Opcije pristupačnosti Pristupačnost

O djelatniku

doc. dr. sc. Ivan Lorencin

Zvanje: docent
Lokacija: Rovinjska 14 - ured 2
E-mail: E-mail
URL službenih stranica na Webu: https://fipu.unipu.hr/fipu/ivan.lorencin
Organizacijska jedinica: Fakultet informatike u Puli
Godina diplomiranja:2018.
Godina doktoriranja:2022.
Na sveučilištu od:2024.

Nastava

prijediplomski

diplomski

Životopis

Ivan Lorencin je docent na Fakultetu informatike Sveučilišta Jurja Dobrile u Puli. Izabran je u znanstveno zvanje znanstvenog suradnika u području elektrotehnike, strojarstva i računarstva te je urednik časopisa "Mathematics". Autor je i suautor preko 90 znanstvenih radova, od kojih se 31 nalazi u časopisima koji spadaju u prvu i drugu kvartilu. Kao voditelj, sudjeluje u znanstvenom projektu "Razvoj inteligentnog sustava za procjenu točke maksimalne snage fotonaponskog sustava s primjenom na autonomnim plovilima". Kao suradnik, sudjeluje u znanstvenim projektima: Interreg InnoHPC, Erasmus + WICT i PESHES, Znanstvenom centru izvrsnosti za znanost o podacima (osnivač MZO-a), CEI projektu "COVIDAi" (305.6019-20) i znanstvenoj podršci Sveučilišta u Rijeci: "Razvoj inteligentnog ekspertnog sustava za online dijagnosticiranje raka mjehura". Ivan Lorencin sudjeluje u znanstvenim istraživanjima vezanim uz umjetnu inteligenciju, robotiku i računarstvo visokih performansi. Godine 2022. završio je poslijediplomski doktorski studij s disertacijom pod naslovom "Inteligentni sustav za dijagnostiku karcinoma mokraćnog mjehura"na Tehničkom fakultetu sveučilišta u Rijeci na kojemu je i diplomirao 2018. godine.

 

 

Izabrane publikacije

Exellence:

 

  • Lorencin, I., Baressi Šegota, S., Anđelić, N., Blagojević, A., Šušteršić, T., Protić, A., ... & Car, Z. (2021). Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine, 11(1), 28.
  • Baressi Šegota, S., Lorencin, I., Anđelić, N., Musulin, J., Štifanić, D., Glučina, M., ... & Car, Z. (2022). Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients. Mathematics, 10(16), 2925.

Q1:

  • Lorencin, I., Anđelić, N., Španjol, J.; Car, Z. (2020). Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis. Artificial Intelligence in Medicine, 102, 101746.
  • Lorencin, I., Baressi Šegota, S., Anđelić, N., Mrzljak, V., Ćabov, T., Španjol, J., & Car, Z. (2021). On Urinary Bladder Cancer Diagnosis: Utilization of Deep Convolutional Generative Adversarial Networks for Data Augmentation. Biology, 10(3), 175.
  • Anđelić, N., Baressi Šegota, S., Lorencin, I., Jurilj, Z., Šušteršič, T., Blagojević, A., ... & Car, Z. (2021). Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm. International Journal of Environmental Research and Public Health, 18(3), 959.
  • Blagojević, A., Šušteršič, T., Lorencin, I., Šegota, S. B., Anđelić, N.,et al. (2021). Artificial intelligence approach towards assessment of condition of COVID-19 patients-Identification of predictive biomarkers associated with severity of clinical condition and disease progression. Computers in biology and medicine, 104869.
  • Musulin, J., et al. (2021). Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. International Journal of Environmental Research and Public Health, 18(8), 4287.
  • Baressi Šegota, S., et al. (2021). Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach. Biology, 10(11), 1134.
  • Šušteršič,et al. (2021). Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union. Frontiers in Public Health, 1567.

 

Q2:

  • Anđelić, N., Baressi Šegota, S., Lorencin, I., Mrzljak, V., & Car, Z. (2021). Estimation of COVID-19 epidemic curves using genetic programming algorithm. Health Informatics Journal, 27(1), 1460458220976728.
  • Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation. Energies, 12(22), 4352.
  • Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020). Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron. Computational and Mathematical Methods in Medicine, 2020.

 

Izabrani projekti

  1. Project "Use of Regressive Artificial Intelligence {Al) and Machine Learning (ML) Methods in Modelling of COVID-19 spread - COVIDA"; (Ref. No. 305.6019-20), Central European Initiative (co-researcher)
  2. Project "Science Center for Excellence in Data Science and Cooperative Systems; DATACROSS Project - Advanced Methods and Technology in Data Science and Co-operative Systems", Ministry of science and education Republic of Croatia, coresearcher
  3. Danube Transnational Program, "High-Performance Computing for Effective Innovation in the Danube Region";, Multilateral International Project, coresearcher (budget 2 M EUR)
  4. Erasmus + KA2 Call, "Development and Implementation of System for Performance Evaluation for Serbian HEIS and Systems - PESHES", Multilateral International Project, (budget 1 M EUR), member
  5. Erasmus + Project WICT