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International Journal Research on Metaverse
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30626927     DOI : https://doi.org/10.47738/ijrm
Core Subject : Science,
Computer Science & IT
Virtual and augmented reality technologies Network infrastructure and architecture for the metaverse Digital economy and transactions in the metaverse Social and cultural aspects of virtual environments Development and design of content in the metaverse Impact of the metaverse on industries such as education, healthcare, entertainment, and business Regulation, policy, and ethics in the metaverse IJRM aims to foster interdisciplinary dialogue and collaboration, contributing to the body of knowledge that drives the adoption and evolution of metaverse technologies. Papers published in IJRM are grounded in rigorous research methods and are expected to articulate their implications for theory and practice clearly. Authors are encouraged to state their contributions to the state-of-the-art in the field explicitly. Subject Area and Category: The International Journal Research on Metaverse focuses on virtual and augmented reality, network infrastructure, digital economy, social and cultural impacts, content development, industry-specific applications, regulation and ethics, and practical case studies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 51 Documents
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Hybrid Ensemble Learning for Anomaly Detection in Metaverse Transactions Using Isolation Forest, Autoencoder, and XGBoost S. Prakash; S. Aruna Mary; G. Sudhagar; Malathy Batumalay
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.46

Abstract

The rapid expansion of metaverse platforms has increased the volume and complexity of digital transactions, creating a greater need for reliable anomaly detection systems. This study proposes a hybrid ensemble learning framework that integrates Isolation Forest, Autoencoder, and XGBoost using a meta learning approach to detect anomalous transactions in metaverse environments. The framework combines unsupervised and supervised learning to identify structural irregularities, behavioral deviations, and contextual patterns associated with high-risk activities. Using a transaction dataset containing behavioral, contextual, and numerical features, the hybrid model was evaluated against its individual components. The results show that the proposed framework achieves superior accuracy, precision, recall, and ROC AUC values compared to standalone models. The analysis of feature importance indicates that quantitative variables, including transaction amount, session duration, and risk score, provide the strongest predictive contribution, while contextual and behavioral factors improve model interpretability and generalization. Principal Component Analysis further visualizes the separation between normal and anomalous clusters, confirming that the hybrid ensemble effectively captures latent relationships within high-dimensional transaction data. Overall, the findings demonstrate that the proposed approach provides a robust and scalable solution for detecting irregular patterns in metaverse-based blockchain transactions. This model also offers practical implications for real-time financial risk assessment and digital security management in decentralized virtual economies.
Ensemble Machine Learning Framework for Predicting User Engagement and Risk Patterns in Metaverse Transactions Supinda Lertlit
International Journal Research on Metaverse Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i2.47

Abstract

The rapid expansion of metaverse ecosystems has introduced new challenges in understanding user behavior, engagement, and financial risk within virtual transactions. This study proposes an ensemble machine learning framework that integrates LightGBM, XGBoost, and Random Forest algorithms to predict user engagement and transaction risk in metaverse environments. The model leverages temporal and behavioral features, including session duration, transaction amount, activity intensity, and short-term risk variations, to capture dynamic patterns of user interaction. Using a time-series dataset of metaverse transactions, the ensemble achieved a Mean Absolute Error (MAE) of 2.15, a Mean Squared Error (MSE) of 16.13, and an R² score of 0.9652, demonstrating exceptional predictive accuracy and generalization capability. Feature importance analysis revealed that both behavioral persistence and short-term temporal variability are critical determinants of risk. The findings highlight the effectiveness of ensemble learning for real-time risk detection, behavioral monitoring, and adaptive governance in digital economies. This study contributes to the development of intelligent, interpretable, and scalable AI-driven risk management systems for emerging metaverse platforms.
Enhancing Trust and Transparency in Metaverse Financial Systems Through Explainable Artificial Intelligence for Risk Assessment Rana Saad Mohammed
International Journal Research on Metaverse Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i2.48

Abstract

This study proposes an Explainable Artificial Intelligence (XAI) model for financial risk assessment in the Metaverse ecosystem by combining predictive accuracy with interpretability through the XGBoost algorithm. The model was trained on behavioral, transactional, and demographic data to capture complex relationships influencing user financial risk. The evaluation results showed strong predictive performance, with an R² value of 0.813 and a 5-fold cross-validation R² of 0.816, indicating robustness and generalization. Feature importance analysis identified High-Value Purchase Pattern and New Users as the most significant predictors, followed by Login Frequency and Transaction Amount, highlighting the importance of user activity and experience in determining financial risk. Residual diagnostics confirmed that the prediction errors were normally distributed and unbiased, demonstrating that the model was accurate and fair across different risk levels. The integration of explainability mechanisms allows stakeholders to interpret and validate AI-driven decisions, promoting transparency and accountability. This research contributes to the advancement of trustworthy and ethical AI systems in virtual economies, offering a practical framework for transparent financial risk management within the Metaverse.
Hybrid LSTM-Based Traffic Anomaly Detection for Smart Mobility in Metaverse Cities Othman Atti Alsulami
International Journal Research on Metaverse Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i2.49

