Choupanı, Roya

Choupanı, Roya

Profile URL

https://hdl.handle.net/20.500.12416/9044

Name Variants

Choupani, R.
Choupany, R.
Choupani, Roya

Job Title

Dr. Öğr. Üyesi

Main Affiliation

Bilgisayar Mühendisliği

Status

Former Staff

Full item page

Sustainable Development Goals

13

CLIMATE ACTION

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH

0

Research Products

3

GOOD HEALTH AND WELL-BEING

2

Research Products

15

LIFE ON LAND

0

Research Products

17

PARTNERSHIPS FOR THE GOALS

0

Research Products

14

LIFE BELOW WATER

0

Research Products

4

QUALITY EDUCATION

0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES

0

Research Products

6

CLEAN WATER AND SANITATION

0

Research Products

10

REDUCED INEQUALITIES

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION

0

Research Products

2

ZERO HUNGER

1

Research Products

1

NO POVERTY

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY

0

Research Products

5

GENDER EQUALITY

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS

0

Research Products

This researcher does not have a Scopus ID.

This researcher does not have a WoS ID.

Scholarly Output

23

Articles

4

Views / Downloads

1842/938

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

23

Scopus Citation Count

45

WoS h-index

3

Scopus h-index

3

Patents

0

Projects

0

WoS Citations per Publication

1.00

Scopus Citations per Publication

1.96

Open Access Source

3

Supervised Theses

1

Google Analytics Visitor Traffic

Journal	Count
10th International Conference on Computer Vision Theory and Applications	1
1st International Conference on Advances in Multimedia -- JUL 20-25, 2009 -- Colmar, FRANCE	1
2015 10th International Conference on Information, Communications and Signal Processing (ICICS)	1
2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018 -- 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018 -- 10 April 2018 through 13 April 2018 -- Thessaloniki -- 137423	1
8th IEEE International Conference on Big Data (Big Data) -- DEC 10-13, 2020 -- ELECTR NETWORK	1

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Scopus Quartile Distribution

Competency Cloud

Scholarly Output Search Results

Now showing 1 - 10 of 23

Citation - Scopus: 1
Lung Inflammatory Classification of Diseases Using X-Ray Images
(Institute of Electrical and Electronics Engineers Inc., 2021) Mohanned, H.H.; Sürücü, S.; Choupani, R.
Recently, studies in inflammatory diseases categorization become of interest in the research community, especially with the sudden outbreak of the Covid-19 virus. Transfer learning proved to be the state-of-the-art when it comes to image classification problems, or related tasks. These methods achieve good results in this type of applications. Lately, this pre-trained embedding became even popular due to X-ray related studies for early Covid-19 diagnosis. In this study, we investigate the X-ray image classification problem using the transfer learning method. We fine-tuned and trained our model using pre-trained models such as AlexNet, VGG16, DenseNet etc, and a baseline deep neural network. We then evaluated this model in terms of classification evaluation metrics. The study shows that DenseNet achieves high accuracy compared to the other pre-trained and baseline CNN models. © 2021 IEEE
Optimized Multiple Description Coding for Temporal Video Scalability
(Springer Verlag, 2013) Choupani, R.; Wong, S.; Tolun, M.
The vast application of video streaming over the Internet requires video adaptation to the fluctuations of the available bandwidth, and the rendering capabilities of the receiver device. On the other hand, the available video coding standards are designed for optimum bit rate which makes them susceptible to packet losses. A combination of video adaptation methods and error resilient methods can make the video stream more robust against networking problems. In this paper, an optimization for combining scalable video coding with multiple description coding schemes have been proposed. Our proposed method is capable of creating balanced descriptions with optimum coding efficiency.
Citation - WoS: 2
Multiple Description Scalable Coding for Video Transmission Over Unreliable Networks
(Springer-verlag Berlin, 2009) Choupanı, Roya; Choupani, Roya; Tolun, Mehmet Reşit; Wong, Stephan; Tolun, Mehmet R.; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Developing real time multimedia applications for best effort networks such as the Internet requires prohibitions against jitter delay and frame loss. This problem is further complicated in wireless networks as the rate of frame corruption or loss is higher in wireless networks while they generally have lower data rates compared to wired networks. On the other hand, variations of the bandwidth and the receiving device characteristics require data rate adaptation capability of the coding method. Multiple Description Coding (MDC) methods are used to solve the jitter delay and frame loss problems by making the transmitted data more error resilient, however, this results in reduced data rate because of the added overhead. MDC methods do not address the bandwidth variation and receiver characteristics differences. In this paper a new method based on integrating MDC and the scalable video coding extension of H.264 standard is proposed. Our method can handle both jitter delay and frame loss, and data rate adaptation problems. Our method utilizes motion compensating scheme and, therefore, is compatible with the current video coding standards such as MPEG-4 and H.264. Based on the simulated network conditions, our method shows promising results and we have achieved tip to 36dB for average Y-PSNR.
Face Photograph Recognition via Generation from Sketches using Convolutional Neural Networks
(2019) Karasolak, Mustafa; Choupani, Roya
Face photo-sketch matching is an important problem for law enforcement agencies in terms of identifying suspects. In this study, a new sketch-photo generation and recognition technique is proposed by using residual convolutional neural network architecture. The suggested RCNN architecture consists of 6 convolutions, 6 ReLU, 4 poolings, 2 deconvolution layers. The proposed architecture is trained with face photos and sketches. Sketches are supplied as an input to the RCNN architecture and, generated face photos are obtained as the output. Then, the generated face photos are compared with the photos of the people in the database. Structural Similarity Index (SSIM) is used to measure the pairwise similarity and the photo with the highest index score is matched. CUHK Face Sketch Database containing 188 images is tested. In the experiments, 148, 20, and 20 images are used for training, validation, and testing, respectively. Data augmentation applied to 148 training images produced 444 images. Experimental results show that the success of the training curve is 90.55% and the validation success is 91.1%. True face recognition success from generated face images with SSIM is 93.89% for CUHK Face Sketch database (CUFS) and 84.55% AR database.
Client-server communication in remote control
(2002) Choupani, Roya
The client server communication model has been used in remote controlling of devices. The main feature in this study is that the Internet has been used as the common media to transmit controlling data and receive information. Client server model on the Internet restricts the access to client computers and has the disadvantage of unknown platform on the client side. This problem has been solved by means of platform independent programming and Java applets. Socket interface available in application layer of TCP/IP protocol suit has been used to establish reliable connection between clients and server. Security issues have been dealt with in the server side by checking the IP addresses of requesting clients.
Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error
(2015) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
