Aydın, Zafer
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Aydin, Zafer
Aydın, Zafer
Aydın, Zafer
Job Title
Doç. Dr.
Email Address
zafer.aydin@agu.edu.tr
Main Affiliation
02. 04. Bilgisayar Mühendisliği
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
13
CLIMATE ACTION
0
Research Products
15
LIFE ON LAND
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
2
ZERO HUNGER
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
14
LIFE BELOW WATER
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
5
GENDER EQUALITY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
4
QUALITY EDUCATION
0
Research Products
1
NO POVERTY
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
3
GOOD HEALTH AND WELL-BEING
12
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
Documents
57
Citations
684
h-index
14
Documents
55
Citations
510
Scholarly Output
67
Articles
34
Views / Downloads
2/5
Supervised MSc Theses
8
Supervised PhD Theses
6
WoS Citation Count
221
Scopus Citation Count
411
WoS h-index
10
Scopus h-index
13
Patents
0
Projects
1
WoS Citations per Publication
3.30
Scopus Citations per Publication
6.13
Open Access Source
33
Supervised Theses
14
| Journal | Count |
|---|---|
| Turkish Journal of Electrical Engineering and Computer Sciences | 3 |
| 9th International Conference on Computational Intelligence and Communication Networks (CICN) -- SEP 16-17, 2017 -- Final Int Univ, Girne, CYPRUS | 2 |
| -- 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- Izmir; Altin Yunus Resort ve Thermal Hotel -- 137780 | 2 |
| IEEE-Acm Transactions on Computational Biology and Bioinformatics | 2 |
| Computers in Biology and Medicine | 2 |
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67 results
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Now showing 1 - 10 of 67
Article YSA Kullanılarak Mamogramlardan Dokusal Öznitelik Tabanlı Meme Kanseri İlgi Bölgesi Sınıflandırılması(2020) Taşdemir, Sena Büşra Yengeç; Tasdemir, Kasim; Aydin, ZaferRadyoloji uzmanlarının mamografi görüntülerine bakarak yaptığı meme kanseriteşhislerinde tip bir hata oranı yüzde otuzlara kadar çıkmaktadır. Kanserin teşhisbaşarısını artırmak adına bu çalışmada uzmanlara yardımcı olacak yeni birBilgisayar Yardımlı Teşhis sistemi, kanserli ve normal dokuyu ayırt etmek içinönerilmektedir. Önerilen sistemde kontrast limitli histogram eşitleme (CLAHE)yöntemiyle iyileştirilen görüntülerin iki boyutlu parçacık dönüşümlerinden (2B–DWT) Haralick ve HOG öznitelikleri çıkarılmıştır. Özniteliklerin sayısını azaltmasıiçin temel bileşenler analizi (PCA) algoritması kullanılmıştır. Seçilen öznitelikler çokkatmanlı algılayıcı (MLP) mimari yapısına sahip yapay sinir ağına (YSA) girdi olarakverilmiştir. Çok katmanlı algılayıcı üzerinde Adam eniyileme yapıldığında %81tespit doğruluğu yakalanmıştır. Ayrıca, diğer bir çok temel makine öğrenmesi vederin öğrenme yöntemleri denenerek karşılaştırma sonuçları detaylı olaraksunulmuştur. Sınırlı sayıda veri kümesi kullanıldığında transfer öğrenim kullanılsadahi derin öğrenme yöntemlerinin tespit başarısı azalmıştır. Buna karşılık doğru önişleme, öznitelik seçilimi ve makine öğrenmesi yaklaşımları kullanıldığı zamangeleneksel bilgisayarlı görü yöntemleri daha başarılı sonuçlar vermiştirConference Object Citation - Scopus: 3Ceph-Based Storage Server Application(Institute of Electrical and Electronics Engineers Inc., 2018) Azgınoglu, Nuh; Eren, Mehmet Akif; Celik, Mete; Aydin, ZaferCeph is a scalable and high performance distributed file system. In this study, a Ceph-based storage server was implemented and used actively. This storage system has been used as a disk of 40 virtual servers in 4 different Proxmox servers. Performance evaluation of the system has been conducted on virtual servers that holds Windows and Linux based operating systems. © 2018 Elsevier B.V., All rights reserved.Article Citation - WoS: 10Citation - Scopus: 13A Noise-Aware Feature Selection Approach for Classification(Springer, 2021) Sabzekar, Mostafa; Aydin, ZaferA noise-aware version of support vector machines is utilized for feature selection in this paper. Combining this method and sequential backward search (SBS), a new algorithm for removing irrelevant features is proposed. Although feature selection methods in the literature which utilize support vector machines have provided acceptable results, noisy samples and outliers may affect the performance of SVM and feature selections method, consequently. Recently, we have proposed relaxed constrains SVM (RSVM) which handles noisy data and outliers. Each training sample in RSVM is associated with a degree of importance utilizing the fuzzy c-means clustering method. Therefore, a less importance degree is assigned to noisy data and outliers. Moreover, RSVM has more relaxed constraints that can reduce the effect of noisy samples. Feature selection increases the accuracy of different machine learning applications by eliminating noisy and irrelevant features. In the proposed RSVM-SBS