Arslan, Şefik Şuayb
Loading...
Profile URL
Name Variants
Arslan, Şuayb Şefik & Arslan Suayb S. & Arslan, Şuayb Ş. & Arslan , Şuayb Şefik
Job Title
Email Address
arslans@mef.edu.tr
Main Affiliation
02.02. Department of Computer Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Research Topics
Domains
Physical Sciences
Fields
Computer Science
Subfields
Computer Networks and CommunicationsInformation Systems
Specific Research Areas
Advanced Data Storage Technologies
Caching and Content Delivery
Error Correcting Code Techniques
Blockchain Technology Applications and Security
Cooperative Communication and Network Coding
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
0
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
0
Research Products
7AFFORDABLE AND CLEAN ENERGY
0
Research Products
8DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
10REDUCED INEQUALITIES
0
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
13CLIMATE ACTION
0
Research Products
14LIFE BELOW WATER
0
Research Products
15LIFE ON LAND
1
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
17PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| MEF University | 34 |
| Centre Tecnologic de Telecomunicacions de Catalunya | 27 |
| Massachusetts Institute of Technology | 24 |
| Boğaziçi University | 14 |
| Pamukkale University | 12 |
1 / 11
Data obtained from OpenAlex
Scholarly Output
43
Articles
17
Views / Downloads
2952/943
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
131
Scopus Citation Count
210
Patents
11
Projects
3
WoS Citations per Publication
3.05
Scopus Citations per Publication
4.88
Open Access Source
17
Supervised Theses
0
| Journal | Count |
|---|---|
| 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | 3 |
| 2021 29th Signal Processing and Communications Applications Conference (SIU) | 2 |
| 2022 7th International Conference on Computer Science and Engineering (UBMK) | 2 |
| IEEE International Symposium on Information Theory (ISIT) -- JUN 26-JUL 01, 2022 -- Espoo, FINLAND | 2 |
| Journal of Vision | 2 |
Current Page: 1 / 7
Scopus Quartile Distribution
Competency Cloud
43 results
Scholarly Output Search Results
Now showing 1 - 10 of 43
Master Term Project The Passanger Load Factor Prediction of Airline Transport(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Karakoç, Kalender; Arslan, Şuayb Ş.Turkish Airlines is one of the most preferred leading European air carriers with global network coverage thanks to its strict compliance with flight safety, reliability, product line, service quality and competitiveness. Turkish Airlines maintains its identity as the flag carrier of Turkey.Conference Object Citation - Scopus: 1Improved Bounds on the Moments of Guessing Cost(IEEE, 2022-06-26) Arslan, Suayb S.; Haytaoglu, ElifGuessing a random variable with finite or countably infinite support in which each selection leads to a positive cost value has recently been studied within the context of "guessing cost". In those studies, similar to standard guesswork, upper and lower bounds for the rho-th moment of guessing cost are described in terms of the known measure Renyi's entropy. In this study, we non-trivially improve the known bounds using previous techniques along with new notions such as balancing cost. We have demonstrated that the novel lower bound proposed in this work, achieves 5.84%, 18.47% higher values than that of the known lower bound for rho = 1 and rho = 5, respectively. As for the upper bound, the novel expression provides 10.93%, 5.54% lower values than that of the previously presented bounds for rho = 1 and rho = 5, respectively.Master Term Project Humpback Whale Indentification With Convolutional Neural Networks(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Can, Duygu; Arslan, Şuayb Ş.The migration patterns of humpback whales are tracked with conventional photoidentification techniques for decades. The distinct markings on whale flukes serve as unique fingerprints for these creatures. This study aims to identify humpback whales according to their fluke images using ResNET, a deep neural network architecture to help the conservation efforts for this endangered species by automatizing the process. We experimented with different train/test split schemes and initializations to obtain the best classifying model. Although we were limited with a small sized training set of 200 images, using state-of-the-art image processing and data augmentation methods we obtained a high accuracy of 0.94 for 11 distinct whales. This project is served as an friendly interface to dive deep into the field of image recognition with Convolutional Neural Networks.Article Minimum Repair Bandwidth Ldpc Codes for Distributed Storage Systems(IEEE, 2023) Pourmandi, Massoud; Pusane, Ali Emre; Arslan , Şuayb Şefik; Haytaoğlu, ElifIn distributed storage systems (DSS), an optimal code design must meet the requirements of efficient local data regeneration in addition to reliable data retention. Recently, lowdensity parity-check (LDPC) codes have been proposed as a promising candidate that can secure high data rates as well as low repair bandwidth while maintaining low complexity in data reconstruction. The main objective of this study is to optimize the repair bandwidth characteristics of LDPC code families for a DSS application while meeting the data reliability requirements. First, a data access scenario in which nodes contact other available nodes randomly to download data is examined. Later, a minimum-bandwidth protocol is considered in which nodes make their selections based on the degree numbers of check nodes. Through formulating optimization problems for both protocols, a fundamental trade-off between the decoding threshold and the repair bandwidth is established for a given code rate. Finally, conclusions are confirmed by numerical results showing that irregular constructions have a large potential for establishing optimized LDPC code families for DSS applications.Conference Object Citation - WoS: 5Citation - Scopus: 7Cloud2hdd: Large-Scale Hdd Data Analysis on Cloud for Cloud Datacenters(IEEE, 2020-02-01) Zeydan, Engin; Arslan, Şefik ŞuaybThe main focus of this paper is to develop a distributed large scale data analysis platform for the opensource data of Backblaze cloud datacenter which consists of operational hard disk drive (HDD) information collected over an observable period of 2272 days (over 74 months). To carefully analyze the intrinsic characteristics of the hard disk behavior, we have exploited a large bolume