IEEE i-COSTE 2025 – NPS Australia Submission System

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.

Potential Use of Natural Fibre Reinforced Composites as Alternative Materials for Wind Turbine Blades – Short Review

With the rise and implementation of decarbonization policies at global level, there has been a rapid and increasing shift towards renewable energy sources. Wind energy has been one such alternative and greener source of energy which uses the wind turbine blades (WTBs) to harness and convert the kinetic energy of wind into electrical energy. On the downside, WTBs have poor recyclability attributes, which tend to be costly and complex, as they are manufactured from high-strength composite materials to meet their lightweight and intricate airfoil shape requirements. With many of the wind turbines approaching the end of their lifetime in service in the coming years, a surge in the amount of WTBs destined for the landfill is forecasted to surge in the upcoming decades. This could result in an ecological issue which could be worsened if sustainable measures are not considered and implemented in the medium and long term. This study aims to review the use of alternative sustainable materials, namely Natural Fibre Reinforced Composites (NFRCs), which could replace presently-used composites such as Glass Fibre Reinforced Composites (GFRCs) and Carbon Fibre Reinforced Composites (CFRCs). In the first part of this study, some typically used NFRCs and their key attributes for WTBs fabrication were reviewed. The potential of using fully biodegradable NFRCs for WTBs and their associated benefits were also discussed. Finally, the notable advantages and limitations observed when integrating NFRCs into modern WTBs were further discussed in this short review study. The findings of this study clearly show that the use of NFRCs in WTBs can largely enhance sustainability in the wind renewable energy sector while concurrently addressing some of the ecological factors associated with their disposal stage.

Trends in Cybersecurity Threats and Mitigation Strategies: A Decade of Global Evidence

This paper contributes to cybersecurity research by providing a decade-long analysis (2015–2024) and forecast analysis (2025-2030) of global cybersecurity threats, integrating statistical testing with trend visualization to evaluate both frequency and severity of incidents. Unlike prevailing assumptions of linear growth, the study demonstrates that while overall volumes have not increased significantly, specific attack types such as ransomware and phishing remain dominant and financially damaging. Furthermore, the comparative analysis of defense mechanisms reveals that incident response workflows, rather than specific technologies, largely determine resolution effectiveness. These insights offer evidence-based guidance for policymakers, practitioners, and organizations seeking to strengthen resilience through adaptive, data-driven cybersecurity strategies.

Web-based Explainable Machine Learning Model for Early-Stage Heart Disease Detection

• Proposed a web-based system using a Voting Ensemble (VE), a soft-voting classifier that combines K-Nearest Neighbors (KNN), Gradient Boosting (GB), and CatBoost with optimized hyperparameter for improved heart disease prediction.

• Evaluated model balance using 10-fold cross-validation with a held-out test set, ensuring robust performance and generalization.

• Integrated SHAP to give feature-level interpretability, enhancing clinical trust in model predictions.

• Developed a web-based application for real-time heart disease risk prediction, demonstrating potential for seamless clinical deployment.

Conference Site: IEEE i-COSTE 2025