RTU Zinātniskās darbības rezultāti un resursi: Personāls

Informācija par autoru

Vārds, Uzvārds:

Artūrs Ķempelis

Struktūrvienība:

(33500) Informācijas tehnoloģijas institūts

E-pasts:

Arturs.Kempelis_1(at)rtu.lv

Publikācijas

Atrasti 4 rezultāti tiek attēloti no 1 līdz 4.

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
(2024)

Artūrs Ķempelis, Inese Poļaka, Andrejs Romānovs, Antons Patļins
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Machine Learning-based Forecasting of Sensor Data for Enhanced Environmental Sensing
(2023)

Marta Narigina, Artūrs Ķempelis, Andrejs Romānovs
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Machine Learning-based Sensor Data Forecasting for Precision Evaluation of Environmental Sensing
(2023)

Artūrs Ķempelis, Marta Narigina, Eduards Osadcijs, Antons Patļins, Andrejs Romānovs
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Using Computer Vision and Machine Learning Based Methods for Plant Monitoring in Agriculture: A Systematic Literature Review
(2022)

Artūrs Ķempelis, Andrejs Romānovs, Antons Patļins
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Informācija par autoru

Publikācijas

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems (2024)

Machine Learning-based Forecasting of Sensor Data for Enhanced Environmental Sensing (2023)

Machine Learning-based Sensor Data Forecasting for Precision Evaluation of Environmental Sensing (2023)

Using Computer Vision and Machine Learning Based Methods for Plant Monitoring in Agriculture: A Systematic Literature Review (2022)

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
(2024)

Machine Learning-based Forecasting of Sensor Data for Enhanced Environmental Sensing
(2023)

Machine Learning-based Sensor Data Forecasting for Precision Evaluation of Environmental Sensing
(2023)

Using Computer Vision and Machine Learning Based Methods for Plant Monitoring in Agriculture: A Systematic Literature Review
(2022)