Arsan, Taner

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A., Taner
Taner Arsan
ARSAN, Taner
Arsan,Taner
Arsan, TANER
A.,Taner
Taner ARSAN
Arsan, Taner
Taner, Arsan
ARSAN, TANER
Arsan, T.
T. Arsan
TANER ARSAN
Arsan,T.
Arsan T.
Job Title
Doç. Dr.
Email Address
arsan@khas.edu.tr
Main Affiliation
Computer Engineering
Status
Website
ORCID ID
0000-0002-4453-3218
Scopus Author ID
6506505859
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAB-2736-2019

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Scholarly Output

70

Articles

22

Citation Count

140

Supervised Theses

13

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Now showing 1 - 10 of 70
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    Conference Object
    Citation - Scopus: 0
    Deciphering the Cluster-Specific Marker Genes Via Integration of Single Cell Rna Sequencing Datasets
    (Institute of Electrical and Electronics Engineers Inc., 2023) Rinch,W.A.; Arsan, Taner; Sogunmez,N.; Altaf,A.; Alsan,H.F.; Arsan,T.; Computer Engineering
    Experimental data from brain tissues are critical for tackling the problems in brain development and revealing the underlying mechanisms of disease states. However, obtaining the brain tissue is a major challenge. Human brain organoids hold remarkable promise for this goal, but they suffer from substantial organoid-to-organoid variability. We performed a data-driven analysis on single-cell RNA-sequencing data using 17775 cells isolated from 2 individual organoids. The main goal was to accurately integrate the data coming from unmatched datasets, cluster the cells based on their similarity levels and predict the differentially expressed genes per cell types to reveal novel brain cell types and markers. This research opens a way to map human brain cells and develop novel and precise machine learning algorithms for accurate scRNA-Seq data analysis. © 2023 IEEE.
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    Conference Object
    Citation - Scopus: 5
    Fast Multi-View Face Trackingwith Pose Estimation
    (2008) Meynet, Julien; Arsan, Taner; Arsan, Taner; Mota, Javier Cruz; Thiran, Jean-Philippe Philippe H.; Computer Engineering
    In this paper a fast and an effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier extracts faces of any pose from the background. Then more specific classifiers discriminate between different poses. The tree of classifiers is trained by hierarchically sub-sampling the pose space. Finally Condensation algorithm is used for tracking the faces. Experiments show large improvements in terms of detection rate and processing speed compared to state-of-the-art algorithms.
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    Conference Object
    Citation - Scopus: 0
    Audience Tracking and Cheering Content Control in Sports Events
    (IEEE, 2020) Yeşilyurt, Gözdenur; Arsan, Taner; Dursun, Sefa; Kumas, Osman; Çakir, Nagehan; Arsan, Taner; Computer Engineering
    Swearing cheers encountered in sports competitions do not comply with sports ethics and morals. Even if this kind of cheering is a group, the entire tribune block is penalized in accordance with the current rules. This method is not preventive and individual punishment should be used. The aim of this study is to determine the individuals who cheer with swearing content. In this study, the person detection is made with the multi-task cascaded convolutional neural network. Moreover, facial landmarks representing the facial regions and the regions related to them are determined as a result of this process. The mouth region is also determined by means of these important points removed, and finally the mouth is determined according to the equation. The face recognition is carried out because the person would be in a state of yelling if the mouth opening ratio exceeds the threshold value by determining the rate of opening. Landmarks extracted from the facial regions for the face recognition are transformed into feature vectors by FaceNet, and the model is created by classifying these vectors with classifiers to use in recognition process. When evaluated in terms of industry, face recognition and detection systems find a wide field of study.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Transmitter Source Location Estimation Using Crowd Data
    (Pergamon-Elsevier Science Ltd, 2018) Öğrenci, Arif Selçuk; Arsan, Taner; Arsan, Taner; Computer Engineering
