##The personal page of Piotr Artiemjew##
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    Basic information:
    dr hab. Piotr Artiemjew, prof. UWM
    Associate Professor in Decision Systems,
    Head of the Discipline Team for Technical Informatics and Telecommunications,
    Faculty of Mathematics and Computer Science,
    University of Warmia and Mazury in Olsztyn
    Member of Polish Artificial Intelligence Society,
    Member of European Association for Artificial Intelligence: EurAI
    Member od International Rough Set Society,
    Member of the Working Group on Artificial Intelligence (GRAI)
    Member of working group - KIS11 - Automation and robotics of technological processes,
    Member of Institutional Review Board (IRB) of Foundation for the Development of Ophthalmology
    Topic editor in the computer science discipline of the Technical Sciences,
    Research Supervisor of the Scientific Circle of Robotics UWM,
    IBM Data Science Practitioner - Instructor,
    He held the position of Deputy Dean for Information Technology Development and Education (8 years' experience).

    Awards:
    Distinction at PP-RAI Contest for the Most Influential Article on Rough Sets co-authored by Polish Researchers in 2020-2021 for paper 69. link
    Young researcher distinctions at Professor Zdzisław Pawlak Best Paper Awards for paper 91. Among all tracks of the FedCSIS conference (including all technical sessions within each track): link
    Paper 102 nominated for CSEDU 2024 Best Industrial Paper Award.
    First and Second Degree Distinction for a Diloma Thesis promoted by Piotr Artiemjew: In the framework of the Engineer 4 Science Competition for the best thesis organized in cooperation and under the auspices of the Polish Section of the IEEE and GovTech Poland: Title of the Work: Waste type detection based on real-time image detection using computational intelligence methods. Author: Bartosz Jankowski, Eng., and for the thesis: Applying reinforcement learning to master and optimize gameplay in Super Mario Bros, Author: Arkadiusz Nowacki, Eng.

    Reviewer of journals: Information Sciences, Artificial intelligence review, Science of the Total Environment, ISA Transactions, Archives of Computational Methods in Engineering, PLoS One, Journal of Universal Computer Science, Applied, Soft Computing, Computational and Mathematical Methods in Medicine, Knowledge and Information Systems, Information, Advances in Robotics Research, Etri Journal, Journal of Universal Computer Science, Ecological indicators, Sustainability, Information Technology and Control, Engineering Applications of Artificial Intelligence, Informatica, Personal and Ubiquitous Computing, Electronics, Book of Springer series: Studies in Computational Intelligence, European Journal of Operational Research, Applied Sciences, Machine Learning and Knowledge Extraction, Bio-Algorithms and Med-Systems, Computation, Advances in Computer Science Research, Mobile Networks and Applications, International Journal of Machine Learning and Cybernetics, Health and Technology, Air Quality, Agriculture, Atmosphere & Health, Technical Sciences, Oceanologia, International Journal of Approximate Reasoning, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Soft Computing, Revista Maderas Ciencia y Tecnologi­a, Journal of Intelligent Information Systems, Cognitive Computation, Agronomy Research, IEEE Sensors Journal, Sensors, IEEE Access, Entropy, International Journal of Imaging Systems and Technology(Wiley), Mathematical Biosciences and Engineering,F Symmetry, Cancers, International Journal of Knowledge-Based and Intelligent Engineering Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Expert Systems (Wiley), ETRI JOURNAL (Wiley), IEEE Internet of Things Journal, Knowledge-Based Systems.

