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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Feb 2018
Next issue: May 2018
Avg review time: 107 days


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

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


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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.

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Fuzzy Integral and Cuckoo Search Based Classifier Fusion for Human Action Recognition

AYDIN, I. See more information about AYDIN, I. on SCOPUS See more information about AYDIN, I. on IEEExplore See more information about AYDIN, I. on Web of Science
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Download PDF pdficon (1,399 KB) | Citation | Downloads: 152 | Views: 134

Author keywords
classification, optimization, feature extraction, fuzzy logic, signal processing

References keywords
recognition(13), activity(10), human(8), sensors(6), computing(6), fuzzy(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01001
SCOPUS ID: 85043298309

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The human activity recognition is an important issue for sports analysis and health monitoring. The early recognition of human actions is used in areas such as detection of criminal activities, fall detection, and action recognition in rehabilitation centers. Especially, the detection of the falls in elderly people is very important for rapid intervention. Mobile phones can be used for action recognition with their built-in accelerometer sensor. In this study, a new combined method based on fuzzy integral and cuckoo search is proposed for classifying human actions. The signals are acquired from three axes of acceleration sensor of a mobile phone and the features are extracted by applying signal processing methods. Our approach utilizes from linear discriminant analysis (LDA), support vector machines (SVM), and neural networks (NN) techniques and aggregates their outputs by using fuzzy integral. The cuckoo search method adjusts the parameters for assignment of optimal confidence levels of the classifiers. The experimental results show that our model provides better performance than the individual classifiers. In addition, appropriate selection of the confidence levels improves the performance of the combined classifiers.

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

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References Weight

Web of Science® Citations for all references: 1,333 TCR
SCOPUS® Citations for all references: 1,716 TCR

Web of Science® Average Citations per reference: 53 ACR
SCOPUS® Average Citations per reference: 69 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 2018-03-22 13:17 in 152 seconds.

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

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