Example of International Journal of Reasoning-based Intelligent Systems format
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Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format
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Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format Example of International Journal of Reasoning-based Intelligent Systems format
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open access Open Access

International Journal of Reasoning-based Intelligent Systems — Template for authors

Categories Rank Trend in last 3 yrs
Engineering (all) #261 of 297 down down by 70 ranks
Computer Science (all) #199 of 226 down down by 29 ranks
journal-quality-icon Journal quality:
Low
calendar-icon Last 4 years overview: 130 Published Papers | 59 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/07/2020
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open access Open Access
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Quality:  
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SJR: 1.432
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IEEE

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Wiley

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Quality:  
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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.

0.5

CiteRatio for International Journal of Reasoning-based Intelligent Systems from 2016 - 2020
Year Value
2020 0.5
2019 0.5
2018 0.6
2017 0.5
2016 0.6
graph view Graph view
table view Table view

0.13

17% from 2019

SJR for International Journal of Reasoning-based Intelligent Systems from 2016 - 2020
Year Value
2020 0.13
2019 0.111
2018 0.125
2017 0.126
2016 0.143
graph view Graph view
table view Table view

0.363

57% from 2019

SNIP for International Journal of Reasoning-based Intelligent Systems from 2016 - 2020
Year Value
2020 0.363
2019 0.841
2018 0.557
2017 0.286
2016 0.473
graph view Graph view
table view Table view

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 17% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 57% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

International Journal of Reasoning-based Intelligent Systems

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Inderscience Publishers

International Journal of Reasoning-based Intelligent Systems

Approved by publishing and review experts on SciSpace, this template is built as per for International Journal of Reasoning-based Intelligent Systems formatting guidelines as mentioned in Inderscience Publishers author instructions. The current version was created on 19 Jul 2020 and has been used by 184 authors to write and format their manuscripts to this journal.

i
Last updated on
19 Jul 2020
i
ISSN
1755-0556
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Impact Factor
Low - 0.311
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
plainnat
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Beenakker, C. W. J. (2006). ‘Specular Andreev Reflection in Graphene’. Phys. Rev. Lett., Vol 97, No 6, pp. 067007.

Top papers written in this journal

Journal Article DOI: 10.1504/IJRIS.2009.028020
Modelling morality with prospective logic
Luís Moniz Pereira1, Ari Saptawijaya

Abstract:

This paper shows how moral decisions can be drawn computationally by using prospective logic programs. These are employed to model moral dilemmas, as they are able to prospectively look ahead at the consequences of hypothetical moral judgments. With this knowledge of consequences, moral rules are then used to decide the appro... This paper shows how moral decisions can be drawn computationally by using prospective logic programs. These are employed to model moral dilemmas, as they are able to prospectively look ahead at the consequences of hypothetical moral judgments. With this knowledge of consequences, moral rules are then used to decide the appropriate moral judgments. The whole moral reasoning is achieved via a priori constraints and a posteriori preferences on abductive stable models, two features available in prospective logic programming. In this work, we model various moral dilemmas taken from the classic trolley problem and employ the principle of double effect as the moral rule. Our experiments show that preferred moral decisions, i.e., those following the principle of double effect, are successfully delivered. Additionally, we consider another moral principle, the principle of triple effect, in our implementation. We show that our prospective logic programs allow us to explain computationally different moral judgments... read more read less

Topics:

Moral disengagement (68%)68% related to the paper, Social cognitive theory of morality (66%)66% related to the paper, Moral reasoning (65%)65% related to the paper, Social intuitionism (61%)61% related to the paper, Morality (54%)54% related to the paper
62 Citations
open accessOpen access Journal Article
A new approach to soft sets, soft multisets and their properties
B. K. Tripathy1, K. R. Arun1

Abstract:

Soft set theory is a new mathematical approach to vagueness introduced by Molodtsov (1999). This is a parameterised family of subsets defined over a universal set using a set of parameters. In this paper, we introduce the notion of characteristic function of a soft set, which helps us in defining the basic operations on soft ... Soft set theory is a new mathematical approach to vagueness introduced by Molodtsov (1999). This is a parameterised family of subsets defined over a universal set using a set of parameters. In this paper, we introduce the notion of characteristic function of a soft set, which helps us in defining the basic operations on soft sets concisely; several concepts associated with it efficiently and make the proofs of properties more elegant. We rectified the definition of complement of a soft set and the earlier definition of complement is now called as the negation of a multiset. Like the crisp multisets, soft multiset is a notion which allows multiple occurrences of elements in a model. So far, more than one attempt has been made to define this concept. Out of these the one put forth by Majumdar (2012) is the most appropriate one and so we use it in this paper. We redefined the concepts of complement of a soft multiset, null soft multiset and absolute soft multiset and introduced many operations on soft multisets like the union and intersection of soft multisets and cardinality of soft multisets. Also, we defined the concepts of addition and deletion of elements from a soft multiset. Two new operations, called the addition and difference of two soft multisets are introduced. We establish several properties of these operations on soft multisets including the De Morgan's Law, associative and distributive properties. A real life example is being used for the purpose of illustration of the notions and concepts. read more read less

Topics:

Soft set (67%)67% related to the paper, Multiset (61%)61% related to the paper, Complement (set theory) (55%)55% related to the paper, Intersection (set theory) (52%)52% related to the paper, Universal set (51%)51% related to the paper
30 Citations
open accessOpen access Journal Article DOI: 10.1504/IJRIS.2013.057273
Chaotic time series prediction using Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS)
Mahboobeh Parsapoor1, Urban Bilstrup1

Abstract:

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recu... In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy. read more read less

Topics:

Artificial neural network (54%)54% related to the paper, Linear model (53%)53% related to the paper, Fuzzy logic (52%)52% related to the paper, Fuzzy control system (51%)51% related to the paper
View PDF
29 Citations
Journal Article DOI: 10.1504/IJRIS.2010.029812
Bayesian reasoning for Laban Movement Analysis used in human-machine interaction
Jörg Rett1, Jorge Dias1, Juan-Manuel Ahuactzin

Abstract:

We present the implementation of computational Laban Movement Analysis (LMA) for human-machine interaction using Bayesian reasoning. The research field of computational human movement analysis is lacking a general underlying modelling language, i.e., how to map the features into symbols. With such a semantic descriptor, the r... We present the implementation of computational Laban Movement Analysis (LMA) for human-machine interaction using Bayesian reasoning. The research field of computational human movement analysis is lacking a general underlying modelling language, i.e., how to map the features into symbols. With such a semantic descriptor, the recognition problem can be posed as a problem to recognise a sequence of symbols taken from an alphabet consisting of motion-entities. LMA has been proven successful in areas where humans are observing other humans' movements. LMA provides a model for observation and description and a notational system (Labanotation). To implement LMA in a computer, we have chosen a Bayesian approach. The framework allows us to model the process, learn the dependencies between features and symbols and to perform online classification using LMA-labels. We have chosen the application 'social robots' to demonstrate the feasibility of our solution. read more read less

Topics:

Laban Movement Analysis (66%)66% related to the paper, Labanotation (57%)57% related to the paper, Bayesian inference (51%)51% related to the paper
View PDF
27 Citations
Journal Article DOI: 10.1504/IJRIS.2009.028019
Prospective logic agents

Abstract:

As we face the actual possibility of modelling agent systems capable of non-deterministic self-evolution, we are confronted with the problem of having several different possible futures for any single agent. This issue brings the challenge of how to allow such evolving agents to be able to look ahead, prospectively, into such... As we face the actual possibility of modelling agent systems capable of non-deterministic self-evolution, we are confronted with the problem of having several different possible futures for any single agent. This issue brings the challenge of how to allow such evolving agents to be able to look ahead, prospectively, into such hypothetical futures, in order to determine the best courses of evolution from their own present, and thence to prefer amongst them. The concept of prospective logic programs is presented as a way to address such issues. We start by building on previous theoretical background, on evolving programs and on abduction, to construe a framework for prospection and describe an abstract procedure for its materialisation. We take on several examples of modelling prospective logic programs that illustrate the proposed concepts and briefly discuss the ACORDA system, a working implementation of the previously presented procedure. We conclude by elaborating about current limitations of the system... read more read less

Topics:

Multi-agent system (52%)52% related to the paper, Logic programming (51%)51% related to the paper
26 Citations
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13. What is Sherpa RoMEO Archiving Policy for International Journal of Reasoning-based Intelligent Systems?

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 International Journal of Reasoning-based Intelligent Systems. 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 International Journal of Reasoning-based Intelligent Systems?

The 5 most common citation types in order of usage for International Journal of Reasoning-based Intelligent Systems 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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