Yazılım Mühendisliği Bölümü

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Citation - WoS: 0
Citation - Scopus: 0
A combined spatial and frequency based texture model for organsegmentation in computed tomography examinations
(Amer Scientific Publishers, 2014) Jafari, Aref; Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan; Yazılım Mühendisliği
The 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.
Citation - WoS: 1
Citation - Scopus: 4
A Comparative Evaluation Of Popular Search Engines On Finding Turkish Documents For A Specific Time Period
(Univ Osijek, Tech Fac, 2017) Bitirim, Yiltan; Gorur, Abdul Kadir; 107251; Bilgisayar Mühendisliği
This study evaluates the popular search engines, Google, Yahoo, Bing, and Ask, on finding Turkish documents by comparing their current performances with their performances measured six years ago. Furthermore, the study reveals the current information retrieval effectiveness of the search engines. First of all, the Turkish queries were run on the search engines separately. Each retrieved document was classified and precision ratios were calculated at various cut-off points for each query and engine pair. Afterwards, these ratios were compared with the six years ago ratios for the evaluations. Besides the descriptive statistics, Mann-Whitney U and Kruskal-Wallis H statistical tests were used in order to find out statistically significant differences. All search engines, except Google, have better performance today. Bing has the most increased performance compared to six years ago. Nowadays: Yahoo has the highest mean precision ratios at various cut-off points; all search engines have their highest mean precision ratios at cut-off point 5; dead links were encountered in Google, Bing, and Ask; and repeated documents were encountered in Google and Yahoo.
Citation - WoS: 186
Citation - Scopus: 245
A comprehensive survey on recent metaheuristics for feature selection
(Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan Ezgi; 234173; Yazılım Mühendisliği
Feature 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.
Citation - WoS: 7
Citation - Scopus: 13
A Concept-based Sentiment Analysis Approach for Arabic
(Zarka Private Univ, 2020) Nasser, Ahmed; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Concept-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.
Citation - WoS: 12
Citation - Scopus: 12
A Constraint Programming Approach To A Real-World Workforce Scheduling Problem For Multi-Manned Assembly Lines With Sequence-Dependent Setup Times
(Taylor & Francis Ltd, 2024) Guner, Funda; Gorur, Abdul K.; Satir, Benhur; Kandiller, Levent; Drake, John H.; 54700; Çankaya Meslek Yüksekokulu; Endüstri Mühendisliği
For 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.
Citation - WoS: 3
Citation - Scopus: 4
A new robust Harris Hawk optimization algorithm for large quadratic assignment problems
(Springer London Ltd, 2023) Dokeroglu, Tansel; Ozdemir, Yavuz Selim; 234173; Yazılım Mühendisliği
Harris 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.
Citation - WoS: 0
Citation - Scopus: 0
A Two-Stage Matching Method for Multi-Component Shapes
(Univ Suceava, Fac Electrical Eng, 2015) Hassanpour, Reza; Yazılım Mühendisliği
In this paper a shape matching algorithm for multiple component objects has been proposed which aims at matching shapes by a two-stage method. The first stage extracts the similarity features of each component using a generic shape representation model. The first stage of our shape matching method normalizes the components for orientation and scaling, and neglects minor deformations. In the second stage, the extracted similarity features of the components are combined with their relative spatial characteristics for shape matching. Some important application areas for the proposed multi-component shape matching are medical image registration, content based medical image retrieval systems, and matching articulated objects which rely on the a-priori information of the model being searched. In these applications, salient features such as vertebrae or rib cage bones can be easily segmented and used. These features however, show differences from person to person on one hand and similarities at different cross-sectional images of the same examination on the other hand. The proposed method has been tested on articulated objects, and reliable registration of 3-dimensional abdominal computed tomography images.
