Piotr Duda

Orcid: 0000-0001-7182-1349

According to our database1, Piotr Duda authored at least 48 papers between 2011 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2023
The Proper Use of Fibre-Optic Sensors to Monitor the Condition of the Steam Boiler Hanger Rods.
Sensors, September, 2023

A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps.
J. Artif. Intell. Soft Comput. Res., March, 2023

The <i>L</i><sub>2</sub> convergence of stream data mining algorithms based on probabilistic neural networks.
Inf. Sci., 2023

Evaluation of Homogeneity of Effervescent Tablets Containing Quercetin and Calcium Using X-ray Microtomography and Hyperspectral Analysis.
Proceedings of the Bioinformatics and Biomedical Engineering, 2023

The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach.
Proceedings of the Artificial Intelligence and Soft Computing, 2023

2022
Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain.
J. Artif. Intell. Soft Comput. Res., 2022

Dynamic Signature Verification Using Selected Regions.
Proceedings of the Artificial Intelligence and Soft Computing, 2022

2021
The Streaming Approach to Training Restricted Boltzmann Machines.
Proceedings of the Artificial Intelligence and Soft Computing, 2021

2020
On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification.
IEEE Trans. Cybern., 2020

Analysis of microtomographic images in automatic defect localization and detection.
Mach. Vis. Appl., 2020

A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment.
J. Artif. Intell. Soft Comput. Res., 2020

On Training Deep Neural Networks Using a Streaming Approach.
J. Artif. Intell. Soft Comput. Res., 2020

Visual Hybrid Recommendation Systems Based on the Content-Based Filtering.
Proceedings of the Artificial Intelligence and Soft Computing, 2020

On a Streaming Approach for Training Denoising Auto-encoders.
Proceedings of the Artificial Intelligence and Soft Computing, 2020

2019
Corrigendum to 'How to adjust an ensemble size in stream data mining?' Information Sciences, vol. 381 (2017), pp. 46-54.
Inf. Sci., 2019

On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine.
Proceedings of the Neural Information Processing - 26th International Conference, 2019

Resource-Aware Data Stream Mining Using the Restricted Boltzmann Machine.
Proceedings of the Artificial Intelligence and Soft Computing, 2019

2018
New Splitting Criteria for Decision Trees in Stationary Data Streams.
IEEE Trans. Neural Networks Learn. Syst., 2018

Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks.
Inf. Sci., 2018

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.
Int. J. Neural Syst., 2018

Online GRNN-Based Ensembles for Regression on Evolving Data Streams.
Proceedings of the Advances in Neural Networks - ISNN 2018, 2018

Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

On Ensemble Components Selection in Data Streams Scenario with Gradual Concept-Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2018

2017
How to adjust an ensemble size in stream data mining?
Inf. Sci., 2017

On applying the Restricted Boltzmann Machine to active concept drift detection.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

On ensemble components selection in data streams scenario with reoccurring concept-drift.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Heuristic Regression Function Estimation Methods for Data Streams with Concept Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2017

2016
A method for automatic adjustment of ensemble size in stream data mining.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions.
Proceedings of the Artificial Intelligence and Soft Computing, 2016

2015
A New Method for Data Stream Mining Based on the Misclassification Error.
IEEE Trans. Neural Networks Learn. Syst., 2015

2014
Decision Trees for Mining Data Streams Based on the Gaussian Approximation.
IEEE Trans. Knowl. Data Eng., 2014

The CART decision tree for mining data streams.
Inf. Sci., 2014

The Parzen kernel approach to learning in non-stationary environment.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

A novel application of Hoeffding's inequality to decision trees construction for data streams.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

2013
Decision Trees for Mining Data Streams Based on the McDiarmid's Bound.
IEEE Trans. Knowl. Data Eng., 2013

On a Splitting Criterion for Decision Trees in Data Streams.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013

On the Application of Orthogonal Series Density Estimation for Image Classification Based on Feature Description.
Proceedings of the Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions - Selected Papers from KICSS'2013, 2013

Adaptation of Decision Trees for Handling Concept Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2013

2012
A New Fuzzy Classifier for Data Streams.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On Resources Optimization in Fuzzy Clustering of Data Streams.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On Fuzzy Clustering of Data Streams with Concept Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On the Uniform Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Time-Varying Environment.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On the Strong Convergence of the Recursive Orthogonal Series-Type Kernel Probabilistic Neural Networks Handling Time-Varying Noise.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On Pre-processing Algorithms for Data Stream.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

On the Strong Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Non-stationary Environment.
Proceedings of the Artificial Intelligence and Soft Computing, 2012

2011
On the Weak Convergence of the Orthogonal Series-Type Kernel Regresion Neural Networks in a Non-stationary Environment.
Proceedings of the Parallel Processing and Applied Mathematics, 2011

On the Cesaro Orthogonal Series-Type Kernel Probabilistic Neural Networks Handling Non-stationary Noise.
Proceedings of the Parallel Processing and Applied Mathematics, 2011

On the Weak Convergence of the Recursive Orthogonal Series-Type Kernel Probabilistic Neural Networks in a Time-Varying Environment.
Proceedings of the Parallel Processing and Applied Mathematics, 2011


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