Piotr Artiemjew

Associate Professor

University of Warmia and Mazury in Olsztyn
Country flagPoland

Research Interests

Explore related searches

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Piotr Artiemjew is an Associate Professor at the University of Warmia and Mazury in Olsztyn, Poland. His research areas include machine learning applications in medical diagnostics, robotics, environmental science, and the study of bee biology. He has published numerous articles addressing topics such as the Extreme Learning Machine framework, the use of gas sensors in detecting bee diseases, and the impacts of climate variability on lagoon ecosystems.

Articles (14)

Year:

2025

Year:

2025

Year:

2025

Beekeeping is an extremely difficult field of agriculture. It requires efficient management of the bee nest so that the bee colony can develop efficiently and produce as much honey and other bee products as possible. The beekeeper, therefore, must constantly monitor the contents of the bee comb. At the University of Warmia and Mazury in Olsztyn, research is being carried out to develop methods for efficient management of the apiary. One of our research goals was to test whether a gas detector (MCA-8) based on six semiconductor sensors—TGS823, TGS826, TGS832, TGS2600, TGS2602, and TGS2603 from the company FIGARO—is able to recognize the contents of bee comb cells. For this purpose, polystyrene and wooden test chambers were created, in which fragments of bee comb with different contents were placed. Gas samples were analyzed from an empty comb, a comb with sealed brood, a comb with open brood, a comb with carbohydrate food in the form of sugar syrup, and a comb with bee bread. In addition, a sample of gas from an empty chamber was tested. The results in two variants were analyzed: (1) Variant 1, the value of 270 s of sensor readings from the sample measurement (exposure phase), and (2) Variant 2, the value of 270 s of sensor readings from the sample measurement (measurement phase) with baseline correction by subtracting the last 600 s of surrounding air measurements (flushing phase). A five-time cross-validation 2 (5xCV2) test and the Monte Carlo cross-validation 25 (trained and tested 25 times) were performed. Fourteen classifiers were tested. The naive Bayes classifier (NB) proved to be the most effective method for distinguishing individual classes from others. The MCA-8 device brilliantly differentiates an empty comb from a comb with contents. It differentiates better between an empty comb and a comb with brood, with results of more than 83%. Lower class accuracy was obtained when distinguishing an empty comb from a comb with food and a comb with bee bread, with results of less than 73%. The matrix of six TGS sensors in the device shows promising versatility in distinguishing between various types of brood and food found in bee comb cells. This capability, though still developing, positions the MCA-8 device as a potentially invaluable tool for enhancing the efficiency and effectiveness of beekeepers in the future.

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (8)

Marek Kruk

University of Warmia and Mazury in Olsztyn

POLAND
View details

Jacek Rapinski

University of Warmia and Mazury in Olsztyn

POLAND
View details

Andrzej Zajac

-

POLAND
View details

Szymon Nitkiewicz

Assistant Profesor

University of Warmia and Mazury in Olsztyn

POLAND
View details

Manuel Ojeda-Aciego

Full Professor

University of Malaga

SPAIN
View details

Robert Barański

lecturer

AGH University of Science and Technology

POLAND
View details

Jerzy Wilde

University of Warmia and Mazury in Olsztyn

POLAND
View details

Irina Perfilieva

Professor

Cracow University of Technology

POLAND
View details
ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.