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ORCIDPiotr S. Maciag
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2020 – today
- 2023
[j4]Piotr S. Maciag, Robert Bembenik, Aleksandra Piekarzewicz
, Javier Del Ser, Jesus L. Lobo, Nikola K. Kasabov:
Effective air pollution prediction by combining time series decomposition with stacking and bagging ensembles of evolving spiking neural networks. Environ. Model. Softw. 170: 105851 (2023)- [j3]Piotr S. Maciag, Robert Bembenik, Artur Dubrawski:
Discovery of crime event sequences with constricted spatio-temporal sequential patterns. J. Big Data 10(1): 98 (2023) - 2022
- [c7]Piotr S. Maciag, Wojciech Sitek, Lukasz Skonieczny, Henryk Rybinski:
A Comparative Study of Short Text Classification with Spiking Neural Networks. FedCSIS 2022: 79-88 - 2021
- [j2]Piotr S. Maciag, Marzena Kryszkiewicz, Robert Bembenik, Jesus L. Lobo, Javier Del Ser:
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks. Neural Networks 139: 118-139 (2021) - [i4]Piotr S. Maciag, Robert Bembenik, Artur Dubrawski:
Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns. CoRR abs/2112.01863 (2021) - 2020
- [c6]Piotr S. Maciag, Marzena Kryszkiewicz, Robert Bembenik:
Online Evolving Spiking Neural Networks for Incremental Air Pollution Prediction. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [j1]Piotr S. Maciag, Nikola K. Kasabov, Marzena Kryszkiewicz, Robert Bembenik:
Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area. Environ. Model. Softw. 118: 262-280 (2019) - [c5]Piotr S. Maciag, Robert Bembenik:
A Novel Breadth-first Strategy Algorithm for Discovering Sequential Patterns from Spatio-temporal Data. ICPRAM 2019: 459-466 - [c4]Piotr S. Maciag:
A Framework for Discovering Frequent Event Graphs from Uncertain Event-based Spatio-temporal Data. ICPRAM 2019: 656-663 - [c3]Piotr S. Maciag, Marzena Kryszkiewicz, Robert Bembenik:
Discovery of closed spatio-temporal sequential patterns from event data. KES 2019: 707-716 - [p1]Piotr S. Maciag:
Efficient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach. Intelligent Methods and Big Data in Industrial Applications 2019: 183-199 - [i3]Piotr S. Maciag, Marzena Kryszkiewicz, Robert Bembenik, Jesus L. Lobo, Javier Del Ser:
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks. CoRR abs/1912.08785 (2019) - 2018
- [c2]Piotr S. Maciag:
Efficient Discovery of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data. FedCSIS 2018: 47-56 - 2017
- [c1]Piotr S. Maciag:
A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data. BDAS 2017: 115-126 - [i2]Piotr S. Maciag:
Efficient Discovering of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data. CoRR abs/1707.00670 (2017) - [i1]Piotr S. Maciag:
Discovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report. CoRR abs/1708.08674 (2017)
Coauthor Index
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