    Conference programme or organising committees member including: Asia-Pacific Conference on Robotics and Autonomous Systems (APCRAS), International Symposium on Intelligent Robotics and Systems (ISoIRS), International Conference on Advanced Machine Learning Technologies and Applications (AMLTA), International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE), International Symposium Advances in Artificial Intelligence and Applications (AAIA) at FedCSIS, International Joint Conference on Rough Sets (IJCRS), International Conference on Ambient Systems, Networks and Technologies (ANT), International Conference on Informatics, Electronics & Vision (ICIEV), International Conference on Computer Systems and Applications (AICCSA), Workshop on Collaboration of Humans, Agents, Robots, Machines and Sensors (CHARMS) at IRC, International Workshop on Deep and Transfer Learning (DTL), International Conference on Information and Knowledge Management (CIKM), International Conference on Advances in Computation, Communications and Services (ACCSE), ACS/IEEE International Conference on Computer Systems and Applications (ACS/IEEE), International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), International KES Conference - Intelligent Decision Technologies (KES IDT-2019), IEEE International Conference on Robotic Computing (IRC), International Workshop on Language Technologies and Applications (LTA) at Federated Conference on Computer Science and Information Systems (FedCSIS), Formal Approaches to Vagueness in Relation to Mereology (FVRM) - a symposium of FEDCSIS, International Workshop on Innovative Technologies and Applications for Assisted Living (ITAAL) at FNC, International Conference on Cognitive Analytics, Granular Computing, and Three-Way Decisions (CCGT), AI-ML Systems, International Conference on Control, Robotics and Informatics (ICCRI), International Symposium on Rough Sets: Theory and Applications (RSTA), International Conference on Machine Learning (MLDM), International Conference on Advances in Sensors, Actuators, Metering and Sensing (ALLSENSORS), International Conference on Educational Data Mining (EDM), International Conference on Information Systems Development (ISD).

    Papers 2024:

    106) Perfilieva, I., Madrid, N., Ojeda-Aciego, M., Artiemjew, P., Niemczynowicz, A.: A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine, arXiv, 2024, https://arxiv.org/abs/2406.17427

    105) Weiss, A., Młyński, M., Artiemjew, P.: On the evaluation of classification quality - the robustness of the AUC of Balance Accuracy Curve (BAC) to anomalies in the classification process, Will be in proceedings of 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2024, Seville, Spain, accepted

    104) Szpakowska, A., Artiemjew, P.: Three-Dimensional Path Planning: Navigating through Rough Mereology, https://doi.org/10.48550/arXiv.2405.09282

    103) Perfilieva, I., Artiemjew, P., and Niemczynowicz, A.: Randomness Versus Backpropagation in Machine Learning, 23rd International Conference on Artificial Intelligence and Soft Computing 2024 (ICAISC 2024), Will be published in LNAI series, accepted

    102) Malinowski, J.; Artiemjew, P. and Cybowski, W. (2024). Prototyping Educational and Scientific Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap Motion Integration Case Study. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 1, ISBN 978-989-758-697-2, ISSN 2184-5026, pages 542-549.

    101) Perfilieva, I, Madrid, N., Ojeda-Aciego, M, Artiemjew, P. and Niemczynowicz, A. :Extreme Learning Machine as a New Learning Paradigm: Pros and Cons: European Symposium on Computational Intelligence and Mathematics, Paper will be included in the Special Issue of ESCIM 2024, published by Springer in its Series: Studies in Computational Intelligence, accepted

    100) Artiemjew, P.; Cybulski, R.; Emamian, M.; Grzybowski, A.; Jankowski, A.; Lanca, C.; Mehravaran, S.; Młyński, M.; Morawski, C.; Nordhausen, K.; Pärssinen, O. and Ropiak, K. (2024). Predicting Children's Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1092-1099. DOI: 10.5220/0012435500003636

    Papers 2023:

    99) B±k, B.; Wilk, J.; Artiemjew, P.; Siuda, M.; Wilde, J. The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors. Sensors 2023, 23, 9811. https://doi.org/10.3390/s23249811

    98) R. Cybulski and P. Artiemjew, "Data Streaming in Concept-Dependent Granulation," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 6033-6039, doi: 10.1109/BigData59044.2023.10386395

    97) Szpakowska, A., Artiemjew, P., Cybowski, W. (2023). Navigational Strategies for Mobile Robots Using Rough Mereological Potential Fields and Weighted Distance to Goal. In: Campagner, A., Urs Lenz, O., Xia, S., ¦lęzak, D., W±s, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_38

    96) Niemczynowicz, A., Kycia, R. A., Jaworski, M., Siemaszko, A., Calabuig, J. M., García-Raffi, L. M., Schneider, B., Berseghyan, D., Perfiljeva, I., Novak, V., Artiemjew, P.: Selected aspects of complex, hypercomplex and fuzzy neural networks, https://doi.org/10.48550/arXiv.2301.00007

    Papers 2022:

    95) Szkoła, J., Artiemjew, P., B±k, B., Wilk, J., & Wilde, J. (2022). Sekwencyjne sieci neuronowe w analizie danych uzyskanych z pomiarów przeprowadzonych za pomoc± elektronicznego nosa opartego na półprzewodnikowych czujnikach gazu w pasiece ze zgnilcem amerykańskim. In (ed.), 59 Naukowa Konferencja Pszczelarska, Materiały z Konferencji online, Puławy 8-9 marca 2022 (pp. 53-54).

