Yazılım Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/2147

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Citation Count: Dökeroğlu, Tansel; Küçükyılmaz, Tayfun; Talbi, El-Ghazali (2024). "Hyper-heuristics: A survey and taxonomy", Computers and Industrial Engineering, Vol. 187.
Hyper-heuristics: A survey and taxonomy
(2024) Dökeroğlu, Tansel; Küçükyılmaz, Tayfun; Talbi, El-Ghazali; 234173
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 hyper-heuristics 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 Count: Çağıltay, Nergiz Ercil; Toker, Sacip; Çağıltay, Kürşat (2024). "Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities", Open Praxis, Vol. 16, No. 1, pp. 70-81.
Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities
(2024) Çağıltay, Nergiz Ercil; Toker, Sacip; Çağıltay, Kürşat; 113411
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 Count: Oğul, Burçin Buket; Gilgien, Matthias; Özdemir, Suat. (2022). "Ranking surgical skills using an attention-enhanced Siamese network with piecewise aggregated kinematic data", International Journal of Computer Assisted Radiology and Surgery, Vol.17, No.6, pp.1039-1048.
Ranking surgical skills using an attention-enhanced Siamese network with piecewise aggregated kinematic data
(2022) Oğul, Burçin Buket; Gilgien, Matthia; Özdemir, Suat
Purpose: 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.
Citation Count: Deniz, Ayça;...et.al. (2022). "Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm", Expert Systems, Vol.39, No.5.
Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm
(2022) Deniz, Ayça; Kızılöz, Hakan Ezgi; Sevinç, Ender; Dökeroğlu, Tansel; 234173
The 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.
Citation Count: Medeni, İhsan Tolga; Medeni, Tunç; Tolun, M. (2011). "Tacıt Knowledge Vısualızatıon Through Organızatıonal Explıcıt Knowledge Warehouses: A Proposal For Research Methodology Desıgn And Executıon", International Journal of eBusiness and eGovernment Studies, Vol.3, No.2, pp.91-100.
Tacıt Knowledge Vısualızatıon Through Organızatıonal Explıcıt Knowledge Warehouses: A Proposal For Research Methodology Desıgn And Executıon
(2011) Medeni, İhsan Tolga; Medeni, Tunç; Tolun, Mehmet
Knowledge 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 organization
Citation Count: Akba, Fırat...et al. "Yeşil BHT Bilgi ve Haberleşme Teknolojileri Akademisyen ve Uygulayıcılar Açısından Bir İnceleme", 4. Mühendislik ve Teknoloji Sempozyumu, 2011.
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, Mehmet; 181215
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet R. "Multiple Description Scalable Coding for Video Transmission over Unreliable Networks", Embedded Computer Systems: Architectures, Modeling, and Simulation, pp. 58-67, 2009.
Multiple Description Scalable Coding for Video Transmission over Unreliable Networks
(2009) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.; 1863
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 up to 36dB for average Y-PSNR.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet. "Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error", 10th International Conference on Computer Vision Theory and Applications, pp. 117-123, 2015.
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 Count: Dokeroglu, Tansel ; Sevinc, E. (2022). "An island parallel Harris hawks optimization algorithm", Neural Computing and Applications, Vol.34, No.21, pp.18341-18368.
An island parallel Harris hawks optimization algorithm
(2022) Dokeroglu, Tansel; Sevinc, Ender; 234173
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 Count: Uzun, Yusuf;...et.al. (2022). "An intelligent system for detecting Mediterranean fruit fly", Journal of Agricultural Engineering, Vol.53, No.3.
An intelligent system for detecting Mediterranean fruit fly
(2022) Uzun, Yusuf; Tolun, Mehmet Resit; Eyyuboglu, Halil Tanyer; Sarı, Filiz
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 Count: Medeni, İ.T.; Aktaş, Z.A.; Tolun, M.R. "Bilgi Biliminin Mühendislik Gereksinimi ve Bilgi Mühendisliği", Elektrik Elektronik Bilgisayar Biomedikal Mühendisleri Eğitimi 4. Ulusal Sempozyumu, 2009.
