Example of Computational Intelligence and Neuroscience format
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Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format
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Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format Example of Computational Intelligence and Neuroscience format
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open access Open Access
recommended Recommended

Computational Intelligence and Neuroscience — Template for authors

Publisher: Hindawi
Categories Rank Trend in last 3 yrs
Mathematics (all) #13 of 378 up up by 53 ranks
Computer Science (all) #37 of 226 up up by 33 ranks
Neuroscience (all) #45 of 110 up up by 34 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 559 Published Papers | 3026 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/06/2020
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Related Journals

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Quality:  
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CiteRatio: 12.7
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

5.4

15% from 2019

CiteRatio for Computational Intelligence and Neuroscience from 2016 - 2020
Year Value
2020 5.4
2019 4.7
2018 3.3
2017 2.0
2016 1.3
graph view Graph view
table view Table view

0.605

13% from 2019

SJR for Computational Intelligence and Neuroscience from 2016 - 2020
Year Value
2020 0.605
2019 0.534
2018 0.397
2017 0.326
2016 0.295
graph view Graph view
table view Table view

1.711

14% from 2019

SNIP for Computational Intelligence and Neuroscience from 2016 - 2020
Year Value
2020 1.711
2019 1.495
2018 1.117
2017 0.659
2016 0.89
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 14% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Computational Intelligence and Neuroscience

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Hindawi

Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence, where neuroscientists, cognitive scientists, engineers, psychologists, physicists, computer scientists, and artificial in...... Read More

Mathematics

i
Last updated on
16 Jun 2020
i
ISSN
1687-5265
i
Impact Factor
High - 1.728
i
Acceptance Rate
41%
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
unsrt
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker. “Specular andreev reflection in graphene”. Phys. Rev. Lett., vol. 97, no. 6, 067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1155/2011/156869
FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
Robert Oostenveld1, Pascal Fries1, Eric Maris1, Jan-Mathijs Schoffelen1

Abstract:

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyz... This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages. read more read less

Topics:

Software (55%)55% related to the paper, EEGLAB (53%)53% related to the paper
View PDF
6,162 Citations
open accessOpen access Journal Article DOI: 10.1155/2011/879716
Brainstorm: a user-friendly application for MEG/EEG analysis
François Tadel1, Sylvain Baillet2, John C. Mosher3, Dimitrios Pantazis4, Richard M. Leahy1

Abstract:

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the... Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI). read more read less
View PDF
1,957 Citations
open accessOpen access Journal Article DOI: 10.1155/2018/7068349
Deep Learning for Computer Vision: A Brief Review.
Athanasios Voulodimos1, Nikolaos Doulamis2, Anastasios Doulamis2, Eftychios Protopapadakis2

Abstract:

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision ... Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. read more read less

Topics:

Deep belief network (64%)64% related to the paper, Deep learning (59%)59% related to the paper, Artificial neural network (54%)54% related to the paper, Object detection (53%)53% related to the paper, Pose (52%)52% related to the paper
View PDF
993 Citations
open accessOpen access Journal Article DOI: 10.1155/2011/720971
Unveiling the biometric potential of finger-based ECG signals
André Lourenço, Hugo Silva1, Ana Fred1

Abstract:

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG ... The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications. read more read less

Topics:

Signal (51%)51% related to the paper, Signal processing (51%)51% related to the paper, Normalization (image processing) (51%)51% related to the paper
View PDF
770 Citations
open accessOpen access Journal Article DOI: 10.1155/2016/3289801
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Srdjan Sladojevic1, Marko Arsenovic1, Andras Anderla1, Dubravko Culibrk2, Darko Stefanovic1

Abstract:

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of trai... The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. read more read less

Topics:

Plant disease (62%)62% related to the paper, Deep learning (60%)60% related to the paper, Convolutional neural network (57%)57% related to the paper, Contextual image classification (55%)55% related to the paper, Artificial neural network (53%)53% related to the paper
View PDF
689 Citations
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Computational Intelligence and Neuroscience format uses unsrt citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Computational Intelligence and Neuroscience in LaTeX?

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

2. Do you follow the Computational Intelligence and Neuroscience guidelines?

Yes, the template is compliant with the Computational Intelligence and Neuroscience 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 Computational Intelligence and Neuroscience?

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 Computational Intelligence and Neuroscience citation style.

4. Can I use the Computational Intelligence and Neuroscience 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 Computational Intelligence and Neuroscience.

5. Can I use a manuscript in Computational Intelligence and Neuroscience 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 Computational Intelligence and Neuroscience that you can download at the end.

6. How long does it usually take you to format my papers in Computational Intelligence and Neuroscience?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Computational Intelligence and Neuroscience.

7. Where can I find the template for the Computational Intelligence and Neuroscience?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Intelligence and Neuroscience's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Computational Intelligence and Neuroscience's guidelines?

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.

9. Computational Intelligence and Neuroscience an online tool or is there a desktop version?

SciSpace's Computational Intelligence and Neuroscience 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.

10. I cannot find my template in your gallery. Can you create it for me like Computational Intelligence and Neuroscience?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Computational Intelligence and Neuroscience?”

11. What is the output that I would get after using Computational Intelligence and Neuroscience?

After writing your paper autoformatting in Computational Intelligence and Neuroscience, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational Intelligence and Neuroscience'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 Computational Intelligence and Neuroscience?

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 Computational Intelligence and Neuroscience. 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 Computational Intelligence and Neuroscience?

The 5 most common citation types in order of usage for Computational Intelligence and Neuroscience 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 Computational Intelligence and Neuroscience?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Intelligence and Neuroscience's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Computational Intelligence and Neuroscience 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 Computational Intelligence and Neuroscience Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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