Yazılım Mühendisliği Bölümü
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Article Citation - WoS: 5Citation - Scopus: 8Predicting the Severity of Covid-19 Patients Using a Multi-Threaded Evolutionary Feature Selection Algorithm(Wiley, 2022) Kiziloz, Hakan Ezgi; Sevinc, Ender; Dokeroglu, Tansel; Deniz, AycaThe COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.Conference Object Detection of Stylometric Writeprint From the Turkish Texts(Ieee, 2020) Canbay, Pelin; Sever, Hayri; Sezer, Ebru Akcapinar; Sever, Hayri; Bilgisayar MühendisliğiAuthorship attribution studies aim to extract information about the author by analyzing the data in the text form. With the increase of anonymous authors in digital environments, the need for these works is increasing day by day. Although there exists lots of studies focuse on stylometric writeprint detection in different languages using different attributes, there is no standard feature set and detection algorithm to be evaluated in these studies. Giving priority to Turkish texts, in this study, which features are more distinctive for determining stylistic writeprint of text, and which methods will contribute to increase the success to be achieved are shown with experimental studies.Article Big Data Reduction and Visualization Using the K-Means Algorithm(2022) Akyol, Hakan; Kızılduman, Hale Sema; Dökeroğlu, TanselA huge amount of data is being produced every day in our era. In addition to high-performance processing approaches, efficiently visualizing this quantity of data (up to Terabytes) remains a major difficulty. In this study, we use the well-known clustering method K-means as a data reduction strategy that keeps the visual quality of the provided huge data as high as possible. The centroids of the dataset are used to display the distribution properties of data in a straightforward manner. Our data comes from a recent Kaggle big data set (Click Through Rate), and it is displayed using Box plots on reduced datasets, compared to the original plots. It is discovered that K-means is an effective strategy for reducing the amount of huge data in order to view the original data without sacrificing its distribution information qualityConference Object Citation - Scopus: 1Component-Based Project Estimation Issues for Recursive Development(Springer, 2008) Altunel, Yusuf; Tolun, Mehmet R.In this paper we investigated the component-based specific issues that might affect project cost estimation. Component-based software development changes the style of software production. With component-based approach the software is developed as the composition of reusable software components. Each component production process must be treated as a stand-alone software project, which needs individual task of management. A typical pure component-based development can be considered as decomposition/integration activities successively applied at different levels and therefore results in recursive style of development. We analyzed and presented our results of studies on the component-based software development estimation issues from recursive point of view.Article Citation - WoS: 2Citation - Scopus: 4An Intelligent System for Detecting Mediterranean Fruit Fly [Medfly; Ceratitis Capitata (Wiedemann)](Pagepress Publ, 2022) Eyyuboglu, Halil Tanyer; Sari, Filiz; Uzun, Yusuf; Tolun, Mehmet ResitNowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.Conference Object Yeşil BHT Bilgi ve Haberleşme Teknolojileri Akademisyen ve Uygulayıcılar Açısından Bir İnceleme(2011) Akba, Fırat; Medeni, İhsan Tolga; Medeni, Tunç Durmuş; Tolun, Mehmet Reşit; Öztürk, MehmetArticle Citation - WoS: 227Citation - Scopus: 291A Comprehensive Survey on Recent Metaheuristics for Feature Selection(Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan EzgiFeature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.Article Citation - WoS: 20Citation - Scopus: 29Creating Consensus Group Using Online Learning Based Reputation in Blockchain Networks(Elsevier, 2019) Ozsoy, Adnan; Oztaner, Serdar Murat; Sever, Hayri; Bugday, AhmetOne of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group. (C) 2019 Elsevier B.V. All rights reserved.Article Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu(2019) Oyucu, Saadin; Polat, Huseyin; Sever, Hayriİnsanlar arasındaki en önemli iletişim yöntemi olan konuşmanın, bilgisayarlar tarafından tanınması önemli bir çalışma