    The problem of transmitter source localization in a dense urban area has been investigated where a supervised learning approach utilizing neural networks has been adopted. The cellular phone network cells and signals have been used as the test bed where data are collected by means of a smart phone. Location and signal strength data are obtained by random navigation and this information is used to develop a learning system for cells with known base station location. The model is applied to data collected in other cells to predict their base station locations. Results are consistent and indicating a potential for effective use of this methodology. The performance increases by increasing the training set size. Several shortcomings and future research topics are discussed. (C) 2017 Elsevier Ltd. All rights reserved.
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    Conference Object
    Citation - Scopus: 10
    Big Data Platform Development With a Domain Specific Language for Telecom Industries
    (IEEE Computer Society, 2013) Senbalci,C.; Arsan, Taner; Altuntas,S.; Bozkuş, Zeki; Bozkus,Z.; Arsan,T.; Computer Engineering
    This paper introduces a system that offer a special big data analysis platform with Domain Specific Language for telecom industries. This platform has three main parts that suggests a new kind of domain specific system for processing and visualization of large data files for telecom organizations. These parts are Domain Specific Language (DSL), Parallel Processing/Analyzing Platform for Big Data and an Integrated Result Viewer. In addition to these main parts, Distributed File Descriptor (DFD) is designed for passing information between these modules and organizing communication. To find out benefits of this domain specific solution, standard framework of big data concept is examined carefully. Big data concept has special infrastructure and tools to perform for data storing, processing, analyzing operations. This infrastructure can be grouped as four different parts, these are infrastructure, programming models, high performance schema free databases, and processing-analyzing. Although there are lots of advantages of Big Data concept, it is still very difficult to manage these systems for many enterprises. Therefore, this study suggest a new higher level language, called as DSL which helps enterprises to process big data without writing any complex low level traditional parallel processing codes, a new kind of result viewer and this paper also presents a Big Data solution system that is called Petaminer. © 2013 IEEE.
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    Master Thesis
    İç Mekan Konumlandırma Sistemlerinde Konum Belirlemesinin Geliştirilmesi
    (2024) Türker, Mehmet Nasuhcan; Arsan, Taner; Arsan, Taner; Computer Engineering
    Son yıllarda, kapalı alan konumlandırma teknolojileri önemli ölçüde gelişmiş ve birçok uygulama alanında büyük potansiyele sahiptir. Kapalı alan konumlandırma belirleme, özellikle akıllı ev sistemleri, endüstriyel otomasyon, inşaat, sağlık ve konum tabanlı hizmetler gibi birçok alanda önemli bir rol oynamaktadır. Bu alandaki teknolojik gelişmeler, mevcut kapalı alan konumlandırma yöntemlerinin doğruluğunu ve hassasiyetini sürekli olarak artırmayı amaçlamaktadır. Bu tez, Federe Kalman Filtresi uygulanarak, Ultra Geniş Bant teknolojisinde görüş hattı dışı (NLOS) senaryoları tarafından oluşan konum sapmasını azaltmaya odaklanmaktadır. Federe Kalman Filtresinin NLOS senaryolarında kullanımı, konum sapmasında dikkate değer bir azalmayı göstermiştir. Bu tez, Federe Kalman Filtresini, kapalı mekân ayarlarında görüş hattı (LOS) ve görüş hattı dışı (NLOS) koşullar altında alınan ölçümleri analiz etmek için kullanmaktadır. Bu çalışmanın bulguları, Ultra Geniş Bant teknolojisi alanında gelecekte yapılacak olan araştırmalar için umut verici bir temel sunarak zorlayıcı çalışma ortamlarında iyileştirilmiş performans ve azaltılmış hata payı ile bu alanın güçlü taraflarını göstermektedir. Federe Kalman Filtresi, ortalama doğruluk iyileştirmesi olarak yaklaşık %96,64'ünü gösterdi. Başlangıçta 0,30 metreye ulaşan hata payı, Federe Kalman Filtresinin entegrasyonu ile 0,0072 metreye önemli ölçüde azaltılmıştır. Benzer şekilde, görüş hattı dışı (NLOS) senaryolarında yaklaşık %96'lık bir iyileştirme gözlemlenmiştir.
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    Conference Object
    Citation - WoS: 16
    Citation - Scopus: 21
    Review of Bandwidth Estimation Tools and Application To Bandwidth Adaptive Video Streaming
    (IEEE, 2012) Arsan, Taner; Arsan, Taner; Computer Engineering
    Streaming video is very popular in today's best effort delivery networks. Streaming video applications should not only have a good end-to-end transport performance but also have a Quality of Service (QoS) provisioning in network infrastructure. Bandwidth estimation schemes have been used to improve the QoS of multimedia services and video streaming applications. To ensure the video streaming service quality some other components such as adaptive rate allocation and control should be taken into consideration. This paper gives a review of bandwidth estimation tools for wired and wireless networks and then introduces a new bandwidth adaptive architecture for video streaming. © 2012 IEEE.
