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.
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
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.
Graph-based Representation Learning for Financial Fraud and Anomaly Transaction Detection
Financial fraud detection has emerged as a critical challenge in modern banking systems, with fraudulent transactions causing billions in annual losses worldwide. Traditional rule-based and statistical methods struggle to adapt to sophisticated fraud patterns and evolving attack vectors. This paper proposes a graph-based representation learning approach leveraging Graph Neural Networks to capture complex relational patterns in financial transaction networks. The methodology constructs heterogeneous transaction graphs encoding structural and temporal information, enabling detection of both known fraud patterns and novel anomalies. Experimental evaluations on real-world datasets demonstrate superior performance compared to traditional machine learning and deep learning baselines, with F1-scores reaching 0.947 and AUC-ROC values exceeding 0.985. The results confirm the effectiveness of graph-based representation learning for addressing imbalanced fraud detection while maintaining low false positive rates.
Feature-Based Detection of Bot Traffic and Click Fraud in Mobile Advertising: A Comparative Analysis
Mobile advertising fraud has emerged as a critical security challenge, causing substantial financial losses across digital ecosystems. This paper presents a comprehensive comparative analysis of machine learning algorithms for detecting bot traffic and click fraud through feature-based approaches. We engineer and evaluate temporal, behavioral, and device-specific features across multiple classification algorithms including Random Forest, XGBoost, LightGBM, and deep learning architectures. Experimental results on real-world advertising datasets demonstrate that ensemble methods achieve superior performance with accuracy exceeding 98%, while deep learning approaches provide enhanced robustness against sophisticated fraud patterns. Feature importance analysis reveals that temporal activity patterns and device consistency metrics serve as primary discriminators between legitimate and fraudulent traffic. Our findings provide actionable deployment guidelines for advertising platforms balancing detection accuracy with computational efficiency.
Explainable Attack Path Reasoning for Industrial Control Network Security Based on Knowledge Graphs
Industrial control systems face escalating cyber threats that exploit protocol-specific vulnerabilities. This paper develops an explainable attack path reasoning framework integrating knowledge graph construction with large language model-assisted semantic analysis. The methodology constructs a domain-specific ontology capturing ICS assets, vulnerabilities, and attack techniques aligned with MITRE ATT&CK for ICS. A graph-based inference engine performs multi-hop reasoning to identify attack chains while generating human-interpretable explanations satisfying regulatory requirements. The LLM-assisted log analysis component extracts semantic patterns from heterogeneous industrial protocols including Modbus, DNP3, and IEC 60870-5-104. Experimental evaluation on public ICS datasets demonstrates 94.7% attack path identification accuracy with 89.3% explainability satisfaction scores. The framework achieves 12.8% improvement in adversarial robustness compared to baseline graph neural network approaches while maintaining real-time inference capabilities.
Deep Reinforcement Learning for Route Optimization in E-commerce Return Management
E-commerce return volumes continue to surge, presenting significant operational challenges for reverse logistics management. Traditional route optimization approaches struggle with the dynamic nature of return collection requests characterized by unpredictable arrivals, stringent time windows, and dispersed locations. This paper proposes a deep reinforcement learning framework combining graph attention networks with proximal policy optimization for dynamic return routing. The approach encodes spatial relationships between pickup locations and learns adaptive policies through continuous environmental interaction. Experimental evaluation on synthetic benchmarks and real-world data demonstrates substantial improvements: 15.8% reduction in routing distance and 11.4 percentage point improvement in on-time pickup rates compared to genetic algorithm baselines. Computational analysis shows 0.28-second inference times enabling real-time adaptation. Results validate practical viability for intelligent reverse logistics optimization.
Deep Learning in Cardiovascular CT Imaging: Evolution, Trends, and Clinical Translation from 2020 to 2025
Cardiovascular diseases remain the leading cause of mortality worldwide, necessitating advanced diagnostic approaches. Deep learning has revolutionized cardiac computed tomography (CT) imaging analysis over the past five years, transforming from experimental algorithms to clinically validated tools. This review examines the architectural evolution from convolutional neural networks to transformer-based models, analyzing their application across anatomical segmentation, coronary artery analysis, and functional risk assessment. We synthesize findings from 15 high-impact studies published between 2020 and 2025, documenting performance improvements in cardiac chamber segmentation (Dice coefficients 0.88-0.95), stenosis detection (sensitivity 90-97%), and mortality risk prediction (C-index 0.70-0.82). Technical challenges including limited annotated datasets, cross-scanner generalization, and regulatory barriers continue to impede widespread clinical adoption. Emerging foundation models and multimodal integration represent promising directions for next-generation cardiovascular AI systems.
Comparative Analysis of Unsupervised Learning Approaches for Anomalous Billing Pattern Detection in Healthcare Payment Integrity
Healthcare payment integrity faces substantial challenges from anomalous billing patterns that undermine financial sustainability and compromise resource allocation effectiveness. This research develops a systematic comparative framework evaluating five unsupervised learning algorithms—Isolation Forest, Local Outlier Factor, DBSCAN, One-Class SVM, and Autoencoder—for detecting aberrant billing behaviors within medical claims databases. Through empirical analysis of Medicare Part B data spanning 142,738 provider records, we quantify detection accuracy, computational efficiency, and pattern recognition capabilities across distinct algorithmic approaches. Isolation Forest demonstrates superior performance with 0.847 F1-score and 3.2-second processing time per 10,000 claims, while Autoencoders reveal 23.6% higher sensitivity to complex multivariate anomalies. The analysis identifies critical tradeoffs between precision-recall balance and scalability constraints, establishing quantitative benchmarks for algorithm selection in operational fraud detection systems. Our findings indicate that ensemble configurations combining density-based and reconstruction-error methodologies yield 15.8% improvement over single-algorithm deployments.
