Journal of Mobile Multimedia

The Interactive Virtual Reality Learning Platform for Rice Milling Process

2026-01-11T17:09:27+01:00

Rice production plays a vital role in the economies of Southeast Asia; however, the increasing adoption of automated and digitally integrated rice milling systems has posed significant challenges for workforce training, particularly for inexperienced operators. To address this gap, this study proposes an Interactive Virtual Reality Learning Platform (IVRLP) designed to support immersive, experiential training for rice milling operations. The study adopts a mixed-methods approach, integrating quantitative pre- and post-training assessments with qualitative user feedback to evaluate the platform’s effectiveness. A total of 31 participants from rice milling cooperatives in Vientiane, Lao PDR, engaged with the IVRLP, which simulates key operational processes such as cleaning, separation, and polishing. Statistical analysis using a paired-samples t-test revealed significant improvements in learning outcomes. Specifically, process familiarity increased from M = 3.12 (SD = 0.84) to M = 4.08 (SD = 0.63), with a paired-samples t-test result of t = 6.85, df = 30, p < 0.001, while operational confidence improved from M = 3.05 (SD = 0.91) to M = 4.15 (SD = 0.58), t = 7.42, df = 30, p < 0.001. In addition, descriptive findings indicate high levels of user satisfaction, with 80.9% of participants reporting enhanced understanding and 78.5% expressing willingness to recommend the system.

Knowledge Conversion Dynamics in Multicultural LEGO® SERIOUS PLAY® Learning Environments: SECI Model and Constructionism Integrative Study

2026-03-08T01:43:00+01:00

Multicultural learning environments are increasingly prevalent in higher education; however, empirical understanding of how cultural context shapes knowledge conversion during collaborative, hands-on learning remains limited. This study examines how knowledge conversion unfolds within LEGO® SERIOUS PLAY® (LSP) activities by integrating Nonaka and Takeuchi’s SECI (Socialization, Externalization, Combination, Internalization) model with constructionist learning principles and multimodal interaction theory. A qualitative comparative case-study design was employed. Two graduate cohorts – Thai learners (n=9, Thai-medium instruction) and International learners from East and Southeast Asian backgrounds (n=10, English-medium instruction) – participated in an identical LSP-based Smart City design workshop under controlled conditions. Data were collected from group presentation videos, individual reflection recordings, and instructor observations, and analysed using a hybrid deductive–inductive coding framework with frequency counts of Smart City-related coded episodes as a structured basis for cross-cohort comparison. The findings suggest distinct, culturally patterned knowledge-conversion trajectories. Thai learners appeared to demonstrate tacit synchrony, metaphor-first externalization, holistic combination, and experiential–emotional internalization. International learners appeared to exhibit explicit verbal coordination, verbal-first externalization, analytical combination, and cognitively oriented internalization. These patterns suggest that SECI processes may not operate uniformly across cultural contexts but appear to be mediated by communication styles, cognitive orientations, and culturally situated meaning-making practices. Based on these findings, the study proposes the Multicultural SECI Flow Model, which explains how constructionist artefacts may function as intercultural mediating objects that support convergence toward shared understanding across divergent cultural learning pathways. The study contributes to Knowledge Management scholarship and provides practical implications, including facilitation guidelines and a practitioner checklist, for culturally responsive LSP-based learning in higher education, corporate training, and digital collaboration contexts.

Layer Expansion Techniques for Quantum Neural Networks in COVID-19 Image Classification Using Expressibility and Entangling Capability

2026-04-05T11:07:16+02:00

The COVID-19 pandemic claimed numerous lives, and led to the development of new tools for disease treatment. Initial diagnosis using X-rays can now identify COVID-19-infected individuals, and significant research has promoted neural networks for X-ray image classification. Quantum technology is also gaining interest, with intriguing properties such as superposition and entanglement. This study proposed a Quantum Neural Network (QNN) for X-ray image classification to investigate the relationship techniques between layer expansion and quantum circuits, measured by expressibility and entanglement capability. The results showed that both metrics significantly affected model accuracy. Seven circuits were tested, each with six layers, to examine their impact on model performance. The experiments yielded an accuracy of 95%, with highly effective image classification circuits exhibiting a balanced relationship between entanglement capability and expressibility.

Detection and Mitigation of SQL Injection-based Attacks in Web Security

2026-05-16T23:21:16+02:00

Crafty tactics like nested request bodies, encoding schemes, and JavaScript Object Notation (JSON) operators are now used by attackers to trick and bypass conventional Web application firewalls. The proposed machine learning-based system for detecting and mitigating SQL injection attacks is designed not just to protect against conventional SQLi attacks but also against JSON-based SQLi attacks, NoSQL injection attacks, hybrid attacks, and conventional WAF evasion techniques. The proposed system utilizes a stacking ensemble of Random Forest, Gradient Boosting, and Logistic Regression classifiers with manually constructed features that represent various properties of queries instead of using conventional static rule-based techniques or deep learning models. The detector is integrated into an application process that facilitates query inspection, batch analysis, decision explanation, and mitigation actions. The application is made available via a Flask-based REST API. To add structural variety, the dataset is constructed from public payload sources and augmented methodically. To support detections and to provide potential WAF rules, feature-level explanations are employed. The proposed work is also extended to incorporate a privacy-preserving federative learning framework to show its efficacy in collaborative environments. The system’s overall goal is to provide a modern, API-driven application as a complementary injection attack detection and monitoring layer suitable for quick, easy deployment.