The use of large datasets for predictive modeling in intelligent databases is frequently hindered by structural deficiencies, most notably missing records. Conventional imputation methods, such as mean or median substitution, distort statistical variance and fail to capture multivariate relationships among features. This paper presents a dynamic, AI-based data quality management framework that autonomously identifies and corrects structural anomalies prior to predictive modeling. Utilizing Classification and Regression Trees (CART), the system intelligently imputes missing categorical and numerical values by learning localized patterns from the observed portion of the dataset. To validate the integrity of the repaired database, the framework was evaluated on a credit risk dataset using a Tri-Ensemble of gradient boosting algorithms (XGBoost, LightGBM, and CatBoost), achieving an Area Under the ROC Curve (AUC) score of 0.9393. These results demonstrate that predictive, tree-based data imputation preserves the statistical distribution of the original data and substantially enhances the accuracy of downstream machine learning tasks. The proposed framework offers a scalable, automated solution for data preprocessing in distributed and intelligent database environments.
Explainable Artificial Intelligence (XAI) is rapidly being integrated into project and program management decision-support tools, driven by the widely held assumption that model transparency improves human decision quality. This assumption has not been subjected to rigorous empirical testing in program management contexts. This paper presents the design, theoretical framework, experimental methodology, and pilot study findings of a controlled behavioural experiment evaluating whether program managers who receive XAI-augmented risk predictions make measurably better decisions than those receiving prediction-only outputs or no AI assistance. Three experimental conditions are defined: unaided judgment, prediction-only AI output, and XAI-augmented AI output with SHAP-derived feature explanations. Four dependent variables are measured: decision accuracy, intervention targeting precision, decision latency, and stakeholder communication quality. A pilot study conducted with 18 practitioners provides preliminary support for the primary hypotheses, with moderate-to-large effect size estimates confirming adequate statistical power for the full study. The framework is grounded in Dual Process Theory, Bounded Rationality, and the Technology Acceptance Model. The study addresses a critical empirical void at the intersection of explainable AI and program governance and is positioned to produce the first empirically validated evidence base on the decision-level value of XAI in IT program risk management.
As organizations transition to hyper-connected, multi-cloud architectures, the mobility of data has far outpaced the technical frameworks meant to govern it. Regulatory mandates GDPR, CCPA, and CPRA articulate purpose limitation and informed consent as foundational principles, yet a persistent gap exists between what these laws require and what distributed systems actually enforce. In cloud-native environments, the metadata that records user consent and allowable use is routinely lost or overwritten as data crosses microservice boundaries, IoT pipelines, and third-party APIs. This article examines the architectural roots of that failure through three documented enforcement actions: the 2026 FTC settlement with General Motors over its Smart Driver telematics program, Tesla's internal camera-data misuse, and the Toyota-Progressive Insurance opt-out breakdown. Each case traces the same systemic flaw, consent does not travel with the data it authorizes. In response, this article proposes a Sticky Governance framework built on cryptographically signed consent tokens, service mesh enforcement, and dynamic resource provisioning, together with a revised audit methodology appropriate for information systems professionals operating in automated, high-velocity data environments. Analysis of three documented enforcement actions reveals a consistent pattern: purpose-scope metadata was absent from inter-organizational API payloads in all cases examined, enabling downstream data misuse that produced a 2026 FTC consent order, internal privacy violations affecting video footage from tens of thousands of enrolled vehicles, and insurance opt-out failures persisting across at least one underwriting cycle. The Sticky Governance Framework operationalizes cryptographic consent propagation and automated purpose-matching at the service-mesh enforcement layer, directly addressing the architectural gap through which governance context is stripped in transit. Policy recommendations include mandatory consent-token schema standards for cross-organizational API ecosystems, governance-metadata preservation clauses in cloud service contracts, and audit frameworks grounded in cryptographic evidence rather than document-review sampling.
This article proposed AI-ADS, an AI-Driven Accelerated Diagnostic System that integrates automated blood analysis, rapid X-ray interpretation, and rapid laboratory orchestration into a single latency-aware architecture. A physiology-structured blood analyzer, a reliability-gated fusion module with uncertainty estimation and budgeted early departures, and a dual-path radiograph encoder with semantic and frequency cues are all part of the proposed solution. In order to translate the speed of diagnostics into actual clinical throughput, AI-ADS employs a digital-twin controller to further model laboratory operations, and constraint-aware scheduling to reduce turnaround-time tails while prioritizing uncertain or critical cases. With an AUROC (macro) of 0.962, an AUPRC (macro) of 0.931, and F1 = 92.2% (Table 1; Fig. 1), the experimental results demonstrate that multimodal fusion improves diagnostic quality in comparison to single-stream baselines. Probability trustworthiness is enhanced by calibration and uncertainty gating, as shown in Table 2 and Figure 2, where ECE = 0.021, Brier = 0.074, and error@high-confidence = 1.9%. Table 3 and Figure 3 show that AI-ADS offers real-time inference with a throughput of 18.4 cases/sec, an early-exit rate of 71%, and a P50/P95 end-to-end latency of 50/120 ms. Table 4 and Figure 4 show that the lab's critical-test compliance increased from 81.2% to 93.5% after using the digital-twin CRL scheduler, and that the median TAT decreased from 49 to 34 minutes. The 90th percentile TAT also decreased from 92 to 62 minutes. Clinical timeliness, diagnostic reliability, and accuracy can all be improved with AI-ADS, as shown here.
This study evaluated the performance of three major SQL injection (SQLi) detection categories—deep learning models, security tools, and structured detection frameworks. Experiments were conducted on a benchmark SQLi dataset derived from publicly available and synthetically augmented SQL traffic, with performance evaluated using accuracy, F1-score, AUC, latency, and false positive rate. Using this dataset containing diverse SQLi variants, the research compared hybrid CNN–LSTM–Autoencoder and Transformer-based models against widely used tools (SQLMap, Acunetix, Microsoft Defender for SQL, CodeScan Labs) and established frameworks (IDE, DIAVA, SQL Shield, ASTF). Deep learning models achieved the highest accuracy (≥0.99), followed by frameworks (0.86–0.96), while tools recorded the lowest detection capability (0.75–0.92). ANOVA results (F = 11.12, p = 0.0013) confirmed statistically significant performance differences. The findings demonstrate the superiority of deep learning—especially hybrid architectures integrating structural, sequential, and latent features—in detecting modern SQLi attacks. This comparative analysis provides empirical evidence supporting the prioritization of adaptive neural models in database security environments.
