Journal of Computing Innovations and Applications
The Journal of Computing Innovations and Applications (CIA) stands at the forefront of computational research and technological advancement, serving as a premier platform for groundbreaking discoveries in computing sciences. This peer-reviewed international journal bridges the crucial gap between theoretical innovation and practical implementation, publishing cutting-edge research that transforms how we understand and apply computing technologies. CIA uniquely focuses on the intersection of emerging computational methods with their real-world applications, featuring work that spans artificial intelligence, quantum computing, cybersecurity, and intelligent systems. With its rigorous double-blind review process and commitment to rapid publication, the journal connects leading researchers, industry pioneers, and practitioners who are shaping the future of digital innovation. CIA particularly values research that demonstrates novel solutions to contemporary challenges, fostering a dynamic exchange between academia and industry that drives technological progress and societal advancement.
Trustworthy Artificial Intelligence in High-Stakes Decision Systems: A Cross-Domain Systematic Review of Explainability, Fairness, Privacy, Robustness, and Governance
Artificial intelligence (AI) systems are increasingly entrusted with consequential decisions in finance, healthcare, cybersecurity, public infrastructure, and online platforms. As these systems move from research prototypes to operational deployment, their value depends not only on predictive accuracy but on whether stakeholders can trust them to behave transparently, fairly, privately, robustly, and accountably. This paper presents a cross-domain systematic review of trustworthy AI in high-stakes decision systems, synthesizing 196 recent studies that span financial risk and regulatory technology, clinical and biomedical analytics, network and software security, social-media information integrity, energy and sustainability, and commerce and mobility. We organize the literature along five trustworthiness pillars-explainability, fairness, privacy preservation, robustness and safety, and accountability and governance-and three cross-cutting methodological enablers: multimodal and multi-source data fusion, graph-based learning, and large language models (LLMs) with agentic orchestration. For each domain we characterize the dominant tasks, data modalities, and evaluation practices; for each pillar we summarize representative techniques and their tensions. Our coverage analysis reveals an uneven landscape: explainability and robustness dominate finance and security, privacy concentrates in healthcare and consumer platforms, and fairness remains comparatively underexplored beyond credit scoring. We further discuss recurring trade-offs-privacy versus utility, fairness versus accuracy, transparency versus performance-and the emerging risks introduced by LLM-based and multi-agent systems, including hallucination, memory poisoning, and over-refusal. We conclude with open challenges and a research agenda emphasizing standardized evaluation, lifecycle governance, and the integration of trustworthiness guarantees into agentic AI.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
An Empirical Comparison of Few-Shot Example Selection Strategies for In-Context Learning on Public Reasoning and QA Benchmarks
In-context learning allows large language models to adapt to a new task by conditioning on a small set of labelled demonstrations placed inside the prompt, and a growing body of work shows that the demonstrations chosen can shift task accuracy by more than ten absolute points. Four families of selection strategies dominate current practice: random sampling, similarity-based retrieval, diversity-based coverage, and complexity-based ranking. Their relative strengths across task types have not been examined inside a single controlled grid. This work offers an empirical comparison of six representative strategies drawn from these four families on four widely used public benchmarks — GSM8K, MMLU, BIG-Bench Hard, and CommonsenseQA — with two open-weight instruction-tuned backbones (Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.2) and a robustness check on StrategyQA. Every strategy is evaluated under the same shot budget and prompt template, and stability is quantified across random seeds. No single strategy dominates the spread of tasks: similarity-based retrieval excels on commonsense QA, complexity-based ranking leads on multi-step arithmetic and algorithmic reasoning, a similarity-plus-diversity hybrid delivers the most stable average accuracy, and the gap between the best and worst strategies is moderate at 3.1 points. These findings support a task-aware view of demonstration selection and suggest that selection can be tuned at the task-type level
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
A Comparative Empirical Study of Semantic Signal Enhancement Methods for User Interest Features in CTR Prediction: Applicability of TF-IDF Weighting, Sentence-BERT Embeddings, and LDA Topic Fusion
User interest feature engineering in recommendation and advertising platforms increasingly relies on semantic signals derived from item text, user queries, and tag annotations. Practitioners lack a unified empirical comparison of the dominant fusion paths under a shared evaluation protocol. This study reports a comparative empirical analysis of three representative semantic signal enhancement methods — TF-IDF weighting, Sentence-BERT embeddings, and latent Dirichlet allocation (LDA) topic distributions — applied to user interest features for click-through rate (CTR) prediction. All three methods are evaluated on four public datasets (MovieLens-25M, MIND-small, Amazon Reviews 2023, and KuaiRec 2.0) using the Deep Interest Network and the Deep Interest Evolution Network as fixed CTR backbones. Sentence-BERT yields a mean AUC lift of 1.71 percent over the identifier-only baseline, while TF-IDF and LDA deliver 0.81 percent and 0.29 percent, respectively. Granularity analysis indicates that TF-IDF peaks on short text such as titles and tags, Sentence-BERT scales monotonically with document length, and LDA only matches TF-IDF once content exceeds roughly one hundred tokens. Cost-benefit profiling places Sentence-BERT with cached item vectors on the accuracy-latency Pareto frontier for mid-to-long text, while TF-IDF remains preferable in short-text, cold-start, and long-tail regimes.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Transparent Humanitarian Funding UI: Data-Grounded LLM-Compatible Explanations of OCHA FTS Funding Flows
