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Piotr 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
A Comparative Study of Short Text Classification with Spiking Neural Networks. FedCSIS 2022: 79-88 - 2021
- [j2]Piotr S. Maciag
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
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
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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