Online-Journals.org (International Association of Online Engineering)
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Digital Transformation of Music Education in Perspective of Internet-Assisted Signature Pedagogy
Get PDFCurrently the professional work of musicians tends to shift from the traditional classroom to the Internet environment. Two decades ago Shulman pointed out the need to reexamine the fundamental signatures due to the development of Web-based technologies that redesign the professions. While he suggests the method of critique as a signature pedagogy for teaching arts, that is proposed by his followers as a model of dialogue between equal voices, the perspectives of this method at the Internet-assisted learning environments are curious for study. In such a context the purpose of this study was determined: Do the competence of self-critique is promoted through the Internet-assisted professional learning of music based on the signature pedagogy? The research was organized at Kazan Federal University, 78 undergraduate students of music education were involved. The methodology used questionnaires, implemented by the google forms, and statistical methods. The results demonstrated that students who actively use Internet resources on the computer for making music or training performing skills demonstrate higher levels of self-critique competence in professional training. Based on the analysis was concluded, in conditions of lacking personal interactions due the shifting to the Internet environment musicians need to demonstrate their competences in critical self-assessment
Improving Monitoring of Heart Rate Using an RGB Camera and OpenCL Architecture: Towards a Heterogenous Embedded System Implementation
Get PDFConventional heart rate (HR) monitoring typically relies on contact sensors, but recent advancements demonstrate the potential of non-contact methods using RGB cameras for photoplethysmography (PPG)-based HR analysis. This study presents a real-time, non-contact HR monitoring system that applies signal processing techniques to accurately derive HR from facial video data. Our approach mitigates environmental and motion-induced noise through image enhancement and signal filtering while utilizing Fourier analysis to extract physiological signals from the processed PPG data. Implemented on a heterogeneous CPU-GPU system with high-level synthesis (HLS) for parallel acceleration, our proposed system achieves a substantial improvement in processing efficiency, outperforming the baseline method by a factor of 3.53 in processing time. These results underscore the system’s potential for integration into embedded healthcare monitoring applications, offering a pathway for reliable, non-invasive physiological monitoring
Lung Cancer Survival Period Prediction: Exploring Machine Learning Approaches
Get PDFLung cancer imposes the highest disease burden among all cancers and has the highest expected mortality rate, with 1.8 million deaths annually. It also has the lowest five-year survival rate, averaging at 20% among all diagnosed cancers. Machine learning (ML) offers a novel approach that has been utilized in healthcare for early detection, treatment planning, and survival time estimation. In this study, we applied various supervised, ensemble, and unsupervised ML algorithms to surveillance, epidemiology, and end results (SEER) lung cancer data to predict disease-specific survival (DSS) at 0.5-year, one-year, three-year, and five-year intervals. Our results show that ML models were effective in predicting short-term survival outcomes, but their ability to predict three-year and five-year survival was suboptimal. The limited performance of the models to predict survival outcomes may be attributed to the class imbalance that inherently exists in lung cancer patients. It may also be an indication of limited capacity of the selected features to predict long-term survival. Among the models tested, logistic regression (LR) and XGBoost were most robust algorithms to predict survival outcomes using given features. K-nearest neighbour (K-NN) and deep neural network (DNN) showed relatively weak performance as compared to other models in survival prediction. Additionally, the study found that household income, a socioeconomic factor, was the most significant predictor of survival across all time intervals. These findings highlight the potential of ML in survival prediction, particularly in the short term for lung cancer. The study also emphasizes the importance of addressing socioeconomic disparities as part of public health strategies to improve lung cancer outcomes
Loneliness among Students Before and After the COVID-19 Pandemic in Da Nang, Vietnam
Get PDFLoneliness stood out as a significant emotional experience during the COVID-19 pandemic, especially in times of enforced social distancing. This study examines the phenomenon of loneliness and its effects on adolescent mental health in Da Nang City throughout the pandemic. Data collection took place online in early 2023, approximately 18 months after lockdown restrictions in Da Nang were lifted. A total of 1,262 students from grades 6 to 12 participated in the study. Key findings include: (1) loneliness was a dominant emotional response among adolescents during lockdown compared to post-pandemic; (2) major contributors to loneliness included illness, social isolation, and insufficient parental attention and care; (3) although many adolescents reported brief and mild feelings of loneliness, a notable percentage experienced persistent and intense loneliness; and (4) support from family and peers was vital in mitigating the mental health challenges associated with loneliness. These insights highlight the critical role of social connections in addressing the enduring psychological impacts of loneliness
Research in Online Teacher Professional Development: A Systematic Mapping Review
Get PDFThis systematic mapping study examines 115 studies on online teacher professional development (OTPD) programs, including online-only and blended learning (BL) formats. Findings reveal that most programs combine synchronous and asynchronous phases, emphasizing collaborative, action-oriented, and reflective learning approaches. Frequently used technologies include discussion forums, videos, and learning management systems (LMS), while innovative tools such as virtual reality (VR) and artificial intelligence (AI) are less commonly implemented. Approximately half of the programs involve instructor guidance. These findings offer valuable insights into the design and implementation of OTPD and underline the need for further research into content-specific strategies and the effective use of emerging technologies