Abstract

The rapid development of Metaverse technologies has created new opportunities for modeling and managing intelligent transportation systems in virtual urban environments. However, ensuring efficient and stable mobility within these digital ecosystems requires accurate and interpretable anomaly detection mechanisms. This study proposes a Hybrid LSTM–Isolation Forest (HLIF-Net) framework for identifying traffic anomalies in smart mobility simulations using the METR-LA dataset. The model integrates a deep learning-based Long Short-Term Memory (LSTM) network for sequential traffic prediction with an Isolation Forest algorithm that detects anomalies from residual prediction errors. The proposed framework was trained using twelve-step input sequences of normalized traffic speed data and evaluated across 34,260-time samples. Experimental results demonstrated strong model stability, with a Mean Squared Error (MSE) of 0.0021 on training data and 0.0025 on validation data. Approximately 3 percent of traffic instances were classified as anomalous, reflecting potential irregularities such as congestion, sudden speed changes, or sensor inconsistencies. Temporal and spatial analyses further revealed that anomalies tend to cluster during periods of instability and concentrate in high-mobility regions. These findings confirm that the HLIF-Net framework provides a robust, data-driven solution for real-time anomaly detection and intelligent mobility management in Metaverse city environments.
AI-Powered Digital Twin of Urban Road Networks for Real-Time Traffic Congestion Prediction in the Metaverse Heri Subangkit; Iqbaluddin Syam Had; Gupita Nurmalita Sari
International Journal Research on Metaverse Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i2.50

Abstract

This study presents the development of an artificial intelligence-powered digital twin framework designed to predict urban traffic congestion using a Long Short-Term Memory (LSTM) deep learning model. Historical traffic data collected from multiple city road segments were analyzed to capture temporal dependencies and fluctuations in vehicular flow patterns. The proposed model was trained using a 30-day look-back window and optimized through parameter tuning, achieving a Root Mean Squared Error (RMSE) of 2,726.36 and a Mean Absolute Error (MAE) of 2,154.84. These results demonstrate the model’s capability to accurately represent complex non-linear relationships inherent in urban traffic dynamics. The temporal analysis revealed distinct bi-modal patterns corresponding to morning and evening rush hours, while a 30-day heatmap visualization highlighted recurring congestion peaks and low-traffic intervals. The integration of predictive analytics into a digital twin environment enables real-time visualization of congestion propagation, supporting data-driven planning and decision-making within a metaverse-based urban simulation. This framework establishes a methodological foundation for intelligent transportation systems that leverage artificial intelligence, digital twin technology, and virtual environments to enhance traffic forecasting, operational efficiency, and smart city management.
A Digital Twin-Enabled Deep Learning Framework for Remaining Useful Life Prediction of Turbofan Engines N. Ismayil Knai; B. Arun; B. V. Manikandan; S. Sheik Abdullah; D. Lakshmi
International Journal Research on Metaverse Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i2.51

Abstract

Accurate prediction of the Remaining Useful Life (RUL) of industrial machinery is essential for developing intelligent predictive maintenance and digital twin systems. This study proposes a Long Short-Term Memory (LSTM) neural network model to estimate the RUL of turbofan engines by analyzing multivariate time-series sensor data obtained from the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. The model was designed to capture temporal dependencies within the sensor readings in order to learn the complex patterns of degradation that occur over time. Four datasets, namely FD001, FD002, FD003, and FD004, were examined, and the FD001 dataset was selected as the baseline because it represents a single operational condition with a clearly defined degradation trend. The trained LSTM model achieved a Mean Absolute Error (MAE) of 38.533 and a Root Mean Square Error (RMSE) of 51.069, showing that it can closely follow the actual degradation trajectory with a high degree of accuracy. Correlation analysis identified several key sensors, including sensor_7, sensor_12, sensor_20, and sensor_21, as the most influential variables for predicting RUL. The findings indicate that deep learning models can effectively represent mechanical degradation and can be integrated into digital twin frameworks to enable real-time health monitoring, proactive maintenance scheduling, and data-driven decision-making in industrial environments.
Deep Learning-Based Digital Twin Framework for Real-Time Water Network Anomaly Detection Hussain Ahmad Awad; Mohammed-Issa Riad Jaradat
International Journal Research on Metaverse Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i3.56