Humanitarian funding dashboards expose complex relationships among donors, recipients, sectors, reporting statuses, and response-plan gaps. This paper evaluates a transparent funding-interface design in which data-grounded, LLM-compatible explanations are generated for OCHA Financial Tracking Service (FTS) flow records and paired with accessible legends for funding gaps and reporting certainty. The study conducts a complete reproducible experiment on downloaded FTS/HDX CSV data for the Occupied Palestinian Territory. The primary incoming-flow file contains 394 records and 37 fields, with $794.41M in reported incoming flows, 57 source organizations, 73 destination organizations, and 20 normalized cluster labels. The linked requirements file identifies the 2026 OPT Flash Appeal (FPSE26) with $4.06B in requirements, $459.73M in reported funding, and an official 11.0% funded value, leaving a measured $3.60B gap. Four generation conditions were evaluated on every flow: a minimal label, a generic narrative baseline, a gap-aware explanation, and a constrained audit explanation. Metrics included entity coverage, numeric grounding, unsupported-claim rate, traceability, legend completeness, actionability, reading ease, length, and an aggregate UI-fit score. The proposed constrained explanation achieved a mean UI-fit score of 98.3, compared with 28.2 for minimal labels, 13.7 for generic narratives, and 52.8 for gap-aware explanations. Results show that transparent funding explanations improve semantic completeness when they explicitly bind donor, recipient, sector, amount, status, boundary scope, and plan-level gap context. The included code, processed data, tables, and figures reproduce all reported findings.
Recovering Design Tokens from Figma-like UI Layers Using LLM-Guided Schema Synthesis
Design tokens encode a design system's reusable decisions for color, spacing, typography, and component semantics, yet production teams often inherit screens whose layer names, styles, and component nesting are inconsistent. This paper presents SALT, a deterministic layer-analysis and constrained LLM-guided schema-synthesis pipeline for recovering design tokens from Figma-like UI layer JSON and paired high-fidelity screenshots. The method parses vector-style layer trees, extracts style observations, clusters noisy color and dimension values, recovers component groups from frame hierarchy, and emits a Design Tokens Community Group compatible JSON schema. Full experiments were executed on a 300-screen EGFE-compatible benchmark included with this submission. The benchmark contains 9,164 total layers, 6,374 leaf layers, 8,150 color observations, 12,450 spacing observations, 3,281 typography observations, and 2,190 annotated component groups. Six methods were evaluated: raw unique values, screenshot-palette extraction, rounded-value heuristics, k-means clustering, schema generation without semantic naming, and SALT. SALT recovered spacing tokens with F1=0.800, typography tokens with F1=0.909, and component hierarchy with F1=1.000; its color-token F1 was 0.538, while the rounded-16 heuristic reached the highest color-value F1 of 0.645. SALT was the only method that produced nonzero reference-path schema coverage, achieving 0.571, and it generated a compact 22-token schema in 0.244 s on the complete benchmark. The results show that layer-aware semantic synthesis is necessary for usable token schemas, while value-only color clustering remains sensitive to neutral color collisions.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
A Comparative Evaluation of Transfer Learning Methods for Cross-Context Behavioral Generalization Assessment in Autism Spectrum Disorder Interventions
This study presents a comparative evaluation of three transfer learning paradigms—domain adaptation, few-shot learning, and multi-task learning—for assessing cross-context behavioral generalization in autism spectrum disorder (ASD) interventions. Behavioral skill generalization across distinct intervention environments remains a persistent barrier to long-term ASD intervention effectiveness. The distributional discrepancies inherent in behavioral data collected from clinical, school, and home settings create domain shift challenges that standard recognition models cannot adequately address. Drawing primarily on the Self-Stimulatory Behavior Dataset (SSBD) for the video-based comparative analysis, while using the Autism Brain Imaging Data Exchange (ABIDE) as an auxiliary multi-site domain-shift reference and the Expanded Stereotype Behavior Dataset (ESBD) as a published benchmark, this paper establishes an evaluation framework with four quantitative metrics: cross-context recognition accuracy, generalization stability index, few-shot adaptation efficiency, and behavioral transfer success rate. Within this benchmark-informed framework, domain adaptation yields the highest source-proximal accuracy, few-shot learning shows the strongest low-label adaptation, and multi-task learning exhibits the most stable cross-context profiles. These findings provide practical guidance for selecting computational tools to support more scalable and objective ASD intervention evaluation.
