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215 papers
This article explains how ontology-based semantic layers work and advocates for their use as versatile enterprise tools. They add shared meaning, context, and governance to data, improving integration, analytics, oversight, and AI reliability across organizational systems.
Background: Antimicrobial resistance (AMR) is projected to contribute to millions of deaths in the coming decades, and the conventional antibiotic-discovery pipeline has, by most accounts, not kept pace with it. Artificial intelligence (AI) is frequently proposed as a corrective, though whether that promise has translated into demonstrable clinical benefit is less often examined directly. Methods: We conducted a narrative-systematic review of peer-reviewed and preprint literature on AI applications in AMR diagnostics and antimicrobial discovery, searching PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library through mid-2026, and organized findings across four domains: rapid phenotypic diagnostics, genomic and metagenomic resistome prediction, explainable AI, and de novo drug design. Results: AI-enabled diagnostics reduced susceptibility-testing turnaround from a conventional 36–72 hours to under 2–4 hours in several platforms; genomic language models such as DNABERT outperformed conventional classifiers by 12–18% in resistance-gene classification; explainable AI methods, SHAP in particular, linked model predictions to known resistance mechanisms; and generative frameworks yielded antimicrobial peptide candidates with confirmed in vitro and in vivo activity. Nearly all of this evidence, however, derives from retrospective, single-center validation, and no AI-based AMR tool has yet secured regulatory clearance anywhere. Conclusion: AI has moved convincingly beyond proof-of-concept in AMR diagnostics and discovery, but its path to the clinic now depends less on further algorithmic refinement than on prospective validation, equitable data representation, and interpretability standards that clinicians can reasonably trust.
Digital Twin (DT) technologies enable the creation of virtual replicas of learning environments, supporting personalized and real-time educational interventions. However, the integration of Artificial Intelligence (AI) within DT-enabled e-learning introduces critical challenges related to explainability, security, and learner privacy, and lacks a standardized operational framework. This study proposes and validates a comprehensive framework that operationalizes explainability and security for AI models in DT-based e-learning environments, balancing predictive performance, interpretability, and data protection. A quantitative experimental design involving approximately 300 learners evaluates three AI model variants: baseline, explainability-focused, and privacy/ security-enhanced. Predictive modeling employs temporal learner representations with ensemble predictors. Explainability is implemented through post-hoc interpretability techniques such as SHAP and Integrated Gradients, while privacy protection is ensured using Differential Privacy (DP) and Role-Based Access Control (RBAC). Multilevel mixed-effects models are utilized to assess predictive accuracy, explanation fidelity, and privacy guarantees, expressed as $\varepsilon $ -values. Results indicate that incorporating explainability mechanisms increases user trust by approximately 0.8–1.2 points on a Likert scale and enhances explanation fidelity by 25–30%. Integrating privacy controls produces a modest reduction in predictive AUC (up to 8%) but significantly mitigates data leakage risks. The proposed framework offers a standardized and reproducible evaluation suit for the certified deployment of explainable and secure AI systems in DT-enabled e-learning, facilitating transparent trade-offs between performance, interpretability, and privacy.
Digital twin (DT) technology real-time digital counterparts of physical assets has advanced rapidly across critical sectors. In the 6G era, the integration of DTs with ultra-low latency communication, edge intelligence, and artificial intelligence (AI) promises predictive control, enhanced collaboration, and resilient research ecosystems. Yet, this same convergence expands the attack surface: physical tampering, edge compromise, model hijacking, and adversarial AI pose risks that current security standards only partially address. Existing frameworks such as ISO/IEC 27001, 3GPP SA3, ETSI PDL, GDPR, and NIST AI RMF each contribute, but none fully cover end-to-end DT synchronisation, AI governance, or federated research data protection. This article presents a layered predictive security framework for 6G-enabled DTs in university research management and big data protection. The framework integrates provenance anchoring, anomaly detection, risk forecasting, and explainability dashboards with secure network slicing and federated identity management. We map threats to controls, assess coverage of international standards, identify critical gaps, and propose future standardisation directions. A university case study illustrates practical deployment. The work highlights the urgency of harmonising security and AI standards to ensure interoperable, trustworthy, and privacy-preserving DT ecosystems in next-generation communication systems.
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The quick development of financial technologies and digital transactions has made fraud detection and regulatory compliance more difficult. This study introduces a Financial Digital Twin with Explainable AI over 6G (FinDT-XAI6G) to enhance real-time fraud detection and compliance monitoring in financial systems. The proposed method leverages the ultra-low latency, high bandwidth, and pervasive intelligence capabilities of 6G networks to enable seamless synchronization between digital twins and real-world financial activities. Common modeling approaches ensure interoperability across financial firms, regulatory bodies, and auditing authorities. Embedded artificial intelligence systems continuously examine vast amounts of transactional data, behavioral patterns, and contextual indications to spot anomalies that could be signs of fraud. Financial firms may now fully and transparently explain automated judgments to regulators thanks to Explainable AI (XAI) modules that enhance interpretability. Blockchain-based audit trails also guarantee data integrity, accountability, and traceability across distributed infrastructures. The integration of 6G connectivity allows for real-time monitoring, cross-border compliance validation, and instant anomaly reporting, even in scenarios with huge data volumes. Comparative studies reveal that our 6G-driven digital twin approach significantly improves detection accuracy, reduces false positives, and expedites compliance verification when compared to traditional methods. Additionally, its scenario modeling capabilities enable the proactive assessment of emerging compliance risks in dynamic regulatory and commercial contexts. Overall, this study demonstrates how financial fraud prevention and compliance assurance in next-generation digital economies can be revolutionized by 6G intelligence-powered standardized, AI-integrated digital twins.
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.
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.
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.
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.
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.
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.
Earth AI
Piloting the world's first double-blind AI evaluations
The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Generative AI
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
