Articles in journals

  1. Zymbler M., Goglachev A. PaSTiLa: Scalable Parallel Algorithm for Unsupervised Labeling of Long Time Series // Lobachevskii Journal of Mathematics. 2024. Vol. 45, No. 3. P. 1333–1347. PDF DOI: 10.1134/S1995080224600766 (accepted for publication)
  2. Zymbler M., Kraeva Y. High-Performance Time Series Anomaly Discovery on Graphics Processors // Mathematics. 2023. Vol. 11, No. 14. Article 3193. PDF DOI: 10.3390/math11143193 WOS:001039018700001 Scopus
  3. Kraeva Y., Zymbler M. A Parallel Discord Discovery Algorithm for a Graphics Processor. Pattern Recognition and Image Analysis. 2023. Vol. 33, No. 2. P. 101–112. PDF DOI: 10.1134/S1054661823020062 WOS:001022883000004 Scopus
  4. Zymbler M., Goglachev A. Fast Summarization of Long Time Series with Graphics Processor. Mathematics. 2022. Vol. 10, No. 10. Article 1781. PDF DOI: 10.3390/math10101781 WOS:000801317400001 Scopus
  5. Mohapatra M., Parida A.K., Mallick P.K., Zymbler M., Kumar S.  Botanical Leaf Disease Detection and Classification Using Convolutional Neural Network: A Hybrid Metaheuristic Enabled Approach. Computers. 2022. Vol. 11, No. 5. Article 82. PDF DOI: 10.3390/computers11050082 WOS:000803360000001
  6. Das A.K., Mishra D.K., Das K., Mallick P.K., Kumar S., Zymbler M., El-Sayed H. Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine. Mathematics. 2022. Vol. 10, No. 7. Article 1121. PDF DOI: 10.3390/math10071121 WOS:000781442300001 Scopus
  7. Nayak D.R., Padhy N., Mallick P.K., Zymbler M., Kumar S. Brain Tumor Classification Using Dense Efficient-Net. Axioms. 2022. Vol. 11, No. 1. Article 34. PDF DOI: 10.3390/axioms11010034 WOS:000757035700001 Scopus
  8. Zymbler M., Ivanova E. Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS. Mathematics. 2021. Vol. 9, No. 17. Article 2146. PDF DOI: 10.3390/math9172146 WOS:000694382500001 Scopus
  9. Pradhan A., Mishra D., Das K., Panda G., Kumar S., Zymbler M. On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model. Mathematics. 2021. Vol. 9, No. 17. Article 2095. PDF DOI: 10.3390/math9172095 WOS:000694331000001 Scopus
  10. Zymbler M., Grents A., Kraeva Ya., Kumar S. A Parallel Approach to Discords Discovery in Massive Time Series Data. Computers, Materials & Continua. 2021. Vol. 66, No. 2. P. 1867–1876. PDF DOI: 10.32604/cmc.2020.014232 WOS:000594856200001 Scopus
  11. Reddy A.V.N., Krishna C.P., Mallick P.K., Satapathy S.K., Tiwari P., Zymbler M., Kumar S. Analyzing MRI Scans to Detect Glioblastoma Tumor Using Hybrid Deep Belief Networks. Journal of Big Data. 2020. Vol. 7. Article 35. PDF DOI: 10.1186/s40537-020-00311-y WOS:000596096600001 Scopus
  12. Zymbler M., Kraeva Ya. Discovery of Time Series Motifs on Intel Many-Core Systems. Lobachevskii Journal of Mathematics. 2019. Vol. 40, No. 12. P. 2124–2132. PDF DOI: 10.1134/S199508021912014X WOS:000514534200013 Scopus 
  13. Kumar S., Tiwari P., Zymbler M. Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big Data. 2019. Vol. 6. Article 111. PDF WOS:000599145900001 Scopus DOI: 10.1186/s40537-019-0268-2
  14. Kumar S., Zymbler M. A Machine Learning Approach to Analyze Customer Satisfaction from Airline Tweets. Journal of Big Data. 2019. Vol. 6. Article 62. PDF WOS:000599136700001 Scopus DOI: 10.1186/s40537-019-0224-1
  15. Zymbler M. Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems. Journal of Computing and Information Technology. 2018. Vol. 26, No. 4. P. 209–221. PDF DOI: 10.20532/cit.2018.1004382 Scopus
  16. Movchan A.V., Zymbler M.L. Parallel Algorithm for Local-best-match Time Series Subsequence Similarity Search on the Intel MIC Architecture. Procedia Computer Science. 2015. Vol. 66. P. 63–72. PDF DOI: 10.1016/j.procs.2015.11.009 WOS:000373782500008 Scopus
  17. Pan C.S., Zymbler M.L. Encapsulation of Partitioned Parallelism into Open-Source Database Management Systems. Programming and Computer Software. 2015. Vol. 41, No. 6. P. 350–360. PDF DOI: 10.1134/S0361768815060067 WOS:000364955300005 Scopus

