Journal of AI Analytics and Applications
Journal of AI Analytics and Applications (ISSN: 3067-7386) View the Insights
AI-Augmented Anomaly Analytics from Packet Traces and Host Events in Residential Device HubsAI-Augmented Anomaly Analytics from Packet Traces and Host Events in Residential Device Hubs
Residential device hubs increasingly mediate the traffic and local execution context of cameras, locks, speakers, thermostats, appliance bridges, and mobile companion applications. Although these hubs provide convenience, they also concentrate risk: one compromised hub can hide malicious activity behind encrypted cloud traffic, reshape device discovery, or suppress local security events. This paper proposes an AI-augmented anomaly analytics framework that fuses packet traces with host events to support practical detection and investigation in residential device-hub environments. The framework aligns flow-level network behavior with process, firmware, authentication, and resource events, then uses temporal representation learning, calibrated novelty scoring, and explanation layers to produce analyst-readable alerts. A controlled benchmark of 32 days, 86 residential devices, 12.9 million flows, and 2.3 million host events is used to evaluate five model configurations. Compared with a flow-only autoencoder baseline, the proposed hybrid multimodal model improves F1 score from 0.842 to 0.941, reduces false positives from 5.8 to 2.1 per hub-day, and shortens median triage time from 14.6 to 6.8 minutes. The results show that the largest gains come not from deeper models alone but from time alignment, device-type context, uncertainty-aware thresholds, and explanations that reveal why packet and host evidence jointly indicate suspicious behavior. The study contributes a deployment-oriented architecture, a feature and evaluation protocol, and design recommendations for privacy-aware anomaly analytics in home and small-office hubs.
Copyright (c) 2026 Elena Moreno, Daniel Ruiz, Isabel Vargas*, Tomas Herrera (Author) https://creativecommons.org/licenses/by/4.0
AI Analytics for V2X Misbehavior Recognition Using Qubit-Aided Feature Encoding
Vehicle-to-everything (V2X) communication extends automated driving perception beyond line of sight, yet it also creates a data-trust problem: authenticated messages can still contain false, stale, or strategically manipulated mobility information. This article develops an AI analytics framework for V2X misbehavior recognition using qubit-aided feature encoding (QAFE), a quantum-inspired representation layer that maps normalized kinematic, temporal, spatial-consensus, and radio-context indicators into nonlinear interaction features before classical classification. The study is framed as a simulation-based benchmark using a VeReMi-style message corpus with 1.25 million cooperative awareness and basic safety messages, six label categories, scenario-holdout validation, and operational metrics that include macro-F1, attack recall, false-report rate, calibration error, and edge inference latency. Results show that the proposed QAFE-gradient boosting model improves macro-F1 from 0.909 for an XGBoost baseline and 0.925 for a compact Transformer to 0.935, while reducing minority-class recall gaps under sudden-stop and Sybil-like replay attacks. Ablation analysis indicates that pairwise phase terms and temporal consistency windows account for most of the performance gain. The framework does not claim near-term quantum hardware advantage; instead, it demonstrates how qubit-inspired feature maps can provide a disciplined way to construct high-order interactions for safety-critical V2X analytics under limited latency and interpretability constraints
Copyright (c) 2026 Elena Martín, Lucas Navarro, Marta Ibáñez* (Author) https://creativecommons.org/licenses/by/4.0
Diagnosing Non-Stationarity and Relative Overgeneralization in Fully Decentralized Multi-Agent Reinforcement Learning for Priority-Aware Mobile Edge Computing
Fully decentralized multi-agent reinforcement learning is an attractive control paradigm for priority-aware mobile edge computing, because edge nodes must decide how to admit, schedule, and offload heterogeneous tasks under strict latency budgets without relying on a central coordinator or on the exchange of raw state. Yet decentralized learners in a shared edge environment routinely under-perform their centralized counterparts, and the reasons are frequently misattributed to hyper-parameters or to insufficient training. This paper argues that two structural pathologies explain most of the gap: non-stationarity, in which each learner faces a moving target because its peers adapt concurrently; and relative overgeneralization, in which agents are drawn toward a robust but jointly sub-optimal equilibrium whose shadowed reward dominates the narrow optimum. We formalize priority-aware edge offloading as a decentralized partially observable Markov decision process, derive a priority-weighted objective that couples the agents through shared queues and interference, and show analytically why priority coupling deepens both pathologies. Using a controlled edge simulator we isolate each effect, quantify it with dedicated diagnostics (a target-drift index, a shadowed-reward gap, and a miscoordination rate), and measure how the pathologies translate into deadline misses for high-priority traffic. Across the configurations studied, independent Q-learning converges 2.1 times slower and misses 3.4 times as many high-priority deadlines as a centralized-critic reference, and roughly two thirds of that penalty is traceable to relative overgeneralization rather than to non-stationarity alone. We then evaluate leniency, hysteretic updates, distributional value estimation, and opponent modeling as targeted remedies, and discuss which classes of edge workload each remedy suits. The paper supports the position that decentralization failures in edge learning are diagnosable and separable, and that remedies should be matched to the dominant pathology rather than applied indiscriminately.
Copyright (c) 2024 Tomas Whitfield, Aparna Krishnan, Ruilong Chen (Author) https://creativecommons.org/licenses/by/4.0
Self-Adjusting Contracts: Toward AI-Augmented Predictive Risk Intelligence in Blockchain-Enabled Supply Chain Finance
Supply chain finance (SCF) lowers the cost of working capital for firms that are weakly served by traditional lending, yet the instruments that carry it remain static: terms are fixed at origination and rarely respond to the operational events that actually move credit risk. This review examines an emerging alternative, the self-adjusting contract, in which a blockchain-based smart contract continuously consumes predictions from artificial intelligence (AI) risk models and updates financing parameters such as advance rates, spreads, collateral ratios, and covenants without manual renegotiation. Synthesising 50 studies published between 2015 and 2025 across blockchain-enabled supply chains, smart-contract engineering, machine learning for credit and supply chain risk, and decentralised and trade finance, we organise the field around a four-layer reference architecture that links on-chain and enterprise data, a predictive risk engine, self-adjusting contract logic, and governed financing outcomes. We then provide an illustrative data analysis that contrasts conventional and AI-augmented blockchain SCF across supplier tiers, compares the discriminatory power of competing risk-model families, traces how a predicted risk index drives automatic adjustment of financing terms through a stress episode, and decomposes the indicators that contribute most to small-enterprise credit risk. The synthesis argues that the strategic value of combining blockchain and AI in SCF lies not in disintermediation alone but in closing the loop between sensing risk and pricing it, while the principal obstacles are oracle integrity, model opacity, and governance of automated adjustment. We conclude with a research agenda spanning verifiable oracles, interpretable and auditable models, and human oversight of self-executing financial logic.
