Journal of Science, Innovation & Social Impact
Journal of Science, Innovation & Social Impact (JSISI) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research at the interface of scientific discovery, technological innovation, and societal impact. The journal welcomes interdisciplinary studies that advance both theoretical knowledge and practical applications, with a focus on topics such as emerging scientific breakthroughs, innovation-driven solutions, societal challenges, policy implications, and the broader social consequences of scientific and technological advancements.
Wind-Assisted Propulsion Systems in the Carbon-Neutral Era: A Systematic Review of Multi-Modal Technologies
The global shipping industry faces substantial pressure to reduce greenhouse gas emissions to comply with the International Maritime Organization's stringent reduction policies. Consequently, wind-assisted propulsion systems (WAPs) have garnered extensive research interest due to their utilization of clean energy and the widespread availability of wind resources. However, previous reviews predominantly focused on literature published before 2020 or concentrated on single systems, lacking a systematic comparative analysis of multiple wind propulsion technologies. This review systematically examines WAPs, focusing on their development path, current status, and future trends. Our findings delineate three distinct developmental stages in WAPs research: aerodynamic fundamentals (2000–2015), performance optimization (2015–2020), and system integration (2021–present). Four main technologies dominate current research directions: rigid sails, Flettner rotors, kite systems, and hybrid configurations. With technological advancements, theoretical research has shifted from complex computational fluid dynamics toward systematic evaluations of the economic and environmental benefits of entire ship systems. Nevertheless, notable research gaps persist, including the absence of a comprehensive life cycle assessment framework, insufficient real-world operational data from commercial deployments, and inadequate maturity in control strategies for integrated systems. Ultimately, this review offers a comprehensive knowledge framework for researchers and practitioners, providing data-driven recommendations for technological advancement and carbon neutrality policy formulation.
Copyright (c) 2026 Journal of Science, Innovation & Social Impact
Evaluating Prompt Engineering Strategies for Few-Shot Cyber Threat Intelligence Entity and Relation Extraction from Multi-Source Reports
The proliferation of multi-source cyber threat intelligence reports---spanning vulnerability databases, government advisories, vendor analyses, and open-source feeds---has outpaced the capacity of human analysts to extract structured knowledge about adversary tactics, techniques, and procedures. While large language models present a promising avenue for automating this extraction under low-resource conditions, no systematic empirical comparison of prompt engineering strategies exists for the cyber threat intelligence domain. This study evaluates six prompt engineering strategies---zero-shot, one-shot, three-shot, five-shot, retrieval-augmented five-shot, and chain-of-thought five-shot---across four publicly available cyber threat intelligence named entity recognition datasets (DNRTI, CyNER, AnnoCTR, APTNER) and one relation extraction corpus, using GPT-4, GPT-3.5-turbo, and Llama-3-70B. The retrieval-augmented five-shot strategy achieves the highest named entity recognition F1 of 0.753 on CyNER with GPT-4, narrowing the gap with the fine-tuned SecureBERT baseline to 2.8 percentage points. Chain-of-thought prompting yields the lowest expected calibration error (0.108), suggesting its value for uncertainty-aware intelligence triage. Cross-source extraction variance reaches 12.2 F1 points between the easiest and hardest corpora, underscoring the challenge of heterogeneous intelligence fusion. These findings offer actionable guidance for deploying prompt-based extraction in operational threat intelligence pipelines aligned with the NIST Cybersecurity Framework and national cyber defense priorities.
