Example of Complex Adaptive Systems Modeling format
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Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format Example of Complex Adaptive Systems Modeling format
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open access Open Access

Complex Adaptive Systems Modeling — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Applied Mathematics #125 of 548 down down by None rank
Modeling and Simulation #95 of 290 down down by None rank
Computer Science Applications #268 of 693 down down by None rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 27 Published Papers | 93 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 10/07/2020
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 6.1
SJR: 1.882
SNIP: 1.743
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Elsevier

Quality:  
High
CiteRatio: 4.9
SJR: 0.774
SNIP: 1.158
open access Open Access

Inderscience Publishers

Quality:  
Good
CiteRatio: 3.1
SJR: 0.322
SNIP: 1.178
open access Open Access

Inderscience Publishers

Quality:  
Good
CiteRatio: 2.3
SJR: 0.293
SNIP: 0.839

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.

3.4

29% from 2019

CiteRatio for Complex Adaptive Systems Modeling from 2016 - 2020
Year Value
2020 3.4
2019 4.8
2018 3.5
graph view Graph view
table view Table view

0.291

31% from 2019

SJR for Complex Adaptive Systems Modeling from 2019 - 2020
Year Value
2020 0.291
2019 0.222
graph view Graph view
table view Table view

0.951

11% from 2019

SNIP for Complex Adaptive Systems Modeling from 2018 - 2020
Year Value
2020 0.951
2019 0.856
2018 0.96
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 29% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 31% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

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

Complex Adaptive Systems Modeling

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Springer

Complex Adaptive Systems Modeling

Approved by publishing and review experts on SciSpace, this template is built as per for Complex Adaptive Systems Modeling formatting guidelines as mentioned in Springer author instructions. The current version was created on 10 Jul 2020 and has been used by 398 authors to write and format their manuscripts to this journal.

Complex networks

i
Last updated on
10 Jul 2020
i
ISSN
2194-3206
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
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
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

open accessOpen access Journal Article DOI: 10.1186/2194-3206-1-3
Complex adaptive systems modeling with repast simphony

Abstract:

This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular archi... This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years. Includes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration. Include a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system. We discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts. read more read less
View PDF
506 Citations
open accessOpen access Journal Article DOI: 10.1186/S40294-016-0017-8
Analysis and visualization of large networks with program package Pajek
Andrej Mrvar1, Vladimir Batagelj1

Abstract:

Pajek is a program package for analysis and visualization of large networks (networks containing up to one billion of vertices, there is no limit—except the memory size—on the number of lines). It has been available for 20 years. The program, documentation and supporting material can be downloaded and used for free for noncom... Pajek is a program package for analysis and visualization of large networks (networks containing up to one billion of vertices, there is no limit—except the memory size—on the number of lines). It has been available for 20 years. The program, documentation and supporting material can be downloaded and used for free for noncommercial use from its web page: http://mrvar.fdv.uni-lj.si/pajek/ read more read less

Topics:

Web page (54%)54% related to the paper, Visualization (52%)52% related to the paper
View PDF
149 Citations
open accessOpen access Journal Article DOI: 10.1186/2194-3206-1-7
Adaptive pedestrian behaviour for the preservation of group cohesion
Giuseppe Vizzari1, Lorenza Manenti1, Luca Crociani1

Abstract:

A crowd of pedestrians is a complex system in which individuals exhibit conflicting behavioural mechanisms leading to self-organisation phenomena. Computer models for the simulation of crowds represent a consolidated type of application, employed on a day-to-day basis to support designers and decision makers. Most state of th... A crowd of pedestrians is a complex system in which individuals exhibit conflicting behavioural mechanisms leading to self-organisation phenomena. Computer models for the simulation of crowds represent a consolidated type of application, employed on a day-to-day basis to support designers and decision makers. Most state of the art models, however, generally do not consider the explicit representation of pedestrians aggregations (groups) and their implications on the overall system dynamics. This work is aimed at discussing a research effort systematically exploring the potential implication of the presence of groups of pedestrians in different situations (e.g. changing density, spatial configurations of the environment). The paper describes an agent-based model encompassing both traditional individual motivations (i.e. tendency to stay away from other pedestrians while moving towards the goal) and an adaptive mechanism representing the influence of group presence in the simulated population. The mechanism is designed to preserve the cohesion of specific types of groups (e.g. families and friends) even in high density and turbulent situations. The model is tested in simplified scenarios to evaluate the implications of modelling choices and the presence of groups. The model produces results in tune with available evidences from the literature, both from the perspective of pedestrian flows and space utilisation, in scenarios not comprising groups; when groups are present, the model is able to preserve their cohesion even in challenging situations (i.e. high density, presence of a counterflow), and it produces interesting results in high density situations that call for further observations and experiments to gather empirical data. The introduced adaptive model for group cohesion is effective in qualitatively reproducing group related phenomena and it stimulates further research efforts aimed at gathering empirical evidences, on one hand, and modelling efforts aimed at reproducing additional related phenomena (e.g. leader-follower movement patterns). read more read less

Topics:

Population (52%)52% related to the paper, Crowd simulation (51%)51% related to the paper, Cohesion (computer science) (50%)50% related to the paper
View PDF
90 Citations
open accessOpen access Journal Article DOI: 10.1186/2194-3206-1-1
Complex Adaptive Systems Modeling: A multidisciplinary Roadmap
Muaz A. Niazi1

Abstract:

PAC Codes 07.05.Tp, 89.75.-k, 89.75.Fb Mathematics Subject Classification (2010) 05C82, 68T42, 00A72, 92C42

Topics:

Complex adaptive system (51%)51% related to the paper, Systems modeling (51%)51% related to the paper, Multidisciplinary approach (50%)50% related to the paper
View PDF
61 Citations
open accessOpen access Journal Article DOI: 10.1186/S40294-016-0016-9
Sentiment analysis and the complex natural language

Abstract:

There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML tech... There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity. read more read less

Topics:

Sentiment analysis (65%)65% related to the paper, Language model (54%)54% related to the paper, Natural language (53%)53% related to the paper, Context (language use) (51%)51% related to the paper, Overfitting (51%)51% related to the paper
View PDF
61 Citations
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Frequently asked questions

1. Can I write Complex Adaptive Systems Modeling in LaTeX?

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Yes, the template is compliant with the Complex Adaptive Systems Modeling 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 Complex Adaptive Systems Modeling?

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 Complex Adaptive Systems Modeling citation style.

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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 Complex Adaptive Systems Modeling.

5. Can I use a manuscript in Complex Adaptive Systems Modeling 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 Complex Adaptive Systems Modeling that you can download at the end.

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7. Where can I find the template for the Complex Adaptive Systems Modeling?

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13. What is Sherpa RoMEO Archiving Policy for Complex Adaptive Systems Modeling?

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 Complex Adaptive Systems Modeling. 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 Complex Adaptive Systems Modeling?

The 5 most common citation types in order of usage for Complex Adaptive Systems Modeling are:.

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

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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 Complex Adaptive Systems Modeling Endnote style according to Elsevier guidelines.

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