Identifying Cross-Market Risk Contagion Amplifiers via Graph Attention Networks: Empirical Evidence from U.S. Financial Stress Periods
The propagation of financial distress across heterogeneous asset markets has become one of the most pressing challenges for systemic risk monitoring in the post-crisis regulatory environment. This paper investigates cross-market risk contagion among U.S. equity, bond, and credit derivative markets through a graph attention network (GAT) framework embedded in a multilayer network structure. Constructing a directed, attention-weighted graph with 30 nodes spanning three market layers, and drawing on daily data from January 2018 to December 2023, the analysis identifies a persistent set of risk amplifier nodes whose elevated outgoing attention scores and centrality measures designate them as the primary drivers of cross-market stress transmission. The high-yield CDS index node emerges as the most consistently identified amplifier across three distinct stress episodes—the March 2020 pandemic shock, the 2022 interest rate stress cycle, and the March 2023 regional banking turmoil—while the financial sector equity index and long-duration bond node occupy episode-specific secondary amplifier roles. The multilayer network representation captures contagion pathways that single-layer analyses systematically underestimate, particularly the derivative-to-equity transmission channel active under tail-risk conditions. These findings carry direct implications for counterparty risk monitoring and macro-prudential early-warning design in the context of central clearing and financial stability regulation.
Gradient Boosting and SHAP-Based Analysis of Flip IRR Determinants in Residential Solar Tax Equity Transactions Under Section 48E Policy Uncertainty
The partnership flip structure remains the dominant mechanism for monetizing federal investment tax credits in U.S. residential solar photovoltaic financing, yet the pricing of the flip internal rate of return and the determination of ITC transfer discount rates have historically depended on bilateral negotiation rather than systematic quantitative benchmarks. This paper presents a data-driven framework applying gradient boosting regression and SHAP-based interpretability analysis to a curated dataset of 87 residential solar portfolio-level tax equity transactions completed between 2018 and 2024, in which each transaction represents an aggregated investment fund comprising hundreds to thousands of individual rooftop installations structured through third-party ownership platforms. XGBoost and LightGBM models are trained to predict flip IRR intervals and ITC discount rates from 22 transaction-level and macroeconomic features, achieving a mean absolute error of 0.19 percentage points on held-out test transactions. SHAP value decomposition identifies the applicable MACRS bonus depreciation profile, benchmark Treasury rates, and contracted revenue escalation assumptions as the three highest-impact determinants of investor return, which in turn governs flip timing, while the deficit restoration obligation provision—frequently treated as legal boilerplate—emerges as a material constraint-relaxing mechanism that enables fuller depreciation utilization in high-bonus-election transactions. A Monte Carlo simulation framework, constrained by model-predicted investor IRR thresholds, generates sponsor net present value distributions across tax-driven capital structures scenarios, revealing that full bonus depreciation under a §48E domestic content scenario improves mean sponsor NPV by 19 percent relative to baseline. The framework is evaluated against the policy landscape established by the One Big Beautiful Bill Act signed into law on July 4, 2025, including the accelerated termination of §48E eligibility for solar facilities, the permanent restoration of 100 percent bonus depreciation, and the preservation of credit transferability under Section 6418. Findings support a scalable, transparent pricing infrastructure capable of reducing transaction friction and lowering capital formation costs across U.S. residential solar markets.
Behavioural Feature Analysis for Anomalous Click Detection in Mobile Advertising Environments: Toward In-App Browser-Specific Detection
Mobile in-app browsers (IABs), implemented through WebView components, have become a dominant channel for rendering advertising content within mobile applications. This environment presents distinctive challenges for ad fraud detection due to its constrained JavaScript execution context, opaque rendering pipeline, and limited access to conventional browser-level signals. This paper presents a behavioral feature analysis targeting anomalous click detection in mobile advertising environments, with particular attention to in-app browser (IAB) deployment contexts. A multi-dimensional feature taxonomy is proposed, organized across four analytical dimensions: temporal patterns, gestural characteristics, device fingerprinting, and network-level attributes. Using three publicly available datasets—TalkingData AdTracking (approximately 200 million click records), FDMA 2012 BuzzCity, and the labeled real-time bidding (RTB) logs reported in recent literature—this study evaluates the discriminative capacity of each feature dimension for characterizing click behavior anomalies indicative of fraudulent activity. Because the publicly available datasets used in this study originate from general mobile advertising platforms rather than IAB-specific instrumentation, the results are interpreted as evidence for mobile ad fraud detection that is directionally—but not conclusively—applicable to IAB environments. The analysis indicates that temporal aggregation features and device consistency metrics provide the strongest detection signals across the evaluated mobile advertising datasets. A discussion on the trade-off between detection granularity and user privacy constraints under data minimization principles is also provided. The findings contribute empirical guidance for practitioners developing fraud mitigation strategies within mobile advertising environments, including IAB contexts.
Detecting Semantic Mismatches in XBRL Tag Mapping for SEC 10-K Filings: A Text Comparison and Historical Consistency Analysis
The accuracy of eXtensible Business Reporting Language (XBRL) tag mapping in SEC financial filings directly affects the reliability of automated financial analysis conducted by millions of investors through the EDGAR system. This study investigates semantic mismatches between financial statement line-item labels and their corresponding XBRL taxonomy elements in 10-K annual reports filed with the U.S. Securities and Exchange Commission. Drawing on SEC Financial Statement Data Sets and the XBRL US Data Quality Committee (DQC) validation rule library, this research analyzes custom tag usage patterns across filer categories and industry sectors over the period 2014–2024, with cross-sectional tabulation of filer-category rates at selected benchmark years (2014, 2017, 2019, and 2020) and aggregate trend data through 2024 drawn from SEC Office of Structured Disclosure publications. A tiered text comparison approach combining lexical similarity scoring (TF-IDF and BM25) with domain-specific contextual features is applied to evaluate the semantic alignment between reported line items and assigned taxonomy tags. Cross-period consistency analysis and SIC-code industry peer benchmarking are employed to identify anomalous tag selection changes that may indicate data quality degradation rather than substantive business changes. The findings reveal persistent heterogeneity in custom tag rates across industries and filer sizes, with specific tag mapping patterns that warrant targeted validation checkpoints. The proposed lightweight verification methods are designed for integration into existing disclosure management workflows without requiring complex computational infrastructure.