An Empirical Comparison of ReAct, Reflexion, Plan-and-Solve, and Tree-of-Thought Planning Strategies on Financial Question Answering and Numerical Reasoning Tasks
Large language model agents increasingly automate reasoning- and decision-intensive financial workflows, yet the comparative effectiveness of competing planning strategies on finance-specific tasks remains unclear. We conduct a controlled empirical comparison of four widely adopted planning strategies --- ReAct, Reflexion, Plan-and-Solve, and Tree-of-Thought --- on four public financial benchmarks spanning multi-step numerical reasoning (FinQA), multi-turn numerical dialogue (ConvFinQA), hybrid tabular-textual question answering (TAT-QA), and long-document question answering (DocFinQA). Using a shared GPT-4o backbone, a common tool set, and a unified evaluation protocol, we measure execution accuracy, exact-match correctness, per-task-type performance, and per-query token cost across three random seeds. Plan-and-Solve offers the best accuracy-per-dollar on purely numerical tasks, delivering a moderate 2.8-point improvement over ReAct on FinQA at roughly one-seventh the token budget of Tree-of-Thought. ReAct with retrieval dominates on long-document DocFinQA, outperforming Plan-and-Solve by 4.1 points. Tree-of-Thought attains the single highest accuracy on the compound-arithmetic subset of TAT-QA (71.4%) but costs 7.2× more tokens per query than Plan-and-Solve. A manual error typology across 400 failures confirms that each strategy repairs a distinct failure class, and that no single strategy dominates all four financial task types. The findings clarify an existing design-space question rather than propose new methodology.
An Empirical Comparison of High-Order Feature Interaction Operators for Conversion Rate Prediction in Sparse, High-Cardinality Message-Ads Traffic: Accuracy, Efficiency, and Offline--Online Consistency
Post-click conversion rate (CVR) prediction on message-ads traffic exposes feature interaction operators to an extreme regime of sparsity, label imbalance, and serving-latency constraints. While a decade of recommender research has produced an abundance of operators that differ in their treatment of explicit versus implicit, low-order versus high-order interactions, published comparisons typically optimize for click-through rate on dense public logs and seldom isolate the operator from confounding training pipelines. This study conducts a controlled empirical comparison of seven high-order interaction operators---plain MLP, FM, DeepFM, DCN, DCN-V2, xDeepFM, and AutoInt---across Criteo, Avazu, and Ali-CCP under a unified training protocol. We measure offline AUC and LogLoss, per-sample parameters, FLOPs, and inference latency, and further stratify AUC by user-activity quantile and by categorical-feature density. On Ali-CCP CVR, DCN-V2 attains the highest AUC (0.6289) while DCN matches it within 0.0011 AUC at 0.83× the latency; xDeepFM's compressed interaction component contributes the largest efficiency penalty without a proportionate accuracy gain. Rank correlation between offline AUC and an online CVR proxy drops from 0.93 on high-activity users to 0.41 on cold-start users, echoing documented offline--online inconsistencies. The findings provide operator-selection guidance grounded in measured efficiency and subgroup stability rather than on headline AUC deltas.
Sparse, Dense, or Hybrid? Comparing Retrieval Strategies for Biomedical Question Answering with Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) has emerged as a dominant paradigm for grounding large language models in external knowledge, yet the choice of retrieval strategy remains underexplored in the biomedical domain. This study presents an empirical comparison of four retrieval strategies---BM25 (sparse), Contriever (general-purpose dense), MedCPT (domain-specific dense), and a reciprocal rank fusion hybrid combining BM25 with MedCPT---within a standardized RAG pipeline for biomedical question answering. Experiments are conducted on three established benchmarks: PubMedQA, MedQA, and BioASQ Task B. Evaluation spans retrieval quality (Recall@10, Recall@20, MRR@10), end-to-end QA accuracy, and answer faithfulness measured through the RAGAS metric. Results indicate that the hybrid strategy achieves the highest Recall@10 across all three datasets, reaching 0.761 on PubMedQA, 0.697 on MedQA, and 0.768 on BioASQ. The domain-specific MedCPT retriever consistently outperforms the general-purpose Contriever, while BM25 remains a competitive baseline that surpasses Contriever on two of three benchmarks. End-to-end QA accuracy follows a similar pattern, with the hybrid strategy yielding the best performance at 0.741 on PubMedQA and 0.613 on MedQA. Faithfulness analysis reveals that domain-specific retrieval reduces hallucination rates by providing more topically relevant context. These findings offer practical guidance for practitioners selecting retrieval strategies when deploying biomedical RAG applications.
