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Jaroslaw Kurek

University Professor

Warsaw University of Life Sciences

Poland

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About

Jaroslaw Kurek is a University Professor at Warsaw University of Life Sciences in Poland. His research interests include machine learning applications in agricultural sciences, recruitment systems, and tool condition assessment. Recent publications reflect his expertise in comparing neural network architectures and developing explainable AI techniques, particularly in areas such as milling processes and ultrasound imaging.

Articles (25)

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Collaborators (10)

Marek Barlak

National Centre for Nuclear Research

POLAND

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Sylwia Stachowiak

SWPS University of Social Sciences and Humanities

POLAND

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Katarzyna Pentoś

Wroclaw University of Environmental and Life Sciences

POLAND

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Tomasz Wojciechowski

Assistant Professor

Uniwersytet Przyrodniczy w Poznaniu

POLAND

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KAMIL KOSZELA

Head

National Institute of Geriatrics, Rheumatology and Rehabilitation

POLAND

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Bogdan Chojnicki

Leibniz Centre for Agricultural Landscape Research

GERMANY

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Albina Jegorowa

Warsaw University of Life Sciences

POLAND

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Jacek Wilkowski

POLAND

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Michał Kruk

Warsaw University of Life Sciences

POLAND

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Artur Krupa

Assistant Professor

Warsaw University of Life Sciences

POLAND

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Jaroslaw Kurek

Research Interests

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About

Articles (25)

A Multi-Objective Scoring Approach to Contract and Exposure-Aware Re-Ranking in Real-Estate Recommendation

Zero-Shot Vertebral Instance Segmentation on DICOM Spine Radiographs Using Promptable Segment Anything Models

towerbench: A frozen-protocol benchmark harness for two-tower retrieval and autoencoder recommenders with paired bootstrap statistics on public implicit-feedback data

fastnb: A high-performance C++ Naive Bayes classifier for SILVA-based 16S rRNA taxonomy

Comparative Evaluation of CNN and Transformer Architectures for Flowering Phase Classification of Tilia cordata Mill. with Automated Image Quality Filtering

Comparative Evaluation of Sequential Neural Network (GRU, LSTM, Transformer) Within Siamese Networks for Enhanced Job–Candidate Matching in Applied Recruitment Systems

Comparative Evaluation of GPT-4o, GPT-OSS-120B and Llama-3.1-8B-Instruct Language Models in a Reproducible CV-to-JSON Extraction Pipeline

Comparative Study of Conventional Machine Learning versus Deep Learning-Based Approaches for Tool Condition Assessments in Milling Processes

Explainable AI (XAI) Techniques for Convolutional Neural Network-Based Classification of Drilled Holes in Melamine Faced Chipboard

Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard

The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits

Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard

Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear

Automatic Estimation of Drill Wear Based on Images of Holes Drilled in Melamine Faced Chipboard with Machine Learning Algorithms

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Effect of Nitrogen Ion Implantation on the Tool Life Used in Particleboard CNC Drilling

Prediction of Blueberry (<i>Vaccinium corymbosum</i> L.) Yield Based on Artificial Intelligence Methods

Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

The Use of Multilayer Perceptron (MLP) to Reduce Delamination during Drilling into Melamine Faced Chipboard

The Use of Multilayer Perceptron (MLP) to Reduce Delamination during Drilling into Melamine Faced Chipboard

Collaborators (10)

Marek Barlak

Sylwia Stachowiak

Katarzyna Pentoś

Tomasz Wojciechowski

KAMIL KOSZELA

Bogdan Chojnicki

Albina Jegorowa

Jacek Wilkowski

Michał Kruk

Artur Krupa

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