Online-Journals.org (International Association of Online Engineering)
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How Mobile E-Commerce is Revolutionizing Marketing Strategies for Indian MSMEs
Get PDFThis paper presents a multi-theoretical framework to explain how mobile e-commerce is revolutionising marketing strategies among Indian Micro, Small, and Medium Enterprises (MSMEs). By integrating mobile affordance theory, service-dominant logic (S-D logic), and the customer engagement cycle (CEC), the framework addresses the limitations of traditional adoption models like technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and diffusion of innovation (DOI) in capturing strategic, experiential, and customer-centric dynamics. The study highlights how MSMEs employ mobile affordances such as push notifications, unified payments interface (UPI) payments, chatbots, and WhatsApp business APIs to co-create value and engage customers across awareness, acquisition, retention, and advocacy phases. National-level policies, such as digital India, UPI and open network for digital commerce (ONDC), have strengthened infrastructure and availability which in return catalysed the growth of mobile technology. The model provides MSME with direction to utilise mobile technologies, not only to survive but also to create sustainable value and compete successfully. It also has the potential to inspire future empirical research by presenting a conceptual framework that can be used to build new emerging market strategies focused on digitalisation
Integrating Mobile Technology into Accounting Practice Instruction: A Strategy for Application and Exploration of Educational Value
Get PDFAmid the rapid advancement of mobile technology and the ongoing digital transformation of education, accounting practice instruction has encountered persistent challenges, including the disconnection between theoretical frameworks and practical application and delays in updating instructional resources. Although prior research has demonstrated the potential of mobile technology in this field, limitations remain in aligning technological tools with the construction of knowledge systems. Moreover, fragmented learning experiences often lack systematic integration, and existing knowledge delivery mechanisms exhibit insufficient contextual relevance. To address these issues, this study focuses on the integration of mobile technology within accounting practice instruction. A knowledge graph for accounting practice instruction grounded in mobile technology was proposed, alongside a strategy for the targeted delivery of fragmented knowledge. By restructuring the accounting practice knowledge system and leveraging the visualization and intelligent capabilities of mobile technology, the study seeks to achieve systematic knowledge construction and personalized knowledge delivery. These efforts are intended to enhance the practicality, relevance, and adaptability of accounting practice instruction, thereby underscoring the educational value of mobile technology in the domain of accounting education
A Novel Ontology-Based Approach for Analyzing Patient Sentiment Regarding Chronic Diseases
Get PDFSentiment analysis (SA) plays a central role in understanding the multidimensional nature of comments on social media platforms like YouTube, Twitter, Facebook, and health forums. This article presents a multi-aspect, multi-sentiment annotation of chronic disease-related comments, highlighting the importance of connecting aspects, such as disease category and treatment, with corresponding sentiments (positive, negative, and neutral). The integration of ontologies enhances semantic consistency, enabling a comprehensive understanding of user experiences related to chronic diseases. The purpose of this paper is to bridge the gap between unstructured patient-generated content and actionable insights for healthcare professionals. A structured approach employing two ontologies—one for chronic disease aspects and another for sentiment classification—enables the linking of disease-related comments with associated emotions, supporting comprehensive SA. A multi-label classification model is trained to simultaneously predict multiple aspects and sentiments within a single comment, addressing the native complexities of sentiment expression. The article concludes with an evaluation of the model’s predictions against real-world annotated data to assess its effectiveness. Our approach achieves promising results, demonstrating its ability to accurately link comments to their corresponding aspects and sentiments
A Novel 3D Method Based on Region-Growing and Morphology for Lung Segmentation
Get PDFThe purpose of this study is to help in the early detection of lung abnormalities by accurately segmenting the volume of interest from the CT scans. For this, we suggest a novel method that loads the volume by interpolating additional images to increase the resolution, thus improving the efficiency of the region-growing application, without any pre-processing, for segmenting voxels in the range of -1000 and -500 HU, and then applying a combination of chain code and region-growing to repair the lacunae due to blood vessels, trachea branches, and/or lesions that have intensities outside the range of interest. The validation of the segmentation result, using metrics, shows how close our method is to the ground truth, with an accuracy of 99.99%, a dice coefficient of 98.99%, an IoU of 98.02%, a recall of 98.44%, a precision of 99.56%, and an F1-score of 98.99%. Compared to UNET, UNET++, and 3D-UNET. Our method presents better results except for recall, which is higher than ours with a minor difference of 0.09–0.85%
Leveraging Machine Learning for Early Detection of Asthmatic Children in Healthcare
Get PDFBreathing problems are common and often transient in early childhood, making it challenging to predict which children will develop persistent asthma. Early and accurate diagnosis is important to ensure appropriate medical treatment. Current prediction models, based on small and specific sample groups, demonstrate limited precision. Machine learning (ML) techniques, however, show promise for providing more accurate and generalizable predictions compared to traditional models. Method: In this study, we developed ML-based prediction models for childhood asthma using a health dataset. Dimensionality was reduced with using nonnegative matrix factorization (NMF), data imbalance addressed with the synthetic minority oversampling method (SMOTE), and outliers removed using density-based spatial clustering of applications with noise (DBSCAN). Predictions were made with the extreme gradient boosting (XG Boost) algorithm. Key factors associated with asthma included symptoms like dry cough, runny nose, breathing difficulty, and tiredness. The results can help clinicians predict asthma onset early and support timely intervention. Results: According to experimental findings, XG Boost classifier approach provided the most accurate results. Our model achieved 99.62% accuracy and area under the curve (AUC) of 0.992. Conclusions: This study investigates ML methods for predicting asthma onset in children, identifying XG Boost as the most accurate classifier
