Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Students’ perceptions and experiences about the combined inductive-deductive approach in intermediate grammar class
Get PDFThis research study investigates students’ perceptions and experiences about using the combined inductive-deductive approach in teaching English grammar in intermediate grammar class. The study employed a mixed methods design to answer the research questions. We used a questionnaire including four aspects according to ARCS Model to answer the first research question through examining the perceptions of 65 university students majoring English. Then five of 65 students were selected to be interviewed to explore their experiences on the combined inductive-deductive approach to answer the second research question. The results of the questionnaire and the interviews reveal that most students prefer the combined approach to teaching English grammar although they have positive perceptions towards the inductive or the deductive approach. The combined approach can provide students with better understanding of grammar and keep them involved and interactive in the learning process. Therefore, this current study suggests that further research should be conducted to investigate the effectiveness of the combined approach in the process of teaching grammar and teachers’ perceptions towards such approach
Bridge Crack Detection Based on Attention Mechanism
Get PDFWith the strong support of the country for bridge construction and the increase in supervision of the safety of old bridges, the visual-based bridge crack target detection has a problem of incomplete target framing due to the characteristics of the bridge crack target, reflecting the current algorithm model's poor ability to accurately identify targets. In this paper, YOLO V5 algorithm was used to address the issue of poor accuracy in bridge crack target detection, and a relevant bridge crack detection dataset was created. Three attention mechanisms, SENet, ECALayer, and CBAM, were respectively fused to improve the model's feature fusion part, and comparative experiments were conducted. The experimental results show that the improved algorithm has increased from 80.5% to 87% in mAP50-95 indicators compared to the original algorithm
Technical and Economic Evaluation and Optimization of an Off-Grid Wind/Hydropower Hybrid System
Get PDFOwing to global population growth, more fossil fuels are being used to meet the energy demand, which has led to increases in environmental pollution. Therefore, alternative sources such as renewable energy can be employed to meet some of the needs. Renewable energy has been criticized for its related problems such as downtime and high investment costs. In this study, a system was proposed to address these two problems by integrating wind and hydropower turbines, in Khalkhal, Ardabil, Iran as a case study. To appraise the system, an economic model was generalized, and an Ant Colony Optimization (ACO) method aiming to reduce investment costs was, implemented. The results show that to cover 100% of the required energy demand with a hybrid system of wind and hydropower turbines, the total cost of this system will be /kWh. For the case study, to supply the required energy demand, 10 wind turbines and 23 kVA batteries, as well as a 14.6 kW hydropower turbine are required
Suicide and self-harm prediction based on social media data using machine learning algorithms
Get PDFOnline social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigo
Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation
Get PDFA brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance
Exploring the impact of songs on student cognitive and emotional development
Get PDFThis scholarly article investigates the impact of diverse songs on second language (L2) learning, emphasizing cognitive, kinesthetic, and emotional engagement. This research recognizes songs as literary expressions, specifically through the lens of lyrics as literary texts, and explores their historical and ongoing significance in L2 education. Beyond linguistic proficiency, the article delves into the broader implications of integrating songs into language education. Educators observe that incorporating music fosters a positive learning environment, contributing to increased motivation and engagement among learners. The discussion in this article addresses the gaps in the integration of literary texts, including songs, into language education. It criticizes the tendency of tertiary foreign language programs to overemphasize 'literature' at the expense of linguistic aspects and the opposite approach at the lower secondary level, which focuses on linguistic aspects while neglecting literary involvement. The results of this literature review highlight the need to recognize the profound impact that literary texts, including songs, have on individual learners, urging the incorporation of artistic elements into language teaching practices in a more comprehensive and balanced manner. This article emphasizes that literature, through stories and narratives, contributes to the emotional development of learners, fostering empathy and understanding of 'otherness.' This research advocates the balanced and thoughtful integration of literature in FL classrooms, recognizing its capacity to go beyond language instruction and contribute to broader education and individual growth
A study on cultural adaptation of Chinese Students in Indonesia
