Example of Neural Computing and Applications format
Recent searches

Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
Look Inside
Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format Example of Neural Computing and Applications format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Neural Computing and Applications — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Software #68 of 389 up up by 2 ranks
Artificial Intelligence #46 of 227 down down by 11 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2807 Published Papers | 20372 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/11/2022
Related journals
Insights
General info
Top papers
Popular templates
Get started guide
Why choose from SciSpace
FAQ

Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 19.8
SJR: 2.882
SNIP: 3.86
open access Open Access
recommended Recommended

Cambridge University Press

Quality:  
High
CiteRatio: 3.8
SJR: 0.29
SNIP: 1.153
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.2
SJR: 0.427
SNIP: 1.319
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 5.0
SJR: 0.624
SNIP: 1.866

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.

4.774

2% from 2018

Impact factor for Neural Computing and Applications from 2016 - 2019
Year Value
2019 4.774
2018 4.664
2017 4.213
2016 2.505
graph view Graph view
table view Table view

7.3

12% from 2019

CiteRatio for Neural Computing and Applications from 2016 - 2020
Year Value
2020 7.3
2019 6.5
2018 4.9
2017 5.4
2016 5.2
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 2% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 12% 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.

0.713

10% from 2019

SJR for Neural Computing and Applications from 2016 - 2020
Year Value
2020 0.713
2019 0.796
2018 0.637
2017 0.7
2016 0.602
graph view Graph view
table view Table view

1.784

6% from 2019

SNIP for Neural Computing and Applications from 2016 - 2020
Year Value
2020 1.784
2019 1.895
2018 1.521
2017 1.465
2016 1.137
graph view Graph view
table view Table view

insights Insights

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

insights Insights

  • SNIP of this journal has decreased by 6% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Neural Computing and Applications

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Springer

Neural Computing and Applications

Approved by publishing and review experts on SciSpace, this template is built as per for Neural Computing and Applications formatting guidelines as mentioned in Springer author instructions. The current version was created on 02 Nov 2022 and has been used by 723 authors to write and format their manuscripts to this journal.

i
Last updated on
02 Nov 2022
i
ISSN
0941-0643
i
Open Access
Hybrid
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515_x0015_ 4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1007/S00521-015-1920-1
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
Seyedali Mirjalili1

Abstract:

A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social i... A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html. read more read less

Topics:

Metaheuristic (57%)57% related to the paper, Multi-objective optimization (56%)56% related to the paper, Multi-swarm optimization (55%)55% related to the paper, Evolutionary algorithm (54%)54% related to the paper, Genetic algorithm (53%)53% related to the paper
1,897 Citations
Journal Article DOI: 10.1007/S00521-015-1870-7
Multi-Verse Optimizer: a nature-inspired algorithm for global optimization
Seyedali Mirjalili1, Seyed Mohammad Mirjalili, Abdolreza Hatamlou2

Abstract:

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search,... This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html. read more read less

Topics:

Population-based incremental learning (59%)59% related to the paper, Local search (optimization) (55%)55% related to the paper, Genetic algorithm (53%)53% related to the paper, Particle swarm optimization (52%)52% related to the paper, Global optimization (51%)51% related to the paper
1,752 Citations
Journal Article DOI: 10.1007/S00521-013-1362-6
A survey of multi-view machine learning
Shiliang Sun1

Abstract:

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches acco... Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning. read more read less

Topics:

Active learning (machine learning) (68%)68% related to the paper, Algorithmic learning theory (66%)66% related to the paper, Instance-based learning (65%)65% related to the paper, Learning sciences (65%)65% related to the paper, Computational learning theory (63%)63% related to the paper
View PDF
782 Citations
Journal Article DOI: 10.1007/S00521-015-1923-Y
Monarch butterfly optimization
Gai-Ge Wang1, Gai-Ge Wang2, Suash Deb3, Zhihua Cui4

Abstract:

In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm... In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub ( https://github.com/ggw0122/Monarch-Butterfly-Optimization , C++/MATLAB) and MATLAB Central ( http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization , MATLAB). read more read less

Topics:

Population (53%)53% related to the paper, Monarch butterfly (52%)52% related to the paper
778 Citations
Journal Article DOI: 10.1007/S00521-009-0295-6
Pattern classification with missing data: a review
Pedro J. García-Laencina1, José-Luis Sancho-Gómez1, Aníbal R. Figueiras-Vidal2

Abstract:

Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches... Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values. read more read less

Topics:

Missing data (64%)64% related to the paper, Feature (machine learning) (63%)63% related to the paper, Document classification (59%)59% related to the paper, Statistical learning theory (56%)56% related to the paper, Pattern recognition (psychology) (55%)55% related to the paper
View PDF
625 Citations
Author Pic

SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds
1
Choose a template
Select a template from a library of 40,000+ templates
2
Import a MS-Word file or start fresh
It takes only few seconds to import
3
View and edit your final output
SciSpace will automatically format your output to meet journal guidelines
4
Submit directly or Download
Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

''

With SciSpace, you do not need a word template for Neural Computing and Applications.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
Publisher Logos

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

publisher-logos

Easy support from all your favorite tools

Neural Computing and Applications format uses SPBASIC 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 Neural Computing and Applications in LaTeX?

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

2. Do you follow the Neural Computing and Applications guidelines?

Yes, the template is compliant with the Neural Computing and Applications 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 Neural Computing and Applications?

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 Neural Computing and Applications citation style.

4. Can I use the Neural Computing and Applications 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 Neural Computing and Applications.

5. Can I use a manuscript in Neural Computing and Applications 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 Neural Computing and Applications that you can download at the end.

6. How long does it usually take you to format my papers in Neural Computing and Applications?

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

7. Where can I find the template for the Neural Computing and Applications?

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 Neural Computing and Applications'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 Neural Computing and Applications'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. Neural Computing and Applications an online tool or is there a desktop version?

SciSpace's Neural Computing and Applications 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 Neural Computing and Applications?

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 Neural Computing and Applications?”

11. What is the output that I would get after using Neural Computing and Applications?

After writing your paper autoformatting in Neural Computing and Applications, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Neural Computing and Applications'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 Neural Computing and Applications?

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 Neural Computing and Applications. 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 Neural Computing and Applications?

The 5 most common citation types in order of usage for Neural Computing and Applications 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 Neural Computing and Applications?

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 Neural Computing and Applications's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Neural Computing and Applications 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 Neural Computing and Applications Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to Neural Computing and Applications formatting guidelines and citation style.

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

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.

Andreas Frutiger
Researcher & Ex MS Word user
Use this template