alanıdır. Bu araştırma alanında farklı diller temel alınarak birçok çalışma gerçekleştirilmiştir. Literatürdeki çalışmalar konuşma tanıma teknolojilerinin başarımının artmasında önemli rol oynamıştır. Bu çalışmada konuşma tanıma ile ilgili bir literatür taraması yapılmış ve detaylı olarak sunulmuştur. Ayrıca farklı dillerde bu araştırma alanında kaydedilen ilerlemeler tartışılmıştır. Konuşma tanıma sistemlerinde kullanılan veri setleri, özellik çıkarma yaklaşımları, konuşma tanıma yöntemleri ve performans değerlendirme ölçütleri incelenerek konuşma tanımanın gelişimi ve bu alandaki zorluklara odaklanılmıştır. Konuşma tanıma alanında son zamanlarda yapılan çalışmaların olumsuz koşullara (çevre gürültüsü, konuşmacıda ve dilde değişkenlik) karşı çok daha güçlü yöntemler geliştirmeye odaklandığı izlenmiştir. Bu nedenle araştırma alanı olarak genişleyen olumsuz koşullardaki konuşma tanıma ile ilgili yakın geçmişteki gelişmelere yönelik genel bir bakış açısı sunulmuştur. Böylelikle olumsuz koşullar altında gerçekleştirilen konuşma tanımadaki tıkanıklık ve zorlukları aşabilmek için kullanılabilecek yöntemleri seçmede yardımcı olunması amaçlanmıştır. Ayrıca Türkçe konuşma tanımada kullanılan ve iyi bilinen yöntemler karşılaştırılmıştır. Türkçe konuşma tanımanın zorluğu ve bu zorlukların üstesinden gelebilmek için kullanılabilecek uygun yöntemler irdelenmiştir. Buna bağlı olarak Türkçe konuşma tanımanın gelecekteki rotasına ilişkin bir değerlendirme ortaya konulmuştur.Article Tacıt Knowledge Vısualızatıon Through Organızatıonal Explıcıt Knowledge Warehouses: A Proposal For Research Methodology Desıgn And Executıon(2011) Medeni, İhsan Tolga; Medeni, Tunç; Tolun, MehmetKnowledge visualization can be used in several fields from medical imaging to industrial engineering. Although there could be variety of applicable research areas, our consideration will be the tacit knowledge visualization in organizations. This proposal aims to suggest a study to develop a tacit knowledge visualization framework to support know-where requirements of the organizational knowledge. With the implementation of our framework in a software application, it is aimed to create a virtual environment, where subject-based knowledge requirements will be answered by the visualized tacit knowledge of individuals and possibly the relations among individual members of the organizationConference Object Sınıflandırmada Küçük ve Dengesiz Veri Kümesi Problemi(2019) Par, Öznur Esra; Akçapınar Sezer, Ebru; Sever, HayriVerilerinin sınıflandırılması, veri kümesinin küçük ve dengesiz olması durumunda zorlaşmakta ve sınıflama performansını direkt etkilemektedir. Veri setinin küçük olması ve/veya sınıflar arasında dengesizlik olması veri madenciliğinde büyük bir sorun haline gelmiştir. Sınıflama algoritmaları, veri setlerinin yeterli büyüklüğe sahip, dengeli olduğu varsayımı üzerine geliştirilmiştir. Bu algoritmaların çoğu, azınlık sınıfındaki örnekleri göz ardı ederken veya yanlış sınıflandırırken, çoğunluk sınıfa odaklanır. Medikal veri madenciliğinde bazı kısıtlardan dolayı küçük ve dengesiz veri seti problemi ile sıklıkla karşılaşılmaktadır. Çalışma kapsamında erişime açık hepatit veri seti, küçük veri setlerine bölünmüş, oluşturulan her bir veri seti uzaklık tabanlı yöntemlerle çoğaltılmıştır. Çoğaltılan veri setleri dört farklı makine öğrenmesi algoritması (Yapay Sinir Ağları, Destek Vektör Makineleri, Naive Bayes ve Karar Ağacı) kullanılarak sınıflandırılmış, elde edilen sınıflama sonuçları karşılaştırılmıştır.Article Citation - WoS: 36Citation - Scopus: 41Hyper-Heuristics: a Survey and Taxonomy(Pergamon-elsevier Science Ltd, 2024) Kucukyilmaz, Tayfun; Talbi, El-Ghazali; Dokeroglu, TanselHyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies. Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyperheuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics. Future research prospects, trends, and prospective fields of study are also explored.Conference Object Türk Beyin Cerrahlarının Teknolojiye Ulaşım İmkanları(2018) Çağıltay, NergizArticle Citation - WoS: 7Citation - Scopus: 13A Concept-Based Sentiment Analysis Approach for Arabic(Zarka Private Univ, 2020) Sever, Hayri; Nasser, AhmedConcept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features + Lexicon-Based-Features + Word2vec-Features (CBF + LEX+ W2V) features combinations.Article A Combined Spatial and Frequency Based Texture Model for Organ Segmentation in Computed Tomography Examinations(Amer Scientific Publishers, 2014) Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan; Jafari, ArefThe organ segmentation in computed tomography (CT) examination is a tedious and error prone task. The local similarity of the pixels from different organs, and the differences between the pixels of the same organ observed in different examinations are two most challenging problems affecting the segmentation process. In this study, statistical and spectral texture properties are combined with the a-priori knowledge about the human body to develop a model for reliably segmenting organs in CT examinations. The main goal of the developed model is fusing local and global statistics to support spatial-frequency analysis and to maximize the simultaneous localization of energy in both spatial and frequency domains. The feature space dimension is reduced by means of a wrapper technique applied as a pre-processing filter. The proposed classifier utilizes a linear combination (ensemble) of two support vector machines (SVM) where the first SVM classifies the input samples according to their textural information and the second one correct the results of the first classifier by searching the spatial information of those samples in a statistical atlas.Article Citation - WoS: 2Citation - Scopus: 3Single-Machine Scheduling of Indivisible Multi-Operation Jobs(Southern African inst industrial Engineering, 2019) Gorur, A. K.; Cetinkaya, F. C.; Catmakas, H. A.This paper considers a single-machine scheduling problem of multi-operation jobs where each job consists of several operations processed contiguously, rather than being intermingled with the operations of different jobs. That is, the jobs are indivisible. A sequence-independent setup is required if the machine switches from one operation to another. However, no setup is necessary before the first operation of a job if this first operation is the same as the last operation of the immediately previous job. A job is complete when all of its operations have been processed. We investigate the problem for two cases. Makespan, which is the time needed to complete all jobs, is minimised in the first case; whereas the total completion time, which is the sum of the job completion times, is minimised in the second case. We show that the makespan problem is solvable in polynomial time. For the problem of minimising total completion time, we develop a mixed integer linear programming (MILP) model, which is capable of solving small and medium-sized problem instances optimally, and obtain a very small gap between the solution found and the best possible solution for the unsolved large-sized problem instances.Article Citation - WoS: 5Citation - Scopus: 7Ranking Surgical Skills Using an Attention-Enhanced Siamese Network With Piecewise Aggregated Kinematic Data(Springer Heidelberg, 2022) Gilgien, Matthias; Ozdemir, Suat; Ogul, Burcin BuketPurpose Surgical skill assessment using computerized methods is considered to be a promising direction in objective performance evaluation and expert training. In a typical architecture for computerized skill assessment, a classification system is asked to assign a query action to a predefined category that determines the surgical skill level. Since such systems are still trained by manual, potentially inconsistent annotations, an attempt to categorize the skill level can be biased by potentially scarce or skew training data. Methods We approach the skill assessment problem as a pairwise ranking task where we compare two input actions to identify better surgical performance. We propose a model that takes two kinematic motion data acquired from robot-assisted surgery sensors and report the probability of a query sample having a better skill than a reference one. The model is an attention-enhanced Siamese Long Short-Term Memory Network fed by piecewise aggregate approximation of kinematic data. Results The proposed model can achieve higher accuracy than existing models for pairwise ranking in a common dataset. It can also outperform existing regression models when applied in their experimental setup. The model is further shown to be accurate in individual progress monitoring with a new dataset, which will serve as a strong baseline. Conclusion This relative assessment approach may overcome the limitations of having consistent annotations to define