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.
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
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. , , ,
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
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.
Comparative Analysis of Foreign Exchange Market Shock Transmission and Recovery Resilience Among Major Economies Under Geopolitical Conflicts: Evidence from the Russia-Ukraine Crisis
This research presents a comprehensive empirical analysis of foreign exchange market dynamics during the Russia-Ukraine crisis, examining shock transmission mechanisms and recovery resilience across major global economies. Using high-frequency data spanning six months before and after the conflict onset, we develop an innovative dual-metric framework combining volatility clustering analysis with novel recovery trajectory modeling. Our methodology quantifies both immediate shock absorption capabilities and long-term resilience characteristics across G7 currencies and emerging market representatives. The study reveals significant heterogeneity in crisis response patterns, with unexpected resilience demonstrated by certain currencies traditionally considered vulnerable. Through advanced econometric techniques including time-series decomposition and cross-correlation analysis, we identify key macroeconomic factors that determine differential recovery performance. Our findings challenge conventional safe-haven currency assumptions and provide actionable insights for central bank crisis protocols. The research contributes to financial contagion theory by establishing quantitative benchmarks for economic resilience measurement during geopolitical upheavals. Policy implications suggest targeted interventions for enhancing national economic security through improved foreign exchange market stability mechanisms.
Evaluating Tool Selection and Usage Efficiency of LLM-based Agents in Domain-Specific Tasks: A Comparative Analysis
Large language model-based agents demonstrate increasing sophistication in autonomous task execution across diverse domains, yet their tool selection mechanisms and usage efficiency remain underexplored. This study develops a comprehensive evaluation framework for assessing tool selection patterns and usage efficiency in domain-specific environments. We implement a probabilistic assessment methodology that quantifies agent performance across multiple dimensions including selection accuracy, execution latency, and resource optimization. Our experimental protocol encompasses financial analysis, scientific computation, and data processing domains, evaluating six distinct LLM architectures under controlled conditions. Results indicate significant variance in tool selection strategies, with transformer-based agents achieving 23.4% higher efficiency scores compared to retrieval-augmented baselines. The framework reveals systematic patterns in tool invocation sequences, demonstrating domain-specific adaptation capabilities while highlighting critical limitations in cross-domain generalization. Our analysis contributes quantitative insights into agent behavior patterns and establishes baseline metrics for future tool usage optimization research. These findings inform architectural decisions for production deployments where tool efficiency directly impacts computational costs and response latency.
Enhancing Credit Decision Transparency for Small Business Owners: An Explainable AI Approach to Mitigate Algorithmic Bias in Micro-lending
The proliferation of artificial intelligence in small business lending has created unprecedented challenges regarding algorithmic transparency and fairness. Traditional credit assessment models exhibit significant opacity, preventing small business owners from understanding rejection rationales and potentially perpetuating discriminatory practices. This research presents a novel probabilistic explainability framework that integrates SHAP-enhanced feature attribution with specialized bias detection metrics tailored for micro-lending contexts. Our approach addresses the critical gap between high-performing machine learning models and regulatory compliance requirements for transparent financial decision-making. The proposed methodology combines advanced interpretability techniques with multi-objective optimization to maintain predictive accuracy while ensuring algorithmic fairness. Experimental validation demonstrates substantial improvements in explanation quality and bias reduction across diverse small business lending scenarios. The framework provides actionable insights for loan officers while enhancing trust among small business applicants. This work contributes to the emerging field of responsible AI in financial services by establishing technical standards for explainable credit assessment. The research implications extend beyond individual lending decisions to inform broader policy discussions regarding algorithmic accountability in financial inclusion initiatives.