A Comparative Evaluation of Robust Loss Functions for Learning with Noisy Labels: From Synthetic to Real-World Annotations
Label noise is a pervasive challenge in supervised learning, as deep neural networks are prone to memorizing corrupted annotations during training. Robust loss functions have emerged as a principled approach to mitigate this issue without requiring auxiliary clean data or multi-network training pipelines. While numerous noise-tolerant and composite loss functions have been proposed in recent years, existing evaluations typically compare against only a limited subset of baselines under heterogeneous experimental protocols, making cross-method comparisons unreliable. This paper presents a systematic and unified comparative evaluation of eight representative robust loss functions—Cross-Entropy, Mean Absolute Error, Generalized Cross-Entropy, Symmetric Cross-Entropy, Active Passive Loss, Early-Learning Regularization, Generalized Jensen-Shannon divergence, and Active Negative Loss—across four distinct noise conditions: symmetric, asymmetric, instance-dependent, and real-world human annotation noise. All experiments are conducted on CIFAR-10 and CIFAR-100 with their corresponding CIFAR-N human-annotated noisy label sets (available at noisylabels.com), using a unified PreAct-ResNet-18 architecture and identical training protocol. The results reveal that composite and regularization-based losses consistently outperform simpler noise-tolerant alternatives, with the performance gap widening at elevated noise rates. Notably, method rankings observed under synthetic symmetric noise do not reliably transfer to real-world instance-dependent noise scenarios, highlighting the importance of evaluating under diverse noise conditions.
Statistical Anomaly Detection Approach for Field Mapping Validation in Enterprise Payroll Data Migration
Enterprise payroll system migrations from legacy platforms to modern cloud-based solutions present significant data quality challenges, particularly in field mapping validation. This research proposes a statistical anomaly detection framework specifically designed for automated validation of field mappings during Oracle Cloud Payroll to SAP SuccessFactors migrations. The framework employs distribution-based analysis techniques including Kolmogorov-Smirnov tests, Chi-square tests, and multi-threshold outlier detection mechanisms to identify systematic mapping errors, data type inconsistencies, and business rule violations. Experimental validation using a dataset of 50,000 employee records across 120 fields demonstrates that the proposed approach achieves 95.3% detection accuracy while reducing manual validation time by 42%. The framework successfully identified critical anomalies including decimal precision loss in salary calculations, date format inconsistencies in employment records, and null value propagation in benefit deductions. These results establish statistical anomaly detection as a viable automated quality assurance mechanism for enterprise payroll data migrations, offering substantial improvements over traditional rule-based validation methods.
Integrating Ovarian Reserve Biomarkers with Machine Learning for Gonadotoxicity Risk Prediction in Young Female Cancer Patients: A Scoping Review
Cancer treatment-related gonadotoxicity poses substantial threats to reproductive health in young female patients. Current risk stratification frameworks, including ASCO clinical practice guidelines and the Oncofertility Pediatric Initiative Network (O-PIN) classification, rely predominantly on population-level statistics and expert consensus, offering limited capacity for individualized prediction. This scoping review examines the emerging intersection of ovarian reserve biomarkers and machine learning (ML) methodologies for gonadotoxicity risk prediction in young female cancer patients. Through systematic analysis of published literature, publicly available datasets, and clinical decision support tools, this review identifies key biomarkers—including anti-Müllerian hormone (AMH), antral follicle count (AFC), and follicle-stimulating hormone (FSH)—that serve as critical input features for predictive models. ML approaches, particularly random forest classifiers, have demonstrated promising discriminative performance (area under the receiver operating characteristic curve up to 0.87) in predicting primary ovarian insufficiency following chemotherapy. This review further evaluates how artificial intelligence may facilitate personalized fertility preservation pathway selection and clinical workflow integration. Challenges including limited sample sizes, absence of external validation, data heterogeneity, and algorithmic fairness concerns are critically discussed. Recommendations for multicenter prospective registries and standardized evaluation frameworks are proposed to advance clinical translation of AI-assisted oncofertility decision-making.