Abstract

Urban water distribution systems are critical infrastructures that require continuous monitoring to prevent water loss caused by leaks and operational anomalies. Conventional monitoring approaches often struggle to detect early-stage anomalies due to the complexity and temporal dynamics of multivariate sensor data. This study proposes a Deep Learning based Digital Twin framework for real time anomaly detection in urban water networks. The framework integrates a Deep LSTM Autoencoder with a Digital Twin architecture to model normal operational behavior and identify deviations through reconstruction error analysis. The model was trained exclusively on normal operational data and optimized using a Receiver Operating Characteristic based threshold selection strategy. Experimental evaluation on time series sensor data demonstrated that the proposed approach achieved a recall of 0.74 for leak detection and an Area Under the Curve value of 0.750, indicating reliable discrimination between normal and anomalous conditions. The results show that temporal sequence learning significantly enhances anomaly detection capability in complex water distribution systems. By synchronizing real time anomaly detection with a Digital Twin environment, the proposed framework enables proactive monitoring, improved situational awareness, and predictive maintenance support for smart city water management applications.
Predicting User Risk Behavior in the Metaverse Using Hybrid Bidirectional LSTM–GRU Temporal Deep Learning Ammar Salamh Alrawahna; Khaled Mohammed Abdulaziz Aboalganam; Ohoud Abdel Hafiez Ali Khasawneh
International Journal Research on Metaverse Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i3.59

Abstract

The rapid expansion of metaverse environments has created a complex ecosystem where user interactions generate vast amounts of behavioral and transactional data. Understanding and predicting user risk behavior has become a critical challenge for maintaining trust, safety, and economic stability within these digital spaces. This study proposes a hybrid Bidirectional Long Short-Term Memory and Gated Recurrent Unit (LSTM–GRU) deep learning model to forecast user risk behavior based on temporal patterns derived from metaverse transaction data. The model was trained on sequential features, including login frequency, session duration, and transaction amount, to capture both short-term fluctuations and long-term behavioral dependencies. Experimental evaluation demonstrated that the proposed model achieved a Root Mean Square Error (RMSE) of 7.342, a Mean Absolute Error (MAE) of 5.813, and a coefficient of determination (R²) of 0.352. The results indicate that the hybrid architecture effectively learns temporal dynamics and accurately forecasts user risk trends, providing a reliable foundation for proactive risk detection. The consistent convergence between training and validation loss further confirms the model’s stability and generalization capability. Overall, this research contributes to the integration of artificial intelligence in metaverse governance by offering a data-driven approach to behavioral forecasting that can enhance user safety, improve decision-making, and support the development of intelligent, adaptive risk management systems in virtual environments.
Efficient Spatiotemporal Traffic Forecasting for Smart Cities Using DCRNN-Lite in a Digital Twin Environment Hussain Ahmad Awad; Ahmed Suliman Zraikat
International Journal Research on Metaverse Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i3.64

Abstract

Urban traffic congestion remains a critical challenge for smart city development, particularly in the context of real-time monitoring and prediction for intelligent transportation systems. To address this issue, this study proposes a Digital Twin-based urban traffic prediction framework using a lightweight Diffusion Convolutional Recurrent Neural Network (DCRNN-Lite). The proposed model integrates spatial dependencies among road segments through diffusion convolution and temporal traffic dynamics through recurrent modeling, enabling effective spatiotemporal traffic forecasting with reduced computational complexity. Experiments were conducted on the real-world METR-LA dataset, consisting of traffic speed data from 207 sensors deployed across the Los Angeles highway network. The experimental results demonstrate that DCRNN-Lite achieves stable prediction performance, as reflected by low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), consistent convergence behavior, and strong correlation between predicted and actual traffic speeds at both node and city-wide levels. Despite its simplified architecture, the model effectively captures local traffic variations and global mobility trends, making it suitable for real-time deployment. The findings indicate that the proposed approach provides a favorable balance between accuracy and efficiency, highlighting its potential as a core component for digital twin-enabled smart cities and metaverse-based urban traffic management systems.
Multivariate Stock Price Forecasting Using Bidirectional Long Short-Term Memory (BiLSTM) with Technical Indicators Elwira Gross Golacka
International Journal Research on Metaverse Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i3.70

Abstract

Stock price forecasting has become an important research area in financial market analysis due to the highly dynamic and volatile nature of stock market movements. Accurate forecasting models can assist investors and financial analysts in making better investment decisions and managing financial risks more effectively. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM)-based deep learning model for forecasting stock prices using multivariate financial time-series data. The dataset used in this study consists of historical stock market data combined with several technical indicators, including High, Low, Open, Volume, Moving Average (MA10 and MA20), and Relative Strength Index (RSI). Prior to model training, the data underwent preprocessing stages including data cleaning, normalization, and sequence generation using a sliding window approach. The proposed architecture employs a Bidirectional LSTM layer followed by additional LSTM and dense layers to capture complex temporal dependencies and nonlinear patterns in stock price movements. The experimental results demonstrate that the proposed model achieved moderate forecasting performance with an MAE value of 15.5805, RMSE of 21.0883, and R² score of 0.4954. Furthermore, the prediction results show that the model was able to follow the general trend of actual stock price movements despite several deviations during periods of high market volatility. The findings indicate that the BiLSTM architecture has strong potential for financial time-series forecasting and can effectively capture important sequential patterns in stock market data. This study also highlights the challenges of stock price prediction due to the stochastic and nonlinear behavior of financial markets. Future research is recommended to incorporate additional external factors and more advanced deep learning architectures to further improve forecasting accuracy and robustness.

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