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Piotr Artiemjew
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2020 – today
- 2024
- [c40]Jakub Malinowski, Piotr Artiemjew
, Wojciech Cybowski:
Prototyping Educational and Scientific Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap Motion Integration Case Study. CSEDU (1) 2024: 542-549 - [c39]Piotr Artiemjew
Predicting Children's Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models. ICAART (3) 2024: 1092-1099 - [i3]Aleksandra Szpakowska, Piotr Artiemjew
Three-Dimensional Path Planning: Navigating through Rough Mereology. CoRR abs/2405.09282 (2024) - [i2]Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz:
A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine. CoRR abs/2406.17427 (2024) - 2023
- [j22]Beata Bak
The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors. Sensors 23(24): 9811 (2023) - [c38]Radoslaw Cybulski, Piotr Artiemjew
Data Streaming in Concept-Dependent Granulation. IEEE Big Data 2023: 6033-6039 - [c37]Aleksandra Szpakowska
Navigational Strategies for Mobile Robots Using Rough Mereological Potential Fields and Weighted Distance to Goal. IJCRS 2023: 549-564 - [i1]Agnieszka Niemczynowicz, Radoslaw A. Kycia, Maciej Jaworski, Artur Siemaszko, Jose M. Calabuig, Lluís Miquel Garcia-Raffi, Baruch Schneider, Diana Berseghyan, Irina Perfiljeva, Vilém Novák, Piotr Artiemjew
Selected aspects of complex, hypercomplex and fuzzy neural networks. CoRR abs/2301.00007 (2023) - 2022
- [j21]Beata Bak
In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks. Sensors 22(3): 1148 (2022) - [c36]Radoslaw Cybulski, Piotr Artiemjew
Accelerating concept-dependent granulation technique using data decomposition. IEEE Big Data 2022: 6195-6201 - [c35]Radoslaw Cybulski, Piotr Artiemjew:
Application of Random Sampling in the Concept-Dependent Granulation Method. FedCSIS (Position Papers) 2022: 3-11 - [c34]Aleksandra Weiss, Marcin Mlynski
About Classifiers Quality Assessment: Balanced Accuracy Curve (BAC) as an alternative for ROC and PR Curve. FedCSIS 2022: 149-156 - [c33]Piotr Artiemjew
Using ConvNet for Classification Task in Parallel Coordinates Visualization of Topologically Arranged Attribute Values. ICAART (3) 2022: 167-171 - [c32]Jaroslaw Szkola, Piotr Artiemjew
Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems. ICAART (3) 2022: 984-989 - 2021
- [j20]Marek Kruk
The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters. Ecol. Informatics 66: 101462 (2021) - [j19]Piotr Artiemjew
A Novel Ensemble Model - The Random Granular Reflections. Fundam. Informaticae 179(2): 183-203 (2021) - [j18]Piotr Artiemjew
Deep Learning for RFI Artifact Recognition in Sentinel-1 Data. Remote. Sens. 13(1): 7 (2021) - [j17]Jakub Wilk
Classifying the Biological Status of Honeybee Workers Using Gas Sensors. Sensors 21(1): 166 (2021) - [j16]Szymon Nitkiewicz
Requirements for Supporting Diagnostic Equipment of Respiration Process in Humans. Sensors 21(10): 3479 (2021) - [j15]Beata Bak
Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors. Sensors 21(14): 4917 (2021) - 2020
- [j14]Krzysztof Ropiak
On a Hybridization of Deep Learning and Rough Set Based Granular Computing. Algorithms 13(3): 63 (2020) - [j13]Piotr Artiemjew
About Granular Rough Computing - Overview of Decision System Approximation Techniques and Future Perspectives. Algorithms 13(4): 79 (2020) - [j12]Piotr Artiemjew
On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation. Comput. 9(1): 13 (2020) - [j11]Piotr Artiemjew