The Why and How of Generative AI Application in Business for Career Readiness
Get PDFThis paper examines the integration of Generative AI in business courses to enhance students’ career readiness in an AI-driven world. Despite rapid AI growth and adoption across industries, there remains a gap in employee expertise. Using a case study from an undergraduate economics course at Penn State University, the paper explores how AI tools, particularly prompt engineering, can complement traditional learning and help students develop essential skills for the future workforce. The findings suggest that effective AI integration fosters deeper learning, increases productivity, and equips students to navigate an evolving job market. The paper advocates for AI-enhanced learning in the classrooms as crucial for improving student employability
Dissecting Retinal Disease: A Multi-Modal Deep Learning Approach with Explainable AI for Disease Classification across Various Classes
Get PDFThis study investigates the efficacy of various deep learning (DL) models in detecting retinal diseases, specifically focusing on cataract detection. Utilizing a pre-processed fundus images data set classified into normal and cataract classes, we evaluate the performance of ResNet, VGG-16 and VGG-19 models based on accuracy, sensitivity, and specificity in classifying fundus images. The primary objective of this work is to provide explanations on the predictions done by the said DL models in order to ensure the ground-truth verification. The explanation is achieved using the explainable artificial intelligence (XAI) model namely gradient-weighted class activation mapping (Grad-CAM), which helps to visualize and interpret the decision-making process of these models. Through a comprehensive exploratory data analysis (EDA), model training, and evaluation, VGG-19 emerged as the superior model, achieving the highest accuracy, precision, and recall. Grad-CAM heat maps provide insights into the models’ attention in image features, highlighting the impact of cataracts on retinal structure. The study underscores the potential of DL in retinal disease detection and the pivotal role of explainable artificial intelligence (XAI) in enhancing model interpretability. Future directions include exploring more advanced DL architectures and furthering the application of XAI techniques to improve detection systems’ accuracy and transparency
Analysis of Autonomous Wheelchair Navigation Technologies in the Past Five Years: A Systematic Review
Get PDFThis study aims to analyze the latest developments in wheelchair navigation assistance systems. This analysis is obtained by conducting a systematic review of the significance of technologies for performance metrics and control strategies used. The autonomous category, input methods, tools used, technology used, test type, and accuracy were selected as reference metrics that set the comparison criteria, highlight innovative approaches, and discuss the development field of wheelchairs. In this work, to conduct the systematic review, four databases were identified. These include Science Direct, Taylor and Francis Online, Springer Journals, and IEEE Xplore. The pool of keywords set was selected to identify research articles published in the past five years. Inclusion and exclusion criteria were set to select the relevant studies that were consistent with the objectives of this study. Based on these criteria, 46 research papers were selected that met the inclusion requirements. The review study showed that wheelchair technology models such as autonomous control, 3D localization, and brain-computer interfaces (BCIs) were more precise in navigation and increased user independence. This systematic planning will help researchers, engineers, and practitioners make more realistic decisions to fill the gaps in available navigation aids and propose new and improved solutions for innovative assistant applications to ensure safety and accurate navigation. This study has many implications, especially the impact of reducing deaths and serious injuries among people with disabilities who use advanced technology wheelchairs
Neural Network-Based Support System to Improve Alzheimer's Detection Using Magnetic Resonance Imaging
Get PDFEarly and accurate detection of Alzheimer’s is crucial for the quality of life of patients and families. Given the limitations of traditional methods, neural networks offer a promising alternative. This study implemented a neural network- based system to analyze brain magnetic resonance imaging (MRI) scans and detect Alzheimer’s. Using the SCRUM methodology, data acquisition and preparation, network training, and system evaluation were managed. The Inception V3 model achieved 98% accuracy, outperforming other models. This efficacy suggests that convolutional neural networks can significantly improve early detection, reducing the time to diagnosis. The findings support the use of advanced artificial intelligence to improve clinical outcomes and enable more timely interventions in Alzheimer’s patients
Bridging Tacit Knowledge and Explicit Knowledge: An Ontological Model for Effective Knowledge Conversion
Get PDFKnowledge management (KM) involves a structured approach to creating, sharing, utilizing, and organizing knowledge and information within an organization, aiming to enhance its efficiency, productivity, and competitive advantage. A core element of KM is the distinction between tacit and explicit knowledge (EK). Tacit knowledge (TK) refers to personal insights and skills that are difficult to articulate or transfer, as they are deeply embedded in individual experiences. In contrast, EK consists of information that can be easily documented, communicated, and shared. The process of converting TK into EK is essential for fostering innovation and organizational growth, particularly in today’s fast-paced business environment. By translating TK into a more formal, shareable format, it becomes easier for others to access and apply valuable insights. However, capturing TK presents challenges because it is subjective and linked to personal experience. The practice of externalization involves developing creative methods to articulate and share this kind of knowledge, making it accessible and actionable for others. This paper introduces a model for representing TK and outlines strategies for transforming it into explicit forms. It also discusses techniques for effectively capturing this valuable expertise, which is crucial for making informed decisions. Converting TK into EK ensures that organizations can preserve and utilize critical insights across different roles and functions