In video coding, dependencies between frames are being exploited to achieve compression by only coding the differences. This dependency can potentially lead to decoding inaccuracies when there is a communication error, or a deliberate quality reduction due to reduced network or receiver capabilities. The dependency can start at the reference frame and progress through a chain of dependent frames within a group of pictures (GOP) resulting in the so-called drift error. Scalable video coding schemes should deal with such drift errors while maximizing the delivered video quality. In this paper, we present a multi-layer hierarchical structure for scalable video coding capable of reducing the drift error. Moreover, we propose an optimization to adaptively determine the quantization step size for the base and enhancement layers. In addition, we address the trade-off between the drift error and the coding efficiency. The improvements in terms of average PSNR values when one frame in a GOP is lost are 3.70(dB) when only the base layer is delivered, and 4.78(dB) when both the base and the enhancement layers are delivered. The improvements in presence of burst errors are 3.52(dB) when only the base layer is delivered, and 4.50(dB) when both base and enhancement layers are delivered.
Citation - WoS: 4
Citation - Scopus: 7
Multiple Description Coding for Snr Scalable Video Transmission Over Unreliable Networks
(Springer, 2014) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
Streaming multimedia data on best-effort networks such as the Internet requires measures against bandwidth fluctuations and frame loss. Multiple Description Coding (MDC) methods are used to overcome the jitter and delay problems arising from frame losses by making the transmitted data more error resilient. Meanwhile, varying characteristics of receiving devices require adaptation of video data. Data transmission in multiple descriptions provides the feasibility of receiving it partially and hence having a scalable and adaptive video. In this paper, a new method based on integrating MDC and signal-to-noise ratio (SNR) scalable video coding algorithms is proposed. Our method introduces a transform on data to permit transmitting them using independent descriptions. Our results indicate that on average 1.71dB reduction in terms of Y-PSNR occurs if only one description is received.
Citation - Scopus: 3
Scalable Video Transmission Over Unreliable Networks Using Multiple Description Wavelet Coding
(2011) Choupanı, Roya; Choupany, R.; Wong, S.; Tolun, M.; Bilgisayar Mühendisliği
Scalable video coding (SVC) and multiple description coding (MDC) are the two different adaptation schemes for video transmission over heterogenous and best-effort networks such as the Internet. We present a new approach to combine the advantages of SVC and MDC to provide reliable video communication over a wider range of communication networks and/or satisfy application requirements. Our proposed method utilizes 3D discrete wavelet transform and a modified embedded zero tree data structure to group the coefficients in different descriptions. The proposed method reduces the impact of the drift error by organizing the frames in a hierarchical structure. © 2011 AICIT.
Citation - Scopus: 1
Distributed Query Processing and Reasoning Over Linked Big Data
(Springer Science and Business Media Deutschland GmbH, 2022) Mohammed, H.H.; Doğdu, E.; Choupani, R.; Zarbega, T.S.A.
The enormous amount of structured and unstructured data on the web and the need to extract and derive useful knowledge from this big data make Semantic Web and Big Data Technology explorations of paramount importance. Open semantic web data created using standard protocols (RDF, RDFS, OWL) consists of billions of records in the form of data collections called “linked data”. With the ever-increasing linked big data on the Web, it is imperative to process this data with powerful and scalable techniques in distributed processing environments such as MapReduce. There are several distributed RDF processing systems, including SemaGrow, FedX, SPLENDID, PigSPARQL, SHARD, SPARQLGX, that are developed over the years. However, there is a need for computational and qualitative comparison of the differences and similarities among these systems. In this paper, we extend a previous comparative analysis to a diverse study with respect to qualitative and quantitative analysis views, through an experimental approach for these distributed RDF systems. We examine each of the selected RDF query systems with respect to the implementation setup, system architecture, underlying framework, and data storage. We use two widely used RDF benchmark datasets, FedBench and LUBM. Furthermore, we evaluate and examine their performances in terms of query execution time, thus, analyzing how those different types of large-scale distributed query engines, support long-running queries over federated data sources and the query processing times for different queries. The results of the experiments in this study show that SemaGrow distributed system performs more efficiently compared to FedX and Splendid, even though in smaller queries the former performs slower. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 7
Phishing E-Mail Detection by Using Deep Learning Algorithms
(Assoc Computing Machinery, 2018) Hassanpour, Reza; Dogdu, Erdogan; Choupani, Roya; Goker, Onur; Nazli, Nazli

Choupanı, Roya

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Name Variants

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Email Address

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ORCID ID

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Turkish CoHE Profile ID

Google Scholar ID

WoS Researcher ID

Sustainable Development Goals

13

CLIMATE ACTION

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH

0

Research Products

3

GOOD HEALTH AND WELL-BEING

2

Research Products

15

LIFE ON LAND

0

Research Products

17

PARTNERSHIPS FOR THE GOALS

0

Research Products

14

LIFE BELOW WATER

0

Research Products

4

QUALITY EDUCATION

0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES

0

Research Products

6

CLEAN WATER AND SANITATION

0

Research Products

10

REDUCED INEQUALITIES

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION

0

Research Products

2

ZERO HUNGER

1

Research Products

1

NO POVERTY

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY

0

Research Products

5

GENDER EQUALITY

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS