Şengül, Gökhan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/7052
Name Variants
Gokhan, Sengul
Sengul, Gokhan
Sengul,G.
Gökhan, Şengül
Engul G.
Şengül G.
Şengül, Gökhan
G.,Sengul
Sengul, G.
S.,Gokhan
Sengul G.
Ş., Gökhan
G.,Şengül
G., Sengul
Şengül,G.
G., Şengül
S., Gokhan
Ş.,Gökhan
Job Title
Profesor Doktor
Email Address
gokhan.sengul@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Website
ORCID ID
Scopus Author ID
8402817900
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
G-8213-2016

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
4
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
1
Research Products
GENDER EQUALITY5
GENDER EQUALITY
1
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
1
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

41

Citations

344

h-index

12

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Documents

17

Citations

106

Go to WoS profile
Scholarly Output

84

Articles

49

Views / Downloads

105/195

Supervised MSc Theses

9

Supervised PhD Theses

3

WoS Citation Count

217

Scopus Citation Count

332

Patents

0

Projects

0

WoS Citations per Publication

2.58

Scopus Citations per Publication

3.95

Open Access Source

18

Supervised Theses

12

JournalCount
Biomedical Research (India)5
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
3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG2
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings -- 24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- Zonguldak -- 1226052
International Journal of Engineering Education2
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Type: Article

