Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks

Autor: Dalia Grendaite, Linas Petkevicius

Publikováno v: IEEE Access, Vol 12, Pp 27973-27988 (2024)

Algal blooms are a common problem in inland waters, which raise growing awareness on monitoring lakes’ conditions. The on site monitoring is expensive and requires large human resources efforts. This work proposes remote monitoring techniques using

Externí odkaz: https://doaj.org/article/b672a38ddd724c92bf485c3e0da0b837

Modeling carbohydrates oxidation by oxygen catalyzed by bienzyme glucose dehydrogenase/laccase system immobilized into microreactor with carbon nanotubes

Autor: Juozas Kulys, Linas Petkevicius, Romas Baronas

Publikováno v: Journal of Mathematical Chemistry. 59:168-185

This paper presents a mathematical model of a batch stirred tank reactor based on an array of identical spherical porous microbioreactors loaded with non specific glucose dehydrogenase and oxygen reducing enzyme, i.e. laccase. The model was validated

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::625e13e071be76d44695626d8338f5a2
https://doi.org/10.1007/s10910-020-01187-2

Analysis of Information Compression of Medical Images for Survival Models

Autor: Ieva Jonaityte, Linas Petkevicius

Publikováno v: 2021 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream).

In this paper, we investigate the methods for extraction of significant information from medical breast cancer images for survival analysis. In breast cancer diagnostics as well as in the analysis of other medical images it is still common to employ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dbd83798fecc3054164c9c0ee21ef0d5
https://doi.org/10.1109/estream53087.2021.9431461

Probabilistic deep learning for electric-vehicle energy-use prediction

Autor: Kristian Torp, Simonas Šaltenis, Alminas Čivilis, Linas Petkevicius

Publikováno v: SSTD '21: 17th international symposium on spatial and temporal databases: virtual, USA, August 23-25, 2021, New York : Association for Computing Machinery, 2021, p. 85-95
SSTD
Petkevicius, L, Saltenis, S, Civilis, A & Torp, K 2021, Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction . in Proceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021 . Association for Computing Machinery, pp. 85-95, 17th International Symposium on Spatial and Temporal Databases, SSTD 2021, Virtual, Online, United States, 23/08/2021 . https://doi.org/10.1145/3469830.3470915

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f74b430c6766dd6862e4b30a06632190
https://repository.vu.lt/VU:ELABAPDB102949017&prefLang=en_US

Analysis of Deep Neural Network Architectures and Similarity Metrics for Low-Dose CT Reconstruction

Autor: Linas Petkevicius, Jonas Brusokas

Publikováno v: 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream).

Computed tomography (CT) is a widely used imaging technique in the medical field. During CT procedures patients are exposed to high amounts of radiation, posing a tangible threat to their health. Developed low-dose procedures lower exposure but produ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c318d7e8af91d48d2890554207c6b551
https://doi.org/10.1109/estream50540.2020.9108883