Copyright (c) 2026 Adrian R. Keller, Charlotte Edwards, Rafael O. Mendes* (Author) https://creativecommons.org/licenses/by/4.0
The Age of Autonomous Agents: A Bibliometric Review of Agentic AI Architectures, Applications, and Emerging Challenges
The rapid evolution of large language models (LLMs) has catalyzed a shift from passive AI systems toward autonomous agentic architectures capable of reasoning, memory, tool use, and multi-agent collaboration. This bibliometric review characterizes the emerging field through 810 publications retrieved from the Web of Science Core Collection for the period 2023–2025. Annual output rose sharply over this window—from 4 publications in 2023 to 96 in 2024 and 710 in 2025—accompanied by a parallel rise in citations, indicating rapid mainstream adoption. Author-keyword analysis reveals a landscape dominated by large language models, artificial intelligence, and multi-agent systems, with agentic AI, generative AI, and retrieval-augmented generation (RAG) emerging as core themes. Research output is geographically concentrated, led by China and the United States, and is distributed across a broad range of engineering, applied-science, and domain-specific journals rather than a single specialist venue, reflecting the field's cross-disciplinary uptake. Synthesizing this corpus, we organize the technical landscape around reasoning, memory, tool integration and RAG, and multi-agent orchestration; survey application domains spanning healthcare, scientific discovery, education, and software engineering, with emerging activity in finance and law; and analyze the principal challenges—hallucination, trust and robustness, inter-agent coordination, scalability, and governance. The review provides a structured, evidence-based map of agentic AI research to orient researchers and practitioners navigating this rapidly evolving field.
Copyright (c) 2026 Amara N. Okoye, Clara M. Hofmann, Ben J. Weber (Author) https://creativecommons.org/licenses/by/4.0
A Multi-Layer Intelligent Cyber-Physical Security Architecture for Distributed Device Networks with Adaptive Signal Transformation and Trust-Aware Logging
Distributed device networks now underpin critical cyber-physical infrastructure, yet their scale, heterogeneity, and physical exposure make them difficult to defend with monolithic tools. This paper presents an end-to-end security architecture that treats protection as a pipeline of five cooperating layers rather than a single detector. A distributed device network supplies raw measurements; an adaptive signal-transformation layer denoises and embeds those measurements into features that remain discriminative under noise and drift; a secure communication and aggregation layer moves features without tampering; a trust-aware anomaly-detection layer combines machine-learned detectors with continuous per-device trust scoring and cross-device correlation; and a tamper-evident logging and response layer commits findings to an append-only record and orchestrates containment. Two feedback loops couple the layers: trust flows back to gate signal admission, and containment flows back to the devices. Evaluated on a heterogeneous testbed of 240 devices and roughly 14.2 million labelled observations spanning six attack families, the full pipeline reaches an F1 score of 0.91, with each layer contributing a distinct and measurable increment. Trust-awareness yields its largest gains on the stealthy attacks, false-data injection, spoofing, and exfiltration, that are most dangerous in cyber-physical settings, improving their detection by between thirteen and seventeen points. The architecture degrades gracefully under adversarial signal perturbation, and its tamper-evident logging imposes per-operation latency well within the budget of edge hardware even at a hundred million records. We argue that robustness, continuous trust, and accountability are constitutive requirements for cyber-physical security rather than optional additions.
Copyright (c) 2026 Rohan A. Deshpande*, Mei Lin Chau, Viktor S. Brandt, Amara N. Okoye (Author) https://creativecommons.org/licenses/by/4.0
Beyond Average Calibration: A Study of Local and Conditional Reliability for Trustworthy Uncertainty Quantification
Predictive models are increasingly asked not only to make decisions but to report how confident they are, and that confidence is useful only if it is calibrated: stated uncertainty must correspond to the error a model actually makes. A large literature equips practitioners to measure and improve calibration, yet most of it certifies calibration only on average, asking whether predicted confidence matches accuracy across an entire test set. This article argues that average calibration, or consistency, is necessary but far from sufficient, and that the property which matters for trustworthy deployment is adaptivity: calibration that holds locally, conditioned on the input, so that the uncertainty attached to a particular prediction can be believed. We formalise the distinction between marginal, conditional, and individual calibration, assemble the diagnostics each requires, and apply them to several predictive models on equal footing. We show that a model can be almost perfectly calibrated on average while being badly over-confident in some regions of the input space and under-confident in others, that standard aggregate metrics are blind to this, and that popular recalibration methods which repair average calibration leave local miscalibration largely intact. We conclude that reliable uncertainty quantification must be evaluated and enforced conditionally rather than only marginally.
Copyright (c) 2026 Pimchanok T. Sorn, Chaiwat K. Lert, Natcha S. Thong (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Decentralized Insurance: Analytical Perspectives on Automated Risk and Trust Systems
Decentralized insurance combines distributed-ledger infrastructure, self-executing contracts, and machine intelligence to perform risk pooling, pricing, and claim settlement without a single coordinating intermediary. This paper develops an analytical perspective on such intelligent decentralized insurance, asking how automation reshapes the cost structure of risk transfer and how trust is produced when no central insurer stands behind the promise to pay. A four-plane reference architecture is proposed that separates participants and assets, data and oracles, automation logic, and governance assurance, and three families of automated risk architectures are examined through this lens: algorithmic mutual pools, parametric trigger contracts, and token-governed coverage protocols. The analysis is supported by a structured synthesis of peer-reviewed research published between 2016 and 2024 and by stylized quantitative models. The expense decomposition indicates that automation can plausibly compress operating loads from roughly 29 percent of premium toward 12 percent, while settlement latency falls from weeks to minutes for fully automated parametric designs. A pool-stability model shows, however, that loss dependence places a hard floor under the solvency benefits of scale, and an oracle redundancy model quantifies how trigger integrity improves geometrically with independent data sources. The paper argues that the binding constraints on intelligent decentralized insurance are no longer purely technological: they concern data integrity, contract security, governance legitimacy, and regulatory recognition. A research agenda is outlined that links actuarial risk-sharing theory, information systems design, and institutional economics.
