Wieslaw Paja

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Assistant Professor

Rzeszów University

Poland

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About

Wieslaw Paja is an Assistant Professor at Rzeszów University in Poland. His research focuses on the application of machine learning and spectroscopic techniques for the diagnosis of various cancers, including prostate cancer and glioblastoma. Recent publications highlight his work in employing Raman spectroscopy and FTIR methods to identify disease markers in biological samples. Professor Paja actively contributes to advancements in the intersection of machine learning and medical diagnosis.

Recent Grants

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Development of reference spectra and algorithm for analysis of spectroscopic spectra for implementation in medical diagnostics

Open Date: 2022-05-01

Close Date: 2022-11-01

Grant: Close

Intelligent identification of melanocytic skin lesions using machine learning algorithms

Open Date: 2022-04-01

Close Date: 2022-09-01

Grant: Close

Development of feature selection methods by selecting significant values and ranges of attribute values

Open Date: 2019-01-01

Close Date: 2019-01-01

Grant: Close

Development of applications of feature selection methods by generational elimination of features

Open Date: 2018-01-01

Close Date: 2018-01-01

Grant: Close

Development of applications of feature selection methods in biomedical data analysis

Open Date: 2017-01-01

Close Date: 2017-01-01

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2022

Collaborators (8)

Yaroslav Shpotyuk

Assistant Professor

Rzeszów University

POLAND

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Krzysztof Pancerz

John Paul II Catholic University of Lublin

POLAND

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Agnieszka Wosiak

Lodz University of Technology

POLAND

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Paweł Jakubczyk

Rzeszów University

POLAND

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Rafał Podgórski

Lecturer

Uniwersytet Wroclawski

POLAND

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Anna Sroka-Bartnicka

Research Univeristy Professor

Medical University of Lublin

POLAND

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Józef Cebulski

prof.

Rzeszów University

POLAND

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Bartosz Dziadek

Assistant Professor

Rzeszów University

POLAND

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Wieslaw Paja

Research Interests

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About

Recent Grants

Articles (20)

Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease

Serum or urine: Which body fluids show higher sensitivity in detection prostate cancer by Raman spectroscopy

FTIR Markers of Prostate Cancer Tissue and Their Correlation With Medical Parameters of Tumor Aggressiveness

Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning

Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum

Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification atherosclerotic carotid stenosis from serum

Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses

Determination of platinum-resistance of women with ovarian cancer by FTIR spectroscopy combined with multivariate analyses and machine learning methods

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

FTIR, RAMAN and biochemical tools to detect reveal of oxidative Stress-Related lipid and protein changes in fibromyalgia

Fourier transform infrared spectroscopic marker of glioblastoma ob-tained from machine learning and changes in the spectra

Investigation of novel methods for stress level measurements in athletes employing FTIR and Raman spectroscopy techniques

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer

Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges

IoT Integrated Edge Platform for Secure Industrial Application with Deep Learning

Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study

Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays

Collaborators (8)

Yaroslav Shpotyuk

Krzysztof Pancerz

Agnieszka Wosiak

Paweł Jakubczyk

Rafał Podgórski

Anna Sroka-Bartnicka

Józef Cebulski

Bartosz Dziadek

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