Online-Journals.org (International Association of Online Engineering)
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Emotion-Aware Mental Health Intervention Strategies on Mobile Computing Platforms
Get PDFWith the rapid advancement of mobile computing technologies and the continuous growth of global mobile device users, smartphones and other mobile terminals have created new possibilities for the widespread delivery of mental health services. At the same time, the increasing psychological pressure in modern society—especially in the post-pandemic era—has led to a surging demand for mental health interventions. Leveraging mobile computing platforms for accurate emotion recognition and effective intervention has thus become a critical area of research. However, current studies show that unimodal emotion recognition models achieve less than 60% accuracy in complex scenarios, while multimodal fusion methods often overlook the impact of spatiotemporal context, resulting in significant accuracy degradation in cross-regional emotion recognition. Furthermore, existing approaches lack effective strategies to address data sparsity and noise in mobile environments. This study focuses on emotion analysis in mental health service dialogues conducted via mobile networks. We propose a multimodal fusion module and a long-distance emotion fusion module. The former integrates multi-source data—including text, voice, and facial expressions—for comprehensive emotion capture, while the latter constructs a cross-regional emotional feature mapping mechanism to incorporate spatiotemporal contextual information. The contribution of this study lies in overcoming the limitations of existing models in terms of recognition accuracy and adaptability to diverse scenarios, thereby offering a feasible technical framework for mental health interventions on mobile computing platforms and advancing the intelligent development of digital mental health services
Innovative Applications of Augmented Reality and Interactive Mobile Technologies in Music Education
Get PDFAgainst the backdrop of digital transformation in education, the integration of augmented reality (AR) and interactive mobile technologies offers new pathways for intelligent and personalized development in music education. While existing studies have explored the use of AR in musical learning environments, significant gaps remain in the dynamic capture of learner behavior data and its application in personalized adaptation. For instance, current AR-based music visualization systems often lack mechanisms for feedback on user behavior trajectories, and mobile learning platforms struggle to effectively integrate short- and long-term student behavior data in their recommendation modules. To address these limitations, this study proposes a music learning point-of-interest (POI) recommendation model based on spatiotemporal AR expansion and mobile interaction networks. The model operates through the synergistic functions of three components: a long-term behavior dependency module, a short-term behavior dependency module, and a balancing and integration module. Together, they enable precise content recommendation and context-aware learning path guidance. The findings of this study not only provide personalized learning support for music learners but also offer a technical framework and methodological reference for the development of personalized models in digital music education
Adapting Online Education for Deaf and Hard-of-Hearing Adults: Preferences, Perceptions, and Learning Characteristics
Get PDFThis study explores the online education preferences and learning characteristics of deaf and hard-of-hearing adults based on a survey of 51 participants. Findings show that most participants prefer using smartphones to access online resources, while tablets and PCs are less favored. Respondents engage frequently with social media and news websites but use educational platforms, online banking, and government services less often. Key preferences include the use of visual components—such as sign language videos, graphics, and illustrative examples—highlighting the importance of visual learning. Additionally, clear module structures with pre- and post-lesson exercises are valued for improving comprehension. The study concludes that adaptive, mobile-friendly online courses with short, visually focused modules are essential for effective learning among individuals with hearing impairments
Hyperparameter Optimisation for Breast Cancer Detection Using APO and Pre-Trained CNNs
Get PDFEarly detection of breast cancer improves survival rates and treatment outcomes. Mammography remains the key diagnostic technique; however, building deep learning models for reliable categorisation is challenging. This paper presents a groundbreaking method for fine-tuning hyperparameters in two cutting-edge convolutional neural networks (CNNs), ConvNeXtBase and ResNet-50, which employ the Arctic Puffin Optimisation (APO) algorithm. Experiments were performed on two benchmark mammography datasets: CBIS-DDSM and MIAS. The APO-optimised ConvNeXtBase model achieved 98.46% accuracy on the CBIS-DDSM dataset and 99.34% on the MIAS dataset, with precision and recall both at 100% in the latter. These findings indicate that APO increases CNN performance, making it a promising tool for computer-assisted breast cancer diagnosis
Development and Pilot Implementation of a 3D Printed Prototype for Surgical Education and Preoperative Planning in Blount's Disease
Get PDFBlount’s disease is a complex pediatric orthopedic disorder characterized by progressive tibial deformities, necessitating precise surgical planning. Traditional imaging techniques often fail to provide a comprehensive assessment. This study explores the role of 3D-printed anatomical models in enhancing surgical planning and medical training, particularly within the Jordan Royal Medical Services. This study focuses on a 12-year-old patient with a severe multiplanar tibial deformity. A 3D model of the patient’s tibia and fibula was generated using CT scan data, segmented via 3D Slicer software, and printed using FormLabs Form 3BL SLA 3D printer. The printed model was utilized in a pediatric orthopedic workshop attended by over 30 surgeons. Seven surgeons responded to the post-workshop survey. It revealed that all participants found the 3D model beneficial for understanding anatomical deformities, enhancing surgical preparedness, and improving procedural accuracy. However, challenges such as cost, material rigidity, and production time were noted as barriers to widespread adoption. Future research should focus on optimizing material properties and reducing costs to facilitate broader implementation in surgical education and planning in Jordan
Assistive Technology for Inclusive Education: A Single-Case Study of a Child with Spastic Tetraplegia, Visual and Motor Disabilities in Ecuador
