With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors, such as banking, is a major issue because existing fraud detection methods are rule-based and unable to detect unknown patterns. The tactics and techniques used by fraudsters are far more advanced than they are, making machine learning (ML) a valuable approach to improve detection efficiency. While numerous studies have explored machine learning models for credit card fraud detection, most have prioritized accuracy metrics alone, offering little attention to how or why models make decisions. This lack of interpretability creates barriers for financial institutions, where regulatory compliance and user trust are critical. In particular, the systematic application of explainable AI (XAI) techniques such as SHAP and LIME to fraud detection remains scarce. This study addresses this gap by combining high-performing ensemble models (Random Forest and XGBoost) with advanced interpretability methods (SHAP and LIME), providing both strong predictive performance and transparent feature-level explanations. Such integration not only improves fraud detection but also strengthens the trustworthiness and deployability of AI systems in real-world financial contexts. A real-world credit card dataset is used to evaluate both models, and experimental results show that Random Forest achieved higher precision (89.09%) and F1 score (0.9159), while XGBoost yielded better recall (95.56%) and ROC AUC (0.9997). To address the crucial need for interpretability, SHAP and LIME analyses were applied, revealing the most influential features behind model predictions and enhancing transparency in decision-making. Overall, this study demonstrates the potential of integrating explainable artificial intelligence (XAI) into fraud detection systems, thereby enhancing trust and reliability in financial institutions.

AI-driven anomaly detectors in 5G renewable energy IoT and industrial systems lack unified governance: they operate opaquely, exhibit protocol-class bias, and expose training data to inference attacks. This paper presents the Ethical AI Governance Framework (EAGF), which maps four EU AI Act pillars, transparency (C), fairness (RP/FPRP), privacy (P), and accountability (A) to computable engineering metrics that are jointly governed within one training-and-deployment lifecycle: fairness and privacy are co-optimized via a Pareto-guided multi-objective procedure with domain-adaptive fairness loss selection, transparency is structurally controlled through clarity-triggered pruning, and accountability is audited post hoc, with all four scores aggregated into a composite Trust Index (TI). Evaluated across two domains: on a biometric task (10,021 images, ten seeds), EAGF raises TI by \(+38.97\%\) (\(0.565\rightarrow 0.785\)), improves recall parity by \(+15.1\%\), and enhances privacy by \(+18.8\%\); on the real-world Edge-IIoTset intrusion-detection benchmark (157,800 samples, five seeds), EAGF achieves \(+69.3\%\) TI gain (\(0.358\rightarrow 0.606\)) and \(+56.4\%\) FPR parity improvement, with only \(+0.2\) ms forward-pass inference overhead. Joint multi-pillar governance substantially outperforms model-level-only approaches across both domains; the accountability infrastructure contributes a large and explicitly quantified fraction of total TI gains, underscoring that governance readiness requires both algorithmic and operational investments.

Cities face pressure from urban growth and climate risk, yet deployed systems stay single-domain and reactive. This PRISMA-guided rapid review applies operationalized criteria to separate Agentic AI from conventional machine learning for SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Agentic AI is defined by four properties: task-level autonomy, goal-directed planning, tool use, and multi-agent coordination; evidencing at least two marks a system as fully agentic. A two-tier search across five databases with backward citation tracking returned 896 records (2018–2026), of which 60 met the eligibility criteria and 14 satisfied the agentic threshold. The corpus is stratified by study type with a threshold sensitivity analysis. Two contributions follow: a reference architecture specifying how an agentic layer and an urban digital twin exchange state, and a real-data feasibility study on the SEVIR archive testing whether multimodal fusion improves hazard classification. On real data the proposed model is the best-ranked of four but only marginally exceeds a no-change persistence baseline, giving the assumption weak support, not operational evidence. The review reveals a field growing sharply since 2023, clustered in a few urban and climate domains, with almost no validated cross-domain deployment.

Chest radiography (CXR is widely used for triage and follow-up of pulmonary disease, yet COVID-19 classification remains vulnerable to bias, label noise, and domain shift. We propose a multi-stage Bayesian deep learning framework that combines lung segmentation, segmentation-guided classification, calibrated ensembling, and uncertainty estimation to classify four classes (COVID-19, normal, viral pneumonia, bacterial pneumonia) and to grade COVID-19 severity. Models are trained and tested on 1,531 CXRs (100 COVID-19 images from 70 patients; 1,431 non-COVID images from ChestX-ray14) with patient-wise splits. The final ensemble achieves 98.33% test accuracy; COVID-19 sensitivity reaches 100% on this split. Robustness is quantified by stress-testing five image degradations (Gaussian noise, motion/defocus blur, JPEG compression, and downsampling), with macro AUC drops remaining small at moderate severities and larger under strong blur or heavy downsampling. Saliency and context-relevance analyses are used to identify spurious cues. The study is limited by dataset size and lack of external multi-site validation; a planned evaluation on COVIDx and BIMCV-COVID19 + is outlined.

