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(Field of Science):
- information and communication technology (Engineering and Technology)
- computer and information sciences (Natural sciences)
Ministry points: Help
| Year | Points | List |
|---|---|---|
| Year 2024 | 200 | Ministry scored journals list 2024 |
| Year | Points | List |
|---|---|---|
| 2024 | 200 | Ministry scored journals list 2024 |
| 2023 | 200 | Ministry Scored Journals List |
| 2022 | 200 | Ministry Scored Journals List 2019-2022 |
| 2021 | 200 | Ministry Scored Journals List 2019-2022 |
| 2020 | 200 | Ministry Scored Journals List 2019-2022 |
| 2019 | 200 | Ministry Scored Journals List 2019-2022 |
Model:
Points CiteScore:
| Year | Points |
|---|---|
| Year 2023 | 5.5 |
| Year | Points |
|---|---|
| 2023 | 5.5 |
| 2022 | 5.1 |
| 2021 | 4.1 |
| 2020 | 2.8 |
| 2019 | 2.4 |
| 2018 | 10.8 |
| 2017 | 7.4 |
| 2016 | 3.1 |
| 2015 | 0.5 |
Impact Factor:
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Papers published in journal
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total: 3
Catalog Journals
Year 2024
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Floodsar: Automatic mapping of river flooding extent from multitemporal SAR imagery
PublicationFloodsar is an open-source tool for automatic mapping of the flood extent from a time series of synthetic aperture radar (SAR) imagery. Floodsar is unsupervised, however, it requires defining the parameters search space, geographical area of interest, and some river gauge observations (e.g. water levels or discharges) time series that overlap temporarily with the SAR imagery. Applications of Floodsar are mainly in real-time monitoring...
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HMSE: A tool for coupling MODFLOW and HYDRUS-1D computer programs
PublicationA new software HMSE has been developed to facilitate external coupling between two well-known programs for subsurface flow modeling: MODFLOW-2005 (saturated zone flow) and HYDRUS-1D (unsaturated zone flow). Two coupling schemes have been implemented. In the first case the groundwater recharge flux is calculated by HYDRUS-1D assuming a fixed water table position and then passed to MODFLOW input files. In the second case the water...
Year 2023
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AffecTube — Chrome extension for YouTube video affective annotations
PublicationThe shortage of emotion-annotated video datasets suitable for training and validating machine learning models for facial expression-based emotion recognition stems primarily from the significant effort and cost required for manual annotation. In this paper, we present AffecTube as a comprehensive solution that leverages crowdsourcing to annotate videos directly on the YouTube platform, resulting in ready-to-use emotion-annotated...
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