Indiscernibility Mask Key for Image Steganography. Comput. 9(2): 38 (2020) - [j10]Piotr Artiemjew
About Rule-Based Systems: Single Database Queries for Decision Making. Future Internet 12(12): 212 (2020) - [j9]Agnieszka Chojka
RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques. Sensors 20(10): 2919 (2020) - [j8]Beata Bak, Jakub Wilk
Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors. Sensors 20(14): 4014 (2020) - [c31]Krzysztof Ropiak, Piotr Artiemjew
Random Forests and Homogeneous Granulation. ICIST 2020: 185-195 - [e3]Krzysztof Ropiak, Lech Polkowski, Piotr Artiemjew:
Proceedings of the 28th International Workshop on Concurrency, Specification and Programming, Olsztyn, Poland, September 24-26th, 2019. CEUR Workshop Proceedings 2571, CEUR-WS.org 2020 [contents]
2010 – 2019
- 2019
- [j7]Krzysztof Ropiak
Homogenous Granulation and Its Epsilon Variant. Comput. 8(2): 36 (2019) - [c30]Piotr Artiemjew, Pawel Idzikowski:
Building an Ensemble of Naive Bayes Classifiers using Committee of Bootstraps and Monte Carlo splits for a various percentage of random objects from training set. CS&P 2019 - [c29]Piotr Artiemjew
Using r-indiscernibility Relations to Hide the Presence of Information for the Least Significant Bit Steganography Technique. ICIST 2019: 209-220 - [c28]Piotr Artiemjew
Missing Values Absorption Based on Homogenous Granulation. ICIST 2019: 441-450 - [c27]Piotr Artiemjew
Robot Localization in the Magnetic Unstable Environment. IRC 2019: 504-508 - 2018
- [j6]Piotr Artiemjew
Boosting Effect of Classifier Based on Simple Granules of Knowledge. Inf. Technol. Control. 47(2): 184-196 (2018) - [c26]Piotr Artiemjew, Krzysztof Ropiak:
A Novel Ensemble Model - The Random Granular Reflections. CS&P 2018 - [c25]Krzysztof Ropiak, Piotr Artiemjew
A Study in Granular Computing: Homogenous Granulation. ICIST 2018: 336-346 - [c24]Lech Polkowski
Robot Navigation and Path Planning by Means of Rough Mereology. IRC 2018: 363-368 - [c23]Krzysztof Ropiak, Piotr Artiemjew
On Granular Rough Computing: Epsilon Homogenous Granulation. IJCSR 2018: 546-558 - 2017
- [c22]Piotr Artiemjew
Ensemble of Classifiers Based on Simple Granules of Knowledge. ICIST 2017: 343-350 - [c21]Lukasz Zmudzinski
Path Planning Based on Potential Fields from Rough Mereology. IJCRS (2) 2017: 158-168 - [e2]Lech Polkowski, Yiyu Yao, Piotr Artiemjew, Davide Ciucci, Dun Liu, Dominik Slezak, Beata Zielosko:
Rough Sets - International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3-7, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10313, Springer 2017, ISBN 978-3-319-60836-5 [contents] - [e1]Lech Polkowski, Yiyu Yao, Piotr Artiemjew, Davide Ciucci, Dun Liu, Dominik Slezak
Rough Sets - International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3-7, 2017, Proceedings, Part II. Lecture Notes in Computer Science 10314, Springer 2017, ISBN 978-3-319-60839-6 [contents] - 2016
- [c20]Piotr Artiemjew
A New Classifier Based on the Dual Indiscernibility Matrix. ICIST 2016: 380-391 - 2015
- [b1]Lech Polkowski, Piotr Artiemjew:
Granular Computing in Decision Approximation - An Application of Rough Mereology. Intelligent Systems Reference Library 77, Springer 2015, ISBN 978-3-319-12879-5, pp. 1-422 - [c19]Piotr Artiemjew:
The Localization of Mindstorms NXT in the Magnetic Unstable Environment Based on Histogram Filtering. ICAART (2) 2015: 341-348 - [c18]Jacek Szypulski, Piotr Artiemjew
