Maciej Grzenda, Ph.D.

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Selected publications

2021-

• Luckner, M., Wrona, P., Grzenda, M., Łysak, A., Analysing Urban Transport Using Synthetic Journeys. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science - ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14838. Springer, Cham, 2024
• Golik, P., Grzenda, M., Sienkiewicz, E., Hybrid Ensemble-Based Travel Mode Prediction, w: Miliou, I., Piatkowski, N., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14641. Springer, Cham, 2024
• Grzenda, M., Kazmierczak, S., Luckner, M., Borowik, G., Mańdziuk, J., Evaluation of machine learning methods for impostor detection in web applications, Expert Systems with Applications, vol. 231, Elsevier, 2023
• Grzenda, M., Luckner, M., Brzozowski, Ł., Quantifying Parking Difficulty with Transport and Prediction Models for Travel Mode Choice Modelling. In: Mikyska, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds), Computational Science - ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10477. Springer, Cham, 2023
• Grzenda, M., Luckner, M., Wrona, P., Urban Traveller Preference Miner: Modelling Transport Choices with Survey Data Streams in: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Grenoble, France. Lecture Notes in Computer Science, vol 13718. Springer, Cham, 2023
• Wrona, P., Grzenda, M., Luckner, M., Streaming Detection of Significant Delay Changes in Public Transport Systems. in: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science. International Conference on Computational Science, London, 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham, 2022
• H. Murilo Gomes, M. Grzenda, R. Mello, J. Read, M. H. Le Nguyen, A. Bifet, A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams, ACM Computing Surveys, 55, 4, 2022, pp. 75:1-75:42
• M.Grzenda, Quantifying Changes in Predictions of Classification Models for Data Streams. in: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham, 2022, pp. 115-127
• M. Grzenda, J. Legierski, Towards Increased Understanding of Open Data Use for Software Development, Information Systems Frontiers, vol. 23(2), Springer, 2021, pp. 495-513
• N. Kourtellis, H. Herodotou, M. Grzenda, P. Wawrzyniak, A. Bifet, S2CE: a hybrid cloud and edge orchestrator for mining exascale distributed streams, Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems, ACM, 2021, pp. 103-113
• R. Kunicki, M. Grzenda, Towards Increasing Open Data Adoption Through Stream Data Integration and Imputation, IEA/AIE 2021: Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, LNCS, vol. 12798, Springer, Cham, 2021, pp. 15-27

2016-2020

• M. Grzenda, H. M. Gomes, A. Bifet, Delayed Labelling Evaluation for Data Streams. Data Mining and Knowledge Discovery, DOI: 10.1007/s10618-019-00654-y, 34, 2020, pp. 1237-1266
• M. Luckner, M. Grzenda, R. Kunicki, J. Legierski, IoT Architecture for Urban Data-Centric Services and Applications, ACM Transactions on Internet Technology, 2020, vol. 20, no. 3
• M. Grzenda, H. M. Gomes and A. Bifet, Performance measures for evolving predictions under delayed labelling classification, 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, Wielka Brytania, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207256.
• M. Grzenda, A. Bustillo, Semi-supervised roughness prediction with partly unlabeled vibration data streams, in Journal of Intelligent Manufacturing, Springer, 2019, vol. 30, no. 2, pp. 933-945
• M.Grzenda, Analysing the Performance of Fingerprinting-Based Indoor Positioning: The Non-trivial Case of Testing Data Selection, in Advances and Trends in Artificial Intelligence. From Theory to Practice, LNAI, 11606, (Eds.) Wotawa, F. et al., 2019, Springer International Publishing, pp. 457-469
• M. Grzenda, K. Kwasiborska, T. Zaremba, Hybrid Short Term Prediction to Address Limited Timeliness of Public Transport Data Streams, Neurocomputing, Elsevier, Vol. 391, 2020, pp. 305-317
• M. Grzenda, A. Ismail Awad, J. Furtak, J. Legierski (red.), Advances in Network Systems : Architectures, Security, and Applications, Springer International Publishing, 2017
• T. Liebig, S. Peter, M. Grzenda, K. Junosza-Szaniawski, Dynamic Transfer Patterns for Fast Multi-modal Route Planning, in Societal Geo-innovation: Selected papers of the 20th AGILE conference on Geographic Information Science, Springer International Publishing, doi: 10.1007/978-3-319-56759-4_13, 2017, pp. 223-236
• M. Grzenda, K. Kwasiborska, T. Zaremba, Combining Stream Mining and Neural Networks for Short Term Delay Prediction, in Proceedings of International Joint Conference SOCO'17-CISIS'17-ICEUTE'17 Leon, Spain, September 6-8, 2017, ed. : Perez G. et al, Springer International Publishing, doi: 10.1007/978-3-319-67180-2_18, 2018, pp. 188-197
• A. Bustillo, M. Grzenda, B. Macukow, Interpreting tree-based prediction models and their data in machining processes, Integrated Computer-Aided Engineering, 2016, vol. 23, no. 4, pp. 349-367