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    Master Thesis
    Classification of Heart Diseases With Convolutional Neural Networks
    (Kadir Has Üniversitesi, 2021) Koç, Bekir Yavuz; Arsan, Taner; Arsan, Taner; Computer Engineering
    Günümüzde kalp hastalıklarının sayısı ve sıklığı artmaktadır. Bu alanda iyileştirmeler yapılabilmesi için yüksek miktarda harcama yapılmaktadır. Kalbin elektriksel iletimindeki atımlar özel cihazlarla kaydedilebilir ve EKG (Elektrokardiyogram) oluşturulabilir. EKG'den üretilen veriler, Taylor Series algoritması ile faz uzaylarına dönüştürülebilir. Kalp hastalığının tespiti için 44 farklı kişiden alınan verilerle MLII sinyallerinden EKG ve faz uzayları oluşturuldu. Bu kayıtların kalp durumunu belirlemek için hem EKG görüntüleri hem de faz uzayı görüntüleri kullanıldı. Kayıtların kalp durumu görüntülere ve sonuçlara Convolutional Neural Networks (CNNs) yöntemi uygulandı ve SVM (Support Vector Machine) algoritması ile karşılaştırılarak başarı oranı ölçüldü. Ayrıca aynı kayıtlar üzerinden eğitim ve test seti değiştirilerek farklı modellerin başarı oranları karşılaştırıldı. EKG ile faz uzayı görüntülerine CNN algoritmasının verdiği sonuçlardaki farklılık tespit edildi. Nowadays, the number and frequency of heart diseases is increasing. High amounts of expenses are incurred in order to make improvements in this area. The beats in the electrical conduction of the heart can be recorded by special devices and ECG (Electrocardiogram) can be created. Data generated from ECG can be transformed into phase spaces with Taylor Series algorithm. In order to determine the detection of heart disease, ECG and phase spaces were created from MLII signals based on 44 different records. Both ECG images and phase space images were used to determine the heart conditions of these recordings. The heart status of the recordings was measured by applying Convolutional Neural Networks (CNNs) method to the images and results compared with the SVM (Support Vector Machine) algorithm. In addition, the success rates of different models were compared by changing the training and test set over the same records. The success rate between ECG and phase space was also determined.
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    Master Thesis
    Twitter Sentiment Analysis Via Machine Learning
    (Kadir Has Üniversitesi, 2021) Kaşgarlı, Kemal Mahmut; Arsan, Taner; Arsan, Taner; Computer Engineering
    İnsanlar dünyada yaşanan olaylardan kullandıkları ürün ve hizmetlere kadar bir çok konu hakkında sosyal medya platformlarında yorum yapmakta, duygu ve düşüncelerini paylaşmakta ve birbirleriyle iletişim içinde bulunmaktadır. Twitter günümüzde çok popüler olan sosyal medya platformlarından biridir. Bu platformun kullanıcıları tarafından oluşturulan tweetler Metin Madenciliği alanında ve özelinde Duygu analizi çalışmalarında veri bilimcileri için çok iyi birer veri seti kaynağı olabilmektedir. Bu tez çalışmasında tweet verileri Python programlama dili ile Anaconda platformunda yer alan JupyterLab editörü üzerinde metin önişleme sürecinden geçirildikten sonra duygu analizleri yapılmış, metin verisi ikili sınıflandırma yapılarak Negatif ve Pozitif olarak etiketlenmiştir. Tweet metin verileri vektörlere dönüştürülerek Bag of Words ve Tf-idf gibi özellik çıkarımı yöntemi ile işlenmiş ve Destek Vektör Makinesi, Lojistik Regresyon, Naïve Bayes, Rastgele Orman, Extreme Gradient Boost Makine Öğrenmesi algoritmaları ile sınıflandırma tahmin verilerinin doğrulukları karşılaştırılmıştır.
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    Conference Object
    Citation - Scopus: 0
    Joint Visible Light Communications and Positioning with Dimming;
    (Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Panayırcı, Erdal; Kumaş,M.B.; Demiryürek,T.; Arsan,T.; Panayirci,E.; Electrical-Electronics Engineering; Computer Engineering
    In this paper, a novel visible light communication (VLC) and three-dimensional (3D) positioning (VLP) algorithm based on spatial modulation (SM) with dimming capability is proposed. With the help of the signal and pilot symbols observed by a user with two photodetectors (PD) on the receiver, the detection of the transmitted data with the estimated channel and dimming coefficients is performed, and at the same time, the position of the user represented in 3D is determined. The computer simulations conclude that the root mean square (RMS) values of the proposed algorithm in 3D position determination are very low, and the bit error rate (BER) performance is very high. © 2023 IEEE.