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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: May 2018
Next issue: Aug 2018
Avg review time: 109 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  2/2018 - 4

Generic Approach for Interpretation of PCA Results - Use Case on Learner's Activity in Social Media Tools

MIHAESCU, M. C. See more information about MIHAESCU, M. C. on SCOPUS See more information about MIHAESCU, M. C. on IEEExplore See more information about MIHAESCU, M. C. on Web of Science, POPESCU, P. S. See more information about  POPESCU, P. S. on SCOPUS See more information about  POPESCU, P. S. on SCOPUS See more information about POPESCU, P. S. on Web of Science, MOCANU, M. L. See more information about MOCANU, M. L. on SCOPUS See more information about MOCANU, M. L. on SCOPUS See more information about MOCANU, M. L. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,304 KB) | Citation | Downloads: 106 | Views: 122

Author keywords
data engineering, knowledge representation, machine learning, social network services, social computing

References keywords
analysis(13), principal(11), component(10), learning(5), education(5), student(4), social(4), review(4), research(4), advanced(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 27 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02004
SCOPUS ID: 85047847074

Abstract
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Intensive usage of social media tools for educational purposes transformed many previously tackled issues from classical e-Learning systems. Among the most general challenging issues reside in building classification models having the performed activities set as independent variables and final grade as dependent variable. A critical step in data analysis process regards building interpretable models in terms of explaining feature values and ranges along with their influence on target class. We asked whether dimensionality reduction techniques may be effectively used such that high quality interpretable models are obtained. Principal component analysis (PCA) dimensionality reduction technique, scaling and several classical classification techniques were used to create a data analysis pipeline that produces classification models with similar accuracy of initial classification models built on raw available data. Experimental results show that features that characterize the activity performed on each social tool and on all tools are highly interpretable in our classification context. The proposed approach is flexible and can be adapted to similar practical use cases in which a large number of features is difficult to be interpreted and a digest is required as being more useful for bringing a better insight on data.


References | Cited By  «-- Click to see who has cited this paper

[1] M. C. Mihaescu, P. S. Popescu, E. Popescu, "Data analysis on social media traces for detection of "spam" and "don't care" learners", The Journal of Supercomputing, vol. 73, no. 10, pp. 1-22, 2017.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 1]


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[CrossRef] [Web of Science Times Cited 45]


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[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


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[CrossRef]


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References Weight

Web of Science® Citations for all references: 2,053 TCR
SCOPUS® Citations for all references: 2,677 TCR

Web of Science® Average Citations per reference: 79 ACR
SCOPUS® Average Citations per reference: 103 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-08-12 16:29 in 94 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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