Online-Journals.org (International Association of Online Engineering)
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Uncovering the Paradox: Digital Shifts in Human Resource Management through Mobile Technology and Wireless Communication – A Content Co-occurrence Analysis Using Citespace
Get PDFThis study explores digital shifts in human resource management (HRM) through content co-occurrence analysis, examining trends and the role of mobile technologies. A bibliometric content co-occurrence analysis was conducted on 28 papers from 2014 to 2024, sourced from the Scopus database. Cite space software visualized the scientific landscape and themes. The findings reveal that advanced technologies—including artificial intelligence (AI), big data analytics, cloud computing, Internet of Things (IoT), blockchain, and mobile computing—are increasingly integrated into HRM practices. These advancements have reinforced HRM’s centrality in digital transformation, focusing on data-driven decision-making, employee engagement, and strategic alignment with business goals. However, challenges remain, such as data privacy concerns, skill gaps, and balancing technology with human-centric approaches. Mobile technology is growing, particularly in M-learning applications, mobile web and video conferencing, remote laboratories, and wireless networks, in reshaping HRM processes. Integrating mobile computing and smart agent technologies enhances adaptive environments for HR professionals and employees. This study emphasizes the need for research to navigate these complexities and improve HRM outcomes. Future research should focus on mobile architectures integration, cost-effectiveness, applications, and the social impact of next-generation mobile technologies on human resource management
Guest Editorial
Get PDFThis special focus issue belongs to the annual conference on “Innovating for Impact: Digital Technologies for Public Administration, Engineering Application, Environmental Protection, Sustainable Business Operations, and Healthcare.” This conference was organized by Confab 360 Degree in collaboration with Manipal Academy of Higher Education, Dubai Campus; Rushford Business School, Switzerland; and European Global Institute of Innovation and Technology, Malta, on 19th and 20th February 2025. The official venue of the conference was the Manipal Academy of Higher Education, Dubai Campus. The chief patron(s) of the conference were Prof. (Dr.) S. Sudhindra, MAHE, Dubai; Prof. (Dr.) Tufail Syed; Prof. (Dr.) Padmakali Banerjee; and Prof. (Dr.) Nishu Ayedee. The patron(s) of the conference were Prof. (Dr.) Anuj Kumar, Prof. (Dr.) S.K. Pandey, Prof. (Dr.) Sunitha Prabhuram, Prof. (Dr.) Alok Satsangi, and Prof. (Dr.) Kanika Gupta. Prof. (Dr.) Anuj Kumar was also heading the editorial board of the conference. In this conference, more than 400 papers have been received, and 160 papers have been shortlisted for the conference presentation. More than 400 authors have participated in the conference as authors and co-authors of the papers. Out of 160 papers, 10 extended papers have been shortlisted for the special issue of the International Journal of Interactive Mobile Technologies. The authors have reviewed and revised these papers before shortlisting them
Development of Innovative Mobile QR-EFI Simulator in Problem-Based Teaching Factory (PBTF) Model to Enhance Students' 4C Skills
Get PDFThis study addresses the urgent need to bridge the competency gap between vocational education graduates and the rapidly evolving demands of the modern automotive industry, particularly in the increasingly complex electronic fuel injection (EFI) systems. Conventional learning approaches have proven insufficient in equipping students with the necessary technical and non-technical skills to adapt to current technologies. Therefore, this study aims to develop and validate a mobile-based EFI simulator integrated with QR code technology, enabling interactive visualization of the components, data flow, operational processes, and diagnostic procedures of the EFI system in an easily understandable manner. The simulator’s effectiveness is further evaluated through its integration within the Problem-Based Teaching Factory (PBTF) model to enhance students’ 4C skills: collaboration, communication, creativity, and critical thinking. Employing a Research and Development (R&D) approach with the 4-D model (Define, Design, Develop, Disseminate), the study involved 50 Automotive Engineering students at FT-UNP, divided into experimental and control groups. Validity analysis using SEM-PLS confirmed high validity of the simulator, especially in visualization aspects, while effectiveness testing showed that the experimental group using the simulator scored significantly higher in 4C skills than the control group. These results confirm that integrating the Mobile QR-EFI Simulator within the PBTF model significantly improves students’ critical and creative skills, bridges the gap between theory and practice in vocational automotive education, and meets the demand for interactive learning tools aligned with modern industry developments
Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease
Get PDFDementia is term related to many symptoms regarding brain abilities for old people. These symptoms include losing memory and thinking abilities. There are many causes leading to dementia, such as vascular dementia, Parkinson’s disease, and also severe head injury. But one of the biggest reasons is Alzheimer’s disease. Diagnostic of Alzheimer’s is challenging for the psychiatrists. There are many ways to diagnostic Alzheimer’s from conducting tests for memory to thinking skills to being evaluated by a healthcare professional. Brain-imaging as MRI, can be used to diagnose Alzheimer’s dementia earlier. This paper proposes a hybrid model to predict Alzheimer’s early by combining different machine learning (ML) models with deep learning models. Many models in this hybrid are used to get the powerful from each model and increasing the accuracy and to overcome the shortage of other models if it exist. We use two datasets of MRI for the brain from Kaggle. The result shows some hybrid models achieved outstanding results, as MobileNet with KNN scores the highest accuracy of 0.96, precision of 0.96, recall of 0.96, and F1-score of 0.96. This suggests that KNN is highly effective in leveraging the MobileNet. These top classifiers from the hybrid models indicate that combining robust feature extractors such as MobileNet, InceptionV3, and VGG16 with effective ML algorithms such as KNN, MLP, and random forest (RF) provides the best results for Alzheimer’s disease prediction
A Hybrid Model for Alzheimer’s Disease Classification Based on Neural Network Architectures Enhanced by GAN Model
