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Yazılım Mühendisliği Bölümü

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Citation Count: A Comparative Evaluation of Popular Search Engınes On Finding Turkish Documents For A Specific Time Period (2017). Bitirim, Yiltan; Gorur, Abdul Kadir, Tehnicki Vjesnik-Technical Gazette, 24(2), 565-569.
A Comparative Evaluation Of Popular Search Engines On Finding Turkish Documents For A Specific Time Period
(Univ Osijek, Tech Fac, 2017) Bitirim, Yıltan; Görür, Abdül Kadir; 107251
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 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: Nasser, Ahmed; Sever, Hayri (2020). "A Concept-based Sentiment Analysis Approach for Arabic", The International Arab Journal of Information Technology, Vol. 17, No. 5, pp. 778-788.
A Concept-based Sentiment Analysis Approach for Arabic
(2020) Nasser, Ahmed; Sever, Hayri; 11916
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 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: Hassanpour, Reza, "A Two-Stage Matching Method for Multi-Component Shapes", Advances in Electrical and Computer Engineering, 15, No. 1, pp. 143-150, (2013).
A Two-Stage Matching Method for Multi-Component Shapes
(Univ Suceava, 2015) Hassanpour, Reza
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 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: 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: 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: Canbay, Pelin; Sezer, Ebru; Sever, Hayri (2020). "Deep Combination of Stylometry Features in Forensic Authorship Analysis", International Journal of Information Security Science, Vol. 9, no. 3, pp. 154-163.
Deep Combination of Stylometry Features in Forensic Authorship Analysis
(2020) Canbay, Pelin; Sezer, Ebru; Sever, Hayri; 11916
Authorship Analysis (AA) in forensic is a process aim to extract information about an author from his/her writings. Forensic AA is needed for detection characteristics of anonymous authors to make better the security of digital media users who are exposed to disturbing entries such as threats or harassment emails. To analyze whether two anonymous short texts were written by the same author, we propose a combination of stylometry features from different categories in different progress. In the majority of the previous AA studies, the stylometric features from different categories are concatenated in a preprocess. In these studies, during the learning process, no category-specific operations are performed; all categories used are evaluated equally. On the other hand, the proposed approach has a separate learning process for each feature category due to their qualitative and quantitative characteristics and combines these processes at the decision phase by using a Combination of Deep Neural Networks (C-DNN). To evaluate the Authorship Verification (AV) performance of the proposed approach, we designed and implemented a problem-specific Deep Neural Network (DNN) for each stylometry category we used. Experiments were conducted on two English public datasets. The results show that the proposed approach significantly improves the generalization ability and robustness of the solutions, and also have better accuracy than the single DNNs.
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: 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: Cinar, Muhammet Serkan; Genc, Burkay; Sever, Hayri, "Identifying criminal organizations from their social network structures", Identifying criminal organizations from their social network structures, Vol. 27, No. 1, pp. 421-436, (2019).
Identifying criminal organizations from their social network structures
(Tubitak Scientific & Technical Research Council Turkey, 2019) Çınar, Muhammet Serkan; Genç, Burkay; Sever, Hayri; 11916
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 Count: Clarke, Paul; O'Connor, Rory, V; Yilmaz, Murat, "In Search of the Origins and Enduring Impact of Agile Software Development", Proceedıngs Of The 2018 Internatıonal Conference On Software And System Process (Icssp 2018), (2018), pp. 142-146.
In Search of the Origins and Enduring Impact of Agile Software Development
(Assoc Computing Machinery, 2018) Clarke, Paul; O'Connor, Rory V.; Yılmaz,Murat; 55248
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 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: Polat, Hüseyin...et al. (2019). "Otomatik Konuşma Tanıma Sistemlerinde Kullanılan Gerçek Metin Verisinde Biçimbilimsel-Sözdizimsel Hataların Tespiti ve Düzeltmesi", Veri Bilimi, Vol. 2, No. 2, pp. 18-24.
