Arsan, Taner

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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

4

QUALITY EDUCATION
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0

Research Products

6

CLEAN WATER AND SANITATION
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0

Research Products

10

REDUCED INEQUALITIES
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0

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13

CLIMATE ACTION
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0

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14

LIFE BELOW WATER
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0

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2

ZERO HUNGER
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0

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8

DECENT WORK AND ECONOMIC GROWTH
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0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

1

Research Products

17

PARTNERSHIPS FOR THE GOALS
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3

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1

NO POVERTY
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0

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11

SUSTAINABLE CITIES AND COMMUNITIES
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5

Research Products

15

LIFE ON LAND
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1

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3

GOOD HEALTH AND WELL-BEING
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3

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7

AFFORDABLE AND CLEAN ENERGY
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2

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5

GENDER EQUALITY
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0

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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

Research Products
Documents

54

Citations

427

h-index

9

Go to Scopus profile
Documents

27

Citations

301

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Scholarly Output

74

Articles

23

Views / Downloads

59/0

Supervised MSc Theses

15

Supervised PhD Theses

0

WoS Citation Count

232

Scopus Citation Count

342

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

3.14

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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Scholarly Output Search Results

Now showing 1 - 10 of 74
  • Thumbnail Image
    Article
    Career Center System Software Architecture
    (Springer Verlag, 2015) 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.
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    Master Thesis
    Improving the Accuracy of Indoor Positioning System
    (Kadir Has Üniversitesi, 2019) Hameez, Mohammed Muwafaq Noori; 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).
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    Article
    Citation - WoS: 11
    Citation - Scopus: 10
    Early Steps in Automated Behavior Mapping via Indoor Sensors
    (MDPI, 2017) Arsan, Taner; 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.
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    Article
    Citation - WoS: 1
    Chaotic - Deterministic or Random Nature of Earthquakes: a Phase Space Analysis
    (Symmetrion, 2023) Pekcan, Onder; Arsan, Taner
    Using the phase space approach, time series analysis of high EV1 and low EV2 intense two different earthquakes that occurred at the nearly same precise spot, at different times, and were measured with the same sensor of a broadband station were studied. Time series data of strong, large (EV1) and weak, small (EV2) two earthquake events were analyzed by dividing them into three different regions. Fractal dimensions of the EV1 and EV2 were produced using the box-counting algorithm for east-west (BHE), north-south (BHN), and vertical (BHZ) components. The small, weak earthquake, EV2, created a larger fractal dimension in phase space by implying its random nature in all regions. However, EV1 is a strong, large earthquake that presents deterministic oscillatory behavior at a long-time region. Oscillatory behavior can be named surface wave. EV2 exhibits weak, high-frequency ground oscillations similar to fibrillation before and after the earthquake in the long-term areas.
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    Article
    Citation - Scopus: 1
    Trafik Verilerinde Genetik Algoritmalar ve Meta Optimizasyonla Güçlendirilmiş Exponential Smoothing Modeli ile Anomali Tespiti ve Performans Analizi
    (IEEE-Inst Electrical Electronics Engineers inc, 2025) Guler, Ali Kerem; Fuat Alsan, Huseyin; Arsan, Taner
    Bu çalışma, Numenta Anomaly Benchmark'ın (NAB) gerçek zamanlı trafik veri setleri üzerinde tahminleme yapan Third Order Exponential Smoothing modelinin parametrelerini optimize etmek amacıyla genetik algoritma kullanmaktadır. Ayrıca, genetik algoritma optimizasyon sürecini daha verimli hale getirmek için meta-optimizasyon tekniklerinden yararlanılarak anomali tespitindeki doğruluğu önemli ölçüde artıran yenilikçi bir yaklaşım sunmaktadır. Önerilen metodoloji, trafik yönetim sistemlerinde kritik olan veri akışlarındaki sapmaları tespit etmek için çeşitli trafik veri senaryolarına karşı farklı veri setleri üzerinde test edilmiştir. NAB'nin skorlama sistemini kullanarak yapılan karşılaştırmalı performans analizi, bu araştırmada geliştirilen yöntemin mevcut NAB algoritmalarının çoğundan üstün olduğunu ve NAB'nin önde gelen algoritmalarıyla rekabet edebildiğini göstermektedir. 'standart' için 54.32, 'reward_low_FP' için 53.73 ve 'reward_low_FN' için 69.54 skorları elde eden önerilen yaklaşım, sırasıyla NAB algoritmalarının ortalamasına göre %3.13, %2.70 ve %3.24 oranında bir iyileşme sağlamış, önemli bir gelişme kaydetmiştir. Bulgular, önerilen yaklaşımın sadece yüksek hassasiyetle anormallikleri tespit etmekle kalmayıp, aynı zamanda manuel yeniden kalibrasyon gerektirmeden değişen veri özelliklerine dinamik olarak uyum sağladığını göstermektedir. Bu çalışma, güvenilir izleme sağlayan ve potansiyel olarak etkin trafik yönetimi ve planlamayı kolaylaştıran sağlam bir trafik anomali tespit yöntemi önermektedir. Çalışmanın sonuçları, gerçek zamanlı veri izleme ve anormallik tespiti gerektiren diğer alanlara da genişletilebilir, farklı bağlamlar ve gereksinimlere uyum sağlayabilen ölçeklenebilir bir çözüm sunmaktadır.
