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

15

LIFE ON LAND
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1

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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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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

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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

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1

NO POVERTY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

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8

DECENT WORK AND ECONOMIC GROWTH
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0

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11

SUSTAINABLE CITIES AND COMMUNITIES
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5

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5

GENDER EQUALITY
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0

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Documents

52

Citations

416

h-index

9

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Documents

27

Citations

291

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Scholarly Output

73

Articles

22

Views / Downloads

524/8219

Supervised MSc Theses

15

Supervised PhD Theses

0

WoS Citation Count

222

Scopus Citation Count

331

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

3.04

Scopus Citations per Publication

4.53

Open Access Source

30

Supervised Theses

15

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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
IEEE Access2
Computers & Electrical Engineering2
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Scholarly Output Search Results

Now showing 1 - 10 of 73
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Power Control and Resource Allocation in Tdd-Ofdm Based Femtocell Networks With Interference
    (IEEE, 2017) Altabbaa, Mhd Tahssin; Arsan, Taner; 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.
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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: 98
    Citation - Scopus: 142
    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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    Master Thesis
    Indoor positioning system development
    (Kadir Has Üniversitesi, 2015) Alp, Ebru; Dağ, Tamer; Arsan, Taner
    Nowadays, smartphone market penetration continues to grow with developing technology. Accordingly, position detection in closed areas has become an important research area. For instance; finding a direct route to the gate based on location at an airport, determining a route to the destination that could be a shop or cafe at a shopping center or informing about sales discount to increase sales using location are several applicable areas of position estimation. In the thesis, I developed triangulation algorithm more efficient using least square method with the developments of Wi-Fi channel fixing, optimized A and n values used in log normal formula and more than 3 access points. I used synthetic data which is created from sample data and estimate location for comparison to analyzing success rate of algorithm. According to the measurement results, triangulation algorithm with least square method, channel fixing, optimized A and n values, more than 3 Access Points gives accurate location in closed areas more than simple triangulation algorithm does. The thesis will lead to detect position in closed areas and use it in daily lives using triangulation algorithm with least square method.
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    Conference Object
    Citation - Scopus: 4
    Predictive Maintenance Analysis for Industries
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sunetcioglu,S.; Arsan,T.
    In this paper, we are focused on deriving conclusions from sensor parameter data that would enable the detection of potential faults and the prediction of failures. We used Random Forest, Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, and Long Short-Term Memory models to predict faults for sensor data. This analysis, which predicts the failure, has been examined through the pump sensor dataset from Kaggle. It is a binary classification problem, and it performs time series analysis using historical pump sensor data to predict future observations and classify them into a positive label (normal) or a negative label (broken). The pump system must be in perfect condition to ensure continuous power supply. A failure of one of the pumps in the system can lead to a temporary drop in power generation and even a complete outage. This may be avoided if failures are predicted in advance. Therefore, it is important to anticipate failure early to avoid large financial losses. Predictive maintenance is beneficial for industries to prevent these faults and losses. Despite expectations, the Random Forest algorithm outperforms LSTM, followed by Decision Trees. Support Vector Machine and Naive Bayes algorithms show inferior performance compared to Random Forest and LSTM. © 2024 IEEE.
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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.
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    Conference Object
    A Systems Software Architecture for Training Neural, Fuzzy Neural and Genetic Computational Intelligent Networks
    (Institute of Electrical and Electronics Engineers Inc., 2006) Arsan,T.; 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.