Comparative Evaluation of Post-Hoc Feature Attribution Methods on Tabular Financial Data: Faithfulness, Stability, and Computational Efficiency
The deployment of machine learning in credit scoring and fraud detection has intensified regulatory and societal demand for transparent decision-making. Post-hoc feature attribution methods such as SHAP, LIME, Integrated Gradients, and Anchors promise to explain individual predictions, yet their comparative reliability on financial tabular data remains insufficiently characterized. This study conducts a controlled empirical evaluation of four prominent attribution methods across four public financial datasets spanning credit scoring and transaction fraud detection. Three classifiers---XGBoost, Random Forest, and Multilayer Perceptron---serve as the underlying predictive functions. Explanation quality is quantified along three axes: faithfulness measured by Prediction Gap on Important features and infidelity, stability measured by max-sensitivity, and computational efficiency measured by wall-clock time per explanation. Results indicate that TreeSHAP achieves the highest faithfulness and lowest sensitivity on tree-based classifiers, while Integrated Gradients attains competitive faithfulness on neural networks. LIME exhibits the largest variance across repeated runs, raising concerns for regulatory settings that require reproducible explanations. Anchors produce the sparsest explanations at the cost of reduced faithfulness. No single method dominates all evaluation criteria simultaneously, corroborating recent theoretical predictions of an inherent trade-off among explanation desiderata. These findings provide practitioners and regulators with empirically grounded guidance for selecting attribution methods in financial applications.
A Comparative Evaluation of Class Imbalance Handling Techniques for Credit Card Fraud Detection
Credit card fraud detection presents a challenging classification task due to extreme class imbalance, where fraudulent transactions constitute less than 1% of all observations. Selecting an appropriate imbalance handling technique is critical, yet the comparative performance of these techniques under varying imbalance severities remains insufficiently understood. This study conducts a systematic empirical evaluation of nine class imbalance handling techniques across two publicly available fraud detection datasets exhibiting different imbalance ratios (578:1 and 90:1). The techniques evaluated span data-level resampling (SMOTE, ADASYN, Borderline-SMOTE, SMOTE combined with Edited Nearest Neighbors, and random undersampling), algorithm-level cost-sensitive learning (class weighting), and ensemble-based approaches (EasyEnsemble, RUSBoost, and Balanced Random Forest). Each technique is paired with four base classifiers---Logistic Regression, Random Forest, XGBoost, and LightGBM---and assessed using five evaluation metrics: AUC-ROC, PR-AUC, F1-score, Matthews Correlation Coefficient, and recall. Results indicate that ensemble-based methods, particularly EasyEnsemble, achieve the most consistent improvements across both datasets. Hybrid resampling via SMOTE with Edited Nearest Neighbors produces comparable gains among data-level methods. A notable finding is that standard SMOTE, while improving AUC-ROC and F1-score, can reduce PR-AUC relative to the untreated baseline under severe imbalance. Cost-sensitive class weighting emerges as a computationally efficient alternative that preserves strong PR-AUC performance. These findings provide practical guidance for practitioners selecting imbalance handling strategies in fraud detection applications.
