Dr hab. inż., Prof. UR Wiesław Paja

Publikacje

• [współaut.] Mitura P, Młynarczyk G, Kowalski R [et al.] Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2026, Vol. 344, Part 1
• [współaut.] Kęsik J, Terlecki P, Iłżecki M [et al.] Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification atherosclerotic carotid stenosis from serum. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2025, Vol. 326
• [współaut.] Kryska A, Depciuch J, Krysa M [et al.] Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum. - Analytical and Bioanalytical Chemistry, 2025, Vol. 417, iss.29, p. 6655-6663
• [współaut.] Kryska A, Sawic M, Depciuch J [et al.] Machine learning-driven Raman spectroscopy : A novel approach to lipid profiling in diabetic kidney disease. - Nanomedicine: Nanotechnology Biology and Medicine, 2025, Vol. 64
• [współaut.] Mitura P, Klebowski B, Płaza P [et al.] Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2025, Vol. 327
• [współaut.] Mitura P, Klebowski B, Płaza P [et al.] Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine. - Journal of Biophotonics, 2025, Vol. 18, iss. 1
• [współaut.] Mitura P, Klebowski B, Wronecki L [et al.] FTIR Markers of Prostate Cancer Tissue and Their Correlation With Medical Parameters of Tumor Aggressiveness. - Journal of Biophotonics, 2025, Vol. 18, iss. 9
• [współaut.] Mitura P, Płaza P, Starownik R [et al.] Investigation of importance Raman shifts in liquid biopsy diagnostics of prostate cancer. - Scientific Reports, 2025, Vol. 15, iss. 1
• [współaut.] Mitura P, Płaza P, Starownik R [et al.] Serum or urine : Which body fluids show higher sensitivity in detection prostate cancer by Raman spectroscopy. - Microchemical Journal, 2025, Vol. 215
• [współaut.] Tołpa B, Jakubczyk P, Łach K [et al.] Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning. - Analytical Biochemistry, 2025, Vol. 699
• [współaut.] Wojtowicz A, Bentkowska U Feature Importance Ranking Using Interval-Valued Methods and Aggregation Functions for Machine Learning Applications. - Applied Sciences-Basel, 2025, Vol. 15, iss. 22
• [współaut.] Wojtowicz A, Bentkowska U The interval-valued weighted feature ranking method based on aggregation functions W: 2025 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) : Reims, France, 6-10 July 2025 / eds. Kevin Guelton, Christophe Marsala. Piscataway, Institute of Electrical and Electronics Engineers (IEEE): 2025, S. 1-7
• [współaut.] Kęsik J, Jakubczyk P, Khalavka M [et al.] Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses. - Nanomedicine: Nanotechnology Biology and Medicine, 2024, Vol. 62
• [współaut.] Kluz-Barłowska M, Kluz T, Pancerz K [et al.] FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women sufering from ovarian cancer. - Scientific Reports, 2024, Vol. 14, iss. 1
• [współaut.] Kluz-Barłowska M, Kluz T, Sarzyński J [et al.] Determination of platinum-resistance of women with ovarian cancer by FTIR spectroscopy combined with multivariate analyses and machine learning methods. - Scientific Reports, 2024, Vol. 14, iss.1
• [współaut.] Kryska A, Depciuch J, Krysa M [et al.] Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024, Vol. 320
• [współaut.] Sarzyński J, Paja S Identification of outlier cases in information systems using selection of relevant intervals of attribute values. - Procedia Computer Science, 2024, Vol. 246, p. 4610-4616
• [współaut.] Szkoła J, Paja T Application of binary subnetworks to classify images potentially containing melanocytic skin lesions. - Procedia Computer Science, 2024, Vol. 246, p. 4515-4523
• [współaut.] Tołpa B, Trojnar E, Łach K [et al.] FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors. - Nanomedicine: Nanotechnology Biology and Medicine, 2024, Vol. 57
• [współaut.] Balaji B. S, Antonijevic M, Stoean C [et al.] IoT Integrated Edge Platform for Secure Industrial Application with Deep Learning. - Human-centric Computing and Information Sciences, 2023, Vol. 13
• [współaut.] Depciuch J, Czarny W, Płonka A [et al.] Investigation of novel methods for stress level measurements in athletes employing FTIR and Raman spectroscopy techniques. - Measurement, 2023, Vol. 220
• [współaut.] Depciuch J, Jakubczyk P, Pancerz K [et al.] Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer. - Nanomedicine: Nanotechnology Biology and Medicine, 2023, Vol. 48
• [współaut.] Depciuch J, Jakubczyk P, Pancerz K [et al.] Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study. - Bioprocess and Biosystems Engineering, 2023, Vol. 46, iss. 4, s. 599-609
• [współaut.] Depciuch J, Pancerz K, Özgur U [et al.] Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods. - Journal of Raman Spectroscopy, 2023, Vol. 54, iss. 5, p. 501-511
• [współaut.] Guleken Z, Jakubczyk P, Pancerz K [et al.] An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker. - Computer Methods and Programs in Biomedicine, 2023, Vol. 234
• [współaut.] Guleken Z, Suna G, Karaca & [et al.] FTIR, RAMAN and biochemical tools to detect reveal of oxidative Stress-Related lipid and protein changes in fibromyalgia. - Infrared Physics &Technology, 2023, Vol. 133
• [współaut.] Kluz-Barłowska M, Kluz T, Sarzyński J [et al.] FT-Raman data analyzed by multivariate and machine learning as a new methods for detection spectroscopy marker of platinum-resistant women suffering from ovarian cancer. - Scientific Reports, 2023, Vol. 13, iss. 1
• [współaut.] Pancerz K, Burda A, Grochowalski P Ontologiczny generator testów wiedzy z tekstów na przykładzie wiedzy o geografii Polski. - Barometr Regionalny. Analizy i Prognozy, 2023, T. 19, nr 1, s. 41-49
• [współaut.] Tołpa B, Depciuch J, Jakubczyk P [et al.] Fourier transform infrared spectroscopic marker of glioblastoma obtained from machine learning and changes in the spectra. - Photodiagnosis and Photodynamic Therapy, 2023, Vol. 42
• Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges. - Entropy, 2023, Vol. 25, iss. 8
• [współaut.] Pancerz K, Jakubczyk P Determining Reference Spectra for Medical Diagnosis Using Clustering Methods W: 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems / eds. G.A. Tsihrintzis, C. Toro, S.A. Rios, R.J. Howlett, L.C. Jain. Amsterdam, Elsevier: 2023, S. 4700-4705
• [współaut.] Szkoła J, Pancerz K, Sarzyński J [et al.] Identification of Melanocytic Skin Lesions Using Deep Learning Methods W: Progress in Polish Artificial Intelligence Research 4 / edkacja naukowa Adam Wojciechowski, Piotr Lipiński. Łódź, Politechnika Łódzka: 2023, S. 239-244
• [współaut.] Szkoła J, Sarzyński J, Żychowska M A Preliminary Research on Automatic Identification of Melanocytic Skin Lesions from Digital Images W: 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems / eds. G.A. Tsihrintzis, C. Toro, S.A. Rios, R.J. Howlett, L.C. Jain. Amsterdam, Elsevier: 2023, S. 4706-4712
• [współaut.] Depciuch J, Jakubczyk P, Sarzyński J [et al.] Apocynin reduces cytotoxic effects of monosodium glutamate in the brain : a spectroscopic, oxidative load, and machine learning study. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, Vol. 279
• [współaut.] Guleken Z, Bahat P, Toto & [et al.] Blood serum lipid profiling may improve the management of recurrent miscarriage : a combination of machine learning of mid-infrared spectra and biochemical assays. - Analytical and Bioanalytical Chemistry, 2022, Vol. 414, iss. 29-30, p. 8341-8352
• [współaut.] Guleken Z, Bulut H, Bulut B [et al.] Correlation between endometriomas volume and Raman spectra. Attempting to use Raman spectroscopy in the diagnosis of endometrioma. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, Vol. 274
• [współaut.] Guleken Z, Bulut H, Bulut B [et al.] Identification of polycystic ovary syndrome from blood serum using hormone levels via Raman spectroscopy and multivariate analysis. - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, Vol. 273
• [współaut.] Guleken Z, Jakubczyk P, Pancerz K [et al.] Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications. - Talanta, 2022, Vol. 237
• [współaut.] Guleken Z, Tuyji Tok Y, Jakubczyk P [et al.] Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level. - Measurement, 2022, Vol. 196
• [współaut.] Jakubczyk P, Pancerz K, Cebulski J [et al.] Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods. - Photodiagnosis and Photodynamic Therapy, 2022, Vol. 38
• [współaut.] Pancerz K, Stoean C COVID-19 antibody level analysis with feature selection approach. - Procedia Computer Science, 2022, Vol. 207, p. 4268-4275
• [współaut.] Stoean C, Bacanin N, Stoean R [et al.] Semantic segmentation of fetal heart components in second trimester echocardiography. - Procedia Computer Science, 2022, Vol. 207, p. 3085-3092
• Identification of Relevant Medical Parameter Values in Information Systems using Fuzzy Approach. - Procedia Computer Science, 2021, Vol. 192, p. 3915-3921
• [współaut.] Pancerz K, Pękala B, Sarzyński J Application of the Fuzzy Logic to Evaluation and Selection of Attribute Ranges in Machine Learning W: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, Institute of Electrical and Electronics Engineers (IEEE): 2021, s. 1-6
• [współaut.] Knap M A Constructive Induction of Feature using Random Forest Approach. - Procedia Computer Science, 2020, vol. 176, pp. 3318-3323
• [współaut.] Stoean C, Stoean R, Sandita A Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. - PLoS ONE, 2019, vol. 14, iss. 10, Article Number: e0223593
• Tree-based generational feature selection in medical applications. - Procedia Computer Science, 2019, vol. 159, pp. 2172-2178
• Generational Feature Selection using Random Forest Approach W: Proceedings of the International Conference on Information and Digital Technologies 2019, IDT 2019 : Zilina, Slovakia, 25-27 June 2019 / ed. Enrico Zio. [b.m.], Institute of Electrical and Electronics Engineers (IEEE): 2019, S. 354-357
• [współaut.] Pancerz K, Sarzyński J, Gomuła J Determining Importance of Ranges of MMPI Scales Using Fuzzification and Relevant Attribute selection. - Procedia Computer Science, 2018, vol. 126, pp. 2065-2074
• [współaut.] Pancerz K, Grochowalski P Generational feature elimination and some other ranking feature selection methods W: Advances in Feature Selection for Data and Pattern Recognition / editors Urszula Stańczyk, Beata Zielosko, Lakhmi C. Jain. Cham, Springer: 2018, S. 97-112
• A Decision Rule Based Approach to Generational Feature Selection W: Advances in Data Mining. Applications and Theoretical Aspects : 18th Industrial Conference, ICDM 2018, New York, NY, USA, July 11-12, 2018, Proceedings / ed. Petra Perner. Cham, Springer: 2018, S. 230-239
• Generational Feature Elimination to Find All Relevant Feature Subset W: Intelligent Decision Technologies 2017 : proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017), Part 1 / edited by Ireneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain. Cham, Springer: 2018, S. 140-148
• [współaut.] Pancerz K Feature Selection Methods Applied to Severe Brain Damages Data W: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems : September 3-6, 2017, Prague, Czech Republic / Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki (eds.). Warszawa, Polskie Towarzystwo Informatyczne: 2017, S. 199-202
• [współaut.] Wrzesień M, Niemiec R, Rudnicki W Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models. - Geoscientific Model Development, 2016, Vol. 9, Iss. 3, p. 1065-1072
• [współaut.] Pancerz K, Gomuła J Random Forest Feature Selection for Data Coming from Evaluation Sheets of Subjects with ASDs W: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, September 11-14, 2016. Gdańsk, Poland / (eds). Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. Warszawa, Polskie Towarzystwo Informatyczne: 2016, S. 299-302
• [współaut.] Pancerz K, Grochowalski P On selected data preprocessing procedures with the Classification and Prediction Software System (CLAPSS) W: The International Conference on Information and Digital Technologies 2016 : 5-7 July 2016, Rzeszów, Poland / Institute of Electrical and Electronics Engineers. New York, Institute of Electrical and Electronics Engineers (IEEE): 2016, S. 219-226
• A preliminary attempt to attribute selection using Split-and-Rank tool W: The International Conference on Information and Digital Technologies 2016 : 5-7 July 2016, Rzeszów, Poland / Institute of Electrical and Electronics Engineers. New York, Institute of Electrical and Electronics Engineers (IEEE): 2016, S. 215-218
• Feature Selection Methods Based on Decision Rule and Tree Models W: Intelligent Decision Technologies 2016 : Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) - Part II / editors Ireneusz Czarnowski, Alfonso Mateos Caballero, Robert J. Howlett, Lakhmi C. Jain. Cham, Springer: 2016, S. 63-70
• [współaut.] Pancerz K Estimation and feature selection by application of knowledge mined from decision rules models W: Concurrency, Specification & Programming : 24th International Workshop, CS&P 2015, Rzeszow, Poland, September 28-30, 2015 : proceedings. Vol. 2 / Zbigniew Suraj, Ludwik Czaja (eds.). Rzeszów, University of Rzeszow: 2015, S. 57-68
• Medical diagnosis support and accuracy improvement by application of total scoring from feature selection approach W: Proceedings of the Federated Conference on Computer Science and Information Systems : [September 13-16, 2015, Łódź, Poland] / editors Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. Warszawa, Polskie Towarzystwo Informatyczne: 2015, S. 281-286