Arsan, Taner

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12469/6140
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
[email protected]
Main Affiliation
Computer Engineering
Status
Current Staff
Website
ORCID ID
0000-0002-4453-3218
Scopus Author ID
6506505859
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAB-2736-2019

Sustainable Development Goals

15

LIFE ON LAND
LIFE ON LAND Logo

1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

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14

LIFE BELOW WATER
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0

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6

CLEAN WATER AND SANITATION
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0

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3

GOOD HEALTH AND WELL-BEING
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3

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17

PARTNERSHIPS FOR THE GOALS
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3

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4

QUALITY EDUCATION
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0

Research Products

2

ZERO HUNGER
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0

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10

REDUCED INEQUALITIES
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0

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7

AFFORDABLE AND CLEAN ENERGY
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2

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13

CLIMATE ACTION
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0

Research Products

1

NO POVERTY
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0

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
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0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

5

Research Products

5

GENDER EQUALITY
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0

Research Products
Documents

54

Citations

427

h-index

9

Go to Scopus profile
Documents

27

Citations

299

Go to WoS profile
Scholarly Output

74

Articles

23

Views / Downloads

559/8598

Supervised MSc Theses

15

Supervised PhD Theses

0

WoS Citation Count

230

Scopus Citation Count

342

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

3.11

Scopus Citations per Publication

4.62

Open Access Source

30

Supervised Theses

15

JournalCount
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 1941534
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 2045623
2023 31st Signal Processing and Communications Applications Conference, Siu2
Computers & Electrical Engineering2
Symmetry-Culture and Science2
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Scopus Quartile Distribution

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Scholarly Output Search Results

Now showing 1 - 10 of 74
  • Thumbnail Image
    Master Thesis
    Classification of Heart Diseases With Convolutional Neural Networks
    (Kadir Has Üniversitesi, 2021) Koç, Bekir Yavuz; 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.
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    Conference Object
    A Data Science Perspective on Global Trends in Energy Production
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hatira, N.; 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.
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    Conference Object
    Citation - Scopus: 1
    Network Traffic Anomaly Detection Using Quantile Regression with Tolerance
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alsan,H.F.; 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.
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    Book Part
    C# Based Media Center
    (2013) Arsan, Taner; Sen, Rasim; Ersoy, Barkan; Devri, Kadir Kadirhan
    In this paper, we design and implement a novel all-in-one Media Center that can be directly connected to a high-definition television (HDTV). C# programming is used for developing modular structured media center for home entertainment. Therefore it is possible and easy to add new limitless number of modules and software components. The most importantly, user interface is designed by considering two important factors; simplicity and tidiness. Proposed media center provides opportunities to users to have an experience on listening to music/radio, watching TV, connecting to Internet, online Internet videos, editing videos, Internet connection to pharmacy on duty, checking weather conditions, song lyrics, CD/DVD burning, connecting to Wikipedia. All the modules and design steps are explained in details for user friendly cost effective all-in-one media center.
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    Conference Object
    Building Damage Assessment To Facilitate Post-Earthquake Search and Rescue Missions by Leveraging a Machine Learning Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2024) Zaker, M.; Alsan, H.F.; Arsan, T.
    Earthquakes have a severe impact on people's lives and infrastructure. Many emergency institutes and search and rescue missions need accurate post-earthquake response strategies, particularly in building damage assessment. Traditional methods, relying on manual inspections, are inefficient compared to Machine Learning (ML) algorithms. Thus, Random Forest (RF) algorithms stand out because they handle diverse datasets effectively and minimize overfitting. The study outlines the methodology encompassing data preparation, exploratory analysis, feature engineering, and model building, employing a preprocessing pipeline integrating numerical and categorical features. Additionally, Principal Component Analysis (PCA) is applied to reduce dimensionality. The results of the RF model showed an accuracy of 94% and the highest F1-score of 97% among all the grades, demonstrating its efficacy in predicting damage grades post-earthquake. The results can help support better disaster management plans by helping to prioritize rescue operations and allocate resources wisely. © 2024 IEEE.
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    Conference Object
    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.; Sogunmez,N.; Altaf,A.; Alsan,H.F.; Arsan,T.
    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; Mota, Javier Cruz; Thiran, Jean-Philippe Philippe H.
    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
    Audience Tracking and Cheering Content Control in Sports Events
    (IEEE, 2020) Yeşilyurt, Gözdenur; Dursun, Sefa; Kumas, Osman; Çakir, Nagehan; Arsan, Taner
    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
    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.; Altuntas,S.; 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.