Digital twins (DTs) are rapidly emerging as foundational enablers of 6G smart cities, offering real time monitoring, predictive analytics, and autonomous control across transportation, energy, healthcare, and industrial domains. Large scale DT adoption faces critical barriers including cybersecurity vulnerabilities, privacy risks, and the absence of standardized orchestration frameworks. This article presents Fed-DTOrch, a comprehensive end to end architecture that integrates federated intelligence, blockchain based audit trails, and AI governance to achieve secure and privacy preserving DT management. The proposed three tier architecture spans IoT and edge devices, domain specific twins, and a city level orchestrator, employing secure federated learning for model updates, lightweight cryptographic authentication, and tamper proof logging. We quantify the DT threat landscape, perform a standards gap analysis across ISO/IEC 27001, 3GPP TS 33.501, ITU-T IoT risk frameworks, and NIST AI RMF, and introduce a 6G ready security framework incorporating federated AI trust metrics, secure synchronization, and explainable AI audits. Cross domain evaluation across five smart city sectors demonstrates 35-60% privacy gain, 40-55% attack mitigation, 28-40% reliability uplift, 26-30% latency reduction, and >85% compliance readiness with <10% overhead. These results provide the first integrated blueprint that combines federated intelligence, blockchain-based auditability, and standards gap analysis to enable secure, standardized, and interoperable DT orchestration for trustworthy 6G ecosystems.
Li Wang, Xiuming Cheng· IEEE Communications Standard...· 2 citations
Combining hybrid ensemble learning, two-stage feature selection, and XAI approaches provides a computationally efficient, dependable, and interpretable method for PCOS diagnosis and practitioners may find this model to be a useful decision-support tool that improves the accuracy of diagnosis and lessens the need for human interpretation.
Md Rakibul Hasan Efty, Md Naymur Rohman, K. M. Uddin et al.· Health Science Reports· 0 citations
AI-based models, particularly CNN-based DL architectures, demonstrate clinically relevant diagnostic performance with high sensitivity, specificity, and diagnostic odds ratios, supporting their potential role as adjunctive tools in CBCT-based differentiation of OKCs from other odontogenic lesions.
R. Shoorgashti, S. Lesan, S. S. Ehsani et al.· Health Science Reports· 0 citations
This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit, providing intuitive clinical and managerial transparency for public health planning.
The Judicial Relativity Framework is proposed, an AI-assisted decision-support methodology inspired by Einstein's concept of multiple frames of reference and by dimensionality-reduction principles from machine learning that offers augmented rather than automated justice, improving consistency and transparency subject to fairness, explainability, and due-process safeguards.
Prabhat Kumar· International Journal of Adv...· 0 citations
The paper discusses a dynamic stress test using Generative Adversarial Networks (GANs), answers key questions in the context of Explainable AI (XAI) and data privacy, and offers a recommended course of action in implementing and standardised RegTech/SupTech systems to transition regulatory control to an active and proactive science.
Elizabeth Ope, Yejide R. Alli, Ifeoluwa A. Ojo et al.· International Journal of Sci...· 0 citations
The widespread use of pesticides in modern agriculture has substantially improved food production while raising serious concerns regarding contamination and food safety. Considerable scientific effort has focused on improving methods for monitoring and detecting pesticide residues in food to enhance safety assurance. Although chromatography coupled with mass spectrometry remains the gold standard, its routine application is constrained by high costs, labor-intensive sample preparation, and prolonged analysis times. Recent advances in spectroscopic techniques, including surface-enhanced Raman spectroscopy (SERS), Raman spectroscopy, hyperspectral imaging (HSI), and near-infrared (NIR) spectroscopy, offer promising non-destructive, rapid, and sensitive alternatives for pesticide residue detection across diverse food matrices. When integrated with machine learning (ML), these approaches further improve predictive accuracy and analytical robustness. This review synthesizes recent advances in ML-assisted spectroscopic approaches for pesticide residue detection across diverse food matrices, with emphasis on analytical performance, preprocessing strategies, feature engineering, and model selection. Convolutional neural networks (CNNs), support vector machines (SVMs), random forests (RFs), and ensemble learning methods are increasingly used to improve classification and quantitative prediction. Across the reviewed studies, analytical performance was generally strong, with high classification accuracies, while the lowest reported detection limit was achieved using a SERS-CNN platform. Despite these advances, key limitations remain, including reliance on laboratory-spiked samples, small dataset sizes, matrix interference, inconsistent validation strategies, high computational demands associated with high-dimensional spectral data, and limited field validation. Future directions should focus on hybrid AI-driven sensors, IoT integration, advanced data augmentation, QuEChERS-assisted preprocessing, and explainable AI to improve real-world applicability and interpretability.
B. C. Ezenwanne, C. Okoye, Stanley Ebhohimhen Abhadiomhen et al.· Food Research International· 0 citations
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.
Yundian Zeng, Qing Ye, Jike Wang et al.· Journal of Chemical Informat...· 0 citations
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and
K
-stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient (
R
=
0.918
), and the highest reference index (
RI
=
0.951
). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi et al.· 0 citations
The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue-atom distance distributions for affinity prediction.
Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by higher education institutions (HEIs).
Methodology: The study adopts a conceptual research approach grounded in institutional, human capital, and entrepreneurial ecosystem theories. Relevant literature on AI, entrepreneurship, higher education, governance, and innovation ecosystems is synthesised to construct an integrated conceptual framework.
Results: The framework conceptualises responsible AI entrepreneurship as the intersection of AI capability, entrepreneurial innovation, ethical responsibility, and institutional governance. It identifies four interrelated dimensions: AI capability, entrepreneurial innovation, ethical and governance capability, and institutional readiness, and explains how leadership, pedagogy, technological infrastructure, governance systems, and ecosystem collaboration shape universities’ capacity to foster sustainable AI-driven innovation. Particular attention is given to challenges facing developing economies.
Novelty and Contribution: The study advances higher education scholarship by integrating AI capability development, institutional readiness, entrepreneurship, and ecosystem thinking into a unified conceptual model, providing a foundation for future empirical research on responsible AI entrepreneurship ecosystems.
Practical and Social Implications: The framework offers guidance for policymakers and university leaders seeking to strengthen AI governance, institutional readiness, and ecosystem collaboration. It supports the development of ethical, inclusive, and innovation-oriented higher education systems capable of preparing graduates and entrepreneurs for AI-driven economies.
Oluwatosin Omosolape Omodewu, M. Shokunbi· Elicit Journal of Economics...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