Fairness-Accuracy Trade-offs in AI Credit Scoring: A Comparative Evaluation of Reweighting and Resampling Strategies Under Multiple Fairness Constraints
The proliferation of artificial intelligence in financial risk assessment has introduced significant concerns regarding algorithmic fairness, particularly in credit scoring systems where biased predictions can disproportionately affect protected demographic groups. This study presents a comparative evaluation of two predominant pre-processing debiasing strategies—reweighting and resampling—applied to AI-based credit scoring algorithms. Using two publicly available benchmark datasets (the UCI German Credit dataset and the UCI Default of Credit Card Clients dataset), we systematically assess the accuracy-fairness trade-offs under three widely adopted fairness constraints: statistical parity, equal opportunity, and equalized odds. Experimental results across three baseline classifiers (Logistic Regression, Random Forest, and XGBoost) indicate that reweighting achieves a more favorable balance between predictive accuracy and fairness when evaluated under equal opportunity and equalized odds constraints, while resampling demonstrates stronger performance in reducing statistical parity differences. The magnitude of accuracy degradation varies substantially depending on the choice of fairness constraint, with equalized odds imposing the greatest accuracy cost across both datasets. These findings provide evidence-based guidance for financial institutions seeking to implement fairness-aware credit scoring systems and suggest that the selection of debiasing strategy should be contingent upon the specific fairness objective prioritized by regulatory and institutional requirements.
Causal Effect Evaluation of Personalized Reminder Strategies on Government Welfare Program Enrollment: A Propensity Score Matching Approach
Government digital service platforms face persistent challenges achieving optimal enrollment rates for welfare programs including SNAP and Medicaid. This research develops a causal inference framework quantifying personalized reminder intervention effects on enrollment completion, addressing selection bias through Propensity Score Matching, temporal dynamics via Difference-in-Differences, and endogeneity through Instrumental Variables. Methodology validation uses simulated observational data incorporating realistic population heterogeneity and non-random treatment assignment. Results demonstrate personalized reminders achieve 14.3 percentage point enrollment increases (p<0.001) after controlling confounding, with heterogeneous effects across age and digital literacy. The framework provides evidence-based guidance for optimizing government platforms per Executive Order 14058 on customer experience modernization.
AI-Enhanced What-If Scenario Analysis in Supply Chain Digital Twins: A Multi-Objective Trade-Off Perspective on Cost, Resilience, and Carbon Efficiency
Global supply chains face compounding disruptions driven by geopolitical tensions, tariff volatility, climate events, and demand uncertainty. Digital twin technology, which creates dynamic virtual replicas of physical supply chain networks, has emerged as a promising instrument for proactive decision-making through simulation and scenario analysis. This paper presents an analytical framework examining how artificial intelligence enhances What-If scenario analysis within supply chain digital twin environments, with a particular focus on multi-objective trade-offs among cost, resilience, service level, and carbon efficiency. By synthesizing recent literature, publicly available datasets, and U.S. federal supply chain digitalization initiatives, the study delineates the mechanisms through which AI enables multi-source data integration, automated scenario generation, cascading disruption prediction, and Pareto-optimal decision evaluation within digital twin architectures. Rather than proposing a new computational model or system implementation, this work offers a structured analytical perspective that bridges the gap between isolated single-module optimization studies and the integrated cross-functional decision-making that modern supply chains demand. The findings highlight a convergent relationship between resilience optimization and carbon reduction, and identify key technical and organizational challenges for practical implementation at the enterprise level.
AI-Enhanced Cross-Asset Liquidity Contagion Pathway Identification and Dynamic Hedging Strategy Optimization: Evidence from U.S. Equity, Bond, and Derivatives Markets
Cross-asset liquidity risk constitutes a persistent threat to financial stability, as demonstrated by the 2007–2009 Global Financial Crisis, the March 2020 COVID-19 market disruption, and the March 2023 banking stress episode. This study investigates the identification of time-varying liquidity contagion pathways across U.S. equity, fixed-income, and derivatives markets, and the optimization of dynamic hedging strategies informed by such pathways. A temporal graph attention network (T-GAT) is constructed from publicly available data—including FRED macroeconomic indicators, CBOE VIX, and Amihud illiquidity measures derived from Yahoo Finance—to capture directional liquidity spillovers among six representative asset proxies. Transfer entropy serves as the edge-weighting mechanism, and the graph structure is updated on rolling 60-day windows. A Proximal Policy Optimization (PPO)-based deep reinforcement learning agent leverages the T-GAT-derived contagion state to allocate hedging positions across assets. Out-of-sample evaluation over the 2022–2024 period indicates that the T-GAT achieves an F1-score of 0.731 in predicting weekly liquidity stress events, representing a moderate improvement over LSTM and static GCN baselines. The contagion-informed hedging agent yields a Sharpe ratio of 0.847, compared to 0.623 for conventional risk-parity allocation, with reductions in maximum drawdown during the March 2023 stress period.
A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models
Low-light image enhancement (LLIE) has attracted extensive research interest, with approaches spanning convolutional neural networks (CNNs), Retinex-based deep architectures, zero-shot learning, generative adversarial networks (GANs), Transformers, and diffusion models. Despite the proliferation of individual methods, a unified cross-paradigm evaluation under consistent experimental conditions remains absent in the existing literature. This paper presents a systematic comparative study of twelve representative LLIE methods drawn from six distinct algorithmic paradigms. All methods are evaluated on three widely adopted benchmark datasets—LOL-v1, LOL-v2-Real, and SID—using both full-reference metrics (PSNR, SSIM, LPIPS) and no-reference metrics (NIQE, BRISQUE), alongside computational efficiency analysis covering parameter count, floating-point operations, and inference latency. The experimental results indicate that Transformer-based approaches achieve a favorable balance between reconstruction fidelity and perceptual quality, while zero-shot methods offer substantial advantages in inference speed at the cost of quantitative performance. Diffusion-based methods produce perceptually compelling outputs but incur considerable computational overhead. Cross-dataset generalization tests further expose performance degradation across all paradigms when trained and tested on mismatched data distributions. These findings provide practical guidance for selecting LLIE methods under different deployment constraints and evaluation priorities.
