Example of Annals of Mathematics and Artificial Intelligence format
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Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format
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Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format Example of Annals of Mathematics and Artificial Intelligence format
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open access Open Access

Annals of Mathematics and Artificial Intelligence — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Applied Mathematics #201 of 548 down down by 50 ranks
Artificial Intelligence #139 of 227 down down by 48 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 167 Published Papers | 420 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 26/06/2020
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Related Journals

open access Open Access

Springer

Quality:  
High
CiteRatio: 4.1
SJR: 0.337
SNIP: 0.919
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 7.1
SJR: 1.039
SNIP: 1.83
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 18.3
SJR: 2.886
SNIP: 3.548
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 44.2
SJR: 3.811
SNIP: 11.215

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.

0.778

23% from 2018

Impact factor for Annals of Mathematics and Artificial Intelligence from 2016 - 2019
Year Value
2019 0.778
2018 1.011
2017 0.899
2016 0.807
graph view Graph view
table view Table view

2.5

14% from 2019

CiteRatio for Annals of Mathematics and Artificial Intelligence from 2016 - 2020
Year Value
2020 2.5
2019 2.9
2018 2.3
2017 2.3
2016 2.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 23% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 14% 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.

0.369

26% from 2019

SJR for Annals of Mathematics and Artificial Intelligence from 2016 - 2020
Year Value
2020 0.369
2019 0.498
2018 0.346
2017 0.413
2016 0.438
graph view Graph view
table view Table view

0.932

35% from 2019

SNIP for Annals of Mathematics and Artificial Intelligence from 2016 - 2020
Year Value
2020 0.932
2019 1.431
2018 1.09
2017 1.027
2016 0.998
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 26% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 35% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Annals of Mathematics and Artificial Intelligence

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Springer

Annals of Mathematics and Artificial Intelligence

The scope of Annals of Mathematics and Artificial Intelligence is intended to represent a wide range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to Artificial Intelligence areas as diverse as decision suppor...... Read More

Mathematics

i
Last updated on
26 Jun 2020
i
ISSN
1012-2443
i
Impact Factor
Medium - 0.936
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
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Citation Type
Numbered
[25]
i
Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25(7), 4515–4532 (1982). URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1023/A:1016639210559
Coverage for robotics – A survey of recent results
Howie Choset1

Abstract:

This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body pa... This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms. read more read less

Topics:

Any-angle path planning (62%)62% related to the paper, Motion planning (58%)58% related to the paper, Heuristic (computer science) (53%)53% related to the paper, Path (graph theory) (53%)53% related to the paper, Cellular decomposition (51%)51% related to the paper
1,206 Citations
Journal Article DOI: 10.1023/A:1018930122475
Logic programs with stable model semantics as a constraint programming paradigm
Ilkka Niemelä1

Abstract:

Logic programming with the stable model semantics is put forward as a novel constraint programming paradigm. This paradigm is interesting because it bring advantages of logic programming based knowledge representation techniques to constraint programming and because implementation methods for the stable model semantics for gr... Logic programming with the stable model semantics is put forward as a novel constraint programming paradigm. This paradigm is interesting because it bring advantages of logic programming based knowledge representation techniques to constraint programming and because implementation methods for the stable model semantics for ground (variabledfree) programs have advanced significantly in recent years. For a program with variables these methods need a grounding procedure for generating a variabledfree program. As a practical approach to handling the grounding problem a subclass of logic programs, domain restricted programs, is proposed. This subclass enables efficient grounding procedures and serves as a basis for integrating builtdin predicates and functions often needed in applications. It is shown that the novel paradigm embeds classical logical satisfiability and standard (finite domain) constraint satisfaction problems but seems to provide a more expressive framework from a knowledge representation point of view. The first steps towards a programming methodology for the new paradigm are taken by presenting solutions to standard constraint satisfaction problems, combinatorial graph problems and planning problems. An efficient implementation of the paradigm based on domain restricted programs has been developed. This is an extension of a previous implementation of the stable model semantics, the Smodels system, and is publicly available. It contains, e.g., builtdin integer arithmetic integrated to stable model computation. The implementation is described briefly and some test results illustrating the current level of performance are reported. read more read less