Papers in proceedings

  1. Zymbler M., Kraeva Ya., Latypova E., Kumar S., Shnayder D., Basalaev A. Cleaning Sensor Data in Smart Heating Control System. Proceedings of 2020 Global Smart Industry Conference, GloSIC 2020, Chelyabinsk, Russia, November 17–19, 2020. P. 375–381. PDF Scopus WOS:000646231600061 DOI: 10.1109/GloSIC50886.2020.9267813 
  2. Ivanov S., Nikolskaya N., Radchenko G., Sokolinsky L., Zymbler M. Digital Twin of City: Concept Overview. Proceedings of 2020 Global Smart Industry Conference, GloSIC 2020, Chelyabinsk, Russia, November 17–19, 2020. P. 178–186. PDF Scopus WOS:000646231600029 DOI: 10.1109/GloSIC50886.2020.9267879 
  3. Zymbler M., Kraeva Ya., Grents A., Perkova A., Kumar S. An Approach to Fuzzy Clustering of Big Data Inside a Parallel Relational DBMS. 21st International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2019, Kazan, Russia, October 16–18, 2019, Revised Selected Papers. Communications in Computer and Information Science. 2020. Vol. 1223. P. 211–223. PDF Scopus DOI: 10.1007/978-3-030-51913-1_14
  4. Kumar S., Kraeva Ya., Kraleva R., Zymbler M. A Deep Neural Network Approach to Predict the Wine Taste Preferences. Intelligent Computing in Engineering. Select Proceedings of RICE 2019, 4th International Conference on Research in Intelligent and Computing in Engineering, August 8–9, 2019, Hanoi, Vietnam. Advances in Intelligent Systems and Computing. 2020. Vol. 1125. P. 1165–1174. PDF Scopus DOI: 10.1007/978-981-15-2780-7_120
  5. Zymbler M., Polyakov A., Kipnis M. Time Series Discord Discovery on Intel Many-Core Systems. 13th International Conference, PCT 2019, Kaliningrad, Russia, April 2–4, 2019, Revised Selected Papers. Communications in Computer and Information Science. 2019. Vol. 1063. P. 168–182. PDF Scopus WOS:000558285700012 DOI: 10.1007/978-3-030-28163-2_12
  6. Kraeva Ya., Zymbler M. Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL. 20th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2018, Moscow, Russia, October 9–12, 2018, Revised Selected Papers. Communications in Computer and Information Science. 2019. Vol. 1003. P. 149–164. PDF Scopus DOI: 10.1007/978-3-030-23584-0_9
  7. Kraeva Ya., Zymbler M. An Efficient Subsequence Similarity Search on Modern Intel Many-core Processors for Data Intensive Applications. Selected Papers of the XX International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2018, Moscow, Russia, October 9–12, 2018.  CEUR Workshop Proceedings. 2018. Vol. 2277. P. 143–151. URL PDF Scopus
  8. Faizullin A., Zymbler M., Lieftucht D., Fanghänel F. Use of Deep Learning for Sticker Detection During Continuous Casting. Proceedings of 2018 Global Smart Industry Conference, GloSIC 2018, Chelyabinsk, Russia, November 13–15, 2018. IEEE, 2018. Article no. 8570155. PDF WOS:000462287600095 Scopus DOI: 10.1109/GloSIC.2018.8570155
  9. Rechkalov T., Zymbler M. A Study of Euclidean Distance Matrix Computation on Intel Many-Core Processors. 12th International Conference, PCT 2018, Rostov-on-Don, Russia, April 2–6, 2018, Revised Selected Papers. Communications in Computer and Information Science. 2018. Vol. 910. P. 200–215. PDF DOI: 10.1007/978-3-319-99673-8_15 WOS:000521733800015 Scopus
  10. Rechkalov T., Zymbler M. Integrating DBMS and Parallel Data Mining Algorithms for Modern Many-Core Processors. 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, Moscow, Russia, October 10–13, 2017, Revised Selected Papers. Communications in Computer and Information Science. 2018. Vol. 822. P. 230–245. PDF DOI: 10.1007/978-3-319-96553-6_17 Scopus
  11. Rechkalov T., Zymbler M. An Approach to Data Mining Inside PostgreSQL Based on Parallel Implementation of UDFs. Selected Papers of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017), Moscow, Russia, October 9–13, 2017. CEUR Workshop Proceedings. 2017. Vol. 2022. P. 114–121. URL PDF Scopus
  12. Zymbler M. Accelerating Dynamic Itemset Counting on Intel Many-core Systems. Proceedings of the 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO'2017, Opatija, Croatia, May 22–26, 2017. IEEE, 2017. P. 1575–1580. PDF DOI: 10.23919/MIPRO.2017.7973631 WOS:000426903800234 Scopus