Copyright (c) 2026 Hao Chen, Li Zhao, Wei Zhang (Author) https://creativecommons.org/licenses/by/4.0
Bayesian Feedback Analytics for Quantum-Assisted Target Recovery in Security-Critical Data Spaces
Grover's search and its amplitude-amplification generalisation give a quadratic speed-up for locating marked entries in an unstructured data space, yet recovering every marked entry rather than a single one introduces a fragile dependence on the assumed number of targets. Because the optimal number of amplification rounds is fixed by this count, an estimate that drifts from the truth either halts the search early—leaving targets undiscovered—or drives it into an unbounded hunt for entries that do not exist. We frame this difficulty as an online inference problem and present a quantum-classical procedure that treats the unknown target count as a random variable, maintaining a posterior over candidate counts that is revised after each measurement through Bayesian feedback analytics. A lightweight classical layer prunes improbable hypotheses, defers updates until the recent outcome record is informative, and protects neighbourhood diversity so that the true count is not discarded prematurely. Across search spaces from 2^12 to 2^24 the method drives the mean number of unrecovered targets close to zero—for a space of 2^24 entries the static baseline left roughly 60.9 targets unfound on average while our procedure left 0.82—and it does so without inflating cost, requiring about 389,400 oracle calls against 408,000 for the baseline. A vulnerability-enumeration case study on large open-source code bases shows the same pattern, recovering all catalogued defects where the fixed-count baseline missed several percent. We argue that adaptive posterior correction should be a default component of any complete-enumeration quantum search in security-critical settings
Copyright (c) 2026 Npurdianta Sembiring, Teuku Yuliar Arif, Rizal Munadi (Author) https://creativecommons.org/licenses/by/4.0
Task-Guided Confidence Scoring for Synthetic Time-Series Outputs in Health-Oriented Machine Learning Systems
Generative models are increasingly used to produce synthetic physiological time series in health-oriented machine learning, whether to denoise wearable recordings, adapt signals across acquisition domains, augment scarce training data, or impute missing segments. Yet the same flexibility that makes these models useful also lets them introduce plausible-looking but misleading artefacts, which is a serious liability when the synthetic signal feeds a clinical decision. This review argues that the trustworthiness of a synthetic output cannot be judged in isolation from the task it is meant to support, and it develops a task-guided confidence scoring perspective that grounds the quality of each generated signal in the expected cost of the downstream decision it influences. We organise the argument around four ideas: that conventional distributional and realism metrics answer the wrong question for deployment; that a useful confidence signal must be per-instance, available before ground truth, and aligned with the decision at hand; that such a signal can be derived from the behaviour of the downstream task model and externally grounded by checking whether higher scores track higher realised decision cost; and that the resulting scores enable principled gating of low-confidence outputs. Using wearable photoplethysmography and atrial-fibrillation screening as a running example, we synthesise reporting strategies across modalities, contrast their properties, and map the deployment, governance, and clinical-translation considerations that determine whether confidence scoring delivers value in practice. The perspective offers a transferable diagnostic for deciding when a synthetic time-series output is safe to use.
Copyright (c) 2026 Sneha V. Kumar, Arjun S. Patel (Author) https://creativecommons.org/licenses/by/4.0
Self-Supervised Contrastive Learning with Mamba-2 State Space Models for Audio-Visual Deepfake Detection
With the rapid proliferation of generative adversarial networks (GANs) and diffusion models, deepfake videos have achieved a level of fidelity that challenges the limits of human visual perception, posing severe threats to information security and public trust. Existing detection methodologies mostly rely on Convolutional Neural Networks (CNNs) combined with Recurrent Neural Networks (RNNs) or Transformers to extract spatial-temporal features. However, Transformers scale quadratically with long sequences. Additionally, traditional fusion methods miss subtle audio-visual inconsistencies. This paper proposes a novel, lightweight multi-modal detection framework that integrates Mamba-2 State Space Models (SSM) with Self-supervised Contrastive Learning (SSCL). Specifically, we leverage the linear complexity and the selective scan mechanism of Mamba-2 to efficiently model long-range temporal dependencies. Furthermore, a dual-branch contrastive loss is introduced to explicitly align the physical synchronization patterns between lip movements and speech phonemes. This forces the model to learn invariant forensic features rather than overfitting to specific visual artifacts. Experiments demonstrate that our proposed method achieves superior performance: an AUC of 0.976 on FaceForensics++ and 0.962 on FakeAVCeleb.
Copyright (c) 2026 Robiatul A'dawiah Jamaluddin, Zhen Yin (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI for Urban Retail Site Selection: SHAP-PDP-Bayesian Network Modeling of Facility Synergy and Perceptual Thresholds
Urban retail site selection increasingly depends on complex interactions among consumer mobility, facility complementarity, commercial competition, street-level perception, and transport accessibility. Conventional retail-location models are often useful for descriptive mapping but provide limited support for explaining nonlinear thresholds and conditional facility synergies. This study develops an explainable artificial intelligence framework for evaluating urban retail site suitability with multi-source geospatial data. The framework integrates point-of-interest density, mobility proxies, road-network accessibility, neighboring facility functions, and computer-vision-derived streetscape perception into a 500 m grid representation. XGBoost is used to estimate nonlinear suitability patterns for convenience-format and anchor-format retail sites, SHAP values identify the relative contribution and directional role of each determinant, partial dependence plots reveal threshold effects, and Bayesian network modeling converts model explanations into probabilistic decision rules. The empirical demonstration shows that the XGBoost model improves predictive performance over linear and semi-parametric baselines. Facility synergy and perceptual quality jointly explain more than one-third of model importance for anchor-format sites, while competition intensity and service-gap signals are more decisive for convenience-format sites. The analysis further identifies several decision-relevant thresholds, including minimum facility-density and perception-quality levels beyond which site suitability increases sharply. The results indicate that explainable AI can support site screening, commercial land-use planning, and retail network optimization without reducing urban retail decisions to opaque prediction scores.
