Example of Applied Intelligence format
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Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format
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Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format Example of Applied Intelligence format
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open access Open Access

Applied Intelligence — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Artificial Intelligence #55 of 227 up up by 5 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 961 Published Papers | 6517 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 31/10/2022
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Related Journals

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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.

3.325

15% from 2018

Impact factor for Applied Intelligence from 2016 - 2019
Year Value
2019 3.325
2018 2.882
2017 1.983
2016 1.904
graph view Graph view
table view Table view

6.8

36% from 2019

CiteRatio for Applied Intelligence from 2016 - 2020
Year Value
2020 6.8
2019 5.0
2018 4.0
2017 4.0
2016 3.9
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 36% 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.

0.791

9% from 2019

SJR for Applied Intelligence from 2016 - 2020
Year Value
2020 0.791
2019 0.726
2018 0.651
2017 0.6
2016 0.649
graph view Graph view
table view Table view

1.828

16% from 2019

SNIP for Applied Intelligence from 2016 - 2020
Year Value
2020 1.828
2019 1.571
2018 1.624
2017 1.239
2016 1.328
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 16% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Applied Intelligence

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Springer

Applied Intelligence

The international journal of Applied Intelligence provides a medium for exchanging scientific research and technological achievements accomplished by the international community. The focus of the work is on research in artificial intelligence and neural networks. The journal a...... Read More

Artificial Intelligence

Computer Science

i
Last updated on
31 Oct 2022
i
ISSN
0924-669X
i
Impact Factor
High - 1.972
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
SPBASIC
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Citation Type
Numbered
[1]
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1023/A:1008280620621
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Igor Kononenko1, Edvard Šimec1, Marko Robnik-Šikonja1
01 Jan 1997 - Applied Intelligence

Abstract:

Current inductive machine learning algorithms typically use greedy search with limited lookahead. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELIEFF, an extension of RELIEF develo... Current inductive machine learning algorithms typically use greedy search with limited lookahead. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELIEFF, an extension of RELIEF developed by Kira and Rendell l10, 11r, for heuristic guidance of inductive learning algorithms. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning. read more read less

Topics:

Multi-task learning (64%)64% related to the paper, Inductive bias (62%)62% related to the paper, Computational learning theory (59%)59% related to the paper, Inductive transfer (59%)59% related to the paper, Semi-supervised learning (59%)59% related to the paper
722 Citations
open accessOpen access Journal Article DOI: 10.1007/S10489-020-01829-7
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.
Asmaa Abbas1, Mohammed M. Abdelsamea1, Mohammed M. Abdelsamea2, Mohamed Medhat Gaber2
01 Feb 2021 - Applied Intelligence

Abstract:

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited a... Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases. read more read less

Topics:

Convolutional neural network (55%)55% related to the paper
View PDF
644 Citations
Journal Article DOI: 10.1007/S10489-014-0645-7
How effective is the Grey Wolf optimizer in training multi-layer perceptrons
Seyedali Mirjalili1
01 Jul 2015 - Applied Intelligence

Abstract:

This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are co... This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer. read more read less

Topics:

Ant colony optimization algorithms (53%)53% related to the paper, Evolutionary algorithm (53%)53% related to the paper, Multilayer perceptron (52%)52% related to the paper, Particle swarm optimization (51%)51% related to the paper, Population (51%)51% related to the paper
529 Citations
Journal Article DOI: 10.1007/S10489-017-1019-8
Grasshopper optimization algorithm for multi-objective optimization problems
Seyedeh Zahra Mirjalili1, Seyedali Mirjalili2, Shahrzad Saremi2, Hossam Faris3, Ibrahim Aljarah3
01 Apr 2018 - Applied Intelligence

Abstract:

This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximat... This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution. read more read less

Topics:

Population-based incremental learning (63%)63% related to the paper, Multi-objective optimization (63%)63% related to the paper, Benchmark (computing) (51%)51% related to the paper
495 Citations
open accessOpen access Journal Article DOI: 10.1007/S10489-014-0629-7
Audio-visual speech recognition using deep learning
Kuniaki Noda1, Yuki Yamaguchi2, Kazuhiro Nakadai3, Hiroshi G. Okuno2, Tetsuya Ogata1
01 Jun 2015 - Applied Intelligence

Abstract:

Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for reliable speech recognition, particularly when the audio is corrupted by noise. However, cautious selection of sensory features is crucial for attaining high recognition performance. In the machine-learning community, deep l... Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for reliable speech recognition, particularly when the audio is corrupted by noise. However, cautious selection of sensory features is crucial for attaining high recognition performance. In the machine-learning community, deep learning approaches have recently attracted increasing attention because deep neural networks can effectively extract robust latent features that enable various recognition algorithms to demonstrate revolutionary generalization capabilities under diverse application conditions. This study introduces a connectionist-hidden Markov model (HMM) system for noise-robust AVSR. First, a deep denoising autoencoder is utilized for acquiring noise-robust audio features. By preparing the training data for the network with pairs of consecutive multiple steps of deteriorated audio features and the corresponding clean features, the network is trained to output denoised audio features from the corresponding features deteriorated by noise. Second, a convolutional neural network (CNN) is utilized to extract visual features from raw mouth area images. By preparing the training data for the CNN as pairs of raw images and the corresponding phoneme label outputs, the network is trained to predict phoneme labels from the corresponding mouth area input images. Finally, a multi-stream HMM (MSHMM) is applied for integrating the acquired audio and visual HMMs independently trained with the respective features. By comparing the cases when normal and denoised mel-frequency cepstral coefficients (MFCCs) are utilized as audio features to the HMM, our unimodal isolated word recognition results demonstrate that approximately 65 % word recognition rate gain is attained with denoised MFCCs under 10 dB signal-to-noise-ratio (SNR) for the audio signal input. Moreover, our multimodal isolated word recognition results utilizing MSHMM with denoised MFCCs and acquired visual features demonstrate that an additional word recognition rate gain is attained for the SNR conditions below 10 dB. read more read less

Topics:

Audio mining (62%)62% related to the paper, Audio-visual speech recognition (61%)61% related to the paper, Audio signal (60%)60% related to the paper, Convolutional neural network (54%)54% related to the paper, Deep learning (54%)54% related to the paper
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493 Citations
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Applied Intelligence format uses SPBASIC citation style.

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Frequently asked questions

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

2. Do you follow the Applied Intelligence guidelines?

Yes, the template is compliant with the Applied Intelligence 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 Intelligence?

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 Intelligence citation style.

4. Can I use the Applied Intelligence 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 Intelligence.

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

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

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 Intelligence.

7. Where can I find the template for the Applied Intelligence?

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 Intelligence'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 Intelligence'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 Intelligence an online tool or is there a desktop version?

SciSpace's Applied Intelligence 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 Applied Intelligence?

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 Applied Intelligence?”

11. What is the output that I would get after using Applied Intelligence?

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

12. Is Applied Intelligence'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 Intelligence?

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 Intelligence. 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 Intelligence?

The 5 most common citation types in order of usage for Applied Intelligence 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 Intelligence?

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

16. Can I download Applied Intelligence 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 Intelligence 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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