Arsan, Taner
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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.
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
Main Affiliation
Computer Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
3
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
5
Research Products
15
LIFE ON LAND
1
Research Products
17
PARTNERSHIPS FOR THE GOALS
3
Research Products
Documents
52
Citations
415
h-index
9
Documents
27
Citations
288
Scholarly Output
73
Articles
22
Views / Downloads
518/8101
Supervised MSc Theses
15
Supervised PhD Theses
0
WoS Citation Count
218
Scopus Citation Count
330
WoS h-index
7
Scopus h-index
8
Patents
0
Projects
0
WoS Citations per Publication
2.99
Scopus Citations per Publication
4.52
Open Access Source
30
Supervised Theses
15
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| Journal | Count |
|---|---|
| 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 -- 194153 | 4 |
| 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 -- 204562 | 3 |
| 2023 31st Signal Processing and Communications Applications Conference, Siu | 2 |
| IEEE Access | 2 |
| Computers & Electrical Engineering | 2 |
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Now showing 1 - 10 of 73
Master Thesis Indoor positioning system development(Kadir Has Üniversitesi, 2015) Alp, Ebru; Dağ, Tamer; Arsan, TanerNowadays, 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.Conference Object Citation - Scopus: 4Predictive 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.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.Conference Object Citation - Scopus: 4A Software Architecture for Inventory Management System(2013) Arsan, Taner; Başkan, Emrah; Ar, Emrah; Bozkuş, ZekiInventory Management is one of the basic problems in almost every company. Before computer age and integration paper tables and paperwork solutions were being used as inventory management tools. These we very far from being a solution took so much time even needed employees just for this section of organization. There was no an efficient solution available in the many companies during these days. Every process was based on paperwork human fault rate was high the process and the tracing the inventory losses were not possible and there was no efficient logging systems. After the computer age every process is started to be integrated into electronic environment. And now we have qualified technology to implement new solutions to these problems. Software based systems bring the advantages of having the most efficient control with less effort and employees. These developments provide new solutions for also inventory management systems in this context. In this paper a new solution for Inventory Management System (IMS) is designed and implemented. Most importantly this system is designed for Kadir Has University and used as Inventory Management System. © 2013 Springer Science+Business Media.Conference Object Citation - Scopus: 1Capacity Planning for Electricity Utility Call Centers: a Time Series Analysis Approach(Institute of Electrical and Electronics Engineers Inc., 2024) Kavas, E.; Alsan, H.F.; Arsan, T.Electric power systems are crucial for modern society, yet their reliability can be challenged by unforeseen disruptions, causing electricity supply disruptions. Call centers are essential for managing customer inquiries during such outages, acting as communication hubs for electricity utility companies. Effective capacity planning is vital for these call centers to maintain efficient operations and meet customer demands promptly. Proper workforce management ensures that enough skilled agents can handle calls effectively and maintain high service quality. Capacity planning begins with analyzing historical data to understand call volumes, patterns, and peak times. This data analysis identifies trends and factors influencing call patterns, enabling accurate forecasting of future demand and optimizing staffing levels. This paper provides a comprehensive overview of quantitative forecasting methods, focusing on Time Series Analysis applied to a dataset from a Turkish electric utility company that exhibits typical seasonal fluctuations. Specifically, the study examines the performance of AutoRegressive Integrated Moving Average and Seasonal AutoRegressive Integrated Moving Average models. Results indicate that both models perform well, with the Seasonal AutoRegressive Integrated Moving Average model demonstrating slightly superior performance compared to the AutoRegressive Integrated Moving Average model. This suggests that the Seasonal AutoRegressive Integrated Moving Average model may be more suitable for forecasting inbound calls at electricity utility call centers. This paper's detailed analysis and methodology offer valuable insights for optimizing operational efficiency, reducing costs, and enhancing customer satisfaction in dynamic and challenging operational scenarios. © 2024 IEEE.Article New Generation Android Operating System-Basedmobile Application: Rss/News Reader(Springer Verlag, 2015) Arsan, Taner; Erşahin, Mehmet Arif; Alp, EbruRSS (Rich Site Summary)/News Reader is a web-based Android OS application developed by using PhoneGap framework. HTML5 CSS and JavaScript are basically used for implementation instead of native Android programming language. This application has a production process like a web application because it is actually a fully working web program which is wrapped by PhoneGap framework. This means the application could be used on almost every mobile platform with making some basic arrangements.RSS/News Reader mobile application takes advantage of both flexibility of web design and built-in features of the device it is installed. This combination provides a complete mobile application which eliminates the need to use different native languages with its hybrid form. This hybrid structure makes mobile programming faster and easier to implement.In this new generation operating system-based mobile application a combination of PhoneGap framework HTML5 CSS3 JavaScript jQuery Mobile Python and Django is used for implementation. © Springer International Publishing Switzerland 2015.Master Thesis Bandwidth Allocation and Traffic Shaping in Mobile Broadband Networks Using Deep Packet Inspection(Kadir Has Üniversitesi, 2015) Özbilen, Ramazan; Arsan, TanerIn this thesis, it is intended to estimate bandwidth and control mobile data usage by utilizing PCC (Policy and Charging Control) function. According to increase in number of mobile devices, data explosion occurs. It is becoming a must to analyze traffic and sharing resources between subscribers according to their usage habits. It is aimed to provide better a better connected world with service assurance by sharing available bandwidth and estimate it to users according to their needs by protocol level and service based QoS. Due to increase in amount of services like Facebook, Twitter, Mobile TV, in general IP networks, providing service assurance becomes more important day by day. That's why the issue of controlling bandwidth is raised. In the most basic sense, system architecture consists of three main components: A cell phone to generate user based traffic, Gateway GPRS Support Node (GGSN) for Deep Packet Inspection (DPI), and Policy and Charging Rule Function (PCRF) for initiating PCC or Non-PCC rules to GGSN according to services that are needed by user. Shortly, the main idea in this thesis is assigning service based QoS to subscribers to provide better service assurance according to their usage. As thought, the reason of preparing this study is to show the dramatical increase in service based traffic, to explain insufficiency in current bandwidth estimation approaches, and the idea of what can be used in the work of providing better service assurance to an end user. PCRF is the best component for providing required bandwidth when they need.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.Conference Object Citation - Scopus: 1Network 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.Article Career Center System Software Architecture(Springer Verlag, 2015) Arsan, Taner; Çimenli, Safa; Güneş, ErhanIn 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.