2011-2015

• D. Teixidor, M. Grzenda, A. Bustillo, J. Ciurana, Modeling pulsed laser micromachining of micro geometries using machine-learning techniques, in Journal of Intelligent Manufacturing, 2015, Volume 26, Issue 4, pp. 801-814
• S. Grabowski, M. Grzenda, J. Legierski, The Adoption of Open Data and Open API Telecommunication Functions by Software Developers, International Conference on Business Information Systems BIS 2015: Business Information Systems, Lecture Notes in Business Information Processing , 2015, pp. 337-347
• M.Grzenda, Reduction of Signal Strength Data for Fingerprinting-Based Indoor Positioning, Intelligent Data Engineering and Automated Learning, IDEAL 2015, LNCS, Springer, 2015, pp. 387-394
• R. Filasiak, M. Grzenda, M. Luckner, P. Zawistowski, On the Testing of Network Cyber Threat Detection Methods on Spam Example, in Annals of telecommunications, Vol. 69, Issue 7, 2014, pp. 363-377
• M. Grzenda, On the Prediction of Floor Identification Credibility in RSS-Based Positioning Techniques, in Recent Trends in Applied Artificial Intelligence, Moonis Ali et. al. (Eds.), Lecture Notes in Computer Science, vol. 7906, Springer-Verlag, 2013, pp. 610-619
• M. Grzenda, A. Bustillo, The evolutionary development of roughness prediction models, Applied Soft Computing, Elsevier, Vol. 13, Issue 5, 2013, pp. 2913-2922
• M. Grzenda, A. Bustillo, G. Quintana, J. Ciurana, Improvement of surface roughness models for face milling operations through dimensionality reduction, Integrated Computer-Aided Engineering, vol. 19(2), IOS Press, 2012, pp. 179-197
• M. Grzenda, Consumer-oriented heat consumption prediction, in Control and Cybernetics, vol. 41(1), Systems Research Institute, Polish Academy of Sciences, 2012, pp. 213-240
• L. Fuchs, T. Beeneken, M. Leśniewski, M. Grzenda, Benefits of Real Time Control of Sewer Systems (in Polish), Korzyści ze sterowania praca systemów kanalizacji ogólnospławnej w czasie rzeczywistym, in: Gas, Water and Sanitary Technology (Gaz, woda i technika sanitarna) , vol 5, 2012, pp. 218-223
• M. Grzenda, Towards the Reduction of Data Used for the Classification of Network Flows, in Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol. 7209, Springer-Verlag, 2012, pp. 68-77
• H. Łącka, M. Grzenda, On the Evolutionary Search for Data Reduction Method, in Distributed Computing and Artificial Intelligence / Omatu Sigeru [i in.] ( red. ), Advances in Intelligent and Soft Computing, vol. 151, 2012, pp. 527-534
• M. Grzenda, Prediction-Oriented Dimensionality Reduction of Industrial Data Sets, Lecture Notes in Artificial Intelligence, vol. 6703, Springer-Verlag, Berlin, 2011, pp. 233-241
• M. Grzenda, L. Fuchs, T. Beeneken, N. Wronowski, Key aspects of mathematical modelling of sewer systems on the example of a large combined sewer system (in Polish), Kluczowe zagadnienia modelowania matematycznego systemów kanalizacyjnych na przykładzie dużej sieci ogólnospławnej, In: Gas, Water and Sanitary Technology (Gaz, woda i technika sanitarna), vol. 3, 2011, pp. 91-98
• M. Grzenda, A. Bustillo, P. Zawistowski, A Soft Computing System Using Intelligent Imputation Strategies for Roughness Prediction in Deep Drilling, Journal of Intelligent Manufacturing, Springer, vol. 23(5), (2010: DOI), 2012, pp. 1733-1743