Comparative Analysis of Hybrid and Ensemble Learning in Lung Cancer Diagnosis
Get PDFLung cancer remains one of the leading causes of cancer-related deaths globally, primarily due to delayed diagnosis. Early and accurate detection is critical for improving patient survival rates. However, microarray data used in cancer diagnosis pose a significant challenge due to the curse of dimensionality, where a small number of samples are associated with a large number of features, potentially reducing the accuracy (ACC) of prediction models. To address this, we propose a hybrid machine learning (ML) approach that integrates correlation-based feature selection (CFS) and the elephant search algorithm (ESA) for effective feature selection and optimization. Additionally, we introduce an ensemble deep learning (EDL) strategy, combining a deep neural network (DNN) with a bagging classifier and ensemble techniques such as weighted averaging (WA), soft voting (SV), and hard voting. Experiments conducted on a microarray dataset from the national center for biotechnology information (NCBI) evaluated performance using ten metrics. The hybrid approach (CFS+ESA+MLP) achieved an ACC of 97.18%, while the ensemble DL model with hard voting (HV) attained a superior ACC of 98.87%. These results demonstrate the effectiveness of the proposed methodologies in enhancing early lung cancer diagnosis
Retraction: Practical Work of Digital System Course Based on Virtual Laboratory
No full textThe article (https://doi.org/10.3991/ijoe.v17i08.23359) has been retracted by the journal editors on 2025-07-25 because it contains cross-language plagiarism from a dissertation entitled “Pengembangan Model Pembelajaran Online Untuk Praktik Teknik Digital di Perguruan Tinggi,” authored by Muchlas, and published in 2013 which is available online at https://eprints.uad.ac.id/7154/
Enhancing Early Childhood Cognitive Development via Mobile Game-Based Learning Applications: Insights and Practical Experiences
Get PDFEarly childhood cognitive development is foundational for future academic and personal success. While traditional teaching approaches often overlook technology’s potential, gamebased learning applications offer an innovative solution to enhance cognitive abilities in young children. This study evaluates the feasibility and effectiveness of a game-based learning (GBL) application for enhancing early childhood cognitive abilities. Using Research and Development (R&D) methodology, 40 children aged 5–6 were randomly assigned to experimental and control groups for an eight-week intervention. The application was developed through systematic needs analysis, design, development, implementation, and evaluation. ANCOVA analysis of pre- and post-test assessments demonstrated highly significant effects (F = 2154.89, p < 0.001, partial η2 = 0.966), explaining 96.6% of score variations. Expert validation scores exceeded 4.6 out of 5.0, confirming the application’s quality. Qualitative feedback from teachers and parents noted substantial improvements in children’s problem-solving skills, memory retention, and critical thinking. These findings suggest that integrating GBL applications could enhance educational equity and improve cognitive development outcomes across diverse educational settings
Improving Students’ Motivation for Physical Activity Using Digital Media: A Quasi-Experimental Study in Physical Education Using Smartphones and Tablets
Get PDFBased on the potential of digital media (DM) to increase students’ motivation in physical education (PE), this study investigated the effects of a theory-based tablet and smartphone intervention on students’ motivation in PE. A total of 309 students (8th and 9th grade) participated over five weeks. The intervention group (n = 161) carried out DM-supported PE lessons. Basic psychological need satisfaction (BPNS) and behavioral regulation were assessed. Results show no significant differences between groups on BPNS and behavioral regulation. However, BPNS moderated behavioral regulation regardless of the group. The non-significant results on BPNS and behavioral regulation show that the intervention did not lead to a significant improvement in motivation, which may be due to too little autonomy support, too little attractive content, and too little fun for the students. Future studies should investigate students’ preferences for DM in PE and use these findings to develop interventions to support BPNS in PE
My Voice: An Augmentative and Alternative Communication Android App for Children with Apraxia in Arab Countries
Get PDFApraxia of speech (AOS) is a disorder that prevents people from talking. There is a need to develop different augmentative and alternative communication (AAC) tools to help people with AOS express themselves clearly to others. Most mobile apps dedicated to helping people with various disabilities don’t support the Arabic language, which makes them less effective for users in Arab countries. We present in this paper My Voice as an Android smartphone app that acts as an AAC tool for AOS children all around the world. In fact, this app has come to fill an obvious gap in this area of research. It supports both Arabic and English languages, allowing the users in Arabia to benefit from its features in a similar way to other users in other parts of the world. Moreover, the proposed app provides a user-friendly dashboard that can be used to convert written sentences into speech. It has three modes of communication, which are “keyboard,” “phrases,” and “words.” These modes give the user the choice to select the most intuitive method for constructing sentences. To evaluate our work, we have tested My Voice by asking a group of potential users to use it for a month and then fill out a 10-question survey. The feedback from the survey was very encouraging. As a future work, we plan to add new languages, present an iOS version, and include artificial intelligence (AI) techniques