Current Issue [Vol. 12, No. 05] [May 2026]

This study proposes a metaverse-based digital twin system architecture for organ-based physiological anomaly detection and multi-class anomaly classification in the field of healthcare. The proposed system architecture was created by combining synthetic scenario generation, six-dimensional feature extraction, LSTM-based prediction model training, a threshold determination mechanism based on organ functions, a semantic inference-based decision-making module, and Unity-based interactive visualization components. In the proposed system, anomaly detection is performed for the left lung, right lung, and heart using heart rate and respiratory data, and the results are classified as normal, warning, lung anomaly, heart anomaly, and critical condition. Experimental evaluation was performed on a synthetic dataset of 24,000 sample data (each containing 20 time steps). The training model achieved 89.39% accuracy, 82.41% Micro-F1 score, 82.00% Macro-F1 score, 80.34% precision, 84.59% recall and 72.17% Exact Match Accuracy in the test dataset. Consequently, the findings indicate that the proposed approach provides a technically balanced and evaluable solution for multi-label physiological monitoring processes.
Keywords: Digital twin, metaverse, LSTM, physiological time-series, multi-label anomaly detection, healthcare monitoring, Unity visualization

Quantum computing utilizes the basic principles of quantum mechanics, involving superposition and entanglement, for executing computational tasks that are intractable for classical computers [1]. The new paradigm uses the unique quantum properties of qubits for processing as well as storing data, providing exponential speedups of certain problems, such as prime factorization, database search, and simulation regarding complex quantum systems [2], [3].

The study x-rayed the social challenge of youth restiveness in the Niger Delta Region and stressed the role of woodwork and artistic craftsmanship as primary tools to combat youth restiveness through engagement in skills acquisition and self-employment. Four objectives and three research questions guided the study. Relevant concepts and theories were reviewed. Both primary and secondary data were used. 420 copies of questionnaire was used to generate the primary data, which were analysed with descriptive statistics. The results showed that unemployment, poverty and political marginalisation were among the major drivers of youth restiveness, while crime, disruption of economic activities and destruction of property were the most cited consequences. Aggregate item responses showed 73.33% agreement on perceived root causes with a mean score of 3.04, 75.24% agreement on perceived consequences with a mean score of 3.08, and 75.24% agreement that woodwork and artistic craftsmanship could help curb youth restiveness with a mean score of 3.04. Secondary data also showed that Rivers State recorded a youth not in employment, education or training rate of 36.0% and an unemployment rate of 13.4% under the revised labour force methodology. The paper concluded that craft based technical and vocational education, linked to apprenticeship, start-up tools and market access, could reduce idle time and grievance driven recruitment into violent networks when combined with governance reforms and local security partnerships.
Keywords: youth restiveness, woodwork, artistic craftsmanship, Niger Delta.