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

Main Affiliation

Computer Engineering

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.
Citation - WoS: 16
Citation - Scopus: 21
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.
Citation - Scopus: 0
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.
Citation - Scopus: 1
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.
Citation - WoS: 2
Power Control and Resource Allocation in Tdd-Ofdm Based Femtocell Networks With Interference
(IEEE, 2017) Altabbaa, Mhd Tahssin; Arsan, Taner; Arsan, Taner; Panayırcı, Erdal; Panayırcı, Erdal
Femtocell technology is a promising solution for different dilemmas in cellular networks. In femtocell power control the interference experienced by the network is divided into two main tiers according to the type of network whose signal is interfering with another network. In utilizing the functionality of a two-tier network where femtocell technology is deployed a major challenge is in sharing the frequency resource of a macrocell. This paper proposes an enhanced dynamic algorithm bounded by two constraints to optimize the transmission powers of femtocell users in TDD-OFDM based femtocell networks taking into consideration rate enhancement of femtocell mobile stations. We compare our algorithm with the macrocell guard system which allows femtocells to occupy only the subchannels unoccupied by the macrocell.
Citation - WoS: 1
Improvement of Indoor Positioning Accuracy of Ultra-Wide Band Sensors by Using Big Bang-Big Crunch Optimization Method
(Pamukkale Univ, 2018) Arsan, Taner; Arsan, Taner
Ultra-wide Band technology is an emerging technology that offers successful solutions in many indoor positioning systems and performs better than other methods. In this study an indoor positioning system using Ultra-wide Band (UWB) sensors was developed and it was aimed to increase the accuracy level of the standard equipment with the additional algorithms used while reducing the average error. For this purpose the Big Bang-Big Crunch (BB-BC) optimization method has been applied to the experimental indoor positioning system and the positive effect on the measurement accuracy has been proved by the tests made. An area of 39.76 m(2) was selected as a test area of 7.35 m x 5.41 m and three different Ultra-wide Band receivers were installed at a height of 2.85 m on a specially designed ceiling system and a total of 10.920 measurements were taken from 182 test points for 60 seconds. By correcting the measurement results with the Big Bang Big Crunch optimization algorithm the average error was reduced from the previous 20.72 cm to 15.02 cm thus the accuracy of the measurement results were improved.
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.
Citation - WoS: 2
Citation - Scopus: 3
Transmitter Source Location Estimation Using Crowd Data
(Pergamon-Elsevier Science Ltd, 2018) Öğrenci, Arif Selçuk; Arsan, Taner; Arsan, Taner
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.
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.
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.