Search
ISSN:
eISSN:
Disciplines
(Field of Science):
- information and communication technology (Engineering and Technology)
- safety engineering (Engineering and Technology)
- biomedical engineering (Engineering and Technology)
- mechanical engineering (Engineering and Technology)
- management and quality studies (Social studies)
- international relations (Social studies)
(Field of Science)
Ministry points: Help
| Year | Points | List |
|---|---|---|
| Year 2024 | 40 | Ministry scored journals list 2024 |
| Year | Points | List |
|---|---|---|
| 2024 | 40 | Ministry scored journals list 2024 |
| 2023 | 40 | Ministry Scored Journals List |
| 2022 | 40 | Ministry Scored Journals List 2019-2022 |
| 2021 | 40 | Ministry Scored Journals List 2019-2022 |
| 2020 | 40 | Ministry Scored Journals List 2019-2022 |
| 2019 | 40 | Ministry Scored Journals List 2019-2022 |
Model:
Points CiteScore:
| Year | Points |
|---|---|
| Year 2023 | 2.5 |
| Year | Points |
|---|---|
| 2023 | 2.5 |
| 2022 | 2.4 |
| 2021 | 1.2 |
| 2020 | 1.2 |
| 2019 | 1.2 |
| 2018 | 0.9 |
| 2017 | 0.5 |
| 2016 | 0.4 |
| 2015 | 0.1 |
Impact Factor:
Papers published in journal
Filters
total: 2
Catalog Journals
Year 2020
-
Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
-
Collaborative Data Acquisition and Learning Support
PublicationWith the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an...
seen 634 times