Citation - WoS: 2
Citation - Scopus: 3
An intelligent system for detecting Mediterranean fruit fly
(Pagepress Publ, 2022) Uzun, Yusuf; Tolun, Mehmet Resit; Eyyuboglu, Halil Tanyer; Sari, Filiz; Elektronik ve Haberleşme Mühendisliği; Yazılım Mühendisliği
Nowadays, 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.
Citation - WoS: 6
Citation - Scopus: 7
An island parallel Harris hawks optimization algorithm
(Springer London Ltd, 2022) Dokeroglu, Tansel; Sevinc, Ender; 234173; Yazılım Mühendisliği
The Harris hawk optimization (HHO) is an impressive optimization algorithm that makes use of unique mathematical approaches. This study proposes an island parallel HHO (IP-HHO) version of the algorithm for optimizing continuous multi-dimensional problems for the first time in the literature. To evaluate the performance of the IP-HHO, thirteen unimodal and multimodal benchmark problems with different dimensions (30, 100, 500, and 1000) are evaluated. The implementation of this novel algorithm took into account the investigation, exploitation, and avoidance of local optima issues effectively. Parallel computation provides a multi-swarm environment for thousands of hawks simultaneously. On all issue cases, we were able to enhance the performance of the sequential version of the HHO algorithm. As the number of processors increases, the suggested IP-HHO method enhances its performance while retaining scalability and improving its computation speed. The IP-HHO method outperforms the other state-of-the-art metaheuristic algorithms on average as the size of the dimensions grows.
Citation - WoS: 8
Citation - Scopus: 7
Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities
(Pergamon-elsevier Science Ltd, 2021) Timocin, Erdinc; Temucin, Huseyin; Inyurt, Samed; Shah, Munawar; Jamjareegulgarn, Punyawi; 182651
This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7 degrees N, 121.1 degrees E), Yamagawa (31.2 degrees N, 130.6 degrees E), Yakutsk (62.0 degrees N,129.6 degrees E), Townsville (19.6 degrees S, 146.8 degrees E), Hobart (42.9 degrees S, 147.3 degrees E) and Terre Adelie (66.6 degrees S, 140.0 degrees E). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.
Citation - WoS: 0
Citation - Scopus: 1
Component-Based Project Estimation Issues for Recursive Development
(Springer, 2008) Altunel, Yusuf; Tolun, Mehmet R.; 1863; Yazılım Mühendisliği
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.
Citation - WoS: 20
Citation - Scopus: 29
Creating consensus group using online learning based reputation in blockchain networks
(Elsevier, 2019) Bugday, Ahmet; Ozsoy, Adnan; Oztaner, Serdar Murat; Sever, Hayri; 11916; Bilgisayar Mühendisliği
One 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.
Citation - WoS: 0
Detection of Stylometric Writeprint from the Turkish Texts
(Ieee, 2020) Canbay, Pelin; Sezer, Ebru Akcapinar; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship 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.
Citation - WoS: 1
Citation - Scopus: 2
Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities
(int Council Open & Distance Education, 2024) Cagiltay, Nergiz ercil; Toker, Sacip; Cagiltay, Kursat; 113411; Yazılım Mühendisliği
Massive Open Online Courses (MOOCs) now offer a variety of options for everyone to obtain a high -quality education. The purpose of this study is to better understand the behaviours of MOOC learners and provide some insights for taking actions that benefit larger learner groups. Accordingly, 2,288,559 learners' behaviours on 174 MITx courses were analysed. The results show that MOOCs are more attractive to the elderly, male, and highly educated groups of learners. Learners' performance improves as they register for more courses and improve their skills and experiences on MOOCs. The findings suggest that, in the long run, learners' adaptation to MOOCs will significantly improve the potential benefits of the MOOCs. Hence, MOOCs should continue by better understanding their learners and providing alternative instructional designs by considering different learner groups. MOOC providers' decision -makers may take these findings into account when making operational decisions.