Covariate Feature Importance and Cross-Indication Transferability Analysis for Phase III Oncology Trial Outcome Prediction
Oncology drug development suffers from the lowest clinical success rates among all therapeutic areas, with the likelihood of approval (LOA) from Phase I to regulatory approval estimated at 3.4%--5.3%. Accurate prediction of Phase III trial outcomes is essential for optimizing resource allocation and reducing late-stage attrition costs. This study presents a comparative regression analysis framework designed to identify and rank covariate features that influence Phase III oncology trial success, and to evaluate the temporal stability and cross-indication transferability of these features. Using 1,203 Phase III oncology trials registered between 2005 and 2023, sourced from ClinicalTrials.gov and the BioMedTracker database, 42 candidate covariates spanning trial design, molecular characteristics, patient baseline demographics, and historical performance metrics were extracted and analyzed. SHAP-based importance ranking revealed that prior Phase II efficacy endpoints, biomarker-driven patient selection, and sponsor therapeutic area experience constituted the three most influential predictive features. A comparison of five regression techniques demonstrated that gradient boosted regression trees achieved the highest discriminative performance (AUC = 0.823, 95% CI: 0.791--0.855) on the hold-out validation cohort. Cross-indication transferability analysis across six major cancer types showed moderate to strong covariate consistency for trial design features (Spearman ρ = 0.71--0.89) and lower consistency for molecular target features (ρ = 0.43--0.62). These findings provide quantitative evidence supporting the development of more robust technical success probability (PTS) calculation methods.
Benchmarking Robustness-Efficiency Trade-offs of Camera-LiDAR Fusion Strategies for 3D Object Detection Under Environmental Corruptions
Multi-modal sensor fusion combining camera and LiDAR data has become the dominant paradigm for 3D object detection in autonomous driving. The selection among early fusion, late fusion, and deep fusion strategies involves complex trade-offs between detection accuracy, computational efficiency, and robustness under adverse conditions. This paper presents a systematic benchmarking study that quantitatively evaluates these trade-offs on the nuScenes dataset under diverse environmental corruptions and calibration perturbations. Six representative fusion algorithms spanning three fusion categories are evaluated across 10 corruption types at three severity levels, with simultaneous measurement of detection accuracy (mAP, NDS), computational resource consumption (latency, GPU memory), and degradation patterns under spatial misalignment and temporal desynchronization. The results reveal that deep fusion approaches achieve 2.8%--5.1% higher NDS than early fusion under clean conditions, while late fusion strategies demonstrate 12.3%--18.7% lower mean Corruption Error under LiDAR-degraded scenarios. Calibration perturbation analysis shows that soft-association mechanisms reduce mAP degradation by 41.2% compared to hard-association approaches at 0.5-meter spatial misalignment. These findings provide evidence-based guidance for engineering teams selecting sensor fusion configurations under real-world deployment constraints.
Performance Evaluation and Optimization Strategies for Privacy-Preserving Document Classification in Distributed Learning Environments
The proliferation of sensitive documents across healthcare, financial, and governmental sectors necessitates robust privacy-preserving classification mechanisms. This study presents a comprehensive performance evaluation of privacy-preserving document classification within distributed learning frameworks, examining federated learning and differential privacy implementations. Through systematic experimentation on benchmark datasets, we quantify accuracy-privacy trade-offs, communication overhead, and computational costs across various privacy budget configurations. Results demonstrate that adaptive privacy allocation reduces accuracy degradation by 12-18% compared to uniform distribution while maintaining equivalent privacy guarantees. Gradient compression techniques achieve 67% communication reduction with minimal convergence impact. These findings provide actionable deployment guidelines for organizations implementing privacy-preserving document processing systems.
A Multi-Dimensional Coverage Metric with Evolutionary Search for Safety-Critical Scenario Generation in Autonomous Driving Testing
Ensuring autonomous driving safety requires rigorous testing across diverse and safety-critical scenarios. Manual scenario design is labor-intensive and insufficient in capturing edge cases, while random generation produces redundant test cases. This paper proposes a coverage-guided evolutionary search algorithm (CGES) for automated generation of safety-critical test scenarios with quantitative coverage assessment. A parameterized scenario representation is established based on six functional dimensions, and three complementary coverage metrics---scenario parameter space coverage (SPSC), behavioral diversity coverage (BDC), and risk-weighted fault mode coverage (RFMC)---are defined to quantify test adequacy. An adaptive evolutionary search strategy that incorporates risk-prioritized fitness evaluation and diversity-aware selection is designed to efficiently explore high-risk regions of the scenario space. Experiments on CARLA using five operational design domains demonstrate that CGES achieves 17.3% higher composite coverage and discovers 28.6% more unique safety violations than the baselines, while reducing redundant test cases by 41.2%. The proposed metrics provide a quantitative foundation for evaluating the completeness of autonomous driving test suites, contributing to standardized safety validation aligned with NHTSA regulatory requirements.