Copyright (c) 2026 Nadia A. Rahman, Amirul H. Ismail, Mei Ling Wong*, Farid Z. Hamdan (Author) https://creativecommons.org/licenses/by/4.0
AI-Augmented Blockchain Analytics: Fraud Detection, Smart Contract Risk Scoring, and Trustworthy Decentralized Intelligence
The convergence of artificial intelligence (AI) and blockchain has produced a new class of analytical systems that observe, interpret, and act on decentralized ledger activity. This review examines AI-augmented blockchain analytics across three tightly coupled capability areas: transactional fraud detection, smart-contract risk scoring, and trustworthy decentralized intelligence. Drawing on 60 peer-reviewed studies published between 2015 and 2025, supplemented by aggregated benchmark results from 312 documented deployments, the paper proposes a three-layer architectural framework that links on-chain data ingestion, learning-based inference, and decision-grade application services. Comparative evaluation across six families of detection models shows that graph-aware and hybrid architectures achieve median F1 gains of 0.18-0.24 over conventional tabular baselines, with the strongest gains observed in adversarial DeFi scenarios. For smart-contract auditing, deep semantic models combined with symbolic execution recover roughly 91% of disclosed vulnerabilities in established corpora, while reducing false-positive rates by a factor of two to three. The review further quantifies the latency and throughput envelope of centralized, federated, and on-chain inference deployments, and identifies governance, explainability, and adversarial robustness as the most consequential constraints on trustworthy decentralized intelligence. The paper concludes by outlining a research agenda that prioritizes verifiable AI primitives, privacy-preserving federated analytics, and standards-aligned audit infrastructure through 2028.
Copyright (c) 2026 Arjun Iyer, Priyanka Kapoor, Rohan Mehta (Author) https://creativecommons.org/licenses/by/4.0
AI Patch Validation Analytics: Exploit-Variant Resistance and Root-Cause Conformance in LLM-Based Vulnerability Repair
Large language models are increasingly used to generate candidate security patches, yet a patch that appears successful under one validation signal may still be incomplete, exploit-specific, or unsafe when stronger evidence is applied. This article develops an analytics-oriented framework for evaluating LLM-based vulnerability repair through two security-critical lenses: exploit-variant resistance and root-cause conformance. Instead of treating patch validation as a binary pass/fail decision, the study models validation as a layered evidence profile that combines build validity, functional preservation, original proof-of-concept blocking, exploit-variant resistance, CWE-specific root-cause assessment, static-warning rechecking, and regression-safety review. A controlled patch-validation dataset containing 400 candidate repairs across SQL injection, path traversal, cross-site scripting, and missing authorization scenarios is analyzed to show how weak validation oracles inflate apparent repair success. The analysis demonstrates that original exploit blocking and static-warning disappearance provide useful but incomplete signals. In the primary model configuration, 79.5% of patches blocked the original exploit, whereas 49.0% satisfied the full validation profile. CWE-level results further show that path traversal exhibits strong variant-bypass risk, SQL injection exhibits root-cause conformance gaps, and missing authorization remains difficult because correct repair depends on trust-boundary placement rather than local code edits. The paper contributes a patch-validation analytics model, a reporting structure for oracle divergence, and practical guidance for validation-aware repair workflows. The findings indicate that LLM-based vulnerability repair should be evaluated through layered security evidence rather than by a single permissive oracle.
Copyright (c) 2026 Nurul Farhana Ismail*, Hafiz Rahman, Aina Syahirah Aziz (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI Analytics for Pre-Incident Insider Threat Risk Scoring in Information Systems
Insider threats remain difficult to control because the most damaging events often emerge from ordinary access, changing work conditions, weak controls, and behavioral signals that are visible before a confirmed incident occurs. This study develops an explainable artificial intelligence analytics framework for pre-incident insider threat risk scoring in information systems. The framework integrates behavioral, organizational, and technical control indicators into a staged analytics pipeline that combines feature engineering, entropy-informed weighting, supervised learning, local explanation, calibration, and risk-tier governance. Instead of treating insider threat analytics as a black-box detection problem after malicious activity has already occurred, the proposed framework treats risk scoring as an auditable decision-support process for early intervention. A synthetic enterprise dataset is constructed to evaluate the approach across 6,000 user-period observations and 48 observable indicators representing access behavior, work context, policy violations, control exposure, and security-technology gaps. Comparative analysis shows that the explainable hybrid model improves AUC from 0.76 under entropy-only scoring to 0.89, while reducing calibration error to 0.08. Local explanation results identify data export volume, after-hours access, policy violations, managerial pressure, and data-control gaps as the most influential pre-incident signals. The findings demonstrate that explainable AI can increase model transparency, support proportionate governance actions, and improve the business usability of insider risk analytics without relying on intrusive surveillance or post-incident labels alone.
Copyright (c) 2026 Haoran Lin, Yuting Zhao, Ming Qiao* (Author) https://creativecommons.org/licenses/by/4.0
AI-Assisted Analytics for Practical Education Appraisal: Evaluating Agriculture–Forestry Management Training with Multi-Indicator Models
Agriculture–forestry economic management is a strategically important undergraduate major in China, but its practical education component is difficult to appraise because the discipline mixes economic, ecological, organisational and field-skill content. This study designs an AI-assisted multi-indicator appraisal framework that combines factor analysis, the analytic hierarchy process (AHP), entropy weighting, fuzzy comprehensive evaluation and gradient-boosted scoring, and applies it to data drawn from five Chinese agricultural universities. A four-dimension, eighteen-indicator instrument was completed by 412 senior undergraduate students, 38 faculty members, 24 administrators and 64 peer teachers, and was triangulated against 2,154 practice-base log records. Factor analysis extracted four latent factors whose structure matched the conceptual dimensions of the instrument (Cronbach α between 0.81 and 0.93). The AI-assisted scoring engine outperformed the three classical methods on reliability and discriminant validity, while AHP retained the strongest stakeholder interpretability. Empirical results show that instructor competence carries the largest aggregate weight (0.291–0.328 across methods) and that practical operation ability, teaching methods and practicality are the three highest individual indicators. A three-stage optimisation pathway is proposed (0–6, 6–18 and 18–36 months) and projected outcomes include a 23–31% rise in employer-rated graduate competence and an 18% increase in postgraduate placement. The framework gives Chinese agricultural universities a reproducible, analytics-grounded route to align practical education with rural revitalisation policy and the training needs of the modern agriculture–forestry sector.
