Erasmus+ KA171: PhD Students from China Agricultural University Visit FIDIT
As part of our ongoing commitment to international research collaboration and knowledge exchange, Lab for Data Engineering and Computational Linguistics of the Faculty of Informatics and Digital Technologies is pleased to host two visiting PhD students from China Agricultural University in the period from April 1 to May 31, 2025.
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ERASMUS+ KA171: New Insights from the project
In a recent joint study published in Aquaculture (ScienceDirect), we tackled a crucial yet often overlooked question: how do we measure stress in aquatic animals—particularly when they’re exposed to challenging conditions?
We performed a comprehensive review of existing stress-indicating methods in fish and shellfish, distinguishing between two categories:
- Contact indicators, such as measuring cortisol levels in blood or tissues—accurate but invasive and sometimes harmful.
- Non-contact indicators, like monitoring behavior, changes in water chemistry, or using imaging and telemetry—a gentler approach, better suited for animal welfare.
We propose a new classification system to organize these metrics by how invasive they are and how reliably they signal stress.
Key Findings
- Traditional contact methods (e.g., blood cortisol) remain reliable but stressful and often impractical for large-scale use.
- Emerging non-contact techniques—behavioral monitoring, remote imaging, and water-based indicators—show promise for welfare-friendly stress detection.
- The proposed taxonomy helps aquaculturists balance accuracy with animal wellbeing, guiding them toward the most appropriate stress-measurement strategy for specific situations.
The study is available here.
Read MoreObrana teme doktorske disertacije Tomislava Ivanovskog
Dana 20.4. u 12 sati u učionici O-357 Fakulteta za informatiku i digitalne tehnologije doktorand Tomislav Ivanovski obranio je temu doktorskog rada pod nazivom “Predviđanje zrelosti breskve temeljeno na prirodom inspiriranim metaheuristikama” pred povjerenstvom u sastavu prof. dr. sc. Ivo Ipšić, izv. prof. dr. sc. Marija Brkić Bakarić i prof. Boris Vrdoljak te zornim mentorstvom voditeljice projekta prof. dr. sc. Maje Matetić i komentorstvom suradnika doc. dr. sc. Marka Gulića.
Read MoreObjava još jednog rada na međunarodnoj konferenciji DAAAM 2022
Znanstveni rad “Peach firmness prediction using optimized regression trees models” u koautorstvu doktoranda Tomislava Ivanovskog, suradnika prof. dr. sc. Xiaoshuan Zhanga, prof. dr. sc. Tomislava Jemrića, doc. dr. sc. Marka Gulića i voditeljice projekta prof. dr. sc. Maje Matetić prezentiran je i objavljen u zborniku međunarodne konferencije DAAAM 2022.
Read MoreObjava rada na međunarodnoj konferenciji DAAAM 2022
Znanstveni rad “Machine learning for data analysis in football: A survey of methods and problems” u koautorstvu doktoranda Saše Tokića, suradnika doc. dr. sc. Ante Panjkote i voditeljice projekta prof. dr. sc. Maje Matetić prezentiran je i objavljen u zborniku međunarodne konferencije DAAAM 2022.
Read MoreObjava rada u međunarodnom časopisu Sensors
Znanstveni rad “Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data” u koautorstvu člana projekta i doktoranda Dejana Ljubobratovića, suradnika Marka Vukovića, članice projekta izv. prof. dr. sc. Marije Brkić Bakarić, suradnika prof. dr. sc. Tomislava Jemrića i voditeljice projekta prof. dr. sc. Maje Matetić objavljen je u međunarodnom časopisu Sensors (1424-8220) 22 (2022), 15; 5791, 19 (https://www.mdpi.com/1424-8220/22/15/5791/htm).
Read MoreObjava rada u međunarodnom časopisu Electronics
Znanstveni rad “A Smart Tourism Case Study: Classification of Accommodation Using Machine Learning Models Based on Accommodation Characteristics and Online Guest Reviews” u koautorstvu studentice Nole Čumlievski i članice projekta izv.prof. dr. sc. Marije Brkić Bakarić te voditeljice projekta prof. dr. sc. Maje Matetić objavljen je u međunarodnom časopisu Electronics (Q2).
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