Şengül, Gökhan
Loading...
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
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
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
81
Articles
51
Citation Count
128
Supervised Theses
10
80 results
Scholarly Output Search Results
Now showing 1 - 10 of 80
Master Thesis Makine öğrenme algoritmalarıyla yüz tespiti(2020) Şengül, Gökhan; Şengül, Gökhan; Computer EngineeringBir görüntüdeki yüzlerin ve yüz olmayanların varlığının tespiti, yüzün tutumu (Lokalizasyon), ifade ve ayrımcılık gibi yüz uygulamalarının ilk hareketidir. Yüz tanımanın amacı, bir yüzün bir resimde görünüp görünmediğini belirlemektir. Yüzün bulunduğu görüntünün konumunu bilmek veya bulmak, herhangi bir yüz algılama işleme sisteminin en kritik adımlarından biridir. Yüz algılama sisteminde gerçekleştirilen performans, yüzlerin statik olmadığı (sabit değil) ve duruş, renk, aydınlatma durumu ve ölçek açısından büyük farklılıklar göstermesi nedeniyle, uygulamaların doğru çalışmasına anında etki eder. Yukarıda belirtilen tüm sorunların üstesinden gelmek için otomatik bir sistem planlamak ve hedeflemek zordur. Bu nedenle makine öğrenimi algoritmaları, iyi performans gösteren bir yüz algılama sistemi oluşturmak için başarılı uygulama araçlarından biri olarak bilinir. Yüz algılama sorunu, bir resimdeki insan yüzünün bir veya birden fazla yüzünün algılanmasını içeren bilgisayar görüşü için bir görev olarak adlandırılabilir. Yüzün algılanması, yüz analizlerinin önemli hareketlerinden biridir. Bu tezde, yüzlerin özelliklerini ortaya çıkarmak ve ardından yüzleri sınıflandırmak için kullanılan viyola & jones, LBP (Özellik çıkarıcı), K-NN ve SVM (Sınıflandırıcılar) algoritmalarının genel bir incelemesi, sağlam, verimli ve güvenilir bir yüz algılama sistemi oluşturmak ve her yöntemin avantajları ve dezavantajları kısaca ve ayrıntılı olarak açıklanmakta, ardından hangi yaklaşımın diğerlerine göre daha kesin ve güçlü olduğuna karar verilmektedir. Sonunda, binlerce yüz ve yüz olmayan görüntüden oluşan çalışmamızda kullanılan veri setlerine dayalı olarak SVM, K-NN, LBP ve Viola & Jones için bir karşılaştırma ve değerlendirme yapılır. LBP, K-NN, SVM ve Viola & Jones yöntemleri hızı, doğruluğu, öğrenme yeteneği ve basitliği nedeniyle yüz tanıma için kullanılacak uygun yöntemler gibi görünmektedir. Bu çalışmanın sonuçları, eğitim görüntülerinin sayısı artırılarak sistem doğruluğunun iyileştirilebileceğini göstermiştir. Çalışmada aynı ve farklı boyutlarda iki veri seti kullanılmıştır. Yüzler için aynı yüz boyutlarına sahip veri seti kullanılarak SVM kullanılarak LBP için% 85, K-NN kullanılarak LBP için% 100 ve Viola & Jones için% 88 doğruluk elde edilir. Yüz için farklı boyutlardaki veri seti kullanılarak yüzler için SMV kullanılarak LBP için% 83, K-NN kullanılarak LBP için% 57 ve Viola & Jones için% 68 doğruluk elde edilmiştir.Article Citation Count: 8A Comprehensive Assessment Plan for Accreditation in Engineering Education: A Case Study in Turkey(Tempus Publications, 2015) Turhan, Çiğdem; Şengül, Gökhan; Koyuncu, Murat; Information Systems Engineering; Software Engineering; Computer EngineeringThis 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-ACElabel, and a full member signatory of Washington 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.Conference Object Citation Count: 1An Undergraduate Curriculum for Deep Learning(Ieee, 2018) Türkmen, Güzin; Ekin, Cansu Çiğdem; Şengül, Gökhan; Bostan, Atila; Karakaya, Kasım Murat; Computer EngineeringDeep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.Article 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; Information Systems Engineering; Software Engineering; Computer EngineeringThis 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.Article Using Bluetooth Low Energy Beacons for Indoor Localization(International Journal of Intelligent Systems and Applications in Engineering, 2017) Şengül, Gökhan; Karakaya, Murat; Karakaya, Kasım Murat; Computer EngineeringBluetooth Low Energy (BLE) Beacons gain high popularity due to their low consumption of energy and, thereby, long lifetime. Using the BLE protocol, these devices emit advertisement packets at fixed intervals for a short duration. Indoor localization solutions aim to provide an accurate, low cost estimate of sub-room indoor positioning. There are various techniques