http://hdl.handle.net/20.500.12416/2147 Yazılım Mühendisliği Bölümü yayınlarını içerir. Wed, 28 Oct 2026 17:21:14 GMT 2026-10-28T17:21:14Z http://hdl.handle.net/20.500.12416/8472 Hyper-heuristics: A survey and taxonomy Dökeroğlu, Tansel; Küçükyılmaz, Tayfun; Talbi, El-Ghazali 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. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.12416/8472 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.12416/8457 Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities Çağıltay, Nergiz Ercil; Toker, Sacip; Çağıltay, Kürşat 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. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.12416/8457 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.12416/8227 Ranking surgical skills using an attention-enhanced Siamese network with piecewise aggregated kinematic data 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. Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/20.500.12416/8227 2022-06-01T00:00:00Z http://hdl.handle.net/20.500.12416/8182 Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm Deniz, Ayça; Kızılöz, Hakan Ezgi; Sevinç, Ender; Dökeroğlu, Tansel 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. Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/20.500.12416/8182 2022-06-01T00:00:00Z