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    Conference Object
    Citation - WoS: 7
    Big Data Platform Development With a Domain Specific Language for Telecom Industries
    (IEEE, 2013) Şenbalcı, Cüneyt; Altuntaş, Serkan; Bozkuş, Zeki; Arsan, Taner
    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. hi 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.
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    Review
    Citation - WoS: 105
    Citation - Scopus: 149
    Transducer Technologies for Biosensors and Their Wearable Applications
    (Mdpi, 2022) Polat, Emre Ozan; Cetin, M. Mustafa; Tabak, Ahmet Fatih; Bilget Guven, Ebru; Uysal, Bengu Ozugur; Arsan, Taner; Kabbani, Anas
    The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from a biological event into a detectable output. Combined with the suitable bio-receptors and the miniaturization of readout electronics, the functionality and design of the transducers play a key role in the construction of wearable devices for personal health control. Ever-growing research and industrial interest in new transducer technologies for point-of-care (POC) and wearable bio-detection have gained tremendous acceleration by the pandemic-induced digital health transformation. In this article, we provide a comprehensive review of transducers for biosensors and their wearable applications that empower users for the active tracking of biomarkers and personal health parameters.
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    Article
    Integrating Stable Diffusion via Remote Server APIs for Enhanced Parametric Design Workflows
    (Sage Publications Ltd, 2026) Gokmen, Sabri; Alsan, Huseyin Fuat; Arsan, Taner; Ozen, Figen; Keskin, Ebru Ece
    The current advancements of deep learning models offer potential applications for computational design through sets of generated images controlled by parametric inputs, yet they remain disconnected from geometry-driven parametric tools. For this reason, we study the implications of text and image-based generation methods to be used in traditional parametric design procedures. We implement this study by integrating Stable Diffusion and ControlNet to Rhino Grasshopper through a Python-based remote-API plug-in. This API allows a direct connection to the diffusion-based image generation methods without any middleware. Our main contribution is to enable architects and designers to interactively generate and investigate new design ideas in their native parametric design environment. We evaluate potential impact on parametric design education with 15 architecture students using a single GPU server running Stable Diffusion v1.5 across three exercises: Text-to-Image, Image-to-Image using Rhinoceros view captures, and Parametric-Model-to-Image with ControlNet. Quantitative results showed that the API-enabled image generation averaged 4-15 seconds per image, allowing seamless integration with parametric workflows for all 15 students in a classroom setting. Performance evaluations show that our approach offers significantly improved efficiency and responsiveness compared to existing diffusion-based tools, highlighting its suitability for seamless integration within parametric design environments. Qualitative feedback indicated improved design ideation, greater fluency in prompt engineering, and enhanced understanding of parametric logic through iterative visual experimentation. These findings demonstrate the potential of real-time AI integration to augment both conceptual design and parametric design education.
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    Master Thesis
    Implementation of Pci-Dss V3.0 Information Security Standards
    (Kadir Has Üniversitesi, 2015) Taşdemir, Özgür; Arsan, Taner
    Way of doing business has changed with rapid spread of the internet and mobile devices and payment systems must keep up with that. Most of the monetary transactions are done electronically and percentage of internet trade is growing rapidly. information is being more important for companies and individuals when it comes to payment systems. Fraudulent transaction rates have been increased significantly with the positioning of payment systems in public networks such as the internet and Wi-Fi which brings along security breaches. information security requirements and raise of online payment card transactions together with payment card industry demands triggered the founding of PCi DSS information security standards. This thesis describes PCi DSS their requirements for compliancy and implementation of the standards to a company which have more than 2000 employee and stores processes and transmits payment card information.
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    Conference Object
    Citation - Scopus: 1
    Advancing Anomaly Detection in Time Series Data: a Knowledge Distillation Approach With Lstm Model
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kilinc,S.; Camlidere,B.; Yildiz,E.; Guler,A.K.; Alsan,H.F.; Arsan,T.
    This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.