Scholarly Output Search Results

Now showing 1 - 10 of 49
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    A Comprehensive Assessment Plan for Accreditation in Engineering Education: A Case Study in Turkey
    (International Journal of Engineering Education, 2015) Turhan, Çiğdem; Şengül, Gökhan; Koyuncu, Murat
    This paper describes the procedure followed by Computer Engineering and Software Engineering programs at Atilim University, Ankara, Turkey, which led to the granting of five years of accreditation by MUDEK, the local accreditation body authorized by The European Network for Accreditation of Engineering Education (ENAEE) to award the EUR ACE label, and a full member signatory ofWashington Accord of International Engineering Alliance (IEA). It explains the organizational structure established for preparation, determination and measurement of the educational objectives, program outcomes, course outcomes, and the continuous improvement cycle carried out during the preparation period. The aim of the paper is to share methods and experiences which may be beneficial for the other programs that are intended for accreditation.
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    Article
    Citation - WoS: 6
    Citation - Scopus: 12
    Classification of Parasite Egg Cells Using Gray Level Cooccurence Matrix and Knn
    (Scientific Publishers india, 2016) Sengul, Gokhan
    Parasite eggs are around 20 to 80 mu m dimensions, and they can be seen under microscopes only and their detection requires visual analyses of microscopic images, which requires human expertise and long analysis time. Besides visual analysis is very error prone to human procedures. In order to automatize this process, a number of studies are proposed in the literature. But there is still a gap between the preferred performance and the reported ones and it is necessary to increase the performance of the automatic parasite egg classification approaches. In this study a learning based statistical pattern recognition approach for parasite egg classification is proposed that will both decrease the time required for the manual classification by an expert and increase the performance of the previously suggested automated parasite egg classification approaches. The proposed method uses Gray-Level Co-occurrence Matrix as the feature extractor, which is a texture based statistical method that can differentiate the parasite egg cells based on their textures, and the k-Nearest Neighbourhood (kNN) classifier for the classification. The proposed method is tested on 14 parasite egg types commonly seen in humans. The results show that proposed method can classify the parasite egg cells with a performance rate of 99%.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 3
    A Hybrid Approach for Semantic Image Annotation
    (Ieee-inst Electrical Electronics Engineers inc, 2021) Sezen, Arda; Turhan, Cigdem; Sengul, Gokhan
    In this study, a framework that generates natural language descriptions of images within a controlled environment is proposed. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework's object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.
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    Article
    Türkiye’de Engelli Farkındalığı ve Engelli Bireylerin Adalete Web Erişilebilirlikleri Üzerine Bir Değerlendirme
    (Bilgi Dünyası, 2014) Ertürk, Korhan Levent; Şimşek, A. Aslı; Songür, Damla Gülseren; Şengül, Gökhan
    Fiziksel veya zihinsel nedenlerle bazı hareketleri, duyuları veya işlevleri kısıtlı olan bireyler toplumun bir grubunu oluşturmaktadır. türkiye’de bu bireyler ve/veya çevreleri toplumda doğrudan ya da dolaylı olarak çeşitli sorunlarla karşı karşıya kalmaktadırlar. Günümüzde eğitim, sağlık, adalet, sosyal güvenlik gibi alanlarda bu durum sıklıkla görülebilmektedir. Söz konusu bireyler sorunlarıyla ilgilenilmesini ve çözüme kavuşturulmasını istemektedirler. Bir ülkenin gelişmişlik düzeyi anılan sorunların çözümüne yönelik çalışmalar ile doğrudan ilişkilidir. Çalışmamız, bazı hareketleri, duyuları veya işlevleri kısıtlı olan bireylerin ortak bir terimle ifade edilmesi, engelli birey farkındalığının ortaya konulması ve bu bağlamda ilgili bazı web sitelerinin bu bireyler açısından yeterliliğinin sorgulanmasına yöneliktir. Web sitelerinin olabildiğince erişilebilir yapılması engelli kullanıcılara diğer bireyler ile eşit hakların sağlanmasına katkı sağlayabilecek, bilgi ve iletişim kaynaklarını çeşitlendirebilecektir.
  • Thumbnail Image
    Article
    Determination of Measurement Noise, Conductivity Errors and Electrode Mislocalization Effects To Somatosensory Dipole Localization
    (Biomedical Research, 2012) Şengül, Gökhan; Baysal, Uğur
    Calculating the spatial locations, directions and magnitudes of electrically active sources of human brain by using the measured scalp potentials is known as source localization. An accu rate source localization method requires not only EEG data but also the 3-D positions and number of measurement electrodes, the numerical head model of the patient/subject and the conductivities of the layers used in the head model. In this study we computationally deter mined the effect of noise, conductivity errors and electrode mislocalizations for electrical sources located in somatosensory cortex. We first randomly selected 1000 electric sources in somatosensory cortex, and for these sources we simulated the surface potentials by using av erage conductivities given in the literature and 3-D positions of the electrodes. We then added random noise to measurements and by using noisy data; we tried to calculate the positions of the dipoles by using different electrode positions or different conductivity values. The esti mated electrical sources and original ones are compared and by this way the effect of meas urement noise, electrode mislocalizations and conductivity errors to somatosensory dipole lo calization is investigated. We conclude that for an accurate somatosensory source localization method, we need noiseless measurements, accurate conductivity values of scalp and skull lay ers and the accurate knowledge of 3-D positions of measurement sensors.