Get PDFAlzheimer’s disease (AD) is a neurodegenerative disorder marked by progressive cognitive decline, making early and accurate diagnosis vital for timely intervention. This study explores the efficacy of combining generative adversarial networks (GANs), convolutional neural networks (CNNs), and vision transformers (ViTs) for AD classification using magnetic resonance imaging (MRI) data. GANs were employed to generate synthetic brain images, addressing data scarcity by augmenting the dataset. CNNs were then used for feature extraction, accelerating model training, and mitigating overfitting. These extracted features were subsequently fed into ViTs, known for their ability to capture spatial dependencies in image data. Experimental results demonstrated that the proposed GAN-CNN-ViT fusion model achieved high accuracy (96%) and robustness, outperforming traditional machine learning (ML) and deep learning approaches. GAN-generated synthetic images enhanced dataset generalization, improving ViT performance in distinguishing AD patients from healthy controls. Comparative analyses validated the superiority of this approach over recent methods in AD classification. This framework underscores the potential of deep learning techniques in advancing neuroimaging-based disease diagnosis. It holds significant promise for early AD detection, ultimately contributing to improved patient outcomes and quality of life through the integration of cutting-edge computer vision and ML methodologies in medical applications
Enhancing Digital Healthcare through 5G Integration Using a Slotted Bow-Tie 4 × 1 Patch Antenna Array
Get PDFThis paper explores the transformative potential of integrating fifth generation (5G) mobile communication technology into digital healthcare. The advanced features of 5G, such as high data speed, minimal delay, and extensive device connectivity, can enhance healthcare applications, including remote surgeries, teleconsultations, wearable device applications, and big data management. A novel high return loss and high gain slots Bow-Tie microstrip patch antenna array for 5G applications is proposed to support this integration. The antenna design process, simulations, and measurements are detailed, highlighting the antenna’s performance at a frequency of 5.8 GHz. The study concludes that the synergistic combination of 5G technology and the proposed antenna design can significantly improve digital healthcare delivery
Digital Health Record Systems or Applications in the Management of Type 2 Diabetes: A Literature Review
Get PDFThis literature review examines the integration of digital health record systems in the management of diabetes mellitus (DM), particularly type 2 diabetes, and highlights the urgent need for improved patient access to health information through technology. The use of information technology (IT) in the management of diabetes has shown remarkable results, such as increased medication adherence (12.8%–39%), lower HbA1c (0.49%–8%) levels, and lower blood pressure (47.2%–30.8%) levels. It also explores the benefits of mobile health (mHealth) applications, electronic health records (EHR), and personal health records (PHR) in improving self-management and healthcare support via social networks. It emphasizes the need for regular monitoring and communication facilitated by robust IT solutions that enable patients to access and share their health data, ensure effective communication, and support health monitoring. Unlike other reviews, we focus exclusively on proposals that facilitate interaction with medical records for automatic access to patient data. Our main contribution focuses on identifying critical needs to improve diabetes management through technology
Mobile Robotics Training Kit: Enhancing Learning Achievement, Practical Skills, and Problem-Solving Skills of Industrial Electrical Engineering Students
Get PDFThe complexity of microcontroller learning that must combine theoretical concepts and real practices makes it difficult for many students to master the competencies of microcontroller control systems. In addition, it is difficult to achieve practical skills that students must master without a training kit that can interpret the application of microcontroller control systems. Thus, the purpose of this study is to examine the effectiveness of mobile robotic training kits to improve student learning achievement, practical skills, and problem-solving skills. The true-experiment research method used in this study with the number of participants consisted of 76 students, who were divided into 38 experimental groups and 38 control groups randomly selected. The results of this study show that the mobile robotic training kit is significantly effective in improving students' learning achievement, practical skills, and problem-solving skills in the field of microcontroller control systems. This research provides empirical evidence of the importance of implementing a mobile robotics training kit in the learning process to improve students' competencies and prepare them with competencies relevant to the needs of the world of work
Online Engineering Education and Regional Growth: Innovation, Digitalization, and Policy
Get PDFBuilding upon traditional research on engineering education, innovation, and economic growth, this study introduces additional control variables such as air quality and talent concentration and extends the analytical scope to include underdeveloped regions in western China, thus advancing beyond conventional research paradigms. The research investigates the impact of high-quality engineering education on regional economic sustainability across nine diverse Chinese regions from 2014 to 2024. Grounded in regional innovation systems theory, the study examines both the direct effects of educational investment on economic outcomes and indirect effects mediated through innovation, digital transformation, and industrial upgrading. Findings indicate that financial investment in engineering education significantly promotes regional economic growth, although effects differ notably across regions. Developed areas in China experience economic growth primarily driven by innovation, whereas regions such as Henan and Heilongjiang rely more on industrial modernization. Additional control variables, including R&D personnel density and air quality, further influence these relationships. The study emphasized the importance of zoning to promote optimization of school investment
Industry 4.0 and Supply Chain Resilience: A Comprehensive Analysis of Technological Impacts
Get PDFThis study proposes a novel framework to assess the impact of Industry 4.0 (I4.0) technologies on supply chain resilience (SCRE) enhancement. Recognising the dynamic nature of technological advancements and their influence on resilient supply chain (SC), the framework employs an integrated approach. Initially, relevant I4.0 technologies were identified through literature. The I4.0 technologies are then evaluated against critical SCRE network design requirements using integrated multi-criteria decision-making (MCDM) techniques. Results highlight node criticality as a paramount factor in SCRE, while ranking artificial intelligence (AI) as the most impactful I4.0 technology, followed by autonomous vehicles (AV) and digital twin (DT). This study provides a robust and quantifiable roadmap for understanding the role of specific I4.0 technologies in bolstering SCRE