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Jaroslaw Kurek

University Professor

Warsaw University of Life Sciences

Poland

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Research Interests

Drilling Engineering

70%

Industrial Automation

30%

Machining Technology

70%

Wear Analysis

40%

Manufacturing Engineering

30%

Agriculture

20%

Artificial Neural Network

20%

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Articles20

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

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Year:

2025

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

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Year:

2025

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

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Year:

2024

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

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Year:

2024

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

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Year:

2024

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

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Year:

2024

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

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Year:

2024

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

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Year:

2024

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

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Year:

2024

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

Article Link

Year:

2024

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

Article Link

Year:

2023

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

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Year:

2023

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

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Year:

2023

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

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Year:

2023

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

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Year:

2023

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

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Year:

2022

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

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Year:

2022

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

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Year:

2022

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

Article Link

Year:

2022

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

Article Link

Year:

2022

Collaborators8

Marek Barlak

National Centre for Nuclear Research

POLAND

Katarzyna Pentoś

Wroclaw University of Environmental and Life Sciences

POLAND

Tomasz Wojciechowski

Assistant Professor

Uniwersytet Przyrodniczy w Poznaniu

POLAND

Bogdan Chojnicki

Leibniz Centre for Agricultural Landscape Research

GERMANY

Albina Jegorowa

Warsaw University of Life Sciences

POLAND

Jacek Wilkowski

POLAND

Michał Kruk

Warsaw University of Life Sciences

POLAND

Artur Krupa

Assistant Professor

Warsaw University of Life Sciences

POLAND

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