Behavioral Association Analysis for Organized Fraud Ring Detection in Large-Scale User Interaction Data
The proliferation of organized fraud rings poses significant threats to online platforms and financial systems worldwide. This paper presents a comprehensive behavioral association analysis framework for detecting coordinated fraudulent activities in large-scale user interaction data. We construct heterogeneous user interaction graphs from multi-source behavioral data and extract coordinated behavioral fingerprints across accounts through multi-dimensional association signals including fund flows, device sharing patterns, and temporal synchronization features. Community detection algorithms are employed to identify tightly-connected fraud groups within massive interaction networks. Experimental evaluation on real-world datasets demonstrates that our association-based approach achieves superior detection performance compared to traditional methods, with precision rates reaching 94.7% and recall rates of 89.3% on organized fraud ring identification tasks.
Gradient Boosting-Based Demand Variability Estimation for Improved Safety Stock Calculation in Multi-Echelon Aerospace Spare Parts Inventory
Accurate safety stock calculation in multi-echelon aerospace spare parts networks remains a persistent challenge due to intermittent demand patterns and stochastic lead time variability. Traditional statistical approaches to demand variance estimation often fail to capture the complex, non-linear dependencies inherent in aerospace aftermarket environments. This paper investigates the application of gradient-boosting-based machine learning techniques---specifically LightGBM and XGBoost---to improve the estimation of demand variability and lead time variance as direct inputs to safety stock formulas in a three-echelon inventory network. A comparative evaluation is conducted across five estimation methods using a dataset of 2,847 aerospace spare part SKUs spanning 60 months of transactional records. The experimental results indicate that LightGBM reduces the error in estimating demand standard deviation by 18.6% relative to exponential smoothing, translating to a 9.3% reduction in total inventory holding cost while meeting echelon-specific cycle service level (CSL) targets across the network. A component criticality-based stratification analysis further reveals that gradient boosting methods yield the most pronounced improvements for intermittent-demand, high-criticality parts. The findings provide empirical evidence supporting the integration of machine-learning-enhanced variance estimation into existing multi-echelon safety-stock optimization frameworks for aerospace logistics applications.
Language-Driven Interactive Annotation for Pulmonary Nodules in Chest CT: An LLM Prompt-Translation and Multi-Round Refinement Approach
High-quality pixel-level annotation remains a principal bottleneck for medical artificial intelligence, particularly for pulmonary nodule analysis on chest computed tomography, where expert labeling is costly and heterogeneous across institutions. This paper investigates a narrow but practical question: how short free-text descriptions produced by clinicians can be mediated into the spatial prompts expected by foundation segmentation models such as the Segment Anything Model via structured slot extraction, and how a lightweight multi-round refinement loop can stabilize the resulting masks under realistic annotation budgets. We emphasize that the role of the large language model in this study is restricted to structured slot extraction from short English phrases and to classifying each correction utterance into one of four canonical categories; the language model does not predict pixel coordinates, and the spatial initialization itself is driven by a coarse lobe-level anatomical prior, a size heuristic, and a vessel-suppressed point-sampling rule, rather than by free-form visual reasoning. This study is therefore best described as language-mediated structured prompting rather than free-form reasoning segmentation. We do not propose a new backbone or a full clinical system; rather, we study a prompt-translation strategy coupled with bounded interactive correction, evaluated on three public datasets: LIDC-IDRI, LUNA16, and Medical Segmentation Decathlon Task06 Lung. We report Dice, intersection over union, ninety-fifth percentile Hausdorff distance, and per-case annotation time, together with paired Wilcoxon signed-rank tests and bootstrap confidence intervals, so that the magnitude and reliability of any improvement can be evaluated directly. Results suggest a modest improvement in Dice and a reduction in measured per-case annotation time compared with purely geometric prompting; the annotation-time comparison should be read as an engineering-level approximation rather than as a formal reader study, while the interface remains accessible to clinicians without engineering expertise.