Get PDFAs closer cooperation in education between Guangxi and Indonesia, the cross-cultural adaptation of foreign students in target countries has become a very important issue. In terms of society, psychology, learning and culture, the article studies the adaptation of international students who come from Guangxi University of Foreign Language in Indonesia by means of questionnaires and interviews. Their cultural adaptation is generally good, but there are many differences in gender, length of study abroad, cultural experience, etc..This thesis will analyze the factors that affect cross-cultural adaptation and the reasons for the differences, and provide relevant suggestions for further improving at training program
Tourism and education intersection: cultural reproduction of Osing community in the Angklung Caruk music festival
Get PDFMusic festivals are social and cultural events mostly seen as activities to present artistic expressions for the audience to enjoy. If it is investigated closely, the presentation of work at festivals is the final culmination of training and learning, both of which are essential elements of the educational process. This research aims to examine and show that music festivals have educational elements, even in the context of tourism interests. The music festival studied was the Angklung Caruk Festival, a type of traditional music from the Osing community. The festival competes for musical skills between student angklung groups in Banyuwangi, East Java. The research uses ethnographic methods to discover and explain the intersection between festivals' education and tourism dimensions. Data collection uses participant observation techniques, interviews, document study, and documentation. Data analysis uses an ethnographic model, including domain analysis, taxonomy, components, and cultural themes. The research results show that the Angklung Caruk music festival is an arena for competition in the beauty of musical works between student groups to achieve victory. This competition has become a tourism commodity packaged under the title Banyuwangi Festival. To present their best musical works, each angklung group receives intensive and profound education from professional musicians, with the support of the school, government, and parents. Thus, the Angklung  Caruk Festival is an arena for reproducing Osing traditional music culture which has an economic and educational dimension. It has an impact on strengthening the identity and social repositioning of the Osing community in the present and future
AdPisika: an adaptive e-learning system utilizing k-means clustering, decision tree, and bayesian network based on felder-silverman model to enhance physics academic performance
Get PDFAmid the shift to online learning during the COVID-19 outbreak, the academic performance of students has become a concern. To address this, Adaptive Learning Systems (ALS) have emerged, these help in assessing students and delivering personalized content. This study develops an ALS incorporating K-means Clustering, Decision Tree, and Bayesian Network techniques, based on the Felder-Silverman Learning Style Model (FSLSM). The aim is to optimize learning materials based on students' current Knowledge Level (KL) and their Learning Style (LS). The students who utilized the proposed system showed substantial improvements in their performance across the Electromagnetic Spectrum, Light, Electricity, and Magnetism modules, with increases of 28.8%, 41.4%, 31.9%, and 32.9%, respectively. These findings provide strong evidence that the adaptive e-learning system had a significant positive impact on post-test scores compared to pre-test scores, surpassing the outcomes achieved with the traditional learning approach. With a silhouette score of 0.7 for K-Means clustering, an accuracy of 87.5% for Decision Tree, and a 95.1% acceptance value for the distribution of learning objects using the Bayesian Network, the proposed adaptive system demonstrated successful implementation of these machine learning algorithms. Furthermore, the proposed system received "excellent" ratings for functional stability, performance efficiency, compatibility, and reliability, with mean values of 4.49, 4.43, 4.43, 4.8, and 4.47 respectively
Numerical study optimation design of CPU cooling system analysis using CFD method
Get PDFComputers often experience damage to the CPU, especially the mainboard and processor, due to several factors, including human error or excessive use and environmental conditions. Component placement is frequently utilized to improve the CPU room conditions to keep it cool. This research numerically investigates desktop PC processors and heatsink configurations for mechanical engineering vocational learning. The kind of metal material, number of fans, and fan arrangement were all tested at three levels. The computer components in this research are the CPU, heatsink, fan, and processor—a 65-watt Thermal Design Power (TDP) CPU with a constant air intake speed of 5 m/s. The criteria investigated include metal type (steel, aluminum, and copper), cooling design (horizontal, vertical, and mixed), and fan count (2-4-8). The methods used in this research are the Computational Fluid Dynamics (CFD) method and the Taguchi method to examine fluid flow characteristics and temperature. Numerical results show the maximum temperature is 123 °C in the vertical, eight-fan, and steel configurations. Minimum temperature 39.22 °C in mixed configuration, eight fans, and copper. These findings reveal that the kind of metal material, number of fans, and fan arrangement all impact the CPU cooler and heatsink configuration. However, the Taguchi method can provide a more detailed understanding of configuration