Otomatik Konuşma Tanıma Sistemlerinde Kullanılan Gerçek Metin Verisinde Biçimbilimsel-Sözdizimsel Hataların Tespiti ve Düzeltmesi
(2019) Polat, Hüseyin; Sever, Hayri; Oyucu, Saadin; Tekbaş, Şükran; 11916
Türkçe Otomatik Konuşma Tanıma (ASR: Automatic Speech Recognition) sistemlerinde kullanılan akustik model gürbüz bir dil modeli ile desteklenmediği durumlarda kelime hata oranı yüksek çıkmaktadır. İyi dizayn edilmiş bir dil modeli ile akustik modelin birlikte ASR’de kullanılması kelime hata oranını düşürmektedir. ASR için gerekli dil modelinin eğitiminde düz metin verisi kullanılmaktadır. Kullanılan metin verisinin doğruluğu ASR modellerinin eğitimi için oldukça önemlidir. Bu çalışmada, doğal dil işlemeye dayalı bir yöntem kullanılarak Türkçe ASR sisteminin eğitilmesinde kullanılan metin verisi içerisindeki yazım hatalarının tespiti ve düzeltilmesi gerçekleştirilmiştir. Öncelikle metin verisi içerisinde dil bilgisel olarak yanlış yazılmış olan kelimeler bulunmuştur. Bir kelimedeki karakter eksikliği, karakter fazlalığı, karakterlerin yer değiştirmesi veya karakteri yanlış yazılmış olan kelimeler hatalı olarak kabul edilmiştir. Metin verisi içerisinde hatalı olarak kabul edilen kelimeler morfolojik analiz ile tespit edilmiştir. Yanlış kelimelerin yerine atanacak olan kelimeler belirlenmiştir. Yanlış yazılmış olan kelimeler doğru kelimeler ile değiştirilmiştir. Gerçekleştirilen çalışma hatalı kelimeleri tespit etme ve doğru kelimeler ile yer değiştirme işleminde %93 oranında başarı göstermiştir.
Citation Count: Oyucu, Saadin; Sever, Hayri; Polat, Hüseyin (2019). "Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu", Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Vol. 7, No. 4, pp. 834-854.
Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu
(2019) Oyucu, Saadin; Sever, Hayri; Polat, Hüseyin; 11916
İnsanlar 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 sunulmuş ve 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 da Türkçe konuşma tanımanın gelecekteki rotasına ilişkin bir değerlendirme ortaya konulmuştur.
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: 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: Oyucu, Saadin; Polat, Hüseyin; Sever, Hayri (2020). "Sessizliğin Kaldırılması ve Konuşmanın Parçalara Ayrılması İşleminin Türkçe Otomatik Konuşma Tanıma Üzerindeki Etkisi", Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol. 8, No. 1, pp. 334-346.
Sessizliğin Kaldırılması ve Konuşmanın Parçalara Ayrılması İşleminin Türkçe Otomatik Konuşma Tanıma Üzerindeki Etkisi
(2020) Oyucu, Saadin; Polat, Hüseyin; Sever, Hayri; 11916
Otomatik Konuşma Tanıma sistemleri temel olarak akustik bilgiden faydalanılarak geliştirilmektedir. Akustik bilgiden fonem bilgisinin elde edilmesi için eşleştirilmiş konuşma ve metin verileri kullanılmaktadır. Bu veriler ile eğitilen akustik modeller gerçek hayattaki bütün akustik bilgiyi modelleyememektedir. Bu nedenle belirli ön işlemlerin yapılması ve otomatik konuşma tanıma sistemlerinin başarımını düşürecek akustik bilgilerin ortadan kaldırılması gerekmektedir. Bu çalışmada konuşma içerisinde geçen sessizliklerin kaldırılması için bir yöntem önerilmiştir. Önerilen yöntemin amacı sessizlik bilgisinin ortadan kaldırılması ve akustik bilgide uzun bağımlılıklar sağlayan konuşmaların parçalara ayrılmasıdır. Geliştirilen yöntemin sonunda elde edilen sessizlik içermeyen ve parçalara ayrılan konuşma bilgisi bir Türkçe Otomatik Konuşma Tanıma sistemine girdi olarak verilmiştir. Otomatik Konuşma Tanıma sisteminin çıkışında sisteme giriş olarak verilen konuşma parçalarına karşılık gelen metinler birleştirilerek sunulmuştur. Gerçekleştirilen deneylerde sessizliğin kaldırılması ve konuşmanın parçalara ayrılması işleminin Otomatik Konuşma Tanıma sistemlerinin başarımını artırdığı görülmüştür.
Citation Count: Cetinkaya, F. C.; Catmakas, H. A.; Gorur, A. K., "Single-Machine Scheduling of Indivisible Multi-Operatıon Jobs", South African Journal of Industrial Engineering, Vol. 30, No. 1, pp. 78-93, (May 2019).
Single-Machine Scheduling of Indivisible Multi-Operatıon Jobs
(Southern African Inst Industrial Engineering, 2019) Çetinkaya, Ferda Can; Akkocaoğlu Çatmakaş, Hale; Görür, Abdül Kadir; 50129; 57532; 107251
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.