Example of International Journal of Data Analysis Techniques and Strategies format
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Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format
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Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format Example of International Journal of Data Analysis Techniques and Strategies format
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International Journal of Data Analysis Techniques and Strategies — Template for authors

Categories Rank Trend in last 3 yrs
Information Systems and Management #77 of 125 down down by 8 ranks
Applied Mathematics #360 of 548 -
Information Systems #238 of 329 down down by 28 ranks
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 83 Published Papers | 104 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 07/07/2020
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FAQ

Related Journals

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Quality:  
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CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764
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Springer

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CiteRatio: 8.6
SJR: 1.031
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CiteRatio: 7.7
SJR: 1.426
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Springer

Quality:  
High
CiteRatio: 4.1
SJR: 0.337
SNIP: 0.919

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.

1.3

18% from 2019

CiteRatio for International Journal of Data Analysis Techniques and Strategies from 2016 - 2020
Year Value
2020 1.3
2019 1.1
2018 1.1
2017 0.7
2016 0.6
graph view Graph view
table view Table view

0.245

7% from 2019

SJR for International Journal of Data Analysis Techniques and Strategies from 2016 - 2020
Year Value
2020 0.245
2019 0.264
2018 0.271
2017 0.23
2016 0.194
graph view Graph view
table view Table view

0.603

28% from 2019

SNIP for International Journal of Data Analysis Techniques and Strategies from 2016 - 2020
Year Value
2020 0.603
2019 0.839
2018 0.571
2017 0.652
2016 0.324
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 18% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 28% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
International Journal of Data Analysis Techniques and Strategies

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Inderscience Publishers

International Journal of Data Analysis Techniques and Strategies

Approved by publishing and review experts on SciSpace, this template is built as per for International Journal of Data Analysis Techniques and Strategies formatting guidelines as mentioned in Inderscience Publishers author instructions. The current version was created on 07 Jul 2020 and has been used by 283 authors to write and format their manuscripts to this journal.

i
Last updated on
07 Jul 2020
i
ISSN
1755-8050
i
Impact Factor
Medium - 0.824
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
i
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/IJDATS.2011.041335
Logistic regression in data analysis: an overview
Maher Maalouf1

Abstract:

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective a... Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective and how LR can be applied to imbalanced and rare events data. read more read less

Topics:

Data classification (57%)57% related to the paper, Binary data (55%)55% related to the paper, Logistic regression (52%)52% related to the paper
120 Citations
Journal Article DOI: 10.1504/IJDATS.2008.020020
Predicting credit card customer churn in banks using data mining

Abstract:

In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as... In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as the constituents. The dataset was taken from the Business Intelligence Cup organised by the University of Chile in 2004. Since it is a highly unbalanced dataset with 93% loyal and 7% churned customers, we employed (1) undersampling, (2) oversampling, (3) a combination of undersampling and oversampling and (4) the Synthetic Minority Oversampling Technique (SMOTE) for balancing it. Furthermore, tenfold cross-validation was employed. The results indicated that SMOTE achieved good overall accuracy. Also, SMOTE and a combination of undersampling and oversampling improved the sensitivity and overall accuracy in majority voting. In addition, the Classification and Regression Tree (CART) was used for the purpose of feature selection. The reduced feature set was fed to the classifiers mentioned above. Thus, this paper outlines the most important predictor variables in solving the credit card churn prediction problem. Moreover, the rules generated by decision tree J48 act as an early warning expert system. read more read less

Topics:

Credit card (59%)59% related to the paper, Random forest (52%)52% related to the paper, Decision tree (52%)52% related to the paper, C4.5 algorithm (51%)51% related to the paper, Multilayer perceptron (50%)50% related to the paper
120 Citations
Journal Article DOI: 10.1504/IJDATS.2008.020024
Quality function deployment: a comprehensive literature review
J.R. Sharma, A. M. Rawani1, Milind Barahate

Abstract:

In the past few years, various quality standards and quality systems have been attempted for the improvement of the products and services in our lives. One such quality tool which has the ability to generate creative and novel solutions is Quality Function Deployment (QFD). This paper presents a detailed literature review on ... In the past few years, various quality standards and quality systems have been attempted for the improvement of the products and services in our lives. One such quality tool which has the ability to generate creative and novel solutions is Quality Function Deployment (QFD). This paper presents a detailed literature review on the topic and application of QFD. This literature review is based on a reference bank of more than 400 QFDs and its allied publications, organisations, software, tools and web sources. The literature review is extended with thorough descriptions of the adopted methodologies, exemplified with an elaborate and categorical application analysis of its varied functional areas, namely, primary, secondary and tertiary fields, industrial, non-industrial and service applications and methodological progressions. The paper concludes with some of the insights gained from a large number of research papers, publications and other available literature. read more read less