The Rough Granular Approach to Classifier Synthesis by Means of SVM. RSFDGrC 2015: 256-263 - [c17]Piotr Artiemjew
The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions. RSFDGrC 2015: 267-277 - 2014
- [j5]Piotr Artiemjew:
Rough mereology classifier vs. simple DNA microarray gene extraction methods. Int. J. Data Min. Model. Manag. 6(2): 110-126 (2014) - [c16]Piotr Artiemjew
Visual Dictionary Pruning Using Mutual Information and Information Gain. ICAISC (2) 2014: 3-14 - 2013
- [c15]Pawel Drozda
Visual words sequence alignment for image classification. ICCI*CC 2013: 397-402 - [c14]Przemyslaw Górecki, Piotr Artiemjew
Visual words selection based on class separation measures. ICCI*CC 2013: 409-414 - [p2]Piotr Artiemjew
A Review of the Knowledge Granulation Methods: Discrete vs. Continuous Algorithms. Rough Sets and Intelligent Systems (2) 2013: 41-59 - 2012
- [c13]Przemyslaw Górecki, Piotr Artiemjew, Pawel Drozda, Krzysztof Sopyla:
Categorization of Similar Objects using Bag of Visual Words and Support Vector Machines. ICAART (1) 2012: 231-236 - [c12]Piotr Artiemjew
Granular Covering Selection Methods Dependent on the Granule Size. RSKT 2012: 336-341 - 2011
- [j4]Lech Polkowski
Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data. Int. J. Intell. Syst. 26(6): 555-571 (2011) - [c11]Piotr Artiemjew
The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs. Classifier Based on Rough Mereology. ISMIS 2011: 33-42 - [c10]Piotr Artiemjew
The Extraction Method of DNA Microarray Features Based on Experimental A Statistics. RSKT 2011: 642-648 - 2010
- [c9]Przemyslaw Górecki, Piotr Artiemjew
DNA microarray classification by means of weighted voting based on rough set classifier. SoCPaR 2010: 269-272 - [c8]Piotr Artiemjew
Classifiers based on rough mereology in analysis of DNA microarray data. SoCPaR 2010: 273-278
2000 – 2009
- 2009
- [j3]Lech Polkowski
On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology. Int. J. Comput. Intell. Syst. 2(4): 315-331 (2009) - 2008
- [j2]Lech Polkowski
A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data. Trans. Rough Sets 9: 230-263 (2008) - [j1]Lech Polkowski
On Classifying Mappings Induced by Granular Structures. Trans. Rough Sets 9: 264-286 (2008) - [c7]Lech Polkowski, Piotr Artiemjew
Rough Mereology in Classification of Data: Voting by Means of Residual Rough Inclusions. RSCTC 2008: 113-120 - [c6]Piotr Artiemjew
Natural versus Granular Computing: Classifiers from Granular Structures. RSCTC 2008: 150-159 - [c5]Piotr Artiemjew
On Classification of Data by Means of Rough Mereological Granules of Objects and Rules. RSKT 2008: 221-228 - [c4]Piotr Artiemjew
Rough Mereological Classifiers Obtained from Weak Variants of Rough Inclusions. RSKT 2008: 229-236 - [p1]Lech Polkowski
Rough Sets In Data Analysis: Foundations and Applications. Applications of Computational Intelligence in Biology 2008: 33-54 - 2007
- [c3]Lech Polkowski
Granular Computing: Granular Classifiers and Missing Values. IEEE ICCI 2007: 186-194 - [c2]Lech Polkowski, Piotr Artiemjew
On Granular Rough Computing with Missing Values. RSEISP 2007: 271-279 - [c1]Lech Polkowski, Piotr Artiemjew
On Granular Rough Computing: Factoring Classifiers Through Granulated Decision Systems. RSEISP 2007: 280-289
Coauthor Index
aka: Lech T. Polkowski
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last updated on 2024-10-07 21:21 CEST by the dblp team
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