    94) B±k, B., Wilk, J., Artiemjew, P., & Wilde, J. (2022). Wykrywanie zawarto¶ci komórek plastra pszczelego za pomoc± elektronicznego nosa bazuj±cego na czujnikach półprzewodnikowych gazów. In (ed.), 59 Naukowa Konferencja Pszczelarska, Materiały z Konferencji online, Puławy 8-9 marca 2022 (pp. 16-17)

    93) Cybulski, R., Artiemjew, P. "Accelerating concept-dependent granulation technique using data decomposition," 2022 IEEE International Conference on Big Data (Big Data), pp. 6177 -- 6183, IEEE Catalog Number: CFP22BGD-ART, ISBN: 978-1-6654-8045-1

    92) Kruk, M., GoĽdziejewska, A.M. & Artiemjew, P. Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models. Sci Rep 12, 16145 (2022). https://doi.org/10.1038/s41598-022-20604-x

    91) Weiss, A., Młyński, M., & Artiemjew, P. (2022). About Classifiers Quality Assessment: Balanced Accuracy Curve (BAC) as an alternative for ROC and PR Curve. In M. Ganzha, L. Maciaszek, M. Paprzycki, & D. ¦lęzak (Eds.), Proceedings of the 17th Conference on Computer Science and Intelligence Systems (Vol. 30, pp. 149-156). IEEE. https://doi.org/10.15439/2022F262

    90) Cybulski, R., & Artiemjew, P. (2022). Application of Random Sampling in the Concept-Dependent Granulation Method. In M. Ganzha, L. Maciaszek, M. Paprzycki, & D. ¦lęzak (Eds.), Position Papers of the 17th Conference on Computer Science and Intelligence Systems (Vol. 31, pp. 3-11). PTI. https://doi.org/10.15439/2022F264

    89) Artiemjew, P.: (2022). Rough Inclusion Based Toy Decision Systems Generator For Presenting Data Mining Algorithms. Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 25-27, 2022, Gdynia, Poland, 168-171.

    88) Szkoła, J. and Artiemjew, P. (2022). Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 984-989. DOI: 10.5220/0010992300003116

    87) Artiemjew, P. and Tadeja, S. (2022). Using ConvNet for Classification Task in Parallel Coordinates Visualization of Topologically Arranged Attribute Values. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 167-171. DOI: 10.5220/0010793700003116

    86) B±k, B.; Szkoła, J.; Wilk, J.; Artiemjew, P.; Wilde, J. In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks. Sensors 2022, 22, 1148. https://doi.org/10.3390/s22031148

    Papers 2021:

    85) Kruk, M., Artiemjew, P., Paturej, E..: The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters, Ecological Informatics, 2021, 101462, ISSN 1574-9541, Elsevier, (DOI: 10.1016/j.ecoinf.2021.101462)

    84) Artiemjew, P., Ropiak, K.: 'A Novel Ensemble Model - The Random Granular Reflections', Fundamenta Informaticae, 1 Jan. 2021, vol. 179, no. 2, pp. 183-203, 2021(DOI: 10.3233/FI-2021-2020)

    83) B±k, B.; Wilk, J.; Artiemjew, P.; Wilde, J. Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors. Sensors 2021, 21, 4917. https://doi.org/10.3390/s21144917

    82) Nitkiewicz, S.; Barański, R.; Galewski, M.; Zaj±czkiewicz, H.; Kukwa, A.; Zaj±c, A.; Ejdys, S.; Artiemjew, P. Requirements for Supporting Diagnostic Equipment of Respiration Process in Humans. Sensors 2021, 21, 3479. https://doi.org/10.3390/s21103479

