Taner Arsan

Arsan, Taner

Name Variants

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

ORCID ID

0000-0002-4453-3218

Scopus Author ID

6506505859

Full item page

Scholarly Output

66

Articles

18

Citation Count

140

Supervised Theses

12 Scholarly Output Search Results

Now showing 1 - 10 of 66

Twitter Sentiment Analysis Via Machine Learning
(Kadir Has Üniversitesi, 2021) Kaşgarlı, Kemal Mahmut; Arsan, Taner; Arsan, Taner
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.
İç Mekan Konumlandırma Sistemlerinde Konum Belirlemesinin Geliştirilmesi
(2024) Türker, Mehmet Nasuhcan; Arsan, Taner; Arsan, Taner
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.
Review of Bandwidth Estimation Tools and Application To Bandwidth Adaptive Video Streaming
(IEEE, 2012) Arsan, Taner; Arsan, Taner
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.
A Systems Software Architecture for Training Neural, Fuzzy Neural and Genetic Computational Intelligent Networks
(Institute of Electrical and Electronics Engineers Inc., 2006) Arsan,T.; Arsan, Taner; Öǧrenci,A.S.; Saydam,T.
A systems software architecture for training distributed neural, fuzzy neural and genetic networks and their relevant information models have been developed. Principles of on-line architecture building, training, managing, and optimization guidelines are provided and extensively discussed. Qualitative comparisons of neural training strategies have been provided. © 2006 IEEE.
Classification of Heart Diseases With Convolutional Neural Networks
(Kadir Has Üniversitesi, 2021) Koç, Bekir Yavuz; Arsan, Taner; Arsan, Taner
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.
A Data Science Perspective on Global Trends in Energy Production
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Alsan, H.F.; Arsan, T.
As global demand for energy continues to rise, understanding the trends and dynamics of energy generation is crucial to ensure a sustainable and efficient energy future. This study employs data science techniques to analyze global energy production data from 48 countries spanning 2010 to 2023. Initially, we use clustering methods to categorize countries based on their energy production profiles into three distinct groups: high, medium, and low production. This clustering provides insights into the diverse energy strategies and capacities across different regions. Subsequently, we apply and compare two classification models, specifically Random Forest and Gradient Boosting, to predict the dominant energy source for each cluster. Furthermore, we perform a comparative analysis of two forecasting models, SARIMA and Prophet, to predict future renewable energy production for countries with high production profiles, such as the USA and China. The forecasting results show the efficacy of these models in capturing seasonal trends and providing accurate predictions. © 2024 IEEE.
Network Traffic Anomaly Detection Using Quantile Regression with Tolerance
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Guler,A.K.; Yildiz,E.; Kilinc,S.; Camlidere,B.; Arsan,T.
Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. Data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature; therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections. © 2023 IEEE.
Career Center System Software Architecture
(Springer Verlag, 2015) Arsan, Taner; Arsan, Taner; Çimenli, Safa; Güneş, Erhan
In today’s world, thousands of job seekers are looking for a new job. On the other hand, thousands of employers are trying to find new employees. So, this is a chaotic matching problem and it does not have a certain answer. Companies are searching career centers and web-based career software to find an answer for the question of “”Should we find a convenient worker for a certain role and hire this person or not?” Solution is that simple; just have a look at the beginning of the story: university career centers. In this study, a Career Center System Software has been designed and implemented for matching students with their ideal job. Career Center System Software (CCSS) is programmed on C#, MS-SQL and.NET platform. CCSS has been developed on Visual Studio 2010. CCSS is implemented in a way so as to enable the user to apply for the job announcements and to monitor courses and to call for conferences and seminars. Furthermore, CCSS enables companies to view the applicant’s curriculum vitae. All job announcements, educations, seminars and CVs are stored on the database. Software quality and testing shows that CCSS is implemented successfully and ready to use tool as Career Center Software.
Improving the Accuracy of Indoor Positioning System
(Kadir Has Üniversitesi, 2019) Hameez, Mohammed Muwafaq Noori; Arsan, Taner; Arsan, Taner
Indoor positioning applications needs high accuracy and precision to overcome the existing obstacles and relatively small areas. There are several methods which could be used to locate an object or people in an indoor location. Specifically, Ultra-wide band (UWB) sensor technology is a promising technology in indoor environments because of its high accuracy, resistance of interference and better penetrating. This thesis is focused on improving the accuracy of UWB sensor based indoor positioning system. To achieve that, optimization and machine learning algorithms are implemented. The impact of Kalman Filter (KF) on the accuracy is introduced in the implementation of the algorithms. The average localization error is reduced by approximately 54.53% (from 16.34 cm to 7.43 cm), when combining the big bang - big crunch algorithm (BB-BC) with Kalman Filter. Finally, a Hybrid (BB-BC KF K-Means) algorithm is improved and implemented separately, and the best results are obtained from this Hybrid algorithm. Thus, it has been obtained that the average localization error is reduced significantly by approximately 64.26% (from 16.34 cm to 5.84 cm).
Early Steps in Automated Behavior Mapping via Indoor Sensors
(MDPI, 2017) Arsan, Taner; Arsan, Taner; Kepez, Orçun; Kepez, Orçun
Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies we chose Wi-Fi BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m(2) test area (6m x 6 m) 0.86 m for the BLE beacon in a 37.44 m(2) test area (9.6 m x 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m(2) test area (6 m x 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m(2) test area (7.35 m x 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally we demonstrated the use of ultra-wide band sensor technology for BM.