Topics:

Quality function deployment (58%)58% related to the paper, Quality management (55%)55% related to the paper, Quality management system (53%)53% related to the paper, New product development (50%)50% related to the paper, Quality (business) (50%)50% related to the paper
52 Citations
Journal Article DOI: 10.1504/IJDATS.2010.030010
Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for fault classification of monoblock centrifugal pump
N. R. Sakthivel1, V. Sugumaran2, Binoy B. Nair1

Abstract:

Monoblock centrifugal pumps are widely used in a variety of applications. Defects and malfunctions (faults) of these pumps result in significant economic loss. Therefore, the pumps must be under constant monitoring. When a possible fault is detected, diagnosis is carried out to pinpoint it. In many applications, the role of m... Monoblock centrifugal pumps are widely used in a variety of applications. Defects and malfunctions (faults) of these pumps result in significant economic loss. Therefore, the pumps must be under constant monitoring. When a possible fault is detected, diagnosis is carried out to pinpoint it. In many applications, the role of monoblock centrifugal pumps is critical and condition monitoring is essential. Vibration-based condition monitoring and analysis using the machine-learning approach is gaining momentum. In particular, Artificial Neural Networks (ANNs), fuzzy logic and roughsets have been employed for condition monitoring and fault diagnosis. While it is difficult to train the neural network-based fault classifier, the classification accuracy in case of fuzzy logic- and roughest-based fault classifiers is not very high. This paper presents the use of Support Vector Machines (SVMs) and Proximal Support Vector Machines (PSVMs) for classifying faults using statistical features extracted from vibration signals under good and faulty conditions of a monoblock centrifugal pump. The Decision Tree (DT) algorithm is used to select prime features. These features are fed as inputs for training and testing SVMs and PSVMs and their fault classification accuracy is compared. The results are found to be better than neural network-, fuzzy- and roughest-based methods. read more read less

Topics:

Condition monitoring (54%)54% related to the paper, Support vector machine (53%)53% related to the paper, Fuzzy logic (52%)52% related to the paper, Artificial neural network (51%)51% related to the paper
44 Citations
Journal Article DOI: 10.1504/IJDATS.2009.024295
To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models
Richard Ashley1, Randal J. Verbrugge2

Abstract:

It is often unclear whether time series displaying substantial persistence should be modelled as a vector autoregression in levels (perhaps with a trend term) or in differences. The impact of this decision on inference is examined here using Monte Carlo simulation. In particular, the size and power of variable inclusion (Gran... It is often unclear whether time series displaying substantial persistence should be modelled as a vector autoregression in levels (perhaps with a trend term) or in differences. The impact of this decision on inference is examined here using Monte Carlo simulation. In particular, the size and power of variable inclusion (Granger causality) tests and the coverage of impulse response function confidence intervals are examined for simulated vector autoregression models using a variety of estimation techniques. We conclude that testing should be done using differenced regressors, but that overdifferencing a model yields poor impulse response function confidence interval coverage; modelling in Hodrick-Prescott filtered levels yields poor results in any case. We find that the lag-augmented vector autoregression method suggested by Toda and Yamamoto (1995) – which models the level of the series but allows for variable inclusion testing on changes in the series – performs well for both Granger causality testing and impulse response function estimation. read more read less

Topics:

Bayesian vector autoregression (63%)63% related to the paper, Vector autoregression (59%)59% related to the paper, Granger causality (57%)57% related to the paper, Impulse response (55%)55% related to the paper, Unit root (52%)52% related to the paper
43 Citations
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International Journal of Data Analysis Techniques and Strategies format uses plainnat citation style.

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Frequently asked questions

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13. What is Sherpa RoMEO Archiving Policy for International Journal of Data Analysis Techniques and Strategies?

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 Data Analysis Techniques and Strategies. 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 Data Analysis Techniques and Strategies?

The 5 most common citation types in order of usage for International Journal of Data Analysis Techniques and Strategies 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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