    81) Wilk, J., B±k, B., Artiemjew, P., Wilde, J., Siuda, M., Szczurek, A., Maciejewska, M: Strojenie urz±dzeń opartych o półprzewodnikowe czujniki lotnych zwi±zków organicznych poprzez wła¶ciwy dobór klasyfikatorów prowadzi do zwiększenia skuteczno¶ci rozróżniania obiektów w pszczelarstwie : 58 Naukowa Konferencja Pszczelarska, 9-10 marca 2021, Puławy, sesja plenarna, abstrakt, s.. 39-40, (2021)

    80) Niemczynowicz, A., Artiemjew, P. and Nieżurawska-Zaj±c, J.: Supervised Machine Learning Paradigms Approach for Predicting the Work Loyalty of Generation Z: Comparative Analysis - 37th International Business Information Management Association Conference: 30-31 May 2021, Cordoba, Spain, ISBN: 978-0-9998551-6-4, U.S.A. Library of Congress: ISSN: 2767-9640 (2021)

    79) B±k, B., Wilk, J., Artiemjew, P., Wilde, J., Siuda, M., Szczurek, A., Maciejewska, M: Diagnozowanie zgnilca amerykańskiego za pomoc± elektronicznego nosa opartego na półprzewodnikowych czujnikach gazów: 58 Naukowa Konferencja Pszczelarska, 9-10 marca 2021, Puławy, sesja plenarna, abstrakt, s. 38-39, (2021)

    Papers 2020:

    78) Wilk, J.T.; B±k, B.; Artiemjew, P.; Wilde, J.; Siuda, M. Classifying the Biological Status of Honeybee Workers Using Gas Sensors. Sensors 2021, 21, 166.

    77) Artiemjew, P.; Chojka, A.; Rapiński, J. Deep Learning for RFI Artifact Recognition in Sentinel-1 Data. Remote Sens. 2021, 13, 7.

    76) Artiemjew, P.; Rudikova, L.; Myslivets, O. About Rule-Based Systems: Single Database Queries for Decision Making. Future Internet 2020, 12, 212.

    75) Ropiak K., Artiemjew P. (2020) Random Forests and Homogeneous Granulation. In: Lopata A., Butkiene R., Gudoniene D., Sukacke V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_16 (2020)

    74) B±k, B.; Wilk, J.; Artiemjew, P.; Wilde, J.; Siuda, M. Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors. Sensors 2020, 20, 4014.

    73) Kruk, M., Paturej, E., Artiemjew, P.: From explanatory to predictive network modeling of relationships among ecological indicators in the shallow temperate lagoon, Ecological Indicators, vol. 117, issn = 1470-160X, https://doi.org/10.1016/j.ecolind.2020.106637 (2020)

    72) Chojka, A.; Artiemjew, P.; Rapiński, J. RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques. Sensors 2020, 20, 2919

    71) Artiemjew, P.; Kislak-Malinowska, A. Indiscernibility Mask Key for Image Steganography. Computers 2020, 9, 38.

    70) Artiemjew, P. About Granular Rough Computing-Overview of Decision System Approximation Techniques and Future Perspectives. Algorithms 2020, 13, 79.

    69) Ropiak, K.; Artiemjew, P. On a Hybridization of Deep Learning and Rough Set Based Granular Computing. Algorithms 2020, 13, 63.

    68) Wilk, J., B±k, B., Artiemjew, P., Wilde, J., Siuda, M., Szczurek, A., Maciejewska,: Zastosowanie klasycznych metod klasyfikacyjnych dla rozróżniania elementów składowych gniazda pszczelego przy użyciu półprzewodnikowych czujników gazów, koło pszczelarzy w Nakle ¦l±skim, Konferencja w Cieszynie - program 10-12.03.2020, poster

    67) B±k, B., Wilk, J., Artiemjew, P., Wilde, J., Siuda, M., Szczurek, A., Maciejewska, M: Diagnozowanie warrozy na podstawie badania próbek czerwiu pszczelego za pomoc± półprzewodnikowych czujników gazów, koło pszczelarzy w Nakle ¦l±skim, Konferencja w Cieszynie, 10-12.03.2020, sesja plenarna, abstrakt

    66) Artiemjew, P.; Ropiak, K.K. On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation. Computers 2020, 9, 13.

