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ISSN:
eISSN:
1865-7923
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)
- mechanical engineering (Engineering and Technology)
- 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 |
Points CiteScore:
| Year | Points |
|---|---|
| Year 2023 | 5.2 |
| Year | Points |
|---|---|
| 2023 | 5.2 |
| 2022 | 4.2 |
| 2021 | 3.4 |
| 2020 | 2.9 |
| 2019 | 2.3 |
| 2018 | 1.9 |
| 2017 | 0.8 |
| 2016 | 0.6 |
| 2015 | 0.4 |
| 2014 | 0.3 |
Impact Factor:
n/a
Sherpa Romeo:
Papers published in journal
Filters
total: 1
Catalog Journals
Year 2022
-
Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublicationDue to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...
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