Bilgi Biliminin Mühendislik Gereksinimi ve Bilgi Mühendisliği
(2009) Medeni, İhsan Tolga; Aktaş, A. Ziya; Tolun, Mehmet R.; 1863
Yirminci yüzyılın ikinci yarısında bilgisayar, bilgi ve iletişim teknolojilerindeki gelişmeler bilgiye dayalı yeni bilim ve mühendislik disiplinleri oluşturma ihtiyacını doğurmuştur. Bu ihtiyaç nedeniyle doğan yeni bilim ve mühendislik disiplinlerinin gelişiminin aslında (veri, enformasyon ve bilgi ) üçlüsüne yönelik oluşumlar olduğu gözlemlenmektedir.Bu makalede bilgi sözcüğü bu üçlü için genel bir ad olarak kullanılacaktır. Bilgi disiplini bir taraftan, bu üçlü arasındaki bağların örgütler ve bireyler açısından ortaya koyulmasını amaçlar; açık ve örtük bilginin birbirine dönüşümünü sağlamaya çalışırken, diğer taraftan da ortaya çıkan yeni dallar ve var olan dalların bilgi temelli ilişkisini kurmaya yönelik çalışmalar yapmaktadır. Bu üçlünün ve bilgi disiplininin bilim / mühendislik, işletme / yönetim disiplinleriyle olan ilişkisi ve oluşturulacak bir bilgi mühendisliği lisans programının bu kavramlarla olabilecek ilgisi bu bildirinin konusudur.
Citation Count: Akyol, H.; Kızılduman, H.S.; Dökeroğlu, T. (2022). "Big Data Reduction and Visualization Using the K-Means Algorithm", Ankara Science University, Researcher, Vol.2, No.1., pp.40-45.
Big Data Reduction and Visualization Using the K-Means Algorithm
(2022) Akyol, Hakan; Kızılduman, Hale Sema; Dökeroğlu, Tansel; 234173
A 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 quality
Citation Count: Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan E. (2022). "A comprehensive survey on recent metaheuristics for feature selection", Neurocomputing, Vol.494, pp.269-296.
A comprehensive survey on recent metaheuristics for feature selection
(2022) Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan Ezgi; 234173
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 Count: Coşar, Batuhan Mustafa; Say, Bilge; Dökeroğlu, Tansel. (2023). "
Müfredat Tabanlı Ders Çizelgeleme Problemi İçin Yeni Bir Açgözlü Algoritma
(2023) Coşar, Batuhan Mustafa; Say, Bilge; Dökeroğlu, Tansel; 234173
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.
Citation Count: Çağıltay, Nergiz. "Türk Beyin Cerrahlarının Teknolojiye Ulaşım İmkanları", Türk Nöröşirurji Derneği 30. Bilimsel Kongresi, 2018.
Türk Beyin Cerrahlarının Teknolojiye Ulaşım İmkanları
(2018) Çağıltay, Nergiz; 113411
Citation Count: Dokeroglu, Tansel; Ozdemir, Yavuz Selim. (2023). "A new robust Harris Hawk optimization algorithm for large quadratic assignment problems", Neural Computing & Applications, Vol. 35, No. 17, pp. 12531-12544.
A new robust Harris Hawk optimization algorithm for large quadratic assignment problems
(2023) Dokeroglu, Tansel; Ozdemir, Yavuz Selim; 234173
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 Count: Dökeroğlu, Tansel. (2023). "A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients", Peerj Computer Science, Vol. 9.
A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients
(2023) Dökeroğlu, Tansel; 234173
Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
Citation Count: Güner, Funda...et al. (2023). "A Constraint Programming Approach To A Real-World Workforce Scheduling Problem For Multi-Manned Assembly Lines With Sequence-Dependent Setup Times", International Journal Of Production Research.
A Constraint Programming Approach To A Real-World Workforce Scheduling Problem For Multi-Manned Assembly Lines With Sequence-Dependent Setup Times
(2023) Güner, Funda; Görür, Abduel K.; Satır, Benhür; Kandiller, Levent; Drake, John H.; 54700
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 Count: Par, Öznur Esra; Sezer, Ebru Akçapınar; Sever, Hayri (2019). "Small and Unbalanced Data Set Problem in Classification", 27th Signal Processing and Communications Applications Conference (SIU), Sivas Cumhuriyet Univ, Sivas, TURKEY, APR 24-26, 2019.
Small and Unbalanced Data Set Problem in Classification
(2019) Par, Öznur Esra; Sezer, Ebru Akçapınar; Sever, Hayri; 11916
Classification of data is difficult in case of small and unbalanced data set and this problem directly affects the classification performance. Small and / or the imbalance dataset has become a major problem in data mining. Classification algorithms are developed based on the assumption that the data sets are balanced and large enough. The most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Small and unbalanced data set problem is frequently encountered in medical data mining due to some limitations. Within the scope of the study, the public accessible data set, hepatitis, was divided into small and imblanced data subsets, each of the data subsets were oversampled by distance based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree) and the classification scores were compared.
Citation Count: Choupany, Roya; Wong, Stephan; Tolun, Mehmet (2011). "Scalable video transmission over unreliable networks using multiple description wavelet coding", 7th International Conference on Digital Content, Multimedia Technology and Its Applications, IDCTA 201116 August 2011through 18 August 2011, pp. 5-10.
Scalable video transmission over unreliable networks using multiple description wavelet coding
(2011) Choupany, Roya; Wong, Stephan; Tolun, Mehmet; 1863
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.

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