Informačná stránka zamestnanca SAV

Publikačná činnosť

Ing. Michal Staňo

Publikačná činnosť obsahuje údaje z on-line databázy Ústrednej Knižnice SAV.

• STAŇO, Michal - HLUCHÝ, Ladislav - BOBÁK, Martin - KRAMMER, Peter - TRAN, Viet. Federated learning methods for analytics of big and sensitive distributed data and survey. In IEEE 17th international symposium on applied computational intelligence and informatics (SACI 2023) : Proceedings. - Danvers, US : IEEE, 2023, p. 705-710. ISBN 979-8-3503-2109-8. Dostupné na: https://doi.org/10.1109/SACI58269.2023.10158622 (IEEE 17th international symposium on applied computational intelligence and informatics.) Typ: ADMB
  Citácie:

  [1.1] BAABDULLAH, Tahani - ALZAHRANI, Amani - RAWAT, Danda B. - LIU, Chunmei. Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems. In FUTURE INTERNET, 2024, vol. 16, no. 6, pp. ISSN 1999-5903. Dostupné na: https://doi.org/10.3390/fi16060196, Registrované v: WOS
  [1.1] NJUNGLE, Nges Brian - JAHNS, Eric - WU, Zhenqi - MASTROMAURO, Luigi - STOJKOV, Milan - KINSY, Michel A. GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks. In IEEE ACCESS, 2025, vol. 13, no., pp. 61483-61510. ISSN 2169-3536. Dostupné na: https://doi.org/10.1109/ACCESS.2025.3557228, Registrované v: WOS
  [1.1] NUGROHO, Kukuh - HENDRAWAN - ISKANDAR. Comparative Analysis of Federated and Centralized Learning Systems in Predicting Cellular Downlink Throughput Using CNN. In IEEE ACCESS, 2025, vol. 13, no., pp. 22745-22763. ISSN 2169-3536. Dostupné na: https://doi.org/10.1109/ACCESS.2025.3528527, Registrované v: WOS
  [1.2] JAIN, Arpit - JAT, Dharm Singh. High-Performance Federated Learning Techniques for Deep Learning on Medical Dataset. In Smart Innovation, Systems and Technologies, 2024-01-01, 403 SIST, pp. 387-399. ISSN 21903018. Dostupné na: https://doi.org/10.1007/978-981-97-5799-2_33, Registrované v: SCOPUS
  [1.2] SI, Pengbo - LI, Shuangyuan - LIU, Chang - LI, Meng. Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces. In Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology, 2025-01-01, 51, 1, pp. 87-99. ISSN 02540037. Dostupné na: https://doi.org/10.11936/bjutxb2023110015, Registrované v: SCOPUS
  [1.2] UPADHYAY, Tejal - PATADIA, Divya - MITTAL, Sonia. Federated Learning for Enhanced Deep Learning Integration: A Practical Approach. In Lecture Notes in Electrical Engineering, 2024-01-01, 1195 LNEE, pp. 3-14. ISSN 18761100. Dostupné na: https://doi.org/10.1007/978-981-97-3442-9_1, Registrované v: SCOPUS
  [1.2] WANG, Pengfei - YU, Xinrui - YE, Yefei - QI, Heng - YU, Shuo - YANG, Leyou - ZHANG, Qiang. Anomalous Behavior Identification with Visual Federated Learning in Multi-UAVs Systems. In Proceedings of the International Conference on Parallel and Distributed Systems ICPADS, 2023-01-01, pp. 2143-2150. ISSN 15219097. Dostupné na: https://doi.org/10.1109/ICPADS60453.2023.00290, Registrované v: SCOPUS
  [3.1] BEKTEMYSSOVA, G. - BAKIROVA, G. Comparative analysis of federated machine learning algorithms. In Scientific Journal of Astana IT University, 2024, vol. 17, pp. 57-67. doi: 10.37943/17BVCN7579.
  [3.1] SHERPA, L. - BANERJI, N. Federated learning-hope and scope. In IgMin Research. 2023, vol. 1, no. 1, pp. 22-24. doi: 10.61927/igmin112.
• STAŇO, Michal - HLUCHÝ, Ladislav - DORA, Jean Rosemond - NGUYEN, Ngoc Diep. Federated learning for offensive security in cyber-medical systems inspired by Petri nets. In ICCC 2024 - IEEE 11th International Conference on Computational Cybernetics and Cyber-Medical Systems : Proceedings, 2024, 77-82 pp. ISSN 2471-9269. Dostupné na: https://doi.org/10.1109/ICCC62278.2024.10582955 (ICCC 2024 : IEEE 11th International Conference on Computational Cybernetics and Cyber-Medical Systems.) Typ: ADMB
• STAŇO, Michal - HLUCHÝ, Ladislav. A state-of-the-art survey on local training methods in federated learning. In IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings. - Budapest, Hungary : IEEE, 2023, p. 89-92. ISBN 979-8-3503-4294-9. Dostupné na: https://doi.org/10.1109/CINTI59972.2023.10381965 (CINTI 2023 : IEEE 23rd International Symposium on Computational Intelligence and Informatics.) Typ: ADMB
  Citácie:

  [1.1] NJUNGLE, Nges Brian - JAHNS, Eric - WU, Zhenqi - MASTROMAURO, Luigi - STOJKOV, Milan - KINSY, Michel A. GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks. In IEEE ACCESS, 2025, vol. 13, no., pp. 61483-61510. ISSN 2169-3536. Dostupné na: https://doi.org/10.1109/ACCESS.2025.3557228, Registrované v: WOS
  [1.2] KAR, Indrajit - MUKHOPADHYAY, Sudipta - RALTE, Zonunfeli. Toward Foundation Models: A Simple Approach for Building Large Event Recognition Models [LERM] Using Federated Multi-instance Knowledge Distillation. In Smart Innovation Systems and Technologies, 2025-01-01, 421, pp. 51-62. ISBN [9789819601424]. ISSN 21903018. Dostupné na: https://doi.org/10.1007/978-981-96-0143-1_5, Registrované v: SCOPUS
• STAŇO, Michal - HLUCHÝ, Ladislav - KRAMMER, Peter - DLUGOLINSKÝ, Štefan - KVASSAY, Marcel - TRAN, Viet. Classification of tree species by federated learning. In SISY 2023 - IEEE 21st International Symposium on Intelligent Systems and Informatics : Proceedings. - Budapest, Hungary : IEEE, 2023, p. 333-339. ISBN 979-8-3503-4336-6. Dostupné na: https://doi.org/10.1109/SISY60376.2023.10417957 (SISY 2023 : IEEE 21st International Symposium on Intelligent Systems and Informatics.) Typ: ADMB
• DORA, Jean Rosemond - HLUCHÝ, Ladislav** - STAŇO, Michal**. In-memory shellcode runner detection in internet of things (IoT) networks: a lightweight behavioral and semantic analysis framework. In Sensors, 2025, vol. 25, no. 17, art. no. 5425. (2024: 3.5 - IF, Q2 - JCR, 0.764 - SJR, Q1 - SJR). ISSN 1424-8220. Dostupné na: https://doi.org/10.3390/s25175425 Typ: ADCA
  Citácie:

  [1.1] RAI, Andri - IM, Eul Gyu. MemCatcher: An In-Depth Analysis Approach to Detect In-Memory Malware. In APPLIED SCIENCES-BASEL, 2025, vol. 15, no. 21, art. no. 11800. Dostupné na: https://doi.org/10.3390/app152111800, Registrované v: WOS
• HABALA, Ondrej - ŠELENG, Martin - HABALA, Michal - STUHL, Ľubor - STAŇO, Michal - HLUCHÝ, Ladislav. Scalable cloud application deployment service for versatile cloud service deployment and configuration. In Computing and informatics, 2024, vol. 43, no. 6, p. 1416-1431. (2023: 0.7 - IF, Q4 - JCR, 0.258 - SJR, Q3 - SJR). ISSN 1335-9150. Dostupné na: https://doi.org/10.31577/cai_2024_6_1416 Typ: ADDA
• KRAMMER, Peter - HABALA, Ondrej - STAŇO, Michal - HLUCHÝ, Ladislav. Fine-tuning the high-voltage tower pollution model using distribution identification. In SISY 2024 - IEEE 22nd International Symposium on Intelligent Systems and Informatics : Proceedings. - Danvers : IEEE, 2024, p. 53-58. ISBN 979-8-3503-8560-1. Dostupné na: https://doi.org/10.1109/SISY62279.2024.10737530 (SISY 2024 : IEEE 22nd International Symposium on Intelligent Systems and Informatics.) Typ: ADMB
• SKOVAJSOVÁ, Lenka - HLUCHÝ, Ladislav - STAŇO, Michal. A review of multi-objective and multi-task federated learning approaches. In IEEE 23rd world symposium on applied machine intelligence and informatics : SAMI 2025. Proceedings. - Danvers : IEEE, 2025, p. 35-40. ISBN 979-8-3503-7936-5. ISSN 2767-9438. Dostupné na: https://doi.org/10.1109/SAMI63904.2025.10883172 (SAMI 2025 : IEEE 23rd world symposium on applied machine intelligence and informatics.) Typ: ADMB
  Citácie:

  [1.1] CHEN, Guihong - TONG, Mingyong - YIN, Jiao - WANG, Maojie - CAO, Jinli - WANG, Hua. SecureGraphFL: A Privacy-Preserving and Attack-Resilient Federated Learning Framework for Traffic Prediction. In IEEE INTERNET OF THINGS JOURNAL, 2025, vol. 12, no. 21, pp. 44988-44999. ISSN 2327-4662. Dostupné na: https://doi.org/10.1109/JIOT.2025.3599568, Registrované v: WOS
  [1.1] FARRUKH, Halima - ZAFAR, Sidra - REHMAN, Zia Ul - SHAH, Asghar Ali - ALSHAMMRY, Nizal. Blockchain-Based Fraud Detection: A Comparative Systematic Literature Review of Federated Learning and Machine Learning Approaches. In ELECTRONICS, 2025, vol. 14, no. 24, art. no. 4952. ISSN 2079-9292. Dostupné na: https://doi.org/10.3390/electronics14244952, Registrované v: WOS
  [1.1] MILLER, Tymoteusz - DURLIK, Irmina - KOSTECKA, Ewelina - KOZLOVSKA, Polina - NOWAK, Aleksander. Federated Learning for Decentralized Electricity Market Optimization: A Review and Research Agenda. In ENERGIES, 2025, vol. 18, no. 17, art. no. 4682. Dostupné na: https://doi.org/10.3390/en18174682, Registrované v: WOS
  [1.1] TIMOFTE, Edi Marian - DIMIAN, Mihai - GRAUR, Adrian - POTORAC, Alin Dan - BALAN, Doru - CROITORU, Ionut - HRITCAN, Daniel-Florin - PUSCASU, Marcel. Federated Learning for Cybersecurity: A Privacy-Preserving Approach. In APPLIED SCIENCES-BASEL, 2025, vol. 15, no. 12, art. no. 6878. Dostupné na: https://doi.org/10.3390/app15126878, Registrované v: WOS
  [1.2] BAO, Yuhao - GAO, Jinfeng - FENG, Dongqin - LI, Lebao. Edge Hierarchical Asynchronous Federated Learning Model for the Industrial Internet of Things. In Proceedings 2025 11th IEEE International Conference on Privacy Computing and Data Security Pcds 2025, 2025-01-01, pp. 139-144. ISBN [9781665477468]. Dostupné na: https://doi.org/10.1109/PCDS65695.2025.00027, Registrované v: SCOPUS
  [1.2] WEI, Ze - HE, Rongxi - SONG, Chengzhi - CHEN, Xiaojing - LI, Mingyuan. Contribution-Aware Incentive Mechanism for Clustered Federated Learning: A Stackelberg Game Approach. In IEEE Internet of Things Journal, 2026-01-01, 13, 4, pp. 6401-6421. Dostupné na: https://doi.org/10.1109/JIOT.2025.3635589, Registrované v: SCOPUS
• STAŇO, Michal - HLUCHÝ, Ladislav - KRAMMER, Peter - HUCKO, Michal. Docker survey for FLOps efficiency. In 2025 Cybernetics & Informatics (K&I) : 32nd International Conference. - Danvers, US : IEEE, 2025, p. 1-6. ISBN 979-8-3315-4181-1. ISSN 2767-875X. Dostupné na: https://doi.org/10.1109/KI64036.2025.10916451 (2025 Cybernetics & Informatics (K&I) : 32nd International Conference.) Typ: ADMB
  Citácie:

  [1.1] AHMADPANAH, Seyed Hossein - MIRABI, Meghdad - SAHAFI, Amir - ERFANI, Seyed Hossein. ATLAS: adaptive threat-learning algorithm for secure container migration in heterogeneous multi-cloud environments. In CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2025, vol. 28, no. 15, art. no. 987. ISSN 1386-7857. Dostupné na: https://doi.org/10.1007/s10586-025-05720-3, Registrované v: WOS
• ŠELENG, Martin** - DLUGOLINSKÝ, Štefan - STAŇO, Michal - HLUCHÝ, Ladislav. Model for named entity extraction from short fire event-related texts. In Procedia Computer Science, 2025, vol. 256, p. 557-564. (2024: 0.471 - SJR). ISSN 1877-0509. Dostupné na: https://doi.org/10.1016/j.procs.2025.02.152 Typ: ADMB
  Citácie:

  [1.1] BLSTAK, Miroslav - KOPCAN, Jaroslav - SUPPA, Marek - HARVAN, Samuel - FINDOR, Andrej - TAKAC, Martin - SIMKO, Marian. When the Dictionary Strikes Back: A Case Study on Slovak Migration Location Term Extraction and NER via Rule-Based vs. LLM Methods. In PROCEEDINGS OF THE 10TH WORKSHOP ON SLAVIC NATURAL LANGUAGE PROCESSING, SLAVIC NLP 2025 : 10th Workshop on Slavic Natural Language Processing-Slavic NLP, 2025, vol., no., pp. 91-100., Registrované v: WOS
  [1.2] ZHAO, Yufan - ZHANG, Wuyang - CHENG, Yu - XU, Zhaoyang - TIAN, Yexin - WEI, Zijing. Entity Boundary Detection in Social Texts Using BiLSTM-CRF with Integrated Social Features. In 2025 6th International Conference on Computer Engineering and Application Iccea 2025, 2025-01-01, pp. 1615-1619. ISBN [9798331543303]. Dostupné na: https://doi.org/10.1109/ICCEA65460.2025.11103146, Registrované v: SCOPUS
• STAŇO, Michal - ČIERNIK, Matej - HLUCHÝ, Ladislav - BOBÁK, Martin - DORA, Jean Rosemond. Enhancing UML model dynamics: a source code generation approach. In Acta Polytechnica Hungarica : journal of applied sciences at Budapest Tech Hungary, 2026, vol. 23, no. 2, p. 203-217. ISSN 1785-8860. Dostupné na internete: https://acta.uni-obuda.hu/Stano_Ciernik_Hluchy_Bobak_Dora_166.pdf Typ: ADMA
• STAŇO, Michal - HLUCHÝ, Ladislav - SKOVAJSOVÁ, Lenka - HUCKO, Michal - DORA, Jean Rosemond. Integration of hyperspectral data segmentation methods with QGIS and PDAL: from theory to reproducible practice. In IEEE 25th international symposium on computational intelligence and informatics : CINTI 2025. Proceedings. - Danvers : IEEE, 2025, p. 161-166. ISBN 979-8-3315-5291-6. Dostupné na: https://doi.org/10.1109/CINTI67731.2025.11311819 (CINTI 2025 : IEEE 25th international symposium on computational intelligence and informatics.) Typ: ADMB