Research on AI-Driven Personalized Web Interface Adaptation Strategies and User Satisfaction Evaluation
This research investigates AI-driven personalized web interface adaptation strategies and their impact on user satisfaction evaluation. The study addresses the growing need for intelligent interface customization in modern web applications by developing a comprehensive framework that integrates machine learning algorithms with real-time user behavior analysis. The proposed methodology combines user preference learning, dynamic interface element adaptation, and context-aware personalization algorithms to create more intuitive and efficient user experiences. Through extensive experimentation involving 450 participants across diverse demographic groups, the research demonstrates significant improvements in user satisfaction metrics, with average satisfaction scores increasing by 34.7% compared to static interface designs. The study employs multi-dimensional evaluation methods including task completion efficiency, cognitive load assessment, and subjective user feedback analysis. Statistical significance testing validates the effectiveness of the proposed adaptation strategies across different user segments. The findings contribute to the advancement of human-computer interaction research by providing empirical evidence for the benefits of AI-driven interface personalization. The research establishes a foundation for future developments in adaptive user interface technologies and offers practical insights for web developers and UX designers seeking to implement intelligent interface adaptation systems.
Graph Neural Network-Based Anomaly Detection in Financial Transaction Networks
Modern financial ecosystems face unprecedented challenges in detecting sophisticated fraud schemes that exploit complex transaction networks. This paper presents a comprehensive approach utilizing Graph Neural Networks (GNN) to identify anomalous patterns in financial transaction networks. Our methodology constructs heterogeneous graph representations of financial transactions, incorporating temporal dynamics and multi-entity relationships. The proposed adaptive GNN architecture integrates attention mechanisms for suspicious pattern identification and handles dynamic graph structures effectively. Experimental validation demonstrates superior performance compared to traditional machine learning approaches, achieving 94.7% precision and 92.3% recall in fraud detection tasks. The framework addresses scalability concerns while maintaining interpretability requirements for regulatory compliance. Our approach successfully identifies complex fraud networks and money laundering schemes that evade conventional detection methods. The research contributes novel graph construction techniques, adaptive neural network architectures, and comprehensive evaluation methodologies for financial anomaly detection. Results indicate significant improvements in both accuracy and computational efficiency, making real-time deployment feasible for large-scale financial institutions.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
Energy-Aware Scheduling Algorithm Optimization for AI Workloads in Data Centers Based on Renewable Energy Supply Prediction
This paper presents an innovative energy-aware scheduling algorithm that optimizes artificial intelligence workload distribution in data centers through advanced renewable energy supply forecasting. The proposed system integrates a hybrid LSTM-GRU neural network architecture, achieving a correlation coefficient of 0.87 for 24-hour renewable energy forecasts with a mean absolute error of 13%. Our priority-aware scheduling mechanism dynamically categorizes AI workloads based on energy intensity and deadline constraints, enabling optimal alignment with fluctuating renewable energy resources. Experimental evaluation across three geographically distributed data centers over a 12-month period demonstrates measurable improvements: 58% reduction in grid energy dependency, 47% decrease in carbon emissions, and 34% reduction in operational costs while maintaining 96.2% service level agreement compliance. The system architecture employs multi-objective optimization techniques, balancing energy efficiency, performance metrics, and carbon footprint considerations.
Multimodal Deep Learning Framework for Early Parkinson's Disease Detection Through Gait Pattern Analysis Using Wearable Sensors and Computer Vision
This study presents a novel multimodal deep learning framework that integrates wearable sensor data and computer vision techniques for early-stage Parkinson's disease detection through comprehensive gait pattern analysis. The proposed system combines inertial measurement units, accelerometers, and computer vision-based pose estimation to capture multidimensional gait characteristics. A hybrid CNN-LSTM architecture with attention mechanisms processes temporal and spatial features from heterogeneous data sources. Experimental validation on a dataset of 184 participants (92 early-stage PD patients, 92 healthy controls) demonstrates superior performance with 94.2% accuracy, 93.8% sensitivity, and 94.6% specificity. The multimodal fusion approach outperforms unimodal methods by 8.3% in overall classification accuracy. Feature importance analysis reveals stride variability, postural sway metrics, and temporal gait parameters as the most discriminative biomarkers for early PD detection. The system provides clinically interpretable results and demonstrates potential for real-world deployment in healthcare settings.
