Example of IEEE Transactions on Affective Computing format
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Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format
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Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format Example of IEEE Transactions on Affective Computing format
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recommended Recommended

IEEE Transactions on Affective Computing — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Human-Computer Interaction #6 of 120 down down by 2 ranks
Software #20 of 389 down down by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 189 Published Papers | 2478 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/07/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 2.8
SJR: 0.203
SNIP: 0.959
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.0
SJR: 0.733
SNIP: 2.117
open access Open Access
recommended Recommended

Wiley

Quality:  
High
CiteRatio: 14.8
SJR: 1.291
SNIP: 1.718

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

7.512

19% from 2018

Impact factor for IEEE Transactions on Affective Computing from 2016 - 2019
Year Value
2019 7.512
2018 6.288
2017 4.585
2016 3.149
graph view Graph view
table view Table view

13.1

2% from 2019

CiteRatio for IEEE Transactions on Affective Computing from 2016 - 2020
Year Value
2020 13.1
2019 12.8
2018 10.3
2017 9.4
2016 10.0
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 19% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 2% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.309

1% from 2019

SJR for IEEE Transactions on Affective Computing from 2016 - 2020
Year Value
2020 1.309
2019 1.318
2018 1.014
2017 1.13
2016 1.18
graph view Graph view
table view Table view

3.468

12% from 2019

SNIP for IEEE Transactions on Affective Computing from 2016 - 2020
Year Value
2020 3.468
2019 3.099
2018 3.486
2017 3.223
2016 3.08
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 1% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 12% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Affective Computing

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IEEE

IEEE Transactions on Affective Computing

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Transactions on Affective Computing formatting guidelines as mentioned in IEEE author instructions. The current version was created on 17 Jul 2020 and has been used by 895 authors to write and format their manuscripts to this journal.

Human-Computer Interaction

Software

Computer Science

i
Last updated on
17 Jul 2020
i
ISSN
1949-3045
i
Impact Factor
Very High - 3.893
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1109/T-AFFC.2011.15
DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Abstract:

We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, ... We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods. read more read less

Topics:

Emotion classification (54%)54% related to the paper, Affective computing (53%)53% related to the paper
View PDF
3,013 Citations
Journal Article DOI: 10.1109/T-AFFC.2010.1
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Rafael A. Calvo1, Sidney K. D'Mello2

Abstract:

This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assump... This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their effectiveness. Hence, an informed and integrated examination of emotion theories from multiple areas will need to become part of computing practice if truly effective real-world systems are to be achieved. This survey discusses theoretical perspectives that view emotions as expressions, embodiments, outcomes of cognitive appraisal, social constructs, products of neural circuitry, and psychological interpretations of basic feelings. It provides meta-analyses on existing reviews of affect detection systems that focus on traditional affect detection modalities like physiology, face, and voice, and also reviews emerging research on more novel channels such as text, body language, and complex multimodal systems. This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. In this way, models and methods can be compared, and emerging insights from various disciplines can be more expertly integrated. read more read less

Topics:

Affective computing (56%)56% related to the paper
View PDF
1,503 Citations
open accessOpen access Journal Article DOI: 10.1109/T-AFFC.2011.25
A Multimodal Database for Affect Recognition and Implicit Tagging
Mohammad Soleymani1, Jeroen Lichtenauer2, Thierry Pun1, Maja Pantic2

Abstract:

MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. A multimodal setup was arranged for synchronized recording of face videos, audio signals, eye gaze data, and peripheral/central nervous system physiological signals. Twenty-seven pa... MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. A multimodal setup was arranged for synchronized recording of face videos, audio signals, eye gaze data, and peripheral/central nervous system physiological signals. Twenty-seven participants from both genders and different cultural backgrounds participated in two experiments. In the first experiment, they watched 20 emotional videos and self-reported their felt emotions using arousal, valence, dominance, and predictability as well as emotional keywords. In the second experiment, short videos and images were shown once without any tag and then with correct or incorrect tags. Agreement or disagreement with the displayed tags was assessed by the participants. The recorded videos and bodily responses were segmented and stored in a database. The database is made available to the academic community via a web-based system. The collected data were analyzed and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported. These results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol. read more read less

Topics:

Affective computing (53%)53% related to the paper, Modality (human–computer interaction) (51%)51% related to the paper, Eye tracking (50%)50% related to the paper
View PDF
1,162 Citations
open accessOpen access Journal Article DOI: 10.1109/TAFFC.2015.2457417
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Abstract:

Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-... Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size. read more read less

Topics:

Voice analysis (57%)57% related to the paper, Set (psychology) (52%)52% related to the paper, Affective computing (50%)50% related to the paper
View PDF
1,158 Citations
Journal Article DOI: 10.1109/TAFFC.2014.2339834
Feature Extraction and Selection for Emotion Recognition from EEG
Robert Jenke1, Angelika Peer1, Martin Buss1

Abstract:

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientif... Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes. read more read less

Topics:

Feature (machine learning) (65%)65% related to the paper, Feature extraction (64%)64% related to the paper, Feature vector (63%)63% related to the paper, Feature selection (61%)61% related to the paper
743 Citations
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Frequently asked questions

1. Can I write IEEE Transactions on Affective Computing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Affective Computing guidelines and auto format it.

2. Do you follow the IEEE Transactions on Affective Computing guidelines?

Yes, the template is compliant with the IEEE Transactions on Affective Computing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Affective Computing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Affective Computing citation style.

4. Can I use the IEEE Transactions on Affective Computing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Affective Computing.

5. Can I use a manuscript in IEEE Transactions on Affective Computing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Affective Computing that you can download at the end.

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's IEEE Transactions on Affective Computing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in IEEE Transactions on Affective Computing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Affective Computing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Affective Computing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Affective Computing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Affective Computing?

The 5 most common citation types in order of usage for IEEE Transactions on Affective Computing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Affective Computing?

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16. Can I download IEEE Transactions on Affective Computing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Affective Computing Endnote style according to Elsevier guidelines.

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