    Papers 2019:

    65) Krzysztof Ropiak, Lech Polkowski, Piotr Artiemjew: Proceedings of the 28th International Workshop on Concurrency, Specification and Programming CS&P 2019, Olsztyn, Poland, September 24-26, 2019, Proceedings, CEUR Vol-2571, ISSN 1613-0073, http://ceur-ws.org/Vol-2571/

    64) Artiemjew, P, Idzikowski, P.: 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'19: Concurrency, Specification and Programming 2019, CEUR Vol-2571, ISSN 1613-0073

    63) Artiemjew, P: Zastosowanie wybranych technik obliczeń granularnych w procesach odkrywania wiedzy z danych, II ¦wiatowe Forum Nauki Polskiej poza Granicami Kraju, abstract in conference proceedigns, Pułtusk 2019

    62) Kao-Yi Shen, Michinori Nakata, Artiemjew, P.: A Short Survey of Blockchain-Based Applications in the Fields of Business and Finance, iCIMKC2019 The 15th International Conference on Innovation, Management and Knowledge Community, 2019

    61) Artiemjew P., Ropiak K. (2019) Missing Values Absorption Based on Homogenous Granulation. In: Damaąevičius R., Vasiljeviene G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer

    60) Artiemjew P., Kislak-Malinowska A. (2019) Using r-indiscernibility Relations to Hide the Presence of Information for the Least Significant Bit Steganography Technique. In: Damaąevičius R., Vasiljeviene G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer

    59) Ropiak, K.; Artiemjew, P. Homogenous Granulation and Its Epsilon Variant. Computers 2019, 8, 36.

    58) Artiemjew, P., Ropiak, K.: Robot localization in the magnetic unstable environment, 5th Workshop on Collaboration of Humans, Agents, Robots, Machines and Sensors (CHARMS 2019), The Third IEEE International Conference on Robotic Computing (IRC 2019), Naples, Italy

    Papers 2018:

    57) Żmudzinski, Ł., Polkowski, L., Artiemjew, P.: Controlling robot formations by means of spatial reasoning based on rough mereology, Advances in Robotics Research, Vol. 2, No. 3 (2018) 219-236

    56) Artiemjew, P.,Ropiak, K.: A Novel Ensemble Model - The Random Granular Reflections. CS&P08, Berlin 2018

    55) Ropiak, K., Artiemjew, P.: A Study in Granular Computing: Homogenous Granulation. ICIST 2018, Communications in Computer and Information Science 920, Springer, Vilnius, Lithuania, pp. 336-346 (2018)

    54) Ropiak, K., Artiemjew, P.: On Granular Rough Computing: Epsilon Homogenous Granulation. IJCSR 2018, Lecture Notes in Computer Science 11103, Springer, Quy Nhon, Vietnam, pp. 546-558 (2018)

    53) Artiemjew P.: Boosting effect for classifier based on simple granules of knowledge. In: Information Technology And Control (ITC) vol. 47(2), pp. 184-196 (2018)

    52) Polkowski, L., Zmudzinski, L., Artiemjew, P.: Robot Navigation and Path Planning by Means of Rough Mereology. IRC 2018, IEEE Computer Society, Laguna Hills, CA: 363-368 (2018)

    Papers 2017:

    51) Artiemjew P.: Obliczenia granularne stosowane do aproksymacji decyzji: zastosowania idei aproksymacji wywodzacych sie z teorii mereologii przyblizonej. Plenary talk at Congressio mathematica 2017, Rzeszów (2017)

    50) Artiemjew P.: Ensemble of classifiers based on simple granules of knowledge. In: Damasevicius R. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, pp. 343-350 (2017)

    49) 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

    48) 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 II. Lecture Notes in Computer Science 10314, Springer 2017, ISBN 978-3-319-60839-6

    47) Żmudziński, Ł., Artiemjew, P.: Path planning based on potential fields from rough mereology, In: Proceedings of International Joint Conference on Rough Sets, IJCRS'17, Olsztyn, Poland, Lecture Notes in Computer Science (LNCS), vol. 10304: pp. 158-168. Springer, Heidelberg (2017)

    46) Artiemjew, P., Nowak, B., Polkowski, L., Górecki, P.: The Boosting and Bootstrap Ensembles for the Pair Classifier Based on the Dual Indiscernibility Matrix, Chapter in: Thriving Rough Sets - 10th Anniversary - Honoring Professor Zdzisław Pawlak's Life and Legacy & 35 years of Rough Sets, Studies in Computational Intelligence, vol. 708 pp. 425-439, (Springer 2017)

