Use of Augmented Reality in Mobile Devices for Educational Purposes

WOS: 000411952100007Use of technology in education has been widespread in the last decade, thanks to developments and improvements in information and communication technologies, especially in mobile devices. Among the fields in which mobile devices play important roles, education is one of the leading ones. Mobile devices help teachers and learners access educational resources when needed. To increase the reality of virtual learning environments on mobile devices, Augmented Reality (AR) technologies were introduced for mobile platforms, and the term Mobile Augmented Reality (MAR) arose. MAR opens a new door for educators and trainers to experience new methods of teaching for mobile learners. In this chapter, educational use of AR on mobile devices will be explained. Throughout the content of the chapter, readers will be informed about how AR applications changed people's teaching and learning styles

Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

Emiroglu, Bulent Gursel/0000-0002-1656-6450; Cinarer, Gokalp/0000-0003-0818-6746; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000580451100001Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II,n= 77; Grade III,n= 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification

Examining computer gaming addiction in terms of different variables

AbstractThe computer gaming addiction is one of the newer concepts that young generations face and can be defined as the excessive and problematic use of computer games leading to social and/or emotional problems. The purpose of this study is to analyse through variables the computer gaming addiction levels of secondary school students. The research was conducted with survey and causal-comparative quantitative research methods. Furthermore, the quantitative data was obtained by interpreting the data obtained through open-ended questions. Findings reveal a significant difference between computer gaming addiction and variables of gender, daily gaming times and whether or not students play games with people they do not know. However, findings did not show any significant difference between computer gaming addiction and variables of grade or purposes of game playing. According to the findings from qualitative data analysis, students mostly prefer to play skill-based games, while they would want to design action games.Keywords: Computer gaming, daily gaming times, game addiction, purposes of game playing, secondary school students.</jats:p

Integration search strategies in tree seed algorithm for high dimensional function optimization

Kiran, Mustafa Servet/0000-0002-5896-7180; CINAR, Ahmet Cevahir/0000-0001-5596-6767WOS: 000512019400002The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics