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Archives: Paper Submission

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.

XAI-Enhanced Hybrid Models for Effective Chronic Kidney Disease Prediction

Our key contribution lies in developing a hybrid predictive framework that combines machine learning, deep learning, and ensemble methods for highly accurate and interpretable Chronic Kidney Disease (CKD) detection. We addressed data imbalance through SMOTE, SMOTE-Tomek, and SMOTE-ENN, followed by SHAP-based feature selection via XGBoost, where top-ranked features were aggregated across all resampling strategies. Complementing this, statistical analysis with SPSS reinforced the identification of clinically significant features. Multiple machine learning classifiers, ensemble approaches, and advanced deep learning models—including Attention Autoencoder with XGBoost, TabNet, TabPFN, LightCNN, MLP, and DeepCrossNet—were systematically evaluated. DeepCrossNet achieved 97.38% accuracy, while the stacking ensemble attained 97.50% and Random Forest reached 97.71%. Furthermore, SHAP and LIME explanations emphasized GFR and serum creatinine as critical predictors, enhancing clinical trust. Visualization with t-SNE and UMAP confirmed class separability and detected ambiguous cases. Together, these contributions highlight the novelty of integrating SHAP-aggregated features, advanced resampling, and hybrid ML-DL ensembles to advance both accuracy and interpretability in CKD prediction.

Sustainable Development in Transition: Empirical Insights on Green Economy and Carbon Emissions from India

This study contributes by providing empirical evidence on the link between the green economy and carbon emissions in India, a country undergoing rapid economic transition. It fills a critical gap by integrating sustainability and emission dynamics in a single framework, offering insights that extend existing literature. The findings have strong policy relevance, supporting strategies for low-carbon growth and sustainable development pathways in emerging economies.

Women-led Social Entrepreneurship in Fostering SDGs: Myth or Reality? A Phenomenological Perspective

Abstract—The study explores women’s social entrepreneurial approaches that align with the United Nations’ Sustainable Development Goals, reflecting overall sustainable development in the context of Bangladesh, targeting achievement by 2030. The study adopted a phenomenological approach to reveal the lived experiences of six women social entrepreneurs’ journey towards social entrepreneurship. Protocol analysis, conducted through in-depth interviews, was analyzed through a phenomenological lens. Following Husserl’s epoche (bracketing) and eidetic reduction, it captures the essence (noema) and the act of experiencing (noesis) of underlying entrepreneurial approaches towards the SDGs. The findings of the study demonstrate nine key approaches and their alignment with the SDGs and sustainable development of Bangladesh. By examining lived experiences and interpreting realities, it seeks to provide policymakers with actionable insights to facilitate supportive arrangements for schemes, mentorship, and networking. Additionally, it aims to empower educators to serve findings as learning modules and to engage both national and international development organizations in actively involving social entrepreneurs as key partners in sustainability efforts. Future research endeavors will shed light on men and other supportive organizations, addressing the SDGs within the context of different countries.