proposed for this purpose. BLE Beacons are good hardware candidates to assist the creation of such indoor localization solutions. Given the exact position of BLE Beacons, one can attempt to estimate a receiver position according to the received signal power. In this work, we investigated the success of such an indoor localization approach employing multiple BLE Beacons and two different estimation techniques. The results of the experiments indicate that employing multiple BLE Beacons increases the success of prediction techniques considerably.Article Citation Count: 9Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets(Bentham Science Publ Ltd, 2019) İşgör, Sultan Belgin; Şengül, Gökhan; Sengul, Gokhan; Isgor, Yasemin Gulgun; Özkan, Akın; Chemical Engineering; Computer Engineering; Department of Electrical & Electronics EngineeringBackground: Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner's experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis. Objective: In this study, we investigate various combinations of classifiers and feature extractors (i.e. classification models) to maximize the performance of computer vision-based viability analysis. Method: The classification models are tested on novel hemocytometer image datasets which contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562). Results: From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are 0.031 and 0.082, respectively. Conclusion: The experimental results show that KNN and RF with LPQ can be powerful alternatives to the conventional manual cell viability analysis. Also, the collected datasets are released from the "biochem.atilim.edu.tr/datasets/ " web address publically to academic studies.Article Citation Count: 10Classification of parasite egg cells using gray level cooccurence matrix and kNN(Scientific Publishers of India, 2016) Şengül, Gökhan; Computer EngineeringParasite 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.Article Citation Count: 0A Hybrid Approach for Semantic Image Annotation(Ieee-inst Electrical Electronics Engineers inc, 2021) Sezen, Arda; Turhan, Çiğdem; Sengul, Gokhan; Şengül, Gökhan; Computer Engineering; Software EngineeringIn 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.Article Citation Count: 8White Blood Cells Classifications by SURF Image Matching, PCA and Dendrogram(Allied Acad, 2015) Nazlıbilek, Sedat; Ertürk, Korhan Levent; Şengül, Gökhan; Aliew, Fuad; Ercan, Tuncay; Aliew, Fuad; Department of Mechatronics Engineering; Information Systems Engineering; Computer EngineeringDetermination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils, lymphocytes, neutrophils, monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting, size determination and classification of white blood cells. In this paper, we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques.Master Thesis Görüntü işleme teknikleri ile insanların yaş ve cinsiyetlerinin tahmini(2016) Şengül, Gökhan; Şengül, Gökhan; Computer EngineeringYüz görüntülerinden görüntüdeki kişinin cinsiyetini ve yaşını tahmin etme, henüz tam olarak çözülememiş aktif bir araştırma problemidir. Bu problemin çözümü için birçok araştırmacı tarafından farklı yöntemler önerilmiş olmakla birlikte beklenen gereksinimlerle elde edilen performans arasında farklılıklar bulunmaktadır. Bahsedilen bu probleme yönelik olarak bu tez çalışmasında istatistiksel örüntü tanıma tabanlı bir yöntem önerilmiştir. Önerilen yöntemde özellik çıkarıcı olarak HOG (Histogram of Oriented Gradient) ve LBP (Local Binary Pattern) yaklaşımları kullanılmıştır. Ayrıca daha iyi sonuçlar elde etmek üzere bu iki özellik çıkarıcının sonuçları birleştirilerek de kullanılmıştır. Sınıflandırıcı olarak ise SVM (Support Vector Machines) ve KNN (K-Nearest Neighbour) yaklaşımları izlenmiştir. Performans ölçümleri için ise Birini Dışarıda Bırakma ve Hata Matrisleri kullanılmıştır. Ön işleme olarak; tüm görüntü boyutlarını aynı yapmak ve hesaplama zamanını azaltmak üzere boyut indirgeme ve görüntülerdeki aydınlık farklılıklarını ortadan kaldırmak üzere histogram eşitleme işlemleri gerçekleştirilmiştir. Önerilen yöntem FERET, UTD ve FG-NET veritabanlarından elde edilen görüntüler üzerinde test edilmiştir. Sonuç olarak HOG ve LBP özellikleri birarada kullanıldığından cinsiyet tahmininde 100%, yaş aralığı tahmininde ise 99.87% başarı elde edilmiştir.
