Jaroslaw Kurek

University Professor

Warsaw University of Life Sciences

Poland

Research Interests

Explore related searches

Professors in Poland

Contact this professor

ORCID

Google Scholar

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.

Jaroslaw Kurek

Research Interests

Explore related searches

Contact this professor

About

Articles (20)

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 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 (8)

Marek Barlak

Katarzyna Pentoś

Tomasz Wojciechowski

Bogdan Chojnicki

Albina Jegorowa

Jacek Wilkowski

Michał Kruk

Artur Krupa

How do I reach out?

Meet Kite AI