    Papers 2016:

    45) Artiemjew, P., Polkowski, L.: Granular rough computing selected methods for data size reduction, In: Online abstracts of 3rd International Conference on Big Data Analysis & Data Mining September 26-27, 2016 London, UK, 10.4172/2324-9307.C1.006, https://www.scitechnol.com/conference-abstracts-files/2324-9307.C1.006_007.pdf

    44) Artiemjew, P.: Problemy filozoficzne sztucznej inteligencji, IX Festiwal Filozofii "Filozofia i technika", Olsztyn, 2016

    43) Artiemjew, P., Polkowski, L., Nowak, B., Gorecki, P.: The Boosting and Bootstrap Ensembles for the Pair Classifier Based on the Dual Indiscernibility Matrix, In: Booklet of abstracts of International Joint Conference on Rough Sets, IJCRS'16, Santiago, Chile (2016) (extended abstract)

    42) Artiemjew, P., Nowak, B., A., Polkowski, L., T.: A New Classifier Based on the Dual Indiscernibility Matrix. In: Dregvaite G., Damasevicius R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer (2016)

    41) Artiemjew, P. and Szypulski, J.: On Granular Rough Computing: Covering by Joint and Disjoint Granules in Epsilon Concept Dependent Granulation, KES International - IDT16, INTELLIGENT DECISION TECHNOLOGIES - Spain (2016)

    40) Żmudziński, Ł., Augustyniak, A., Artiemjew, P.: Control of Mindstorms NXT robot using Xtion Pro camera skeletal tracking, In: Technical Sciences, vol. 19(1), pp. 71-81, Olsztyn, UWM Publisher (2016)

    Papers 2015:

    39) P. Artiemjew: The Boosting and Bootstrap Ensemble for classiers based on weak rough inclusions, In: Proceedings of International Joint Conference on Rough Sets, IJCRS'15, pp. 267-277, Tianjin, China, Lecture Notes in Computer Science (LNCS), Springer, Heidelberg (2015)

    38) J. Szypulski, P. Artiemjew: The Rough Granular Approach to Classifier Synthesis by Means of SVM, In: Proceedings of International Joint Conference on Rough Sets, IJCRS'15, pp. 256-263, Tianjin, China, Lecture Notes in Computer Science (LNCS), Springer, Heidelberg (2015)

    37) L. Polkowski, P. Artiemjew: Granular Computing in Decision Approximation, An Application of Rough Mereology, In: Series: Intelligent Systems Reference Library, Vol. 77, 452 pages, Springer (2015)
    handbook

    36) P. Artiemjew: The localization of Mindstorms NXT in the magnetic unstable environment based on histogram filtering, In: Proceedings of 7th International Conference on Agents and Artificial Intelligence, ICAART'15, pp. 341-348, Lisbon, Portugal, (2015)

    Papers 2014:

    35) P. Artiemjew, P. Górecki: Visual Dictionary Pruning using Mutual Information and Information Gain, In: L. Rutkowski et al. (Eds.): International Conference on Artificial Intelligence and Soft Computing, ICAISC'14, Zakopane, Lecture Notes in Computer Science (LNCS), vol. 8468, pp. 3-14, Springer International Publishing (2014)

    34) P. Artiemjew: Rough Mereology Classifier vs Simple DNA Microarray Gene Extraction Methods, In: International Journal on Data Mining, Modelling and Management Special Issue: Pattern Recognition, Vol. 6, No. 2, pp.110-126 (2014)

    Papers 2013:

    33) P. Artiemjew: Wybrane Paradygmaty Sztucznej Inteligencji - monografia, Wydawnictwa Polsko-Japońskiej Wyższej Szkoły Technik Komputerowych, Warszawa (2013)
    handbook

    32) P. Artiemjew: A Review of the Knowledge Granulation Methods: Discrete vs Continuous Algorithms, book chapter in: Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, Skowron, A., Suraj, Z. (Eds), Intelligent Systems Reference Library (ISRL) vol. 43, pp. 41-59, Springer-Verlag, Berlin Heidelberg (2013)

