Elie Saad, M. Paprzycki, M. Ganzha, Bădică, A.; Bădică, C.; Fidanova, S.; Lirkov, I.; Ivanović, M., Generalized Zero-Shot Learning for Image Classification—Comparing Performance of Popular Approaches, Information, 2022, Vol 13, 561 (16 pages) DOI: 10.3390/info13120561, PDF

M. Ganzha, A. Denisiuk, P. Sowiński, M. Paprzycki, K. Wasielewska, Multi-Domain Named Entity Recognition for Robotic Process Automation, Proceedings of the 56th Hawaii International Conference on System Sciences, University of Hawaii, Lahaina, 2023, 940-949, PDF

Jan Sawicki, M. Paprzycki, M. Ganzha, Amelia Badica, Exploring Usability of Reddit in Data Science and Knowledge Processing, Scalable Computing; Practice and Experiences, Vol. 23 No. 1, 2022, 9–21, DOI: 10.12694/scpe.v23i1.1957, PDF

Jaisal Singh, Srinivasan Natesan, M. Ganzha, M. Paprzycki, Experimenting with Assamese Handwritten Character Recognition, in: International Conference on Big Data Analytics, Lecture Notes in Computer Science, LNISA, Vol. 13167, Springer, Cham, 2022, 219-229, DOI: 10.1007/978-3-030-96600-3_16, PDF

Michał Kortała, Tatiana, Jaworska, M. Ganzha, M. Paprzycki, Skin Cancer Recognition for Low Resolution Images, in: International Conference on Big Data Analytics, Lecture Notes in Computer Science, LNISA, Vol. 13167, Springer, Cham, 2022, 122-148, DOI: 10.1007/978-3-030-96600-3_10, PDF

Elie Saad, M. Paprzycki, M. Ganzha, Practical Aspects of Zero-Shot Learning, in: ICCS 2022: Computational Science – ICCS 2022, LNCS, Vol. 13351, Springer, Cham, 2022, 88–95, DOI: 10.1007/978-3-031-08754-7_12, PDF

Łukasz Pałys, M. Ganzha, M. Paprzycki, Machine Learning for Bus Travel Prediction, in: Computational Science – ICCS 2022: 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part II, 703–710, DOI: 10.1007/978-3-031-08754-7_72, PDF

Mihir Yadav, Divyansh Mangal, Srinivasan Natesan, M. Paprzycki, M. Ganzha, Assamese Character Recognition Using Convolutional Neural Networks, in: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds), Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications, Algorithms for Intelligent Systems, Springer, Singapore, 2022, 851–859, DOI: 10.1007/978-981-16-6332-1_70, PDF

A. Denisiuk, M. Ganzha, M. Paprzycki, K. Wasielewska, Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant, Proceedings of the 55th Hawaii International Conference on System Sciences, University of Hawaii, Manoa, 2022, 1320-1329, PDF

D. Rakus, Zastosowanie metod analityki danych do analizy zachowań zespołu serwerów, MS Thesis, Warsaw University of Technology, 2021, PDF; Codes used in research reported in the Thesis: ZIP

S. Menon, P. Zarzycki, M. Ganzha, M. Paprzycki, Development of a Neural Network Library for Resource Constrained Speech Synthesis, 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), IEEE, Los Alamitos, 2021, 1-8, DOI: 10.1109/ICRAIE51050.2020.9358310, PDF

P. Niedziela, A. Danilenka, D. Kolasa, M. Ganzha, M. Paprzycki, K. Nalinaksh, Sunday-FL – Developing Open Source Platform for Federated Learning, 2021 Emerging Trends in Industry 4.0 (ETI 4.0), IEEE, Los Alamitos, 2021, 1-6, DOI: 10.1109/ETI4.051663.2021.9619338, PDF

A. Łuczak M. Ganzha, M. Paprzycki, Probability of Loan Default — Applying Data Analytics to Financial Credit Risk Prediction, Intelligent Systems, Technologies and Applications, Springer, Singapore, 2021, 1-16, DOI: 10.1007/978-981-16-0730-1_1, PDF

P. Janus, M. Ganzha, A. Bicki, M. Paprzycki, Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations, Proceedings of the 54th Hawaii International Conference on System Sciences, University of Hawaii, Manoa, 2021, 218-227, PDF

A. Domańska, M. Ganzha, M. Paprzycki, Teaching Bot to Play Thousand Schnapsen, A. Tomar, et.al. (eds.), Machine Learning, Advances in Computing, Renewable Energy and Communication, Springer, Singapore, PDF, DOI: 10.1007/978-981-16-2354-7_57

M. Piesio, M. Ganzha, M. Paprzycki, Applying machine learning to anomaly detection in car insurance sales, International Conference on Big Data Analytics, Springer, Cham, PDF, DOI: 10.1007/978-3-030-66665-1_17

M. Plakhtiy, M. Ganzha, M. Paprzycki, Comparing performance of classiffers applied to disaster detection in Twitter tweets -- preliminary considerations, International Conference on Big Data Analytics, Springer, Cham, PDF, DOI: 10.1007/978-3-030-66665-1_16

A. Byczynska, M. Ganzha, M. Paprzycki, M. Kutka, Evidence quality estimation using selected machine learning approaches, Proceedings of 2020 Conference on Information Communications Technology and Society (ICTAS), IEEE, Piscataway, NJ, 2020, 1-8, PDF, DOI: 10.1109/ICTAS47918.2020.244042

U. Jindal, S. Gupta, V. Jain, M. Paprzycki, Offline Handwritten Gurumukhi Character Recognition System Using Deep Learning, in: L. Jain et.al. (eds.), Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals, Advances in Intelligent Systems and Computing, vol 1064. Springer, Singapore, 2020, 121-133, PDF, DOI: 10.1007/978-981-15-0339-9_11

J. Zalewski, M. Ganzha, M. Paprzycki, Recommender system for board games, 23rd International Conference on System Theory, Control and Computing (ICSTCC), IEEE, Los Alamitos, CA, 2019, 249-254, PDF, DOI: 10.1109/ICSTCC.2019.8885455

J. Fijalkowski, M. Ganzha, M. Paprzycki, S. Fidanova, I. Lirkov, C. Badica, M. Ivanovic, Mining smartphone generated data for user action recognition - Preliminary assessment, in: M. Todorov (ed.), AIP Conference Proceedings 2025, AIP, PDF, DOI: 10.1063/1.5064928

H. Zhou, S. Rahimi, R. Ahmad, M. Paprzycki, Y. Wang, M. Cobb, ORVPF – the Model and its DNC Implementation, Informatica (Lithuania), Vol 16. No 4, 2005, 603-616

B. Doeksen, A. Abraham, J. Thomas, M. Paprzycki, Real Stock Trading Using Soft Computing Models. In: IEEE International Conference on Information Technology: Coding and Computing (ITCC'05), USA, IEEE Press, Los Alamitos, CA, 2005, 162-167

M. Chong, A. Abraham, M. Paprzycki, Traffic Accident Data Analysis Using Machine Learning Paradigms, Informatica: An International Journal of Computing and Informatics, Vol. 29, No. 1, 2005, 89-98

M. Paprzycki, A. Abraham, R. Guo, S. Mukkamala, Data Mining Approach for Analyzing Call Center Performance, B. Orchad et. al. (eds.), Innovations in Applied Intelligence, Springer Verlag, Berlin, 2004, 1092-1101

M. Paprzycki, M. Chong, A. Abraham, Traffic Accident Data Mining Using Machine Learning Paradigms. In: Proceedings of the Fourth International Conference on Intelligent Systems Design and Applications (ISDA'04), Technical University of Budapest Press, 2004, 415-420