1,720,989 research outputs found
Design and Development of a Mobile Spelling Game for Elementary Students Using Genetic Algorithms
No full textDeep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification
No full textCancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy
Integrating Mobile Games in Arabic Orthography Classrooms
No full textConsiderable advances in the capabilities of modern mobile devices have enabled their use as powerful educational tools. Today, mobile learning games are widely used as creative platforms for teaching and learning, offering enjoyable and ubiquitous educational content. This study describes the design and evaluation of a mobile Arabic orthography game aimed at improving Arabic orthographical skills among young learners. In particular, 52 female fourth-grade students participated in this study to answer the following research questions: what impact does the use of mobile Arabic orthography games have on students’ performance, and how do students perceive this learning approach? A mixed-method research design was adopted to answer these research questions, including pre-and post-tests, interviews, and classroom observations. Analysis of the data revealed that, although there were a few challenges involved in using mobile games as a learning tool, significant improvements were found in students’ performance and engagement, and positive attitudes were developed towards using the mobile game. In addition, there was an overall increase in students’ motivation and interaction. The pedagogical implications of these findings can be linked to the gamification of the teaching and learning environments. Teachers are encouraged to consider integrating mobile educational games into their instructional approaches, as they can serve as great incentives to learning (especially for young students).</p
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