Introduction: Drug-drug interactions (DDIs) are a major cause of preventable harm in polypharmacy and remain difficult to anticipate as formularies, indication profiles, and interaction labels evolve. Over the last few years, the DDI modeling landscape has shifted rapidly toward graph-native, multimodal, and contrastive or self-supervised learning, alongside renewed interest in extraction, decision support, and pharmacovigilance pipelines. Objective: This systematic literature review (SLR) synthesizes computational work on DDI prediction, event-type classification, text extraction, and safety signal detection published between 2022 and 2025. We aim to (i) organize recent methods into a feature–method taxonomy, (ii) compare their evaluation setups and reported performance, and (iii) assess progress on generalization, explainability, and clinical translation. Methods: Using a prespecified review protocol and PRISMA 2020 reporting guidance, we searched major bibliographic databases and screened peer-reviewed studies that proposed or evaluated computational methods for DDIs or closely related interaction tasks. Eligible work spans molecular graph and descriptor models, multimodal pharmacological representations, heterogeneous and knowledge graphs, text-based extraction and retrieval, and real-world evidence from EHRs, FAERS, and similar sources. We grouped methods into similarity and matrix-factorization baselines, conventional machine learning, deep neural architectures (CNNs, RNNs, and Transformers), graph neural networks and knowledge-graph representation learning, multimodal fusion, contrastive/self-supervised objectives, and emerging LLM-based frameworks. For each study, we extracted feature modalities, tasks, datasets and splits, metrics, explainability tools, and any form of clinical or user-centred evaluation. Results: Recent work consistently reports improved AUROC/AUPR on DrugBank-derived, TWOSIDES-like, and DDIExtraction benchmarks, driven by substructure-aware GNNs, KG-augmented architectures, multimodal fusion, and inductive or out-of-distribution training regimes. However, most models still rely on a small set of public datasets, heterogeneous and sometimes optimistic split protocols, and limited external or prospective validation. Event-level and long-tailed risk modeling, prompt- or prototype-based learning, and LLM-assisted extraction strengthen coverage of rare but clinically important interaction types, yet uncertainty quantification, label quality assessment, and end-to-end integration into prescribing workflows remain underexplored. Discussion: Between 2022 and 2025, DDI modeling has moved decisively toward graph-centric, multimodal, and contrastive/self-supervised paradigms that clearly advance benchmark performance but only partially close the gap to reliable, mechanism-aware clinical decision support. We distill design guidelines and a research agenda around transparent dataset construction, realistic and standardized evaluation protocols, mechanism- and direction-aware modeling, robustness to novel drugs and regimens, and prospective, clinician-in-the-loop validation.

Rapid urbanization and intensifying climate risks are placing unprecedented pressure on cities to transition toward sustainable and resilient models. Achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action) requires intelligent systems capable of interpreting complex urban dynamics and enabling proactive, adaptive decision-making. This paper presents a PRISMA-guided rapid review examining the role of Agentic Artificial Intelligence (AAI)–autonomous, goal-directed systems with multi-step reasoning, tool use, and multi-agent coordination–in advancing urban sustainability and climate resilience. Studies were required to exhibit at least two attributes: autonomous decision-making, multi-step planning, tool use or environmental interaction, and multi-agent coordination. From 920 records, 70 peer-reviewed studies were synthesized, covering smart mobility, infrastructure planning, waste management, emergency response, climate monitoring, emissions tracking, renewable energy forecasting, and multi-hazard early warning systems. Results show that despite rapid progress, AAI applications remain fragmented and domain-specific. To address this, a unified Agentic AI–Digital Twin framework is proposed, integrating real-time sensing, urban–climate co-simulation, multi-agent coordination, and adaptive decision intelligence. A Pareto-based optimization approach balances competing sustainability goals. Key challenges in interoperability, data governance, ethics, and scalability are identified, alongside a research roadmap for integrated intelligent urban ecosystems.