feature selection algorithm, noisy data have small effect on eliminating irrelevant features. Experimental results using real-world data verify that RSVM-SBS has better results in comparison with other feature selection approaches utilizing support vector machines.Article Citation - WoS: 13Citation - Scopus: 16A Deep Ensemble Approach for Long-Term Traffic Flow Prediction(Springer Heidelberg, 2024) Cini, Nevin; Aydin, ZaferIn the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long-term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short-term memory (LSTM) network and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Experimental results show that the developed DEM model has a mean square error of 0.06 and a mean absolute error of 0.15 for single-step prediction; it shows that achieves a mean square error of 0.25 and a mean absolute error of 0.32 for multi-step prediction. We compared our proposed model with many models in different categories; individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression, decision tree regression, k-nearest-neighbors regression) and other ensemble models such as random-forest regression. These results also support the claim that ensemble learning models perform better than individual models.Article Citation - WoS: 7Citation - Scopus: 11Protein Β-Sheet Prediction Using an Efficient Dynamic Programming Algorithm(Elsevier Sci Ltd, 2017) Sabzekar, Mostafa; Naghibzadeh, Mahmoud; Eghdami, Mandie; Aydin, ZaferPredicting the beta-sheet structure of a protein is one of the most important intermediate steps towards the identification of its tertiary structure. However, it is regarded as the primary bottleneck due to the presence of non-local interactions between several discontinuous regions in beta-sheets. To achieve reliable long-range interactions, a promising approach is to enumerate and rank all beta-sheet conformations for a given protein and find the one with the highest score. The problem with this solution is that the search space of the problem grows exponentially with respect to the number of beta-strands. Additionally, brute force calculation in this conformational space leads to dealing with a combinatorial explosion problem with intractable computational complexity. The main contribution of this paper is to generate and search the space of the problem efficiently to reduce the time complexity of the problem. To achieve this, two tree structures, called sheet-tree and grouping-tree, are proposed. They model the search space by breaking it into sub-problems. Then, an advanced dynamic programming is proposed that stores the intermediate results, avoids repetitive calculation by repeatedly uses them efficiently in successive steps and reduces the space of the problem by removing those intermediate results that will no longer be required in later steps. As a consequence, the following contributions have been made. Firstly, more accurate beta-sheet structures are found by searching all possible conformations, and secondly, the time complexity of the problem is reduced by searching the space of the problem efficiently which makes the proposed method applicable to predict beta-sheet structures with high number of beta-strands. Experimental results on the BetaSheet916 dataset showed significant improvements of the proposed method in both execution time and the prediction accuracy in comparison with the state-of-the-art beta-sheet structure prediction methods Moreover, we investigate the effect of different contact map predictors on the performance of the proposed method using BetaSheet1452 dataset. The source code is available at http://www.conceptsgate.com/BetaTop.rar. (C) 2017 Elsevier Ltd. All rights reserved.Article New Modeling of Reconfigurable Microstrip Antenna Using Hybrid Structure of Simulation Driven and Knowledge Based Artificial Neural Networks(Pamukkale Univ, 2020) Aoad, Ashrf; Aydin, ZaferKnowledge-based modeling has a critical role to embed existing knowledge to improve modeling performance. Since reconfigurable antenna can provide more operational frequencies than the classical antennas, a knowledge-based hybrid structure is used in this work to obtain efficient model and producing optimum new models for a reconfigurable microstrip antenna. The hybrid structure consists of two phases. The first phase generates initial knowledge which is used in knowledge-based modeling structure to obtain design parameters. Artificial neural network based multilayer perceptron can generate necessary knowledge for a knowledge-based model after the training process. Knowledge-based modeling improves the accuracy of the initial model to determine design parameters corresponding to the design target. Source difference, prior knowledge Input and prior knowledge input with difference can be applied to realize an efficient knowledge-based strategy. 