Get PDFIn this research, we designed and developed an adapted keyboard and software for learning to write for an Ecuadorian child with spastic tetraplegia, visual and motor impairment using a computer. The method used in this research was the adaptation of the Human Activity Assistive Technology (HAAT) model. First, functional and non-functional hardware and software requirements were analyzed. Then, the adapted keyboard and software were designed based on the collected requirements. The adapted keyboard consists of ten keys: three circular keys (Esc, Character, Select Character), two rectangular keys (Space, Enter), and five triangular keys (four scroll arrows, Erase). The software is compatible with any screen reader such as JAWS due to the child's visual impairment and allows writing on a computer through an auto-complete of words previously stored in a database. Finally, the keyboard and software were developed based on the previously elaborated designs and considerations of the child with disabilities. Several tests were developed with the JAWS screen reader to corroborate the accessibility and functionality of the adapted keyboard and software. In conclusion, keyboard and software helped to improve the writing learning. The target audience for this article are researchers, families, educators, and students with disabilities
Beta Wavelet Neural Networks for Medical Image Watermarking: A Fast and Robust Approach
Get PDFSmart devices and modern communication technologies now connect medical equipment more easily, which helps improve diagnostic processes. These systems use medical images to support diagnosis and decision-making, so it’s important to protect those images. Digital watermarking offers an effective way to secure medical images by embedding information that can verify authenticity, protect copyright, and ensure traceability throughout the healthcare workflow. To address this issue, this paper presents a robust and efficient medical image watermarking scheme that integrates the fast Beta wavelet transform (FBWT) with wavelet neural networks (WNN). Firstly, we created a library of activation functions containing a newly introduced family of Beta wavelets. Secondly, leveraging multi-resolution analysis (MRA) and fast wavelet transform (FWT), the medical image was decomposed to obtain wavelet coefficients. Finally, this approach embeds a watermark within the least significant contributions of the host medical image using WNN while maintaining high imperceptibility and robustness. Experimental results demonstrate the effectiveness of the scheme in balancing invisibility and resilience against various attacks, making it a promising solution for securing medical images in telemedicine applications. For imperceptibility evaluation, we used the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), with values of PSNR = 81.49 and SSIM = 1.000. In robustness testing, we measured the normalized correlation (NC) and bit error rate (BER), obtaining values of NC = 1.000 and BER = 0
University Students’ Perceptions of Google Translate in Learning English: A Case Study in Vietnam
Get PDFThis study investigates Vietnamese university students’ perceptions of Google translate (GT) in English language learning using the technology acceptance model (TAM). The research examines three key constructs: Perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI). It explores the relationships among them. Data collected from 535 students from a university in Vietnam revealed high levels of acceptance, with strong correlations among the TAM constructs. Female students reported significantly higher levels of PEOU, PU, and BI compared to males, highlighting gender-based differences in perceptions, while no significant differences were observed between first- and second-year students. These findings underscore the potential of GT as a supplementary tool in English-as-a-foreign-language (EFL) learning, enhancing user engagement and academic outcomes. However, overreliance on the tool may hinder critical language skill development, emphasizing the need for guided integration. Practical recommendations include training sessions on effective usage and gender-sensitive pedagogical interventions. Limitations of the study include its focus on a single institution and reliance on self-reported data, suggesting the need for broader, multi-institutional studies and qualitative approaches in future research. The findings contribute to the literature on technology acceptance in language education, providing valuable insights for optimizing GT’s role in EFL learning
The Impact of Mobile Technology on English Writing Teaching: The Relationship between Interactive Feedback and Autonomous Learning Abilities
Get PDFWith the rapid development of information technology (IT), mobile technology has been widely applied in the field of education, particularly in language learning. English writing, as one of the core skills in language acquisition, faces numerous challenges within traditional teaching models, such as limited learner autonomy and the difficulty of meeting individualized needs. In recent years, mobile technology-assisted English writing teaching has become a new research focus, with interactive feedback mechanisms and the enhancement of autonomous learning abilities being identified as key factors influencing instructional effectiveness. This study aims to explore mobile network-based English writing teaching, specifically analyzing how locationaware features and interactive feedback can enhance students’ autonomous learning abilities. In particular, the study investigates the forms and challenges of mobile network-assisted English writing teaching, examining mobile network discovery technologies, similarity calculation, and evolutionary computation methods relevant to English writing teaching, and proposes strategies for enhancing autonomous learning through interactive feedback
Smart Mobile Technologies in Math Education: Improving Elementary Students' Mathematical Communication Skills
Get PDFDespite mathematics being a mandatory subject in elementary schools worldwide, it is often taught using traditional methods that emphasise rote memorisation, frequent homework assignments, and retention. These approaches can feel monotonous and tiresome for 21st-century students. This study investigates the use of interactive technology in selected elementary school math classrooms to explore its potential for enhancing students’ mathematical communication skills. The integration of technology in teaching methods has the potential to improve academic performance and foster excellent mathematical communication proficiency. Data were collected through a questionnaire administered to 30 fourth-grade students from two public elementary schools. The findings indicate that teachers recognise the significant role of interactive technologies in enhancing elementary students’ mathematical communication skills. Furthermore, participants demonstrated improved abstract communication and comprehension following the implementation of interactive learning sessions. The results highlight a clear positive impact of technology on students’ mathematics learning, particularly when applied in hands-on learning environments. Overall, the study concludes that incorporating interactive technologies in math education is more effective than traditional teaching methods, leading to higher academic performance, a deeper understanding of mathematical concepts, and improved student communication skills