Topics:

Constraint programming (70%)70% related to the paper, Constraint logic programming (69%)69% related to the paper, Concurrent constraint logic programming (67%)67% related to the paper, Constraint satisfaction (67%)67% related to the paper, Logic programming (67%)67% related to the paper
967 Citations
Journal Article DOI: 10.1023/A:1016725902970
Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Tim Bedford1, Roger M. Cooke2

Abstract:

A vine is a new graphical model for dependent random variables Vines generalize the Markov trees often used in modeling multivariate distributions They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence A general fo... A vine is a new graphical model for dependent random variables Vines generalize the Markov trees often used in modeling multivariate distributions They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence A general formula for the density of a vine dependent distribution is derived This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions The so-called ‘canonical vines’ built on highest degree trees offer the most efficient structure for Gibbs sampling read more read less

Topics:

Vine copula (65%)65% related to the paper, Gibbs sampling (59%)59% related to the paper, Conditional independence (57%)57% related to the paper, Graphical model (56%)56% related to the paper, Conditional dependence (55%)55% related to the paper
836 Citations
Journal Article DOI: 10.1023/B:AMAI.0000018580.96245.C6
Theoretical Comparison between the Gini Index and Information Gain Criteria
Laura Elena Raileanu1, Kilian Stoffel1

Abstract:

Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classification is decision tree induction. The selection of the attribute used at ea... Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classification is decision tree induction. The selection of the attribute used at each node of the tree to split the data (split criterion) is crucial in order to correctly classify objects. Different split criteria were proposed in the literature (Information Gain, Gini Index, etc.). It is not obvious which of them will produce the best decision tree for a given data set. A large amount of empirical tests were conducted in order to answer this question. No conclusive results were found. In this paper we introduce a formal methodology, which allows us to compare multiple split criteria. This permits us to present fundamental insights into the decision process. Furthermore, we are able to present a formal description of how to select between split criteria for a given data set. As an illustration we apply the methodology to two widely used split criteria: Gini Index and Information Gain. read more read less

Topics:

Information gain ratio (61%)61% related to the paper, Incremental decision tree (59%)59% related to the paper, Decision tree learning (57%)57% related to the paper, Decision tree (55%)55% related to the paper, Tree (data structure) (52%)52% related to the paper
554 Citations
Journal Article DOI: 10.1023/A:1022915830921
NIST Digital Library of Mathematical Functions
Daniel W. Lozier1

Abstract:

The National Institute of Standards and Technology is preparing a Digital Library of Mathematical Functions (DLMF) to provide useful data about special functions for a wide audience. The initial products will be a published handbook and companion Web site, both scheduled for completion in 2003. More than 50 mathematicians, ph... The National Institute of Standards and Technology is preparing a Digital Library of Mathematical Functions (DLMF) to provide useful data about special functions for a wide audience. The initial products will be a published handbook and companion Web site, both scheduled for completion in 2003. More than 50 mathematicians, physicists and computer scientists from around the world are participating in the work. The data to be covered include mathematical formulas, graphs, references, methods of computation, and links to software. Special features of the Web site include 3D interactive graphics and an equation search capability. The information technology tools that are being used are, of necessity, ones that are widely available now, even though better tools are in active development. For example, LaTeX files are being used as the common source for both the handbook and the Web site. This is the technology of choice for presentation of mathematics in print but it is not well suited to equation search, for example, or for input to computer algebra systems. These and other problems, and some partially successful work-arounds, are discussed in this paper and in the companion paper by Miller and Youssef. read more read less

Topics:

Digital Library of Mathematical Functions (67%)67% related to the paper, Digital library (54%)54% related to the paper
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490 Citations
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13. What is Sherpa RoMEO Archiving Policy for Annals of Mathematics and Artificial 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 Annals of Mathematics and Artificial 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 Annals of Mathematics and Artificial Intelligence?

The 5 most common citation types in order of usage for Annals of Mathematics and Artificial Intelligence 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 Annals of Mathematics and Artificial Intelligence Endnote style according to Elsevier guidelines.

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