3D-EM simulation generates the new model in terms of the design parameters of the proposed application. It has three switching states for operating, which are organized by two resistor circuits representing ON/OFF states. Switch positions and geometrical parameters can be used for satisfying design targets between 1 GHz and 6 GHz for the efficient antenna design.Article Citation - WoS: 13Citation - Scopus: 19A Deep Learning Approach With Bayesian Optimization and Ensemble Classifiers for Detecting Denial of Service Attacks(Wiley, 2020) Gormez, Yasin; Aydin, Zafer; Karademir, Ramazan; Gungor, Vehbi C.Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high-profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder-based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi-class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.Article Citation - WoS: 13Citation - Scopus: 13A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization(Amer Medical Assoc, 2021) Yan, Yao; Schaffter, Thomas; Bergquist, Timothy; Yu, Thomas; Prosser, Justin; Aydin, Zafer; Mooney, SeanIMPORTANCE Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. OBJECTIVES To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. DESIGN, SETTING, AND PARTICIPANTS This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. MAIN OUTCOMES AND MEASURES Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. RESULTS In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. CONCLUSIONS AND RELEVANCE In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.Master Thesis Bilgisayar Ağlarında Anormal Durum Tespiti Yapan Öğrenme Yöntemlerinin Geliştirilmesi(Abdullah Gül Üniversitesi, 2018) MUKHANDI, HABIBU SHOMARI; Mukhandi, Habibu Shomari; Aydın, ZaferMakine öğrenmesi, verilerdeki bilginin bir bilgisayar ya da makina tarafından otomatik olarak öğrenilmesi ve karşılaşılan yeni durumlarda anlamlı bilgi ya da davranışların üretilmesini amaçlar. Bir çok uygulama alanı bulunan makine öğrenmesi daha önce hiç karşılaşılmamış olan sıradışı durumların tespit edilmesi için de kullanılmaktadır. Bilgisayar ağlarındaki siber saldırılar, kredi kartı dolandırıcılığı ve internet sitelerinin linklerine yapılan çok sayıda sahte tıklamalar dünya genelinde ekonomileri ciddi oranda zarara uğratabilecek niteliktedir. Bu tezde üç farklı anormal durum tespiti problemi üzerinde çalışılmıştır: bilgisayar ağlarında saldırı tespiti, kredi kartı dolandırıcılığı tespiti ve internet sitelerdeki linklere sahte tıklama tespiti. Anormal durum tespiti için geliştirilen ve optimize edilen modeller arasında rastgele orman, en yakın komşu, destek vektör makinası, logistic regresyon, karar ağacı, AdaBoost, çantalama ve yığınlama gibi sınıflandırma yöntemleri bulunmaktadır. Yöntemlerin hiper-parametreleri eğitim kümelerinde yapılan çapraz doğrulama deneyleri ile optimize edilmiştir. Bir sonraki aşamada optimum hiper-parametre konfigürasyonları kullanılarak eğitilen modeler ile test verilerinde tahmin sonuçları hesaplanmıştır. Bu deneyler neticesinde genel doğruluk oranı ve F-measure skorlarında yüksek başarı elde edilmiştir. Geliştirilen yöntemler arasında en başarılı sonuçlar topluluk modelleri ile elde edilmiştir.Research Project RNA İkincil Yapılarının Çok Boyutlu Gösterimi ve Pre-Mirna Tespiti Için Uygulamaları(TUBİTAK, 2021) Saçar Demirci, Müşerref Duygu; Demirci, Yilmaz MehmetMikroRNA'lar (miRNA'lar), transkripsiyon sonrası gen ekspresyonu düzenleyicileridir. Bir_x000D_ miRNA yüzlerce haberci RNA'yı (mRNA'lar) hedefleyebildiği gibi, bir mRNA farklı miRNA'lar_x000D_ tarafından hedeflenebilir, üstelik tek bir miRNA bir mRNA sekansında çeşitli bağlanma_x000D_ bölgelerine sahip olabilir. Bu nedenle miRNA'ları deneysel olarak araştırmak oldukça_x000D_ karmaşıktır. Bu tür zorlukları aşabilmek için makine öğrenimi (ML) sıklıkla kullanılmaktadır._x000D_ ML analizinin temel kısımları büyük ölçüde giriş verilerinin kalitesine ve verileri tanımlayan_x000D_ özelliklerin kapasitesine bağlıdır. Daha önce miRNA'lar için 1000'den fazla özellik önerilmişti._x000D_ Bu projede, RNA ikincil yapısını temsil eden yeni özellikler ve yüksek doğruluk değerleri_x000D_ sağlayan, dinamik, çok boyutlu grafik gösterimini tanımlamayı hedeflemiştik. Bu çalışmada,_x000D_ ML tabanlı miRNA tahmini için yeni ve kolayca güncellenebilir bir yaklaşım geliştirilmiştir._x000D_ Bilinen insan miRNA'larının ve sözde saç tokalarının random forest (RF), support vector_x000D_ machine (SVM) ve multilayer perceptron (MLP) gibi çeşitli sınıflandırıcılarla_x000D_ sınıflandırılmasıyla binlerce model oluşturulmuştur. Yöntem insan verilerine dayanarak_x000D_ oluşturulmuş olsa da en iyi model miRBase ve MirGeneDB gibi kamu veri tabanlarından_x000D_ insan olmayan saç tokaları üzerinde test edilmiş ve yüksek skorlar üretilmiştir. Ayrıca,_x000D_ yöntemin farklı veriler üzerindeki etkinliğini göstermek için ekspresyon farkları tahmini_x000D_ (differential expression prediction) analizinde de kullanılmıştır. Bu aşamada SARS-CoV-2_x000D_ enfeksiyonunun etkisini ölçen bir veri setinin analizinden elde edilen sonuçlar yayınlanmıştır.