A Comparative Analysis of Telemetry-Driven Anomaly DetectionApproaches for Dual-Purpose Operational and SecurityOptimization in Edge Computing Infrastructure
The proliferation of edge computing nodes in enterprise infrastructure has intensified the demand for anomaly detection methods capable of addressing both operational reliability and cybersecurity resilience within resource-constrained environments. This paper presents a comparative evaluation of six lightweight anomaly detection algorithms—Isolation Forest, One-Class SVM, LSTM-Autoencoder, K-Nearest Neighbors, Random Forest, and lightweight 1D-CNN—assessed across a dual-purpose framework that encompasses operational health monitoring and security threat detection. Experiments are conducted on six publicly available benchmark datasets: NASA C-MAPSS and Microsoft Azure Predictive Maintenance for degradation analysis, alongside Edge-IIoTset, UNSW-NB15, CIC-IDS2017, and TON_IoT for cybersecurity evaluation. Performance is measured through accuracy, F1-score, inference latency, and a proposed Dual-Purpose Efficiency Index under both high-performance server and resource-constrained ARM-based edge configurations. Results indicate that tree-based methods achieve the most favorable accuracy-to-latency ratio for edge deployment, while the LSTM-Autoencoder attains the highest detection quality at substantially greater computational cost. Cross-dataset generalization experiments reveal persistent domain shift challenges, particularly for low-frequency attack categories. These findings provide practical algorithm selection guidance for enterprise edge infrastructure scenarios where operational monitoring and security detection must coexist within constrained computational budgets.
Artificial Intelligence Applications Across Critical Domains: A Comprehensive Survey of Recent Advances in Healthcare, Finance, Security, and Beyond
Artificial intelligence (AI) has emerged as a transformative technology reshaping virtually every sector of modern society. From healthcare diagnostics and drug discovery to financial risk management, cybersecurity, autonomous transportation, and environmental sustainability, AI-driven methodologies have demonstrated remarkable capabilities in processing complex, heterogeneous data and generating actionable insights. This comprehensive survey examines 133 recent research contributions spanning the period from 2023 to 2026, systematically analyzing the state-of-the-art AI techniques employed across diverse application domains. We categorize these contributions into seven major thematic areas: (1) natural language processing and intelligent communication, (2) healthcare and biomedical sciences, (3) financial services and compliance, (4) cybersecurity and privacy preservation, (5) intelligent detection and pattern recognition, (6) explainable AI and cross-domain learning, and (7) document intelligence and sustainable development. For each domain, we discuss the key methodological innovations, evaluate reported performance metrics, identify common challenges, and highlight emerging research trends. Our analysis reveals that deep learning architectures, federated learning frameworks, and differential privacy mechanisms represent the most frequently adopted technical foundations, while multi-modal data fusion and graph-based learning are gaining increasing attention. We conclude by identifying critical open challenges including model interpretability, fairness-aware AI, and the need for robust evaluation benchmarks, and propose directions for future research that can bridge the gap between theoretical advances and practical deployment.
A Comprehensive Survey on Artificial Intelligence Applications Across Critical Domains: Advances in Healthcare, Finance, Cybersecurity, and Emerging Fields
Artificial Intelligence (AI) has rapidly transformed numerous sectors including healthcare, finance, cybersecurity, transportation, and education. This comprehensive survey systematically reviews 120 recent publications spanning these domains, covering advances in natural language processing, deep learning, federated learning, reinforcement learning, differential privacy, and multi-modal data fusion. We organize the reviewed works in strict sequential order, with each reference cited exactly once, providing a coherent narrative that traces the evolution of AI methodologies across application areas. Tables and figures summarize the distribution of methods, domains, and privacy techniques. This survey serves as a unified reference for researchers and practitioners seeking to understand the breadth of contemporary AI applications.
Uplift Modelling and Doubly Robust Causal Learning for Bank Marketing Targeting: Optimizing ROI with Coverage–Incremental Profit Curves
Marketing decisions should optimize incremental business value rather than predictive accuracy alone. A response classifier that ranks customers by subscription probability may inefficiently allocate budget by targeting individuals who would subscribe regardless of marketing intervention. Uplift modeling addresses this limitation by estimating the incremental causal impact of an action and enabling ROI-oriented targeting. This study formulates the “who to contact” problem as a causal decision task using the UCI Bank Marketing Dataset, specifically the bank-additional.csv subset (n = 4,119). Since the dataset includes only contacted clients, the treatment is operationalized as channel assignment, where cellular represents treatment and telephone represents control, as recorded in the contact variable. The leakage feature duration is removed following the dataset documentation. Two causal estimators are implemented: a two-model uplift estimator (T-learner) and a doubly robust learner that regresses an augmented inverse probability weighted (AIPW) pseudo-outcome with cross-fitting. Targeting policies are evaluated on a held-out test set using doubly robust policy evaluation with logistic-regression propensity scores clipped to [0.01, 0.99], and performance is assessed through AUUC, Qini, and coverage–incremental profit curves. On the test set (n = 1,236; seed = 42), the two-model uplift method achieves the best uplift ranking (AUUC = 0.0491, Qini = 0.0246). Outcome prediction AUC is 0.726 for a covariate-only model and 0.722 for an S-learner including the treatment indicator, demonstrating that predictive accuracy on outcomes does not identify incremental impact. Under a profit model with revenue (r = 100) and channel cost (c = 1), the two-model uplift generates 5,627.8 incremental profit at 10% coverage and peaks at 7,517.7 around 40% coverage. Sensitivity analysis further indicates that uplift-based targeting becomes increasingly advantageous as intervention costs rise. Overall, the study demonstrates a reproducible framework for translating causal uplift estimates into ROI-optimized bank marketing decisions.