until 2010

• M. Grzenda, M. Sudoł, W. Gębski, Modelling of a Large-Scale Water Distribution System, (in Polish), in: Gas, Water and Sanitary Technology (Gaz, woda i technika sanitarna) , vol. 3, 2010, pp. 2-6
• P. Zawistowski, M.Grzenda, Handling Incomplete Data Using Evolution of Imputation Methods, in Proc. of 9th International Conference ICANNGA 2009, Kuopio, Finlandia, Lecture Notes in Computer Science, vol. 5495, Springer-Verlag, Berlin, 2009, pp. 22-31
• M. Grzenda, B. Macukow, Heat Consumption Prediction with Multiple Hybrid Models, in Proc. of 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Hiszpania, Lecture Notes in Computer Science, vol. 5581, Springer-Verlag, Berlin, 2009, pp. 1213-1221
• M. Grzenda, SOM-Based Selection of Monitored Consumers for Demand Prediction, in Proc. of 10th International Conference IDEAL 2009, Burgos, Hiszpania, Lecture Notes in Computer Science, vol. 5788, Springer-Verlag, Berlin, 2009, pp. 807-814
• M. Grzenda, Load Prediction Using Combination of Neural Networks and Simple Strategies, in Tenth Scandinavian Conference on Artificial Intelligence SCAI 2008, Sztokholm, Szwecja, Frontiers in Artificial Intelligence, vol. 173, IOS Press, 2008, pp. 106-113, Amsterdam
• M. Grzenda, B. Macukow, Demand Prediction with Multi-Stage Neural Processing, in: Advances in Natural Computation and Data Mining, Xidian University Press, Chiny, 2006 ISBN: 7-5606-1735-2/TP*0431, pp. 131-141
• M. Grzenda, M. Niemczak, Requirements and Solutions for Web-Based Expert System, Proc. of ICAISC 2004, Zakopane, 2004, Lecture Notes in Artificial Intelligence, vol. 3070, Springer-Verlag, Berlin, pp. 866-871
• M. Grzenda, B. Macukow, Evolutionary neural networks based optimization for short-term load forecasting, Control and Cybernetics, 31, no.2, 2002, pp. 371-382
• B.Macukow, M.Grzenda, Towards the Evolution of Neural Networks, Optoelectronics Review, 9/3, 2001, pp. 23-26
• M. Grzenda, B.Macukow, The Role of Weight Domain in Evolutionary Design of Multilayer Perceptrons, Proc. of the IEEE-INNS-ENNS International Joint Conference on Neural Networks IJCNN 2000, Los Alamitos, USA, 2000
• M. Grzenda, B. Macukow, Neural Networks to Simulate Intelligent Behaviour in the Games of Moves, Proceedings of the 13th European Simulation Multiconference, Ghent, Belgia, vol. 2, 1999, pp. 429-436
• M. Grzenda, B. Macukow, Genetic Algorithm and Neural Networks to Solve the Prisoner's Dilemma, Optical Memory and Neural Networks, Allerton Press Inc., USA, vol. 7 no.3, 1998, pp. 171-175