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ISSN:
1546-2218
eISSN:
1546-2226
Disciplines
(Field of Science):
- automation, electronics, electrical engineering and space technologies (Engineering and Technology)
- information and communication technology (Engineering and Technology)
- biomedical engineering (Engineering and Technology)
- chemical engineering (Engineering and Technology)
- civil engineering, geodesy and transport (Engineering and Technology)
- materials engineering (Engineering and Technology)
- mechanical engineering (Engineering and Technology)
- environmental engineering, mining and energy (Engineering and Technology)
- medical biology (Medical and Health Sciences )
- pharmacology and pharmacy (Medical and Health Sciences )
- biotechnology (Natural sciences)
- computer and information sciences (Natural sciences)
(Field of Science)
Ministry points: Help
| Year | Points | List |
|---|---|---|
| Year 2024 | 20 | Ministry scored journals list 2024 |
| Year | Points | List |
|---|---|---|
| 2024 | 20 | Ministry scored journals list 2024 |
| 2023 | 20 | Ministry Scored Journals List |
| 2022 | 20 | Ministry Scored Journals List 2019-2022 |
| 2021 | 20 | Ministry Scored Journals List 2019-2022 |
| 2020 | 20 | Ministry Scored Journals List 2019-2022 |
| 2019 | 20 | Ministry Scored Journals List 2019-2022 |
| 2018 | 20 | A |
| 2017 | 20 | A |
| 2016 | 15 | A |
| 2015 | 15 | A |
| 2014 | 20 | A |
| 2013 | 20 | A |
| 2012 | 30 | A |
| 2011 | 30 | A |
| 2010 | 32 | A |
Model:
Open Access
Points CiteScore:
| Year | Points |
|---|---|
| Year 2022 | 5 |
| Year | Points |
|---|---|
| 2022 | 5 |
| 2021 | 4.9 |
| 2020 | 4.6 |
| 2019 | 3.8 |
| 2018 | 1.9 |
| 2017 | 1.4 |
| 2016 | 2.1 |
| 2015 | 2.3 |
| 2014 | 2.1 |
| 2013 | 1.9 |
| 2012 | 1.7 |
| 2011 | 2 |
Impact Factor:
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Sherpa Romeo:
Papers published in journal
Filters
total: 1
Catalog Journals
Year 2020
-
Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublicationThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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