Example of Applied Computational Intelligence and Soft Computing format
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Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format Example of Applied Computational Intelligence and Soft Computing format
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open access Open Access

Applied Computational Intelligence and Soft Computing — Template for authors

Publisher: Hindawi
Categories Rank Trend in last 3 yrs
Computational Mechanics #10 of 79 up up by 43 ranks
Civil and Structural Engineering #63 of 318 up up by 171 ranks
Computer Networks and Communications #78 of 334 up up by 163 ranks
Computer Science Applications #175 of 693 up up by 341 ranks
Artificial Intelligence #77 of 227 up up by 87 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 53 Published Papers | 267 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 07/07/2020
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Related Journals

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Quality:  
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CiteRatio: 19.8
SJR: 2.882
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SJR: 0.225
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Quality:  
High
CiteRatio: 4.9
SJR: 0.497
SNIP: 1.938

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.

5.0

52% from 2019

CiteRatio for Applied Computational Intelligence and Soft Computing from 2016 - 2020
Year Value
2020 5.0
2019 3.3
2018 1.4
2017 0.4
graph view Graph view
table view Table view

0.371

41% from 2019

SJR for Applied Computational Intelligence and Soft Computing from 2018 - 2020
Year Value
2020 0.371
2019 0.264
2018 0.237
graph view Graph view
table view Table view

1.169

11% from 2019

SNIP for Applied Computational Intelligence and Soft Computing from 2017 - 2020
Year Value
2020 1.169
2019 1.315
2018 1.03
2017 3.591
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 52% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 41% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 11% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Applied Computational Intelligence and Soft Computing

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Hindawi

Applied Computational Intelligence and Soft Computing

Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies ...... Read More

Heuristics

i
Last updated on
07 Jul 2020
i
ISSN
1687-9724
i
Acceptance Rate
11%
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: 10.1155/2018/1407817
On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis
Asriyanti Indah Pratiwi1, Adiwijaya1

Abstract:

Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classifica... Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far. read more read less

Topics:

Sentiment analysis (62%)62% related to the paper, Feature selection (57%)57% related to the paper
View PDF
68 Citations
open accessOpen access Journal Article DOI: 10.1155/2020/7403128
Arabic Sentiment Analysis: A Systematic Literature Review
Abdullatif Ghallab1, Abdulqader M. Mohsen1, Yousef Ali1

Abstract:

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas fo... With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects. read more read less

Topics:

Sentiment analysis (60%)60% related to the paper, Taxonomy (general) (53%)53% related to the paper, Systematic review (52%)52% related to the paper
View PDF
55 Citations
open accessOpen access Journal Article DOI: 10.1155/2020/3738108
Fish Detection Using Deep Learning
Suxia Cui1, Yu Zhou1, Yonghui Wang1, Lujun Zhai1

Abstract:

Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomou... Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects. read more read less

Topics:

Deep learning (57%)57% related to the paper, Artificial neural network (53%)53% related to the paper, Convolutional neural network (52%)52% related to the paper
View PDF
50 Citations
open accessOpen access Journal Article DOI: 10.1155/2018/1439312
Real Time Eye Detector with Cascaded Convolutional Neural Networks
Bin Li1, Bin Li2, Hong Fu1

Abstract:

An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is ad... An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality. read more read less

Topics:

Convolutional neural network (53%)53% related to the paper
View PDF
46 Citations
open accessOpen access Journal Article DOI: 10.1155/2017/8718956
CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Abstract:

This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological st... This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus, estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify the eye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows a closed eye. In addition, this paper presents a process to create an eye image dataset using a wearable camera. This dataset, which was used to evaluate the proposed method, has approximately 20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation. read more read less

Topics:

Gaze (50%)50% related to the paper
View PDF
45 Citations
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Applied Computational Intelligence and Soft Computing format uses unsrt citation style.

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Frequently asked questions

1. Can I write Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing guidelines and auto format it.

2. Do you follow the Applied Computational Intelligence and Soft Computing guidelines?

Yes, the template is compliant with the Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing citation style.

4. Can I use the Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing.

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

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

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

7. Where can I find the template for the Applied Computational Intelligence and Soft Computing?

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 Applied Computational Intelligence and Soft Computing'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 Applied Computational Intelligence and Soft Computing'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. Applied Computational Intelligence and Soft Computing an online tool or is there a desktop version?

SciSpace's Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing?

The 5 most common citation types in order of usage for Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing?

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 Applied Computational Intelligence and Soft Computing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Applied Computational Intelligence and Soft 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 Applied Computational Intelligence and Soft Computing Endnote style according to Elsevier guidelines.

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