Privacy-Preserving Techniques in Credit Risk Assessment: A Comparative Analysis of Differential Privacy, Federated Learning, and Homomorphic Encryption
The proliferation of machine learning in financial services has intensified concerns regarding consumer data privacy during credit risk assessment. This paper presents a comparative study of three privacy-preserving paradigms relevant to credit risk assessment: differential privacy, federated learning, and homomorphic encryption. Through empirical evaluation on a large-scale, representative credit dataset across multiple privacy-preserving configurations, we examine the privacy-utility trade-offs inherent in each approach. Our experimental framework assesses prediction accuracy, computational overhead, and privacy guarantees across multiple configurations. Results demonstrate that differential privacy with epsilon=8.65 achieves a balanced privacy-utility tradeoff with competitive discriminative performance, while homomorphic encryption preserves near-baseline predictive performance during encrypted inference. Federated learning enables collaborative model training across institutions without sharing raw data. The analysis reveals that privacy parameter selection critically impacts model utility, with differential privacy offering quantifiable privacy budgets and homomorphic encryption providing cryptographic security guarantees. These findings provide actionable guidance for financial institutions navigating regulatory compliance requirements while maintaining effective risk management capabilities.
Performance Evaluation of Anomaly-Based Detection Approaches for Zero-Day Attack Early Warning in Cloud Infrastructure
The escalating sophistication of zero-day attacks poses unprecedented challenges to cloud infrastructure security, necessitating advanced detection mechanisms beyond traditional signature-based approaches. This paper presents a comprehensive performance evaluation of anomaly-based detection approaches specifically designed for early warning of zero-day attacks in cloud environments. We systematically analyze multiple detection strategies leveraging multi-source telemetry data, including network traffic patterns, system call sequences, and resource usage metrics. Through extensive experimentation on a realistic cloud infrastructure testbed using synthesized attack scenarios, we compare statistical-, machine learning-, and ensemble-based detection approaches across critical performance dimensions, including detection accuracy, false positive rates, and detection timeliness. Our evaluation reveals significant trade-offs among approaches, with ensemble methods achieving a recall (TPR) of 94.7% while maintaining a false positive rate of 0.20%. The findings provide actionable insights for cloud service providers seeking to optimize their zero-day threat detection capabilities.
Performance Benchmarking and Optimization of Deep Learning-Based Point Cloud Processing Algorithms for Industrial Quality Inspection
The deployment of deep learning-based point cloud processing algorithms in industrial quality inspection systems requires comprehensive performance evaluation frameworks to guide algorithm selection and optimization. This study establishes a systematic benchmarking methodology that evaluates state-of-the-art point cloud neural networks across multiple dimensions, including recognition accuracy, computational efficiency, and robustness under industrial conditions. Through controlled experiments on synthetic and real-world manufacturing datasets, we quantify the performance tradeoffs among competing approaches and identify optimal configuration strategies for different deployment scenarios. Our benchmark framework incorporates standardized metrics for accuracy assessment, efficiency profiling, and robustness evaluation. The empirical findings provide data-driven guidelines for manufacturing enterprises to select appropriate point cloud processing solutions.
