IEEE INTELLIGENT SYSTEMS - Journal - Bridge of Knowledge

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IEEE INTELLIGENT SYSTEMS

ISSN:

1541-1672

eISSN:

1941-1294

Disciplines
(Field of Science):

  • automation, electronics, electrical engineering and space technologies (Engineering and Technology)
  • 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)
  • computer and information sciences (Natural sciences)

Ministry points: Help

Ministry points - current year
Year Points List
Year 2024 140 Ministry scored journals list 2024
Ministry points - previous years
Year Points List
2024 140 Ministry scored journals list 2024
2023 140 Ministry Scored Journals List
2022 140 Ministry Scored Journals List 2019-2022
2021 140 Ministry Scored Journals List 2019-2022
2020 140 Ministry Scored Journals List 2019-2022
2019 140 Ministry Scored Journals List 2019-2022
2018 40 A
2017 40 A
2016 40 A
2015 35 A
2014 35 A
2013 35 A
2012 35 A
2011 35 A
2010 32 A

Model:

Hybrid - transformation agreement

Points CiteScore:

Points CiteScore - current year
Year Points
Year 2023 13.8
Points CiteScore - previous years
Year Points
2023 13.8
2022 10.2
2021 7.5
2020 9
2019 10.4
2018 6.4
2017 4.5
2016 6.6
2015 5.4
2014 4.8
2013 4.8
2012 4.2
2011 4.3

Impact Factor:

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Sherpa Romeo:

https://v2.sherpa.ac.uk/id/publication/23036

Papers published in journal

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Catalog Journals

Year 2024
  • Exploring Neural Networks for Musical Instrument Identification in Polyphonic Audio
    Publication

    - IEEE INTELLIGENT SYSTEMS - Year 2024

    The purpose of this paper is to introduce neural network-based methods that surpass state-of-the-art (SOTA) models, either by training faster or having simpler architecture, while maintaining comparable effectiveness in musical instrument identification in polyphonic music. Several approaches are presented, including two authors’ proposals, i.e., spiking neural networks (SNN) and a modular deep learning model named FMCNN (Fully...

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