  13. Movchan A.V., Zymbler M.L. Parallel Implementation of Searching the Most Similar Subsequence in Time Series for Computer Systems with Distributed Memory. Proceedings of the 10th Annual International Scientific Conference on Parallel Computing Technologies (PCT 2016). Arkhangelsk, Russia, March 29–31, 2016. CEUR Workshop Proceedings. 2016. Vol. 1576. P. 615–628. URL PDF Scopus
  14. Rechkalov T.V., Zymbler M.L. Accelerating Medoids-based Clustering with the Intel Many Integrated Core Architecture. Proceedings of the 9th International Conference on Application of Information and Communication Technologies (AICT'2015), October 14–16, 2015, Rostov-on-Don, Russia. IEEE, 2015. P. 413–417. PDF DOI: 10.1109/ICAICT.2015.7338591 WOS:000380404000088 Scopus
  15. Miniakhmetova M.S., Zymbler M.L. An Approach to Personalized Video Summarization Based on User Preferences Analysis. Proceedings of the 9th International Conference on Application of Information and Communication Technologies (AICT'2015), October 14–16, 2015, Rostov-on-Don, Russia. IEEE, 2015. P. 153–155. PDF DOI: 10.1109/ICAICT.2015.7338536 WOS:000380404000033 Scopus
  16. Movchan A.V., Zymbler M.L. Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators. Proceedings of the 8th International Conference on Similarity Search and Applications, SISAP 2015 (Glasgow, Scotland, UK, October 12–14, 2015). Lecture Notes in Computer Science. Vol. 9371. Springer, 2015. P. 295–306. PDF DOI: 10.1007/978-3-319-25087-8_28 WOS:000374289600028 Scopus
  17. Movchan A.V., Zymbler M.L. Parallel algorithm for local-best-match time series subsequence similarity search on the Intel MIC architecture. Proceedings of the 1st Russian Conference on Supercomputing - Supercomputing Days (RuSCDays 2015). Moscow, Russian Federation, September 28–29, 2015. CEUR Workshop Proceedings. 2015. Vol. 1482. P. 332–343. URL PDF Scopus
  18. Zymbler M.L. Best-match Time Series Subsequence Search on the Intel Many Integrated Core Architecture. Proceedings of the 19th East-European Conference on Advances in Databases and Information Systems, ADBIS 2015 (Poitiers, France, September 8–11, 2015). Lecture Notes in Computer Science. Vol. 9282. Springer, 2015. P. 275–286. PDF DOI: 10.1007/978-3-319-23135-8_19 WOS:000364683000023 Scopus
  19. Miniakhmetov R.M., Movchan A.V., Zymbler M.L. Accelerating Time Series Subsequence Matching on the Intel Xeon Phi Many-core Coprocessor. Proceedings of the 38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO'2015, Opatija, Croatia, May 25–29, 2015. IEEE, 2015. P. 1675–1680. PDF DOI: 10.1109/MIPRO.2015.7160493 Scopus WOS:000380405300217
  20. Pan C.S., Zymbler M.L. Very Large Graph Partitioning by Means of Parallel DBMS. Proceedings of the 17th East-European Conference on Advances in Databases and Information Systems, ADBIS 2013 (Genoa, Italy, September 1–4, 2013). Lecture Notes in Computer Science. 2013. Vol. 8133. Springer, 2013. P. 388–399. PDF DOI: 10.1007/978-3-642-40683-6_29 Scopus
  21. Pan C.S., Zymbler M.L. Taming Elephants, or How to Embed Parallelism into PostgreSQL. Proceedings of the Database and Expert Systems Applications – 24th International Conference, DEXA 2013 (Prague, Czech Republic, August 26–29, 2013). Lecture Notes in Computer Science. 2013. Vol. 8055. Part I. Springer, 2013. P. 153–164. PDF DOI: 10.1007/978-3-642-40285-2_15 Scopus

Editorial activity

  • Scpecial Issues
  1. Special Issue "Parallel Computing and Applications", Mathematics. 2022. Vol. 10, No. 10.
  2. Special Issue "Intelligent Computing in Industry Applications", Mathematics. 2021. Vol. 9, No. 17.
  • Conference Proceedings
  1. Parallel Computational Technologies 2023. Communications in Computer and Information Science. 2023. Vol. 1868
  2. Parallel Computational Technologies 2022. Communications in Computer and Information Science. 2022. Vol. 1618
  3. Parallel Computational Technologies 2021. Communications in Computer and Information Science. 2021. Vol. 1437
  4. Parallel Computational Technologies 2020. Communications in Computer and Information Science. 2020. Vol. 1263
  5. Parallel Computational Technologies 2019. Communications in Computer and Information Science. 2019. Vol. 1063
  6. Parallel Computational Technologies 2018. Communications in Computer and Information Science. 2018. Vol. 910
  7. Parallel Computational Technologies 2017. Communications in Computer and Information Science. 2017. Vol. 753

Modified: 25.03.2024, © Mikhail Zymbler