A Comparative Study of Machine Learning Methods for Automated Customer Service Dialogue Quality Assessment
The rapid expansion of digital customer service channels has created an urgent need for automated quality assessment methods capable of evaluating dialogue interactions at scale. This paper presents a comprehensive comparative study of machine learning approaches for automated assessment of customer service dialogue quality, examining traditional machine learning algorithms, deep learning architectures, and transformer-based models. A multi-dimensional quality assessment framework is proposed, incorporating three primary evaluation categories: information accuracy, communication appropriateness, and process compliance. An experimental evaluation on a customer service dialogue dataset demonstrates that BERT-based models achieve superior overall classification accuracy (94.2%), while traditional methods offer advantages in computational efficiency and interpretability. The analysis reveals significant performance differences across service defect categories, with transformer models excelling at detecting subtle compliance violations and attitude-related issues. These findings provide practical guidance for enterprises seeking to implement standardized quality-monitoring systems that align with consumer protection regulations.
Accuracy Evaluation of Machine Learning-Based Hospital Resource Demand Forecasting During Infectious Disease Surges: A Comparative Analysis
The COVID-19 pandemic exposed critical vulnerabilities in hospital resource allocation during infectious disease surges, necessitating accurate demand forecasting capabilities. This study evaluates machine learning-based prediction algorithms for hospital resource demand (e.g., ICU occupancy) through comparative analysis. We assess time series methods, ensemble learning techniques, and deep learning architectures using historical utilization data from multiple healthcare facilities. Performance metrics, including MAE, RMSE, and MAPE, were computed for short-term and medium-term prediction horizons. Results demonstrate that ensemble approaches achieve higher accuracy than traditional methods. Across 7-21-day horizons, the ensemble model (XGBoost + Random Forest + LSTM) achieved the lowest prediction errors, with a 7-day MAPE of 7.64% and sustained advantages over ARIMA/SARIMA baselines. These findings provide evidence-based guidelines for healthcare coordinators aligned with AHRQ emergency preparedness priorities.
Enhancing Financial Compliance Transparency through Automated Data Governance and Intelligent Risk Reporting
Financial institutions face mounting pressure to maintain regulatory compliance while managing escalating costs and data complexity. This paper presents a comprehensive framework integrating automated data governance mechanisms with intelligent risk reporting capabilities to enhance compliance transparency. The proposed approach addresses three critical dimensions: real-time data quality monitoring through contract-based validation, anomaly detection using machine learning techniques, and automated audit trail generation for regulatory oversight. Experimental validation demonstrates a 43.2% reduction in compliance processing time and 38.7% improvement in data quality metrics compared to traditional manual approaches. The framework provides particular value for small and medium-sized financial institutions by reducing human resource requirements while maintaining rigorous regulatory standards. Implementation results confirm the framework's effectiveness in detecting compliance violations with 94.3% precision and generating comprehensive audit documentation satisfying regulatory transparency requirements.
Identifying Undisclosed Related Party Relationships and Revenue Recognition Irregularities: A Rule-Based Analytical Approach for Audit Planning
Related party transactions and revenue recognition manipulations remain persistent sources of financial statement fraud, posing significant challenges to audit procedures and investor protection. This research develops a rule-based analytical framework designed to identify potential irregularities in corporate financial disclosures during the audit planning phase. The proposed approach integrates network analysis techniques for detecting undisclosed related-party relationships by cross-referencing entity information and time-series pattern-detection methods for identifying suspicious revenue recognition behaviors, including period-end concentration and cash flow divergence. A composite risk scoring mechanism combines multiple indicators to prioritize audit attention. Empirical analysis using SEC EDGAR filings from 847 publicly traded companies demonstrates the framework's effectiveness, achieving a precision rate of 78.3% in flagging high-risk company filings
Comparative Evaluation of Supervised Learning Algorithms for Cancer Treatment Response Prediction Using Clinical and Biomarker Features
Accurate prediction of cancer treatment response remains essential for personalized oncology. This study conducts comprehensive evaluation of six supervised learning algorithms---Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, XGBoost, and LightGBM---for predicting chemotherapy and targeted therapy responses. We developed a feature engineering pipeline integrating neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and treatment history from 847 cancer patients across five tumor types. XGBoost achieved superior performance with AUC-ROC of 0.873, accuracy of 81.4%, and F1-score of 0.796, significantly outperforming baseline Logistic Regression (AUC-ROC 0.742, p < 0.001). SHAP analysis identified NLR (mean |SHAP| = 0.187), tumor stage, and prior treatment lines as the most predictive features. Kaplan-Meier survival analysis stratified by predicted risk demonstrated strong clinical validity, with high-risk patients exhibiting median progression-free survival of 4.7 months versus 14.3 months for low-risk patients (log-rank p < 0.001, hazard ratio 3.24). Gradient boosting algorithms provide optimal balance between predictive accuracy, computational efficiency, and clinical interpretability for treatment response prediction.
