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

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


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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.

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  1/2018 - 1
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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
 
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 (1,399 KB) | Citation | Downloads: 708 | Views: 713

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
Web of Science Accession Number: 000426449500001
SCOPUS ID: 85043298309

Abstract
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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

[1] M. Vrigkas, V. Karavasilis, C. Nikou, & I.A. Kakadiaris, "Matching mixtures of curves for human action recognition," Computer Vision and Image Understanding, vol. 119, pp. 27-40, Feb. 2014,
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 20]


[2] J. Morales, D. Akopian, "Physical activity recognition by smartphones, a survey,". Biocybernetics and Biomedical Engineering, vol. 37, pp. 388-400, May 2017.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12]


[3] L. Bao, S. Intille, "Activity recognition from user-annotated acceleration data," Pervasive computing, vol. 3001, pp. 1-17, Apr. 2004,
[CrossRef]


[4] L. Chen, J. Hoey, C. D. Nugent, D.J. Cook, Z. Yu, "Sensor-based activity recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 42, pp. 790-808, May 2012,
[CrossRef] [Web of Science Times Cited 267] [SCOPUS Times Cited 374]


[5] J. R. Kwapisz, G. M. Weiss, S.A. Moore, "Activity recognition using cell phone accelerometers," ACM SigKDD Explorations Newsletter, vol. 12, pp. 74-82, Dec. 2011,
[CrossRef]


[6] G. Son, S. Kwon, Y. Lim, "Speech Rate Control for Improving Elderly Speech Recognition of Smart Devices," Advances in Electrical and Computer Engineering, vol.17, no.2, pp.79-84, May 2017,
[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[7] C. Catal, S. Tufekci, E. Pirmit, G. Kocabag, G. "On the use of ensemble of classifiers for accelerometer-based activity recognition," Applied Soft Computing, vol. 37, pp. 1018-1022, Dec. 2015,
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 52]


[8] M. Field, D. Stirling, Z. Pan, M. Ros, F. Naghdy, "Recognizing human motions through mixture modeling of inertial data," Pattern Recognition, vol. 48, pp. 2394-2406, Aug. 2015,
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 24]


[9] E. Vats, C. S. Chan, "Early detection of human actions—a hybrid approach," Applied Soft Computing, vol. 46, pp. 953-966, Sept. 2016,
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[10] F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, Y. Amirat, "Physical human activity recognition using wearable sensors," Sensors, vol. 15, 31314-31338, Dec. 2015,
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 101]


[11] A. Mannini, A. M. Sabatini, "Machine learning methods for classifying human physical activity from on-body accelerometers," Sensors, vol. 10, pp. 1154-1175, Feb. 2010.
[CrossRef] [Web of Science Times Cited 233] [SCOPUS Times Cited 307]


[12] O.-A. Schipor, S.-G. Pentiuc, M.-D. Schipor, "Toward automatic recognition of children's affective state using physiological parameters and fuzzy model of emotions," Advances in Electrical and Computer Engineering, vol.12, pp.47-50, May 2012,
[CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 3]


[13] L. Gao, A. K. Bourke, J. Nelson, "Activity recognition using dynamic multiple sensor fusion in body sensor networks," In: Proc of IEEE Engineering in Medicine and Biology Society, San Diego, 2012, pp. 1077-1080,
[CrossRef] [SCOPUS Times Cited 8]


[14] K. Cho, N. Iketani, H. Setoguchi, M. Hattori, M. "Human activity recognizer for mobile devices with multiple sensors," In: IEEE International Conference on Ubiquitous, Autonomic and Trusted Computing, 2009, pp. 114-119,
[CrossRef] [SCOPUS Times Cited 14]


[15] A. Lopez-Mendez, J.R. Casas, "Model-based recognition of human actions by trajectory matching in phase spaces," Image and Vision Computing, vol. 30, pp. 808-816, Nov. 2012,
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 17]


[16] A. Wang, G. Chen, J. Yang, S. Zhao, C.Y. Chang, "A comparative study on human activity recognition using inertial sensors in a smartphone," IEEE Sensors Journal, vol. 16, pp.4566-4578, March 2016,
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 43]


[17] K. Barbe, R. Pintelon, J. Schoukens. "Welch method revisited: nonparametric power spectrum estimation via circular overlap," IEEE Transactions on signal processing, vol. 58, pp. 553-565, Feb. 2010,
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 43]


[18] S.B. Cho, J.H. Kim, "Multiple network fusion using fuzzy logic," IEEE Transactions on Neural Networks, vol. 6, pp. 497-501, Mar 1995,
[CrossRef] [Web of Science Times Cited 133] [SCOPUS Times Cited 147]


[19] S.L. Wu, Y.T. Liu, T. Y. Hsieh, Y.Y. Lin, C.Y. Chen, C.H. Chuang, C. T. Lin, "Fuzzy integral with particle swarm optimization for a motor-imagery-based brain–computer interface," IEEE Transactions on Fuzzy Systems, vol. 25, pp. 21-28, Aug 2016,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 7]


[20] J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning, New York: Springer series in statistics, pp. 241-249, 2001.

[21] V. V. Phansalkar, P. S. Sastry "Analysis of the back-propagation algorithm with momentum," IEEE Transactions on Neural Networks vol. 5, pp. 505-506, May 1994,
[CrossRef] [Web of Science Times Cited 82] [SCOPUS Times Cited 100]


[22] N. Cristianini, B. Scholkopf, "Support vector machines and kernel methods: the new generation of learning machines," Ai Magazine, vol. 23, pp. 31-41, Fall 2002,
[CrossRef]


[23] R. Rajabioun, "Cuckoo optimization algorithm," Applied soft computing, vol. 11, pp. 5508-5518, Dec. 2011,
[CrossRef] [Web of Science Times Cited 330] [SCOPUS Times Cited 395]


[24] A. H. Gandomi, X. S. Yang, A. H. Alavi, "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems," Engineering with computers, vol.29, pp. 17-35, Jan. 2013,
[CrossRef] [Web of Science Times Cited 345] [SCOPUS Times Cited 500]




References Weight

Web of Science® Citations for all references: 1,638 TCR
SCOPUS® Citations for all references: 2,174 TCR

Web of Science® Average Citations per reference: 66 ACR
SCOPUS® Average Citations per reference: 87 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 2018-11-16 10:30 in 167 seconds.




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Stefan cel Mare University of Suceava, Romania


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