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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Nov 2016
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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
Issue 3/2016



IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 16th of April 2017, placing "IoT - " before the paper title in OpenConf.

Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

Starting with Issue 2/2016, the article processing charge is 300 EUR for each article accepted for publication. The charge of 25 EUR per page for papers over 8 pages will not be changed. Details are available in the For authors section.

Thomson Reuters published the Journal Citations Report for 2014. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.529, and the JCR 5-Year Impact Factor is 0.476.

Starting on the 9th of February 2015, we require all authors to identify themselves, when a submission is made, by entering their SCOPUS Author IDs, instead of the organizations, when available. This information will let us better know the publishing history of the authors and better assign the reviewers on different topics.

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  1/2014 - 20

Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

GEMAN, O. See more information about GEMAN, O. on SCOPUS See more information about GEMAN, O. on IEEExplore See more information about GEMAN, O. on Web of Science, COSTIN, H. See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science
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Download PDF pdficon (741 KB) | Citation | Downloads: 384 | Views: 1,744

Author keywords
adaptive neuro-fuzzy classifier, artificial neural networks, handwriting analysis, nonlinear dynamics, tremor

References keywords
parkinson(11), disease(10), system(6), tremor(5), geman(5), analysis(5), processing(4), link(4), data(4), costin(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 133 - 138
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01020
Web of Science Accession Number: 000332062300020
SCOPUS ID: 84894623357

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Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD) is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP) and an Adaptive Neuro-Fuzzy Classifier (ANFC). In general, the results may be expressed as a prognostic (risk degree to contact PD).

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[1] Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease, Drotár, Peter, Mekyska, Ji┼Öí, Rektorová, Irena, Masarová, Lucia, Smékal, Zdenek, Faundez-Zanuy, Marcos, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue 3, Volume 117, 2014.
Digital Object Identifier: 10.1016/j.cmpb.2014.08.007

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

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