Akan, Erhan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/7937
Name Variants
A., Erhan
Erhan, Akan
Akan, Erhan
E.,Akan
E., Akan
Akan,E.
A.,Erhan
Job Title
Araştırma Görevlisi
Email Address
erhan.akan@atilim.edu.tr
Main Affiliation
Department of Electrical & Electronics Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

SDG data is not available
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

4

Articles

0

Views / Downloads

0/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

8

Scopus Citation Count

11

WoS h-index

1

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

2.00

Scopus Citations per Publication

2.75

Open Access Source

0

Supervised Theses

1

Google Analytics Visitor Traffic

JournalCount
2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings -- 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- Kocaeli -- 1721751
24th IEEE International Conference on Electronics, Circuits and Systems (ICECS) -- DEC 05-08, 2017 -- Batumi, GEORGIA1
ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems -- 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 -- 5 December 2017 through 8 December 2017 -- Batumi -- 1346751
Current Page: 1 / 1

Scopus Quartile Distribution

Quartile distribution chart data is not available

Competency Cloud

GCRIS Competency Cloud

Filters

20172
Reset filters

Settings

Has files: falseSubject: gyroscope

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Conference Object
    Citation - WoS: 8
    Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
    (Ieee, 2017) Akan, Erhan; Tora, Hakan; Uslu, Baran
    In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 11
    Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2017) Akan,E.; Tora,H.; Uslu,B.
    In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor. © 2017 IEEE.