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Choupanı, Roya

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Profile URL
https://hdl.handle.net/20.500.12416/9044
Name Variants
Choupani, R.
Choupany, R.
Choupani, Roya
Job Title
Dr. Öğr. Üyesi
Email Address
Main Affiliation
Bilgisayar Mühendisliği
Status
Former Staff
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WoS Researcher ID

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
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0

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3

GOOD HEALTH AND WELL-BEING
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2

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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0

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6

CLEAN WATER AND SANITATION
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0

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14

LIFE BELOW WATER
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0

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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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1

NO POVERTY
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4

QUALITY EDUCATION
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5

GENDER EQUALITY
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10

REDUCED INEQUALITIES
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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15

LIFE ON LAND
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7

AFFORDABLE AND CLEAN ENERGY
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13

CLIMATE ACTION
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17

PARTNERSHIPS FOR THE GOALS
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2

ZERO HUNGER
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1

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This researcher does not have a Scopus ID.
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Scholarly Output

23

Articles

4

Views / Downloads

1853/979

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

24

Scopus Citation Count

45

WoS h-index

3

Scopus h-index

3

Patents

0

Projects

0

WoS Citations per Publication

1.04

Scopus Citations per Publication

1.96

Open Access Source

3

Supervised Theses

1

JournalCount
10th International Conference on Computer Vision Theory and Applications1
1st International Conference on Advances in Multimedia -- JUL 20-25, 2009 -- Colmar, FRANCE1
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 -- 1374231
8th IEEE International Conference on Big Data (Big Data) -- DEC 10-13, 2020 -- ELECTR NETWORK1
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Now showing 1 - 10 of 23
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    Conference Object
    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
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    Master Thesis
    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.
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    Conference Object
    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.
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    Conference Object
    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
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    Conference Object
    Main Issues in Scalable Video Coding: a Review
    (2007) Choupanı, Roya; Choupani, R.; Tolun, M.R.; Tolun, Mehmet Reşit; Bilgisayar Mühendisliği; Yazılım Mühendisliği
    Video streaming over the Internet has gained popularity during the recent years which is mainly the result of the introduction of videoconferencing and videotele-phony. These in turn have made it possible to bring to life many applications such as transmitting video over the Internet and over telephone lines, surveillance and monitoring, telemedicine (medical consultation and diagnosis at a distance), and computer based training and education. The heterogeneous, dynamic and best-effort structure of the Internet however, can not guarantee any specific bandwidth for a connection. Many video coding standards have tried to deal with this problem by introducing the scalability feature as adapting video streams to the fluctuations in the available bandwidths. In this review, we have discussed the main technical features of more common scalable video coding techniques. The main problems of these methods and their applicability together with the available motion compensated video coding methods are discussed as well.
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    Article
    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.
  • Thumbnail Image
    Conference Object
    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.
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    Conference Object
    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.
  • Thumbnail Image
    Conference Object
    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.
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    Conference Object
    Citation - WoS: 10
    Citation - Scopus: 21
    Multi-Label Classification of Text Documents Using Deep Learning
    (Ieee, 2020) Mohammed, Hamza Haruna; Dogdu, Erdogan; Gorur, Abdul Kadir; Choupani, Roya
    Recently, studies in the field of Natural Language Processing and its related applications continue to mount up. Machine learning is proven to be predominantly data-driven in the sense that generic model building methods are used and then tailored to specific application domains. Needless to say, this has proven to be a very effective approach in modeling the complicated data dependencies we frequently experience in practice, making very few assumptions, and allowing the information to talk for themselves. Examples of these applications can be found in chemical process engineering, climate science, healthcare, and linguistic processing systems for natural languages, to name a few. Text classification is one of the important machine learning tasks that is used in many digital applications today; such as in document filtering, search engines, document management systems, and many more. Text classification is the process of categorizing of text documents into a given set of labels. Furthermore, multi-label text classification is the task of categorization of text documents into one or more labels simultaneously. Over the years, many methods for classifying text documents have been proposed, including the popularly known bag of words (BoW) method, support vector machine (SVM), tree induction, and label-vector embedding, to mention a few. These kinds of tools can be used in many digital applications, such as document filtering, search engines, document management systems, etc. Lately, deep learning-based approaches are getting more attention, especially in extreme multi-label text classification case. Deep learning has proven to be one of the major solutions to many machine learning applications, especially those involving high-dimensional and unstructured data. However, it is of paramount importance in many applications to be able to reason accurately about the uncertainties associated with the predictions of the models. In this paper, we explore and compare the recent deep learning-based methods for multi-label text classification. We investigate two scenarios. First, multi-label classification model with ordinary embedding layer, and second with Glove, word2vec, and FastText as pre-trained embedding corpus for the given models. We evaluated these different neural network model performances in terms of multi-label evaluation metrics for the two approaches, and compare the results with the previous studies.