Alert Fatigue Mitigation in Anomaly Detection Systems: A Comparative Study of Threshold Optimization and Alert Aggregation Strategies
Alert fatigue represents a critical challenge in modern monitoring systems, where excessive false positive alerts overwhelm operations teams and diminish system reliability. This research presents a comprehensive comparative analysis of threshold optimization and alert aggregation strategies designed to mitigate alert fatigue in anomaly detection systems. Through systematic evaluation of a wide variety of adaptive alert threshold adjustment algorithms and intelligent alert correlation and aggregation techniques, our proposed framework demonstrates significant improvements in operational efficiency. We propose a framework that integrates dynamic threshold adjustment mechanisms with multi-dimensional alert aggregation strategies, achieving a 67% reduction in false positive rates while maintaining 94% true positive rate, namely alert detection accuracy. Experimental results across diverse monitoring scenarios reveal that hybrid approaches combining temporal-based threshold optimization with semantic alert clustering outperform traditional static threshold methods. The research also comes up with novel evaluation metrics for measuring impact of our proposed framework on alert fatigue mitigation and provides practical guidelines for implementing effective alert management solutions in complex monitoring infrastructures.
Empirical Study on the Impact of ESG Factors on Private Equity Investment Performance: An Analysis Based on Clean Energy Industry
This study investigates the relationship between Environmental, Social, and Governance (ESG) factors and private equity investment performance within the clean energy sector. Using a comprehensive dataset of 286 private equity investments from 2015-2023, we examine how ESG ratings influence financial returns, risk-adjusted performance, and exit valuations. Our methodology employs multiple regression analysis, propensity score matching, and robustness testing to establish causal relationships. The research reveals that higher ESG scores correlate with superior investment performance, with environmental factors showing the strongest predictive power for returns. Clean energy investments with top-quartile ESG ratings demonstrate 23% higher IRR compared to bottom-quartile performers. Social governance metrics exhibit significant impact on portfolio company operational efficiency, while governance factors primarily influence exit timing and valuation multiples. The study contributes to sustainable finance literature by providing empirical evidence for ESG value creation in private markets. Findings suggest that ESG integration enhances due diligence processes and generates sustainable competitive advantages. The research offers practical insights for private equity practitioners seeking to optimize investment strategies through ESG-focused approaches.
An Empirical Study on the Accuracy of Large Language Models in API Documentation Understanding: A Cross-Programming Language Analysis
This study presents a comprehensive empirical evaluation of Large Language Models (LLMs) in understanding API documentation across multiple programming languages. We systematically assess the accuracy and consistency of five prominent LLMs—GPT-4, GPT-3.5, Claude-3, Llama-2, and CodeT5—in interpreting API documentation for Java, Python, JavaScript, and C++. Our evaluation framework employs both automated metrics and human evaluation protocols to measure understanding accuracy, completeness, and cross-language consistency. Results indicate significant variations in LLM performance across different programming languages, with accuracy scores ranging from 67.3% to 89.7%. The study reveals that syntax complexity, documentation structure, and linguistic patterns substantially influence LLM comprehension capabilities. These findings provide critical insights for improving LLM-based code assistance tools and establishing guidelines for effective API documentation design in multi-language development environments.
Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning
This paper presents a novel reinforcement learning approach for anomaly pattern recognition and risk control in high-frequency trading environments. Market manipulation schemes have evolved significantly, requiring advanced computational methods for detection and mitigation. We introduce a comprehensive framework integrating kernel-based dimensionality reduction techniques with sequential deep learning architectures to identify complex manipulation patterns across multiple time scales. Our approach employs multivariate statistical methods for outlier detection while incorporating temporal dependencies through specialized neural network structures. The risk-aware reinforcement learning system optimizes trading policies with explicit consideration of downside risk, utilizing dynamic threshold adjustment mechanisms that adapt to evolving market conditions. We implement multi-objective reinforcement learning to balance return maximization with risk minimization, enabling customizable risk-return profiles aligned with specific investor preferences. Experimental validation on extensive financial market datasets demonstrates superior performance compared to traditional methods, achieving 92% detection accuracy with false positive rates below 3%. The proposed framework demonstrates particular robustness during periods of elevated market volatility, reducing maximum drawdown by 28.5% while maintaining competitive returns. The integration of interpretable components enhances regulatory compliance and trader acceptance in production environments.