An Empirical Evaluation of Prompt Injection Detection and Refusal-Usefulness Tradeoffs Using the deepset/prompt-injections Dataset
Prompt injection is a leading security risk for large language model (LLM) applications because adversaries can embed instructions that override system intent, exfiltrate hidden prompts, or trigger unsafe tool use. This paper presents a fully empirical evaluation of prompt-injection defenses on the public deepset/prompt-injections dataset (662 labeled prompts; 399 benign, 263 injection/attack) using the official train/test split. We compare lightweight detectors that can be deployed as an input gate: a keyword-based rule system, word-level TF-IDF with Logistic Regression (LR), character-level TF-IDF with LR, calibrated linear Support Vector Machines (SVMs), and Complement Naive Bayes. We report attack success rate (ASR), detection F1, and a refusal rate–usefulness tradeoff. On the test split, the best detector is a character TF-IDF + calibrated linear SVM with F1=0.901 and ROC-AUC=0.977, substantially outperforming keyword rules (F1=0.125). When used as a refusal gate, the same family of character models reduces ASR from 1.000 (no gate) to 0.117 at 92.9% usefulness (1 - benign refusal) under a low-false-positive operating point derived from benign-score quantiles on the training split. Error analysis shows that most remaining bypasses are short, multilingual, or typo-heavy injections, indicating that robust defenses require character-level generalization and abstention tuning. Overall, our results quantify the operational tradeoffs between security and usability and provide reproducible baselines for prompt injection detection research.
Temporal Feature Learning Framework for Multi-dimensional Behavior Anomaly Detection in Digital Transaction Platforms
Digital transaction platforms face escalating challenges from sophisticated fraudulent activities that exploit multi-dimensional behavioral patterns across temporal sequences. This research presents a comprehensive temporal feature learning framework designed specifically for detecting anomalous behaviors in complex digital transaction environments. The proposed framework integrates adaptive threshold mechanisms with multi-scale temporal feature extraction, enabling real-time identification of suspicious activities while maintaining low false-positive rates. Through systematic analysis of transaction sequences, user interaction patterns, and network-level behavioral signatures, the framework achieves enhanced detection accuracy across diverse attack vectors including coordinated fraud campaigns and automated malicious account operations. Experimental validation demonstrates superior performance compared to conventional rule-based approaches, with detection precision reaching 94.7% and recall maintaining 91.3% across heterogeneous transaction datasets. The adaptive nature of the framework allows dynamic adjustment to evolving threat landscapes without requiring extensive retraining cycles. , , ,
Accelerating Clinical Trial Recruitment Through Automated Eligibility Screening with Multi-Modal Deep Learning
Clinical trial recruitment remains a critical bottleneck in medical research, with approximately 80% of trials experiencing significant delays due to inadequate patient enrollment. Traditional manual screening approaches require substantial time and resources while yielding suboptimal accuracy in matching patients to appropriate trials. This paper presents a novel multi-modal deep learning framework that integrates structured electronic health record data with unstructured clinical narratives to automate eligibility screening processes. The proposed architecture employs transformer-based encoders for clinical text processing, coupled with specialized neural networks for structured data analysis, unified through an attention-based fusion mechanism. Experimental validation demonstrates substantial improvements over existing methods, achieving 92.3% accuracy in eligibility prediction while reducing screening time by 73%. The framework successfully processes heterogeneous medical data sources, including diagnosis codes, laboratory results, medication histories, and physician notes, enabling rapid identification of suitable trial candidates. Performance analysis across multiple clinical domains confirms the generalizability and robustness of the approach.
Privacy-Preserving Bid Optimization and Incrementality Estimation under Privacy Sandbox Constraints: A Reproducible Study of Differential Privacy, Aggregation, and Signal Loss
The deprecation of third-party cookies has shifted online advertising toward architectures that expose less user-level information and instead rely on coarse on-device signals and differentially private (DP) aggregation for measurement. This transition creates a recurring technical tension: bidding and conversion models require high-fidelity feedback, yet Privacy Sandbox-style constraints enforce signal obfuscation via quantization, missing feature channels, and noisy aggregated reporting. In this paper, we study the end-to-end impact of these constraints on (i) conversion-rate estimation for bid optimization and (ii) incrementality (uplift) estimation for causal measurement. We center the empirical analysis on the publicly released CriteoPrivateAd dataset and the Criteo Uplift Prediction dataset, and we provide a fully reproducible experimental pipeline. Because the official releases are hosted with content-addressed storage and transfer protocols that require authenticated download flows in common research environments, we provide schema-consistent proxy instantiations that match the published feature buckets, label definitions, and scale regimes and that reproduce every table and figure in this manuscript. Across our experimental sweep, we quantify privacy–utility tradeoffs under feature quantization (4–12 bits), user-level DP feature noise (ε∈{0.5,1,2,4,∞}), and DP cohort aggregation at multiple granularities. Results show that (a) removing the “not available” feature bucket drops profit/1k from 47.1908 to 5.6311 (ROI 0.2318→0.0283) while AUC decreases only slightly, highlighting the difference between ranking metrics and economic utility; (b) 8-bit quantization preserves AUC (0.868→0.8677) and yields similar utility in our bidding simulation; and (c) day-level DP aggregation collapses both prediction quality and uplift policy value, while finer aggregation (campaign- and publisher-level) retains partial utility. We discuss implications for Privacy Sandbox measurement APIs and provide engineering guidance for designing robust models under evolving privacy constraints.
Privacy-Preserving Click Pattern Anomaly Detection for Mobile In-App Browser Advertising Fraud
Mobile in-app browser advertising fraud poses significant economic threats to digital marketing ecosystems, costing advertisers billions annually through sophisticated click manipulation schemes. This research presents a privacy-preserving anomaly detection framework specifically designed for identifying fraudulent click patterns within mobile in-app browser environments. The proposed methodology integrates differential privacy mechanisms with temporal sequence analysis to detect abnormal user interaction patterns while maintaining user privacy compliance. Through comprehensive evaluation on real-world advertising datasets containing 2.3 million click events, our approach achieves 94.7% detection accuracy with minimal privacy budget consumption. The framework analyzes multi-dimensional features including click timing intervals, touch pressure distributions, and device sensor signals to distinguish genuine user interactions from automated fraud attempts. Experimental results demonstrate superior performance compared to existing methods while ensuring ε-differential privacy guarantees, achieving optimal balance between detection effectiveness and privacy protection in mobile advertising environments.
Feature Attribution-Based Explainability Analysis for Market Risk Stress Scenarios
The increasing adoption of artificial intelligence in financial risk management has raised concerns about the transparency and interpretability of stress testing outcomes. This paper presents a feature attribution-based framework for explaining market risk stress scenarios through SHAP (SHapley Additive exPlanations) analysis. The proposed approach addresses the critical gap between advanced scenario generation techniques and regulatory requirements for explainable risk assessments. By decomposing portfolio loss predictions into individual risk factor contributions, the methodology enables risk managers to validate whether generated scenarios align with established economic relationships. Experimental results using Federal Reserve stress test data demonstrate that the attribution framework achieves 87.3% consistency with known financial correlations during crisis periods. The validation mechanism successfully identifies spurious risk factors and quantifies the relative importance of interest rates, equity volatility, and credit spreads across different stress intensities. Comparative analysis against traditional sensitivity analysis shows 34.2% improvement in attribution stability and 28.6% better alignment with domain expert assessments. The framework provides actionable insights for regulatory compliance while maintaining computational efficiency suitable for real-time risk monitoring applications.
Machine Learning-Enhanced Dynamic Asset Allocation in Target-Date Investment Strategies for Pension Funds
Target-date funds constitute the dominant default investment vehicle in defined contribution pension systems, managing approximately $3.4 trillion globally. Traditional glide path designs employ static allocation rules failing to adapt to evolving market regimes. This research develops a machine learning framework integrating temporal feature engineering with ensemble prediction models to construct adaptive asset allocation strategies. Our probabilistic optimization transforms static age-based allocation into a dynamic system responsive to macroeconomic indicators, volatility patterns, and correlation structures. Empirical analysis across 15-year backtesting demonstrates ML-enhanced strategies achieve 1.8% annual excess returns while reducing maximum drawdown by 34% compared to conventional glide paths. The framework incorporates gradient boosting machines for regime classification and LSTM networks for return forecasting, establishing differentiable optimization objectives balancing growth with capital preservation. Implementation protocols address overfitting through walk-forward validation and transaction cost constraints.
Leveraging Multi-Modal Attention Mechanisms for Interpretable Biomarker Discovery and Early Disease Prediction
The identification of molecular biomarkers represents a critical challenge in precision medicine, where high-dimensional multi-omics data creates substantial analytical complexity. This study introduces a novel attention-based framework that integrates genomic, transcriptomic, and clinical data through multi-modal attention mechanisms to enable interpretable biomarker discovery and early disease prediction. The proposed architecture employs self-attention layers for feature-level representation learning and cross-modal attention for heterogeneous data integration, addressing the interpretability limitations of conventional black-box approaches. Evaluated on TCGA and UK Biobank datasets, the framework achieves superior predictive performance with AUC scores of 0.924 and 0.897 respectively, while identifying clinically validated biomarker candidates through attention weight visualization. The method demonstrates significant advantages over traditional feature selection techniques and existing deep learning approaches, providing actionable insights for clinical decision-making through transparent feature importance quantification.