Karakaya, Kasım Murat

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Profile URL
https://hdl.handle.net/20.500.14411/8124
Name Variants
Karakaya, Murat
Karakaya, Kasım Murat
K., Kasim Murat
K.,Kasım Murat
Karakaya,K.M.
Kasim Murat, Karakaya
K., Karakaya
K.,Kasim Murat
Karakaya, Kasim Murat
Kasım Murat, Karakaya
K.M.Karakaya
K.,Karakaya
Karakaya,M.
Karakaya,M.
Job Title
Profesör Doktor
Email Address
murat.karakaya@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
4
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
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This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

44

Articles

20

Views / Downloads

148/417

Supervised MSc Theses

4

Supervised PhD Theses

0

WoS Citation Count

138

Scopus Citation Count

200

Patents

0

Projects

0

WoS Citations per Publication

3.14

Scopus Citations per Publication

4.55

Open Access Source

4

Supervised Theses

4

JournalCount
UBMK 2018 - 3rd International Conference on Computer Science and Engineering -- 3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 20 September 2018 through 23 September 2018 -- Sarajevo -- 1435604
2017 IEEE 1st Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 - Proceedings -- 1st IEEE Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 -- 29 May 2017 through 2 June 2017 -- Kyiv -- 1317632
Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 1768262
3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG2
1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings -- 1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 1571112
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Start date: 2021Subject: Activity recognition

Scholarly Output Search Results

Now showing 1 - 1 of 1
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    Article
    Citation - WoS: 49
    Citation - Scopus: 69
    Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications
    (Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, Robertas
    We implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.