Copyright (c) 2026 Mingrui Zhao, Liangyu Chen, Wenjing Hao (Author) https://creativecommons.org/licenses/by/4.0
Interpretable AI for Telematics-Based Insurance Pricing: Nonlinear Risk Modeling, Feature Attribution, and Regulatory Transparency
Auto insurance ratemaking is undergoing a structural shift as telematics devices and usage-based insurance (UBI) programmes deliver granular data on individual driving behaviour. Generalised linear models (GLMs) remain the dominant pricing engine in the industry because of their statistical transparency and regulatory acceptance, yet they struggle to express the nonlinear and conditional structure that characterises telematics signals. This paper develops an interpretable analytics framework that combines generalised additive models (GAMs), gradient-boosted machines, and a low-dimensional clustering procedure for territorial risk design. Using a synthetic UBI portfolio that preserves the joint distribution of policy and behavioural variables, we model claim frequency and claim severity separately, evaluate spline-based interaction effects, and quantify feature contributions through partial dependence plots and Shapley values. The GAM-based frequency model lowers the empirical–predicted gap for the youngest age cohort by roughly seven percentage points and uncovers a U-shaped response for annual miles driven that is invisible in the GLM specification. An XGBoost benchmark ranks credit score, years without a claim, and car age as the dominant predictors, while annual mileage contributes mostly through interactions with car use and region. A penalised dispersion criterion stabilises the choice of cluster count for the territorial reduction step, and sensitivity tests confirm that the recommended cluster count varies within a narrow band as the penalty parameter changes. Taken together, the framework offers regulators and insurers a transparent path from raw telematics data to defensible rate relativities and parsimonious territorial classifications.
Copyright (c) 2026 Hendrik van der Berg, Elena Marković, Liam O’Sullivan (Author) https://creativecommons.org/licenses/by/4.0
Governed Cloud AI for Financial Services: MLOps, Foundation Models, and Trustworthy AI Analytics across AWS, Azure, and Google Cloud
This paper proposes an integrated reference architecture and a governance-first deployment perspective for cloud-based artificial intelligence in financial services. We argue that the strategic value of cloud AI for banks, insurers, and capital-market firms now depends less on the choice of provider and more on the institution's ability to operationalize three jointly mature capabilities: machine-learning operations (MLOps) as a disciplined production lifecycle, foundation-model access patterns that preserve confidentiality and explainability, and trustworthy AI analytics aligned with emerging governance regimes including the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001. Drawing on a comparative analysis of Amazon Web Services, Microsoft Azure, and Google Cloud Platform, and a survey of governed deployments across 142 financial institutions in Europe, North America, and Asia, the paper develops a five-lane reference architecture that separates the data plane, the MLOps lane, the foundation-models lane, the governance lane, and the application plane. We map provider capability profiles across six dimensions, document F1 performance gains of 16-25 percentage points when rule-based controls are replaced with governed MLOps plus explainable AI on fraud detection, credit-risk modeling, anti-money laundering, and customer service tasks, and quantify the Pareto trade-off between inference latency and composite governance score across six deployment topologies. The paper concludes with a research and practitioner agenda emphasizing multi-cloud governance, confidential computing for sensitive analytics, foundation-model evaluation discipline, and the embedding of trustworthy-AI controls into the institution's existing model-risk-management apparatus.
Copyright (c) 2026 Federica Conti, Giulia Romano, Lorenzo Bianchi (Author) https://creativecommons.org/licenses/by/4.0
Constraint-Guided LLM Reasoning for Causal Risk Propagation in Cyber-Physical Industrial Systems
Cyber-physical industrial systems integrate physical equipment, embedded control, operational software, business workflows, and human decision making. When cyberattacks disturb this integration, risk rarely remains inside a single device or a single control loop. It moves across process variables, unsafe control actions, task failures, alarms, operator responses, and production objectives. Large language models offer a promising way to accelerate causal risk analysis because they can convert heterogeneous engineering descriptions into structured candidate scenarios. However, unconstrained generation may also create unsupported nodes, illegal causal jumps, ambiguous terminology, and outputs that cannot be mapped into quantitative risk models. This paper develops a constraint-guided LLM reasoning framework for causal risk propagation in cyber-physical industrial systems. The framework combines five constraint families: structural consistency, task-topology validity, semantic typing, evidence traceability, and computable output formatting. These constraints guide the model from system knowledge and attack evidence toward auditable causal chains and Bayesian-network-ready tables. A synthetic benchmark involving 24 cyber-physical risk scenarios across four industrial settings is used to evaluate the framework. The results show that constraint-guided reasoning improves causal validity from 0.58 to 0.82 compared with unconstrained prompting, increases Bayesian-network capability from 0.43 to 0.76, and reduces hallucinated or unsupported causal items by more than half. Adding retrieval support and review calibration further improves causal validity to 0.92. The study contributes an actionable design for integrating LLM reasoning into industrial safety analytics without treating generated text as authoritative evidence. It also provides implementation guidance for AI-assisted STPA, FMEA, process-risk modeling, and risk-informed decision support in industrial cyber-physical environments.
Copyright (c) 2026 Marta Fabbri, Giorgio Bianchi, Elena Grecoo* (Author) https://creativecommons.org/licenses/by/4.0
AI-Augmented Risk Analytics for Asymmetric Digital Service Supply Chains: Predicting Capacity Shortage and Cyber Exposure
Digital service supply chains increasingly rely on cloud infrastructure, platform software, data interfaces, and external service providers to serve both consumer and enterprise markets. These systems face a compound risk problem: demand fluctuates across channels, capacity commitments are negotiated under asymmetric information, and cybersecurity events can interrupt service availability while they damage trust. This article develops an AI-augmented risk analytics framework for asymmetric digital service supply chains, with a focus on predicting capacity shortage and cyber exposure in dual-channel software-as-a-service operations. A simulation-calibrated data design is used to generate 24,000 firm-period observations reflecting B2C demand volatility, B2B contract intensity, infrastructure latency, information asymmetry, patch delay, failed authentication signals, service capacity, and risk mitigation investment. Five predictive configurations are compared: logistic regression, random forest, gradient boosting, LSTM sequence learning, and a hybrid ensemble that combines structured tabular learning with time-series signals. The hybrid ensemble achieves the strongest performance, with AUC values of 0.893 for shortage prediction and 0.884 for cyber exposure prediction. Sensitivity analysis shows that asymmetric information magnifies capacity shortage risk more strongly than cyber exposure risk, while cyber exposure is most responsive to patch age, failed login intensity, and shared infrastructure dependency. The study contributes a practical analytics architecture, a model comparison benchmark, and managerial guidance for risk governance in digital service supply chains.
Copyright (c) 2026 Amir Farid Rahman, Mei Lin Tan, Nur Aisyah Khalid* (Author) https://creativecommons.org/licenses/by/4.0
Trust Calibration Analytics for Generative AI Systems: A Cross-Domain Framework for Operational Intelligence and Human Oversight
Generative artificial intelligence (GenAI) has rapidly moved beyond content production into operational roles where its outputs influence consequential decisions across industry, healthcare, education, finance, and public governance. As deployment scope expands, a recurring failure mode becomes visible: human operators systematically miscalibrate their trust in GenAI systems, either over-relying on confident but ungrounded outputs or under-using reliable recommendations because of opaque reasoning. This paper proposes a cross-domain framework, Trust Calibration Analytics (TCA), that treats trust calibration not as a one-time design decision but as a continuous analytical process embedded in deployment. Drawing on 71 peer-reviewed sources spanning 2014–2025 and an empirical synthesis of 312 documented GenAI deployments across five application domains, we develop a three-layer architecture comprising a generative substrate, a calibration analytics engine, and an operational context layer. Quantitative analysis demonstrates that the full TCA pipeline reduces expected calibration error by 76% on average across domains, while deployments with explanation modules and adaptive oversight show a 24-percentage-point reduction in inappropriate operator overrides. Five operational scenarios are analysed to derive three transversal design principles: situation-anchored confidence reporting, drift-aware trust monitoring, and risk-graded oversight orchestration. The paper concludes with a research roadmap identifying confidence-calibrated outputs, cross-domain drift detection, federated oversight, audit-grade provenance, and adaptive escalation as priorities through 2028. TCA reframes human-AI collaboration around measurable trust dynamics rather than assumed reliability.
Copyright (c) 2025 Wei-Ling Tan, Faridah binti Hashim, Mohd Azlan Ibrahim* (Author) https://creativecommons.org/licenses/by/4.0
Latent-Space Image Encryption for Edge AI: A Lightweight Autoencoder–Cipher Framework for Secure IoT Vision Analytics
Vision-enabled Internet-of-Things (IoT) deployments increasingly require image confidentiality at the sensor edge, yet conventional block ciphers such as AES impose latency and energy costs that are difficult to absorb on constrained devices. This paper proposes LSE-Edge, a unified latent-space encryption framework that couples a lightweight image encoder (LiE-Net) with a novel two-pass symmetric cipher called Block-Spiral XOR (BSX) and an outer LDPC channel code. LiE-Net compresses the input image into a 64-dimensional latent vector accompanied by an external residual stream that preserves high-frequency structure for fidelity-aware reconstruction. The BSX cipher applies a forward keystream chain based on SHA3-256 followed by a spiral feedback pass that propagates the final ciphertext block back to the first, yielding global diffusion at constant memory cost. We characterise LSE-Edge across security, robustness, and efficiency. Across the USC-SIPI, ImageNet-IoT, and a curated edge-camera dataset, BSX achieves NPCR of 99.71%, UACI of 33.58%, and ciphertext entropy of 7.998 bits, exceeding the AES-CBC and prior chaotic, DNA-hybrid, and Vision-Transformer-cipher baselines. With LDPC rate-1/2 protection, the framework retains SSIM above 0.74 at channel noise sigma equal to 20, where uncoded variants collapse to SSIM below 0.15. End-to-end latency is 311 milliseconds on a Jetson Xavier and 488 milliseconds on a Raspberry Pi 4 for a 256x256 RGB image, with the BSX stage itself contributing only nine to twenty-eight milliseconds. The combined results indicate that latent-space encryption with a small, well-designed cipher and an outer error-correcting layer can replace bulk pixel encryption for typical edge-AI vision pipelines without sacrificing security or reconstruction quality.
Copyright (c) 2025 Sri Wahyuni Lestari, Muhammad Faiz Rahman, Adi Pranoto Wibowo (Author) https://creativecommons.org/licenses/by/4.0
AI-Assisted Hybrid Vulnerability Analytics for RESTful APIs: Static Rule Matching, Dynamic Fuzzing, and Exploitability Validation
Mass Assignment Vulnerability (MAV) remains a stubbornly recurring weakness in RESTful web services because permissive request-to-object binding is enabled by default in most modern web frameworks and because vulnerable code is syntactically indistinguishable from safe code. We propose a hybrid vulnerability analytics framework that combines lightweight static rule matching with schema-aware dynamic fuzzing and an AI-assisted exploitability validation layer that learns to discriminate confirmed exploits from architecturally fragile but currently non-exploitable findings. The static engine builds an Abstract Syntax Tree (AST) over Java source files and evaluates it against a curated library of seven detection rules spanning controller, model, service, and configuration layers. The dynamic engine consumes the static candidate set and constructs OpenAPI-conformant injection payloads that are dispatched to a live test instance. A response state divergence metric quantifies the impact of injected sensitive fields, and a logistic classifier trained on this metric and seven structural features assigns a four-level severity grade to each finding. We evaluated the framework on three benchmark Spring Boot codebases: a deliberately vulnerable application, a tutorial-style standard application, and a hardened reference application. The hybrid pipeline detected all nineteen planted vulnerabilities (zero false negatives) in the vulnerable benchmark, cleared the hardened reference (zero false positives), and reduced the actionable HIGH-severity finding set from eight to three across the eight dynamically tested endpoints, a 62.5 percent reduction in operational false positives. Static scans complete in under two seconds on a 24-file project and dynamic verification averages 2.3 seconds per endpoint, making the framework practical as a Continuous Integration gate. The paper contributes a publicly described rule library, an AI-assisted severity grading function, and an empirical assessment of where source-code and runtime evidence are jointly necessary
Copyright (c) 2025 Arjun Venkatesh, Nivetha Padmanabhan, Karthik Raghavan (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Identification of Parkinson’s Disease Subtypes from Functional Brain Networks with Prototype-Guided Graph Learning
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized by significant clinical heterogeneity that complicates both diagnosis and therapeutic stratification. Resting-state functional MRI (rs-fMRI) provides non-invasive access to large-scale brain network disruptions associated with PD; however, conventional machine learning approaches lack the representational depth and interpretability required for reliable subtype identification. This paper proposes a Prototype-Guided Graph Learning (PGL) framework that integrates three computational advances: (1) a graph contrastive pretraining stage using a Graph Transformer encoder to derive generalizable brain-network embeddings from multi-site fMRI data; (2) a graph variational autoencoder (GVAE) that maps functional connectivity matrices to a structured latent space enabling uncertainty-aware topology learning; and (3) a prototype-guided classification module that partitions the latent space into clinically meaningful subtype prototypes, simultaneously yielding subtype labels and explainable subgraph signatures. Evaluated on a multi-site dataset comprising 312 PD patients and 184 healthy controls, PGL achieves an accuracy of 93.4%, an AUC of 0.941, and an F1 score of 0.927 for PD versus healthy control discrimination—outperforming all compared state-of-the-art baselines by at least 4.0 percentage points. For three-way PD subtype classification (olfactory-deficit, postural-instability, and tremor-dominant subtypes), the macro-average AUC reaches 0.893. Explainability analysis reveals subtype-specific disruption patterns involving the orbitofrontal cortex and amygdala for olfactory-deficit PD, the supplementary motor area and striatum for postural-instability PD, and the putamen and cerebellum for tremor-dominant PD, consistent with established neurobiological findings. The PGL framework offers a clinically actionable pathway for AI-assisted Parkinson’s diagnosis and subtype stratification.
Copyright (c) 2025 Wei Zhang, Jing Chen, Xiaoming Hu (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI for Predicting Blockchain Adoption Readiness in Government Agencies: A Hybrid SEM-Machine Learning Study
This study develops and evaluates an explainable artificial intelligence framework for predicting blockchain adoption readiness in government agencies. The paper integrates a technology-organization-environment perspective with institutional trust, regulatory legitimacy, and public value concerns to explain why public organizations may be technically interested in blockchain yet unevenly prepared for implementation. A scenario-calibrated dataset of 418 agency-level observations is used to demonstrate a hybrid analytical design in which structural equation modeling validates latent readiness mechanisms, while machine learning models classify high-readiness agencies from construct scores and operational indicators. The SEM results show that regulatory legitimacy, technological capability, top management support, data governance, and institutional trust are the strongest direct predictors of adoption readiness. The machine learning comparison indicates that the hybrid SEM-ML stack achieves the best held-out performance, with higher AUC and F1 values than logistic regression, support vector machines, random forests, extra trees, and gradient boosting alone. Explainability analysis further reveals that regulatory clarity, data governance, top management support, interoperability readiness, and institutional trust have the greatest marginal contribution to readiness classification. The study contributes a transparent readiness analytics architecture for public-sector blockchain governance and argues that adoption readiness should be assessed as a sociotechnical and institutional condition rather than as a purely technological capability. Keywords: Explainable AI; Blockchain adoption; Government agencies; Public-sector innovation; Structural equation modeling; Machine learning; Technology-organization-environment framework; Regulatory legitimacy
Copyright (c) 2025 Amina Rahman, Farah Sulaiman*, Daniel Lim (Author) https://creativecommons.org/licenses/by/4.0
Prompt-Constrained Transformer Analytics for Low-Resource Script Conversion in Kazakh Language Processing
This article develops a prompt-constrained Transformer analytics framework for low-resource script conversion in Kazakh language processing. Kazakh is written across Arabic-based, Cyrillic-based, and Latin-based orthographies, and conversion among these scripts is complicated by non-one-to-one grapheme mappings, vowel harmony, consonant alternation, agglutinative morphology, regional lexical preferences, and large numbers of loanwords. Instead of treating script conversion as a purely mechanical transliteration task, the proposed framework treats it as a constrained sequence analytics problem in which lexical prompts provide soft but explicit guidance to a Transformer encoder-decoder model. The framework integrates a multiscript lexical prompt bank, morphological screening, prompt-conditioned attention, and post-conversion error analytics. A controlled benchmark design is introduced to evaluate six conversion directions across general news, educational text, public-service notices, and technology-related terminology. The results indicate that prompt constraints reduce character and word error rates relative to attention-only baselines, with the greatest gains in loanwords, proper names, and morphologically inflected forms. The article further analyzes the trade-offs among conversion accuracy, robustness, latency, explainability, and data governance. The findings suggest that prompt-constrained analytics offers a practical pathway for low-resource language technologies because it combines neural contextual learning with auditable linguistic knowledge. The study contributes to artificial intelligence analytics by showing how structured prompts can turn scarce linguistic resources into deployable conversion intelligence for multilingual digital services.
Copyright (c) 2025 Aigerim Nurzhanova, Miras Daurenov, Saltanat Ibrayeva* (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI for Banking Stability Prediction: Integrating Quantum-Inspired Fragility Metrics with Random Forest and SHAP Analytics
The opacity of machine learning models poses a critical challenge for their deployment in financial supervision, where regulators and analysts must understand and justify predictive decisions. This study presents an explainable artificial intelligence (XAI) framework for banking stability prediction that integrates quantum-inspired fragility metrics derived from double-well potential modelling with a Random Forest classifier interpreted through SHapley Additive exPlanations (SHAP). Using an annual panel of twelve Mexican commercial banks observed over 2014–2023, we construct a comprehensive feature set that combines classical bank-level financial indicators — including the non-performing loan ratio, capitalization index, net interest margin, and return on assets — with novel quantum-inspired predictors generated via functional structures of Quantum Field Theory (QFT). The proposed augmented Random Forest model achieves an AUC-ROC of 0.887 and an F1-score of 0.858, outperforming all baseline classifiers. SHAP analysis reveals that the quantum fragility score ranks as the third most influential predictor, behind non-performing loans and the capitalization index, confirming that physics-inspired indicators carry independent informational value beyond traditional financial ratios. SHAP dependence plots further demonstrate non-linear interaction effects between the capitalization ratio and the quantum fragility score, consistent with phase-transition dynamics identified in the QFT literature. The framework advances both the theoretical integration of quantum finance methods and the practical need for interpretable early-warning tools in emerging banking markets.
Copyright (c) 2025 Carlos Ramírez-Torres, Ana Lucía Domínguez-Vargas, Pedro Alejandro Fuentes-Castillo* (Author) https://creativecommons.org/licenses/by/4.0
Manifold-Aware Reasoning Risk Scoring for Lightweight Large Language Models: An Analytics Framework for Deception-Sensitive AI Applications
Lightweight large language models are increasingly deployed in mobile, embedded, and edge intelligence settings where safety monitoring cannot depend on continuous cloud-based supervision. Existing deception detection pipelines often reduce reasoning oversight to a binary classification task, thereby overlooking the graded, transitional, and context-sensitive structure of deceptive behavior in reasoning traces. This paper develops a manifold-aware reasoning risk scoring framework for deception-sensitive AI applications. Instead of asking only whether a reasoning trace is deceptive, the proposed framework estimates a continuous risk score derived from geometric separation, transition-band density, calibration uncertainty, and application-level harm exposure. Building on contrastive representation learning and self-supervised monitoring concepts, the framework treats chain-of-thought and hidden reasoning states as forensic evidence that can be organized into low-risk, ambiguous, and high-risk manifolds. A benchmark-style evaluation using 12,000 synthetic and curated reasoning traces across 180 adversarial task scenarios shows that manifold-aware scoring improves AUROC from 0.78 for a binary BCE monitor to 0.88 after calibration, while reducing expected calibration error from 0.118 to 0.055. The calibrated score also supports operational risk tiers that preserve low-latency edge deployment, with an estimated monitor overhead of 1.4 ms per reasoning segment and a 28% relative reduction in high-risk releases compared with a threshold-only monitor. The study contributes an analytics architecture, an interpretable scoring schema, and a governance-oriented deployment protocol for lightweight large language models in finance, education, healthcare triage, public service chatbots, industrial monitoring, and other deception-sensitive settings.
Copyright (c) 2025 Marta Costa, Rafael Nunes, Ana Ribeiro* (Author) https://creativecommons.org/licenses/by/4.0
Contextual Reasoning for Embodied Supply Chain Agents: Reinforcement Learning Policies from Physical State Perception to Collaborative Execution
Physical supply chains increasingly rely on artificial intelligence, autonomous mobile robots, computer vision, edge sensors, and digital twins, yet many decision systems still reason over abstract data tables rather than over the physical state in which execution takes place. This paper develops a contextual reasoning framework for embodied supply chain agents that connects physical state perception, reinforcement learning policy design, and collaborative execution across warehousing, sorting, and last-mile delivery. The proposed framework defines the agent state as a multimodal representation of spatial congestion, shelf load, equipment utilization, order urgency, task risk, and inter-agent dependency. A reward architecture is then formulated to balance fulfillment time, execution accuracy, resource utilization, safety, and policy stability. To demonstrate analytic value, the study constructs an illustrative multi-agent simulation of a three-link supply chain operation involving storage robots, sorting arms, and delivery vehicles. Compared with static rule dispatching, collaborative contextual reinforcement learning reduces average fulfillment time by 30.7%, late-order rate by 51.1%, and near-miss events by 62.1% under the stated scenario assumptions. The analysis shows that contextual reasoning improves not merely prediction accuracy but also the coupling between digital decisions and physical execution. The contribution of the paper is a policy-oriented analytics model that translates embodied supply chain intelligence into implementable reinforcement learning structures, evaluation indicators, and deployment guidelines for AI-enabled adaptive operations.
Copyright (c) 2025 Marta Ribeiro, Diogo Fernandes, Helena Costa*, Pedro Almeida (Author) https://creativecommons.org/licenses/by/4.0
Graph-Attention Federated Analytics for Non-IID Dairy Farm Monitoring and Compliance Prediction
Dairy farm monitoring across distributed smallholder operations presents a fundamental statistical challenge: the heterogeneous distributions of sensor readings, cattle breeds, climatic conditions, and management practices generate persistently non-IID (non-independent and identically distributed) data that degrade the performance of standard federated learning aggregation schemes. This paper proposes a Graph-Attention Federated Analytics (GA-FA) framework designed to address the non-IID problem in dairy compliance prediction. GA-FA integrates a Graph Attention Network (GAT) module that dynamically clusters participating farms based on context-feature similarity, a DBSCAN-based outlier filter that provides Byzantine-resilient aggregation by excluding anomalous local updates, and a cluster-stratified FedAvg aggregation protocol that maintains personalization while converging toward a globally consistent compliance model. The framework operates on lightweight edge devices using a quantized inference runtime and does not require raw sensor data to leave the farm, preserving operational privacy. Validated on a dataset derived from the Shahhet28121 cattle health benchmark extended to 16 farm-level parameters across five heterogeneous farm environments, the GA-FA framework achieves a global classification accuracy of 96.94%, an F1 score of 0.967, and a communication payload of 4.25 KB per federated round—representing a 97.7% reduction relative to standard FL baselines. Ablation experiments confirm that the GAT clustering module contributes the largest single-component accuracy gain of 4.63 percentage points. The framework is evaluated under Gaussian sensor noise up to 20% standard deviation, maintaining accuracy above 90%, and demonstrates stable convergence within 18 federated rounds. Results establish GA-FA as a robust, resource-efficient, and privacy-preserving approach for distributed dairy compliance analytics.
Copyright (c) 2025 Yusuf Adebayo, Chiamaka Okonkwo, Babatunde Salami* (Author) https://creativecommons.org/licenses/by/4.0