skill levels and provide a more interpretable means for objective skill assessment. Moreover, the model allows monitoring the skill development of individuals by comparing two activities at different time points.Article Citation - WoS: 15Citation - Scopus: 15A Constraint Programming Approach To a Real-World Workforce Scheduling Problem for Multi-Manned Assembly Lines With Sequence-Dependent Setup Times(Taylor & Francis Ltd, 2024) Kandiller, Levent; Drake, John H.; Guner, Funda; Gorur, Abdul K.; Satir, BenhurFor over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations.Article Citation - WoS: 20Citation - Scopus: 23Investigation of Equatorial Plasma Bubble Irregularities Under Different Geomagnetic Conditions During the Equinoxes and the Occurrence of Plasma Bubble Suppression(Pergamon-elsevier Science Ltd, 2020) Timocin, Erdinc; Inyurt, Samed; Temucin, Huseyin; Ansari, Kutubuddin; Jamjareegulgarn, PunyawiIn this study, we investigated the behavior of equatorial plasma bubble irregularities under different geomagnetic conditions during March 2015 and September 2017. It was used Total Electron Content (TEC) data obtained from SGOC (6,89 degrees N, 79,87 degrees E), IISC (12,94 degrees N, 77,57 degrees E) and HYDE (17,40 degrees N, 78,50 degrees E) receiver stations which located between the trough and the crest of the equatorial ionization anomaly (EIA). We used the Rate of TEC change (ROT) and Rate of TEC change index (ROTI) to represent plasma bubbles irregularities. These indices are a well proxy for the ionospheric fluctuations and can be used to describe features of plasma bubbles irregularities. The equatorial plasma bubble irregularities for all stations were observed between 13 UT and 20 UT (during postsunset period) during equinoxes. The intensity level of ROTI during postsunset periods was greater than 1 TECU min(-1). Also, the values of mean ROTI (ROTIave) between 13 UT and 20 UT have values greater than 0,4 TECU min(-1) while the values of ROTIave at the other hours have values less than 0,4 TECU min(-1). The geomagnetic activity has a significant effect on the occurrence of equatorial plasma bubbles irregularities. The occurrence rate of equatorial plasma bubble irregularities observed during postsunset hours increased as geomagnetic activity increases. It also was observed that the main phases of geomagnetic storms have the triggering effect of storms on equatorial plasma bubble irregularities observed at postsunset hours while the recovery phases of geomagnetic storms have the suppression effect of storms on equatorial plasma bubble irregularities. Asymmetry between two equinoxes was observed. The occurrence rate of equatorial plasma bubble irregularities in the March equinox was much larger than that of the September equinox. The occurrence probability of equatorial plasma bubbles for March Equinox was maximum with 45,1% at 17 UT while the occurrence probability of equatorial plasma bubbles for September Equinox was maximum with 11,5% at 16 UT. The enhancements and reductions in the latitudinal gradient of VTEC show similar behaviors with the occurrence of equatorial plasma irregularities. The EIA during postsunset hours contributes significantly to the occurrence of equatorial plasma bubbles irregularities.Article Citation - WoS: 3Citation - Scopus: 5A New Robust Harris Hawk Optimization Algorithm for Large Quadratic Assignment Problems(Springer London Ltd, 2023) Dokeroglu, Tansel; Ozdemir, Yavuz SelimHarris Hawk optimization (HHO) is a new robust metaheuristic algorithm proposed for the solution of large intractable combinatorial optimization problems. The hawks are cooperative birds and use many intelligent hunting techniques. This study proposes new HHO algorithms for solving the well-known quadratic assignment problem (QAP). Large instances of the QAP have not been solved exactly yet. We implement HHO algorithms with robust tabu search (HHO-RTS) and introduce new operators that simulate the actions of hawks. We also developed an island parallel version of the HHO-RTS algorithm using the message passing interface. We verify the performance of our proposed algorithms on the QAPLIB benchmark library. One hundred and twenty-five of 135 problems are solved optimally, and the average deviation of all the problems is observed to be 0.020%. The HHO-RTS algorithm is a robust algorithm compared to recent studies in the literature.