An Empirical Study of Large Language Models for Threat Intelligence Analysis and Incident Response
The exponential growth of cyber threats necessitates advanced automation in threat intelligence analysis and incident response workflows. This empirical study investigates the application of Large Language Models (LLMs) across critical security operations tasks, including threat intelligence extraction, TTP mapping, and automated response generation. Through systematic evaluation of multiple LLM architectures on real-world cybersecurity datasets comprising 1,000 threat intelligence reports and 500 incident records, we assess performance across entity extraction, threat actor attribution, and remediation recommendation tasks. Our experimental results demonstrate that LLMs achieve F1 scores exceeding 0.88 for Indicator of Compromise (IoC) extraction and reduce incident response time by 64% while maintaining 82% accuracy in MITRE ATT&CK technique mapping. The findings reveal significant efficiency gains with RAG-enhanced configurations showing 19% performance improvement over baseline approaches. This work provides empirical evidence supporting LLM deployment in security operations centers and identifies critical challenges in production environments.
Agentic AI Across Domains: A Comprehensive Review of Capabilities, Applications, and Future Directions
The emergence of large language model-based agents represents a transformative shift in artificial intelligence, enabling autonomous systems capable of perceiving environments, reasoning about complex tasks, and executing multi-step actions. This comprehensive review examines fundamental capabilities underpinning agentic AI, analyzes cross-domain applications spanning software engineering, scientific discovery, and healthcare, and identifies critical technical challenges. Through systematic analysis of recent advances, we establish a unified framework encompassing perception, reasoning, and execution while documenting performance metrics. The synthesis reveals persistent challenges in reliability, evaluation methodology, and safety governance requiring coordinated research efforts. Our findings indicate that while agents demonstrate remarkable capabilities in constrained domains, achieving robust autonomy demands fundamental innovations in coordination, reasoning, and decision-making protocols.
Forecasting Hospital Resource Demand Using Gradient Boosting: An Operational Analytics Approach for Bed Allocation and Patient Flow Management
Hospital resource management faces increasing complexity due to volatile patient demand and capacity constraints. This research presents a hybrid forecasting framework integrating time series decomposition with gradient boosting techniques for predicting hospital bed occupancy and patient flow patterns. Using three years of operational data from a large American hospital system, the proposed approach combines seasonal decomposition methods with LightGBM to capture both temporal patterns and complex non-linear relationships. Experimental results demonstrate mean absolute percentage error of 2.3% for one-day-ahead bed occupancy predictions, representing 18% improvement over standalone machine learning methods and 32% improvement over classical time series approaches. The framework successfully forecasts emergency department volumes with 6.4% mean absolute percentage error while maintaining computational efficiency suitable for daily operational deployment. Implementation case studies reveal measurable operational improvements including 17% reduction in bed assignment times and enhanced equipment utilization. This research contributes a practical methodology for transforming reactive hospital resource management into proactive capacity planning.
Enhancing Financial Audit Efficiency Through RPA Implementation: A Comparative Analysis in Manufacturing Industry
This research investigates the implementation of Robotic Process Automation (RPA) technology in financial audit processes within the manufacturing industry, focusing on comparative analysis between manual and automated procedures. The study examines the deployment of RPA tools including UiPath and Automation Anywhere in automating critical audit functions such as invoice processing, bank reconciliation, and expense categorization. Through a comprehensive case study approach involving a mid-sized manufacturing company, quantitative and qualitative metrics were collected to evaluate performance improvements. Results demonstrate significant efficiency gains with 73% reduction in processing time, 89% decrease in error rates, and annual cost savings of $425,000. The implementation revealed critical success factors including technical infrastructure readiness, organizational support structures, and continuous maintenance protocols. This research contributes to understanding RPA adoption challenges and benefits in financial auditing, providing practical insights for manufacturing enterprises pursuing digital transformation initiatives.
Cross-Modal Artifact Mining for Generalizable Deepfake Detection in the Wild
The proliferation of deepfake content poses unprecedented threats to digital security and information integrity. Existing detection methods suffer from significant performance degradation when confronting cross-dataset scenarios and real-world manipulated media. This paper proposes a novel cross-modal artifact mining framework that integrates frequency-domain analysis with audio-visual consistency verification for enhanced generalization capability. Our approach employs adaptive high-frequency enhancement modules coupled with discrete cosine transform feature extraction to capture subtle manipulation artifacts. The cross-modal attention fusion mechanism effectively leverages temporal alignment inconsistencies between audio and visual streams. Through comprehensive evaluation on six benchmark datasets, our method achieves superior cross-dataset generalization performance with 89.7% average accuracy and demonstrates robust detection capability against diffusion-generated deepfakes. Extensive experiments validate the effectiveness of each proposed component through ablation studies, while robustness analysis confirms resilience against adversarial perturbations and compression artifacts encountered in real-world deployment scenarios.