Yazılım Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/2147
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Article Citation - WoS: 2Citation - Scopus: 4Exploring 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, KursatMassive 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.Article Citation - WoS: 5Citation - Scopus: 7Ranking Surgical Skills Using an Attention-Enhanced Siamese Network With Piecewise Aggregated Kinematic Data(Springer Heidelberg, 2022) Gilgien, Matthias; Ozdemir, Suat; Ogul, Burcin BuketPurpose Surgical skill assessment using computerized methods is considered to be a promising direction in objective performance evaluation and expert training. In a typical architecture for computerized skill assessment, a classification system is asked to assign a query action to a predefined category that determines the surgical skill level. Since such systems are still trained by manual, potentially inconsistent annotations, an attempt to categorize the skill level can be biased by potentially scarce or skew training data. Methods We approach the skill assessment problem as a pairwise ranking task where we compare two input actions to identify better surgical performance. We propose a model that takes two kinematic motion data acquired from robot-assisted surgery sensors and report the probability of a query sample having a better skill than a reference one. The model is an attention-enhanced Siamese Long Short-Term Memory Network fed by piecewise aggregate approximation of kinematic data. Results The proposed model can achieve higher accuracy than existing models for pairwise ranking in a common dataset. It can also outperform existing regression models when applied in their experimental setup. The model is further shown to be accurate in individual progress monitoring with a new dataset, which will serve as a strong baseline. Conclusion This relative assessment approach may overcome the limitations of having consistent annotations to define skill levels and provide a more interpretable means for objective skill assessment. Moreover, the model allows monitoring the skill development of individuals by comparing two activities at different time points.Article Citation - WoS: 5Citation - Scopus: 8Predicting the Severity of Covid-19 Patients Using a Multi-Threaded Evolutionary Feature Selection Algorithm(Wiley, 2022) Kiziloz, Hakan Ezgi; Sevinc, Ender; Dokeroglu, Tansel; Deniz, AycaThe COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.Article Citation - WoS: 2Citation - Scopus: 4An Intelligent System for Detecting Mediterranean Fruit Fly [Medfly; Ceratitis Capitata (Wiedemann)](Pagepress Publ, 2022) Eyyuboglu, Halil Tanyer; Sari, Filiz; Uzun, Yusuf; Tolun, Mehmet ResitNowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.