    31) P. Górecki, P. Artiemjew, P. Drozda, K. Sopyla: Visual Words Selection based on Class Separation Measures, in Proceedings ICCI*CC'13. 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, IEEE Computer Society, New York city (2013)

    30) P. Drozda, K.Sopyla, P. Górecki, P. Artiemjew: Visual Words Sequence Alignment for Image Classification, in Proceedings ICCI*CC'13. 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, IEEE Computer Society, New York city (2013)

    Papers 2012:

    29) P. Czerpak, P. Artiemjew: Job Scheduling Algorithm Based on Multi Criteria Optimization, In: Studies & Proceedings of Polish Association for Knowledge Management, pp. 44 - 54, ISSN 1732-324X (2012)

    28) P. Artiemjew: Review of the Extraction Methods of DNA Microarray Features Based on Central Decision Class Separation vs Rough Set Classifier, In: Foundations of Computing and Decision Sciences, vol. 37(4), pp. 241-254 (2012)

    27) P. Artiemjew, P. Górecki, K. Sopyła: Categorization of Similar Objects Using Bag of Visual Words and k - Nearest Neighbour Classifier, In: Technical Sciences, vol. 15(2), pp. 293-305, UWM Publisher, Olsztyn (2012)

    26) P. Artiemjew: Granular Covering Selection Methods Dependent on the Granule Size, In: Proceedings of 7th International Conference on Rough Sets and Knowledge Technology, RSKT'12 (JRS'12), Chengdu, China, Lecture Notes in Computer Science (LNCS), vol. 7414, pp. 336-341. Springer, Heidelberg (2012)

    25) P. Artiemjew: Rough Granular Computing as a Tool for Approximation of Decision Systems, Poster presentation at INRIA Visual Recognition and Machine Learning Summer School, Grenoble, France, (2012)

    24) P. Górecki, P. Artiemjew, P. Drozda, K. Sopyła: Categorization of Similar Objects Using Bag of Visual Words and Support Vector Machines, In: Proceedings of 4th International Conference on Agents and Artificial Intelligence, ICAART'12, pp. 231-236, Vilamoura, Algarve, Portugal, (2012)

    Papers 2011:

    23) P. Artiemjew: The Extraction Method of DNA Microarray Features Based on Experimental A Statistics, In: Proceedings of 6th International Conference on Rough Sets and Knowledge Technology, RSKT'11, Banff, Canada, Lecture Notes in Computer Science (LNCS), vol. 6954, pp. 642-648. Springer, Heidelberg (2011)

    22) L. Polkowski, P. Artiemjew: Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data, In: International Journal of Intelligent Systems. Special Issue: A Rough Set Approach to Data Mining, James F. Peters, Chien-Chung Chan, Jerzy W. Grzymala-Busse, Wojciech P. Ziarko (eds.), vol 26, issue 6, pp. 555 - 571. Wiley Periodicals, Inc. (2011)

    21) P. Artiemjew: The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs Classifier Based on Rough Mereology, In: Proceedings of 19th International Symposium on Methodologies for Intelligent Systems, ISMIS'11, Warsaw, Poland, Lecture Notes in Artificial Intelligence (LNAI), vol. 6804, pp. 33-42. Springer, Heidelberg (2011)

    20) P. Artiemjew: Stability of Optimal Parameters for Classifier Based on Simple Granules of Knowledge, In: Technical Sciences, vol. 14(1), pp. 57-69. UWM Publisher, Olsztyn (2011)

    Papers 2010:

    19) P. Artiemjew: Classifiers based on Rough Mereology in Analysis of DNA Microarray Data, In: Proceedings of Second International Conference of Soft Computing and Pattern Recognition (SoCPar2010), pp. 273 - 278. IEEE Computer Society, Cergy Pontoise, Paris, France (2010)

    18) P. Górecki, P. Artiemjew: DNA Microarray Classification by Means of Weighted Voting based on Rough Set Classifier, In: Proceedings of Second International Conference of Soft Computing and Pattern Recognition (SoCPar2010), pp. 269 - 272. IEEE Computer Society, Cergy Pontoise, Paris, France (2010)

    17) P. Artiemjew: In Search of Optimal Parameters for Classifier Based on Simple Granules of Knowledge, III International Interdisciplinary Technical Conference of Young Scientists (InterTech 2010), vol. 3, pp. 138 - 142. Poznań University Press, Poznań (2010)