  • Thumbnail Image
    Article
    Single Camera Photogrammetry System for Eeg Electrode Identification and Localization
    (Annals of Biomedical Engineering, 2010) Baysal, Uğur; Şengül, Gökhan
    In this study, photogrammetric coordinate measurement and color-based identification of EEG electrode positions on the human head are simultaneously implemented. A rotating, 2MP digital camera about 20 cm above the subject’s head is used and the images are acquired at predefined stop points separated azimuthally at equal angular displacements. In order to realize full automation, the electrodes have been labeled by colored circular markers and an electrode recognition algorithm has been developed. The proposed method has been tested by using a plastic head phantom carrying 25 electrode markers. Electrode locations have been determined while incorporating three different methods: (i) the proposed photogrammetric method, (ii) conventional 3D radiofrequency (RF) digitizer, and (iii) coordinate measurement machine having about 6.5 lm accuracy. It is found that the proposed system automatically identifies electrodes and localizes them with a maximum error of 0.77 mm. It is suggested that this method may be used in EEG source localization applications in the human brain.
  • Thumbnail Image
    Article
    Classification of Parasite Egg Cells Using Gray Level Cooccurence Matrix and Knn.
    (Biomedical Research, 2016) Şengül, Gökhan
    Parasite eggs are around 20 to 80 μm dimensions, and they can be seen under microscopes only and their detection requires visual analyses of microscopic images, which requires human expertise and long analysis time. Besides visual analysis is very error prone to human procedures. In order to automatize this process, a number of studies are proposed in the literature. But there is still a gap between the preferred performance and the reported ones and it is necessary to increase the performance of the automatic parasite egg classification approaches. In this study a learning based statistical pattern recognition approach for parasite egg classification is proposed that will both decrease the time required for the manual classification by an expert and increase the performance of the previously suggested automated parasite egg classification approaches. The proposed method uses Gray-Level Co-occurrence Matrix as the feature extractor, which is a texture based statistical method that can differentiate the parasite egg cells based on their textures, and the k-Nearest Neighbourhood (kNN) classifier for the classification. The proposed method is tested on 14 parasite egg types commonly seen in humans. The results show that proposed method can classify the parasite egg cells with a performance rate of 99%.
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    The Integrated Usage of Lbp and Hog Transformations and Machine Learning Algorithms for Age Range Prediction From Facial Images
    (Univ Osijek, Tech Fac, 2018) Khalifa, Tariq; Sengul, Gokhan
    Age prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images' size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%.
  • Thumbnail Image
    Article
    Türkiye’de Engelli Farkındalığı ve Engelli Bireylerin Adalete Web Erişilebilirlikleri Üzerine Bir Değerlendirme
    (2014) Ertürk, Korhan Levent; Şimşek, A Aslı; Songur, Damla Gülseren; Şengül, Gökhan
    Fiziksel veya zihinsel nedenlerle bazı hareketleri, duyuları veya işlevleri kısıtlı olan bireyler toplumun bir grubunu oluşturmaktadır. türkiye'de bu bireyler ve/veya çevreleri toplumda doğrudan ya da dolaylı olarak çeşitli sorunlarla karşı karşıya kalmaktadırlar. Günümüzde eğitim, sağlık, adalet, sosyal güvenlik gibi alanlarda bu durum sıklıkla görülebilmektedir. Söz konusu bireyler sorunlarıyla ilgilenilmesini ve çözüme kavuşturulmasını istemektedirler. Bir ülkenin gelişmişlik düzeyi anılan sorunların çözümüne yönelik çalışmalar ile doğrudan ilişkilidir. Çalışmamız, bazı hareketleri, duyuları veya işlevleri kısıtlı olan bireylerin ortak bir terimle ifade edilmesi, engelli birey farkındalığının ortaya konulması ve bu bağlamda ilgili bazı web sitelerinin bu bireyler açısından yeterliliğinin sorgulanmasına yöneliktir. Bunlar ve benzeri web sitelerinin olabildiğince erişilebilir yapılması engelli kullanıcılara diğer bireyler ile eşit hakların sağlanmasına katkı sağlayabilecek, bilgi ve iletişim kaynaklarını çeşitlendirebilecektir
  • Thumbnail Image
    Article
    Citation - Scopus: 12
    Classification of parasite egg cells using gray level cooccurence matrix and kNN
    (Scientific Publishers of India, 2016) Şengül,G.
    Parasite eggs are around 20 to 80 μm dimensions, and they can be seen under microscopes only and their detection requires visual analyses of microscopic images, which requires human expertise and long analysis time. Besides visual analysis is very error prone to human procedures. In order to automatize this process, a number of studies are proposed in the literature. But there is still a gap between the preferred performance and the reported ones and it is necessary to increase the performance of the automatic parasite egg classification approaches. In this study a learning based statistical pattern recognition approach for parasite egg classification is proposed that will both decrease the time required for the manual classification by an expert and increase the performance of the previously suggested automated parasite egg classification approaches. The proposed method uses Gray-Level Co-occurrence Matrix as the feature extractor, which is a texture based statistical method that can differentiate the parasite egg cells based on their textures, and the k-Nearest Neighbourhood (kNN) classifier for the classification. The proposed method is tested on 14 parasite egg types commonly seen in humans. The results show that proposed method can classify the parasite egg cells with a performance rate of 99%. © 2016, Scientific Publishers of India. All rights reserved.