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Archives: Paper Submission

Digital Financial Inclusion for Resilient Digital Ecosystems in Fiji and the Pacific Islands

The research contributes by systematically identifying and synthesising the dual role of emerging technologies, specifically human -centric artificial intelligence (AI) and decentralised digital digital systems, in shaping financial inclusion outcomes. It demonstrates how these technologies simultaneously act as enablers of access and efficiency, while also introducing new barriers related to digital literacy, transparency and trust.

A Multi-Objective Genetic Algorithm-Based Approach for Explainable Healthcare Fraud Detection

• A MOGA-based framework is proposed for healthcare
fraud detection, which simultaneously optimizes classifi
cation performance and feature subset size.
• The framework integrates LIME to enable instance-level
interpretability, facilitating a deeper understanding of
model predictions.
• A comprehensive comparative analysis is conducted to
evaluate model performance and interpretability before
and after feature selection.

Test

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Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications

gentic AI represents an advanced stage of artificial intelligence capable of goal-driven behavior, adaptation, and self-improvement. It reviews what makes agentic AI different from traditional AI and explains how abilities like autonomy, memory, and reasoning support more general capabilities. Instead of proposing a new system, the paper provides a conceptual view of how current frameworks are evolving toward agentic AI.

Multi-Criteria Decision-Support System for Sustainable Management of the Aral Sea Basin under Uncertainty

— The Aral Sea Desiccated Basin faces one of Central Asia’s most severe ecological and socio-economic crises, driven by decades of unsustainable water use and climate change. Developing sustainable management strategies for this region remains complex due to uncertain, incomplete, and often conflicting environmental data. This study presents a hybrid Multi-Criteria Decision-Support System (MCDSS) that integrates Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques to address uncertainty and hesitation in environmental decisions. By incorporating membership, non-membership, and hesitation degrees, the proposed model enables a more realistic evaluation of management alternatives compared to traditional deterministic or fuzzy methods. To further enhance analytical capacity, a Large Multimodal Model (LMM) is introduced to process and fuse satellite imagery, numerical indicators, and expert textual inputs. The LMM supports cross-modal reasoning and improves the interpretation of soil salinity, vegetation degradation, and water resource distribution. A case study on water allocation and land rehabilitation in the Aral Sea Basin demonstrates that the LMM-enhanced IFS–MCDM framework improves decision robustness, adaptability, and transparency. The results suggest that the proposed system can effectively support data-driven, sustainable management strategies for ecologically vulnerable regions under uncertainty.

AI Driven Personalised Learning for Sustainable Higher Education in the Pacific

Personalised learning (PL) is increasingly promoted
as a strategy to enhance engagement and achievement in higher
education. Advances in adaptive systems, artificial intelligence, and
learning analytics enable tailored pathways and real-time feedback.
Meta-analyses report medium-to-large effects for intelligent
tutoring systems (ITS, g ≈ 0.6–0.7) and moderate gains for selfregulated
learning (SRL) interventions (d ≈ 0.69), while adaptive
platforms typically yield small-to-moderate improvements. Largescale
implementations, however, often produce only modest results
with considerable variance across contexts. This paper presents
a PRISMA-guided scoping review (2012–2025, n = 92 studies)
synthesising global evidence on PL modalities and applying
insights to Pacific higher education, where challenges of access,
equity, and cultural fit are acute. The contribution is twofold: first,
it explicitly links global effect-size evidence to equity outcomes
in low-resource contexts; second, it proposes an offline-first,
edge-based AI architecture tailored to Pacific higher education
to mitigate connectivity, privacy, and sustainability barriers.
The paper concludes with a context-sensitive research agenda
emphasising hybrid models, equity-first design, and governance
frameworks for sustainable implementation.

Decision-Making Framework for Sustainable Management of the Aral Sea Dried-Bottom under Uncertainty

The Aral Sea region has been facing severe ecological, social, and economic challenges due to large-scale water mismanagement and climate change impacts. Effective environmental decision-making in this area requires the integration of multiple uncertain and conflicting factors. Traditional decision-making approaches often fail to adequately handle the complexity and vagueness of such problems. This paper proposes a novel decision-making framework based on the theory of Intuitionistic Fuzzy Sets to address uncertainty in the management and ecological rehabilitation of the Aral Sea. The developed approach enables decision-makers to model hesitation and incomplete knowledge more effectively compared to classical fuzzy methods. A case study focused on sustainable water resource allocation and ecological restoration strategies in the Aral Sea basin demonstrates the applicability of the proposed model. The results show that the intuitionistic fuzzy approach provides greater flexibility and reliability in evaluating alternatives, thereby supporting more robust and sustainable environmental management decisions under uncertainty.

Bone Fracture Detection And Localisation In X-Ray Using Real Time Object Detection Model

The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.

Bone Fracture Detection And Localisation In X-Ray Using Real Time Object Detection Model

The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.

SSViT-4.0: A Self-Supervised Hybrid CNN-Transformer Framework for Industrial Visual Anomaly Detection

Detecting industrial visual anomalies remains a critical challenge in smart manufacturing due to scarce labeled defect samples and highly variable texture patterns. Existing anomaly detection (AD) and active learning (AL) approaches often struggle under adversarial conditions, as training typically relies on only normal, unlabeled data. This research proposes SSViT‑4.0, a self-supervised hybrid framework combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for reliable, label-efficient anomaly detection in industrial images. A cross-hierarchical fusion module integrates global ViT self-attention with local CNN feature extraction, overcoming limitations of conventional independent feature streams or supervised fine-tuning. Self-supervised pretraining via reconstruction enables efficient representation learning without manual annotations, while patch-based embeddings and a lightweight anomaly scoring head allow accurate anomaly localization at inference. Experimental results on benchmark datasets (e.g., MVTec AD) demonstrate that SSViT‑4.0 surpasses CNN-only and Transformer-only baselines in detection accuracy and localization, maintaining real-time inference. The framework offers a scalable and efficient solution for automated visual inspection in Industry 4.0 environments.