    Papers 2009:

    16) P. Artiemjew: On strategies of knowledge granulation and applications to decision systems, PhD Dissertation, Polish Japanese Institute of Information Technology, L. Polkowski, Supervisor, Warsaw (2009)

    15) L. Polkowski, P. Artiemjew: On Knowledge Granulation and Applications to Classifier Induction in the Framework of Rough Mereology, International Journal of Computational Intelligence Systems, (IJCIS), vol. 2 - 4, pp. 315 - 331. Atlantis Press, Paris (2009)

    14) L. Polkowski, P. Artiemjew: A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data, Transactions on Rough Sets IX, (TRS), Lecture Notes in Computer Science vol. 5390, pp. 230-263. Springer, Heidelberg (2009)

    13) L. Polkowski, P. Artiemjew: On Classifying Mappings Induced by Granular Structures, Transactions on Rough Sets IX, (TRS), Lecture Notes in Computer Science vol. 5390, pp. 264-286. Springer, Heidelberg (2009)

    Papers 2008:

    12) P. Artiemjew: Natural versus granular computing: Classifiers from granular structures, In: Proceedings of 6th International Conference on Rough Sets and Current Trends in Computing RSCTC'08, Akron, Ohio, USA, vol. 5306, pp. 150-159. Springer, Heidelberg (2008)

    11) L. Polkowski, P. Artiemjew: Rough mereology in classification of data: Voting by means of residual rough inclusions, In: Proceedings of 6th International Conference on Rough Sets and Current Trends in Computing RSCTC'08, Akron, Ohio, USA, vol. 5306, pp. 113-120. Springer, Heidelberg (2008)

    10) L. Polkowski, P. Artiemjew: Classifiers based on granular structures from rough inclusions, In: Proceedings of 12th Int. Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU'08, Magdalena, L., Ojeda-Aciego, M., Vedegay, J.L. (eds.), pp. 1786-1794. Torremolinos (Malaga), Spain, (2008)

    9) P. Artiemjew: On clasification of data by means of rough mereological granules of objects and rules, In: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT'08, Chengdu China, pp. 221-228. LNAI, vol. 5009. Springer, Heidelberg (2008)

    8) P. Artiemjew: Rough mereological classifiers obtained from weak rough set inclusions, In: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT'08, Chengdu China, pp. 229-236. LNAI, vol. 5009. Springer, Heidelberg (2008)

    7) L. Polkowski, P. Artiemjew: Rough Sets In Data Analisis: Fundations and Applications, chapter in book Applications of Computional Intelligence in Biology: "Current Trends and Open Problems, pp. 33-54. SCI, vol. 122. Springer, Heidelberg, New York (2008)

    Papers 2007:

    6) P. Artiemjew: Classifiers from Granulated Data Sets: Concept dependent and Layered Granulation, in Proceedings RSKD'07. The Workshops at ECML/PKDD'07, pp. 1-9. Warsaw Univ. Press, Warsaw (2007)

    5) L. Polkowski, P. Artiemjew: Towards Granular Computing:Classifiers Induced from Granular Struktures, in Proceedings RSKD'07. The Workshops at ECML/PKDD'07, pp. 43-53. Warsaw Univ. Press, Warsaw (2007)

    4) L. Polkowski, P. Artiemjew: Granular Computing: Classifiers and Missing Values, in Proceedings ICCI'07. 6th IEEE International Conference on Cognitive Informatics, IEEE Computer Society, Los Alamitos, CA, 2007, pp. 186-194.

    3) L.Polkowski, P. Artiemjew: On Granular Rough Computing with Missing Values, In Lecture Notes in Artificial Intelligence (Proceedings RSEISP'07) vol 4585, pp. 271-279. Springer, Heidelberg (2007)

    2) L. Polkowski, P. Artiemjew: On Granular Rough Computing: Factoring Classifiers Though Granular Structures, in Lecture Notes in Artificial Intelligence (Proceedings RSEISP'07) vol. 4585, pp. 280-290. Springer, Heidelberg (2007)

    Papers 2006:

    1) P. Artiemjew: On Linear Error Correcting Codes, M.SC, Department of Mathematics and Computer Science, University of Warmia and Mazury, L. Polkowski, Supervisor, Olsztyn (2006)


 

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