports 13 (1), 12817, 2023 | 13 | 2023 |
| Hybrid dft/data-driven approach for searching for new quasicrystal approximants in sc-x (x= rh, pd, ir, pt) systems RA Eremin, IS Humonen, PN Zolotarev, IV Medrish, LE Zhukov, ... Crystal Growth & Design 22 (7), 4570-4581, 2022 | 13 | 2022 |
| Graph neural networks for predicting structural stability of Cd-and Zn-doped γ-CsPbI3 RA Eremin, IS Humonen, AA Kazakov, VD Lazarev, AP Pushkarev, ... Computational Materials Science 232, 112672, 2024 | 12 | 2024 |
| Benchmarking machine learning models for predicting lithium ion migration AD Dembitskiy, IS Humonen, RA Eremin, DA Aksyonov, SS Fedotov, ... npj Computational Materials 11 (1), 131, 2025 | 10 | 2025 |
| Assessing the transport connectivity of urban territories, based on intermodal transport accessibility AS Morozov, GI Kontsevik, SA Mityagin, IA Shmeleva, L Scheider Frontiers in Built Environment 9, 95, 2023 | 10 | 2023 |
Korovin AlexeyArtificial Intelligence Research Institute, Moscow, RussiaVerified email at airi.net