Citation - Scopus: 0
Hand gesture recognition in variable length sequences
(2005) Choupani, R.; Tolun, M.R.; 1863; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Using hand gestures in human computer interaction has been a major challenge during the recent years. Many of the hand gesture recognition systems however, have been based on the recognition of hand postures and estimating the related gesture which is restricted to a few numbers of possible movements. However when dealing with applications such as understanding sign languages which include a large number of classes, an automatic learning method based on matching a sequence of postures with the characterizing feature sequence of each class is necessary. An important characteristic of this method is that each sample sequence of a class may have a variable length and different position of the key features. In this paper a syntactic method has been proposed for classifying the input sequences. An algorithm foe extracting the grammar of the method during training stage is also given.
Citation - WoS: 19
Citation - Scopus: 27
Hyper-heuristics: A survey and taxonomy
(Pergamon-elsevier Science Ltd, 2024) Dokeroglu, Tansel; Kucukyilmaz, Tayfun; Talbi, El-Ghazali; 234173; Yazılım Mühendisliği
Hyper-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.
Citation - WoS: 1
Citation - Scopus: 2
Identifying criminal organizations from their social network structures
(Tubitak Scientific & Technological Research Council Turkey, 2019) Cinar, Muhammet Serkan; Genc, Burkay; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.
Citation - WoS: 17
Citation - Scopus: 19
In Search of the Origins and Enduring Impact of Agile Software Development
(Assoc Computing Machinery, 2018) Clarke, Paul; O'Connor, Rory, V; Yilmaz, Murat; 55248; Yazılım Mühendisliği
The Agile Manifesto is a philosophical touchpoint for all agile software development (ASD) methods. We examine the manifesto and certain agile methods in an effort to identify the major impacts of ASD. We have encountered some difficulty in delineating agile and non-agile software processes, which is partially the result of terminological confusion. It is clear from the volume of published research that ASD has made a significant contribution, and we have identified two lasting impacts: a reduction in iteration durations and a push for reduced levels of documentation. We find that the contemporary use of tooling to automate processes may not be wholly congruent with the manifesto and that many organisations may still rely on business contracts despite calls in the manifesto for greater levels of informal customer collaboration.
Citation - WoS: 19
Citation - Scopus: 22
Investigation 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, Punyawi; 182651
In 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.
Müfredat Tabanlı Ders Çizelgeleme Problemi İçin Yeni Bir Açgözlü Algoritma
(2023) Batuhan,; Say, Bilge; Dokeroglu, Tansel; 234173; Yazılım Mühendisliği
Bu çalışma, iyi bilinen Müfredat Tabanlı Ders Çizelgeleme Problemini optimize etmek için yeni bir açgözlü algoritmayı açıklamaktadır. Açgözlü algoritmalar, en iyi çözümü bulmak için yürütülmesi uzun zaman alan kaba kuvvet ve evrimsel algoritmalara iyi bir alternatiftir. Birçok açgözlü algoritmanın yaptığı gibi tek bir buluşsal yöntem kullanmak yerine, aynı problem örneğine 120 yeni buluşsal yöntem tanımlıyor ve uyguluyoruz. Dersleri müsait odalara atamak için, önerilen açgözlü algoritmamız En Büyük-İlk, En Küçük-İlk, En Uygun, Önce Ortalama Ağırlık ve En Yüksek Kullanılamaz ders-ilk buluşsal yöntemlerini kullanır. İkinci Uluslararası Zaman Çizelgesi Yarışması'nın (ITC-2007) kıyaslama setinden 21 problem örneği üzerinde kapsamlı deneyler gerçekleştirilir. Önemli ölçüde azaltılmış yumuşak kısıtlama değerlerine sahip 18 problem için, önerilen açgözlü algoritma sıfır sabit kısıtlama ihlali (uygulanabilir çözümler) rapor edebilir. Önerilen algoritma, performans açısından son teknoloji ürünü açgözlü buluşsal yöntemleri geride bırakıyor.

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