of data and the benefits of Hadoop ecosystem as our big data processing engine. In other words, we have utilized a special distributed scheme on cloud for cloud HDD data, which is termed as Cloud2HDD. To classify the remaining lifetime of hard disk drives based on health indicators such as in-built S.M.A.R.T (Self-Monitoring, Analysis, and Reporting Technology) features, we used some of the state-of-the-art classification algorithms and compared their accuracy, precision, and recall rates simultaneously. In addition, importance of various S.M.A.R.T. features in predicting the true remaining lifetime of HDDs are identified. For instance, our analysis results indicate that Random Forest Classifier (RFC) can yield up to 94% accuracy with the highest precision and recall at a reasonable time by classifying the remaining lifetime of drives into one of three different classes, namely critical, high and low ideal states in comparison to other classification approaches based on a specific subset of S.M.A.R.T. features.Conference Object Citation - WoS: 14Citation - Scopus: 42An Overview of Blockchain Technologies: Principles, Opportunities and Challenges(IEEE, 2018-05-01) Arslan, Şuayb Şefik; Mermer, Gültekin Berahan; Zeydan, EnginBlokzincir, toplumumuzun birbiriyle iletişim kurma ve ticaret yapma biçiminde devrim yapma potansiyeline sahip, yakın zamanda ortaya çıkmış olan bir teknolojidir. Bu teknolojinin sağladığı en önemli avantaj aracı gerektiren bir oluşumda güvenilir bir merkezi kuruma ihtiyaç duymadan değer taşıyan işlemleri değiş tokuş edebilmesidir. Ayrıca, veri bütünlüğü, dahili orijinallik ve kullanıcı şeffaflığı sağlayabilir. Blokzincir, birçok yenilikçi uygulamanın temel alınacağı yeni internet olarak görülebilir. Bu çalışmada, genel çalışma prensibi, oluşan fırsatlar ve ileride karşılaşılabilecek zorlukları içerecek şekilde güncel blokzincir teknolojilerinin genel bir görünümünü sunmaktayız.Conference Object Citation - WoS: 1Citation - Scopus: 1Adaptive Boosting of Dnn Ensembles for Brain-Computer Interface Spellers(IEEE, 2021-06-09) Çatak, Yiğit; Aksoy, Can; Özkan, Hüseyin; Güney, Osman Berke; Koç, Emirhan; Arslan, Şuayb ŞefikSteady-state visual evoked potentials (SSVEP) are commonly used in brain computer interface (BCI) applications such as spelling systems, due to their advantages over other paradigms. In this study, we develop a method for SSVEP-based BCI speller systems, using a known deep neural network (DNN), which includes transfer and ensemble learning techniques. We test performance of our method on publicly available benchmark and BETA datasets with leave-one-subject-out procedure. Our method consists of two stages. In the first stage, a global DNN is trained using data from all subjects except one subject that is excluded for testing. In the second stage, the global model is fine-tuned to each subject whose data are used in the training. Combining the responses of trained DNNs with different weights for each test subject, rather than an equal weight, provide better performance as brain signals may differ significantly between individuals. To this end, weights of DNNs are learnt with SAMME algorithm with using data belonging to the test subject. Our method significantly outperforms canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods.Article Citation - WoS: 6Citation - Scopus: 7A Reliability Model for Dependent and Distributed Mds Disk Array Units(IEEE Transactions on Reliability, 2019-03-01) Arslan, Şuayb ŞefikArchiving and systematic backup of large digital data generates a quick demand for multi-petabyte scale storage systems. As drive capacities continue to grow beyond the few terabytes range to address the demands of today’s cloud, the likelihood of having multiple/simultaneous disk failures became a reality. Among the main factors causing catastrophic system failures, correlated disk failures and the network bandwidth are reported to be the two common source of performance degradation. The emerging trend is to use efficient/sophisticated erasure codes (EC) equipped with multiple parities and efficient repairs in order to meet the reliability/bandwidth requirements. It is known that mean time to failure and repair rates reported by the disk manufacturers cannot capture life-cycle patterns of distributed storage systems. In this study, we develop failure models based on generalized Markov chains that can accurately capture correlated performance degradations with multiparity protection schemes based on modern maximum distance separable EC. Furthermore, we use the proposed model in a distributed storage scenario to quantify two example use cases: Primarily, the common sense that adding more parity disks are only meaningful if we have a decent decorrelation between the failure domains of storage systems and the reliability of generic multiple single-dimensional EC protected storage systems.Conference Object Citation - Scopus: 2A Visualization Platfom for Disk Failure Analysis(IEEE, 2018-05-01) Arslan, Şuayb Şefik; Yiğit, İbrahim Onuralp; Zeydan, EnginIt has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.Article Citation - WoS: 2Citation - Scopus: 2TALICS3 : Tape library cloud storage system simulator(Elsevier, 2024-07-01) Peng, James; Arslan, Şuayb Şefik; Göker, TurguyHigh performance computing data is surging fast into the exabyte-scale world, where tape libraries are the main platform for long-term durable data storage besides high -cost DNA. Tape libraries are extremely hard to model, but accurate modeling is critical for system administrators to obtain valid performance estimates for their designs. This research introduces a discrete- event tape simulation platform that realistically models tape library behavior in a networked cloud environment, by incorporating real -world phenomena and effects. The platform addresses several challenges, including precise estimation of data access latency, rates of robot exchange, data collocation, deduplication/compression ratio, and attainment of durability goals through replication or erasure coding. Using the proposed simulator, one can compare the single enterprise configuration with multiple commodity library configurations, making it a useful tool for system administrators and reliability engineers. This makes the simulator a valuable tool for system administrators and reliability engineers, enabling them to acquire practical and dependable performance estimates for their enduring, cost-efficient cold data storage architecture designs.