Multi-Dimensional Feature Analysis and Evaluation Methods for Anomalous Fund Flow Identification in Cross-Border Financial Transactions
Cross-border financial transactions are inherently complicated by the multi-currency nature of the transaction, the different regulatory systems, and the various methods used to launder dirty money; anomaly detection is not easy. The paper presents a comprehensive, multi-sided, and characteristic-based analysis framework for anomaly detection in cross-border fund transactions. Using this framework by taking characteristics of transactions, network topology features, and temporal behavior patterns into account, in order to boost detection. A systematic evaluation was conducted on 2.8M transactional data; a combination of graph-structure-based features and time-series behavioral indicators outperformed a single-dimensional approach. After experimentation, this strategy increased the baseline's accuracy by 18.7 percent and recall by 23.4 percent.
Service Architecture and Optimization Strategies in Cloud-Based Big Data Platforms
Cloud-based big data platforms offer unprecedented opportunities for data-driven insights and services. However, the inherent complexities of distributed systems and the diverse needs of applications present significant challenges in service architecture design and optimization. This research investigates service architecture paradigms and optimization strategies for cloud-based big data platforms, focusing on enhancing performance, scalability, reliability, and cost-efficiency. We analyze existing service architectures, identify key performance bottlenecks, and propose novel optimization techniques encompassing resource allocation, service placement, request routing, and data management. The proposed strategies leverage machine learning and adaptive control mechanisms to dynamically adjust system parameters in response to workload variations and resource availability. We evaluate the effectiveness of the proposed techniques through extensive simulations and real-world experiments on a production-scale cloud platform. Our results demonstrate significant improvements in key performance indicators, including response time, throughput, resource utilization, and energy consumption. Furthermore, we provide practical guidelines for designing and deploying optimized service architectures in cloud-based big data environments, enabling organizations to harness the full potential of their data assets. This research contributes to the advancement of efficient and scalable big data services in the cloud computing era.
Multi-Objective Optimization of Process Parameters for Dental Resin 3D Printing Using Improved NSGA-II Algorithm
This study presents an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing process parameters in dental resin 3D printing. The optimization framework addresses three conflicting objectives: dimensional accuracy, surface roughness, and printing time. Five critical process parameters, including layer thickness, exposure duration, print angle, infill density, and lift speed, are investigated using a design of experiments approach based on Response Surface Methodology. The improved NSGA-II incorporates an adaptive mutation operator and an elite-preservation strategy to enhance convergence and solution diversity. Experimental validation using nine dental model resin types demonstrates that the proposed algorithm achieves a 25.3% improvement in dimensional accuracy compared to default parameter settings. The Pareto-optimal solutions provide dental laboratories with flexible parameter configurations that balance quality requirements and production efficiency. Statistical analysis confirms that layer thickness and exposure duration are the most influential parameters affecting print quality.
A Comparative Study of Reinforcement Learning and Metaheuristic Algorithms for Real-Time Last-Mile Delivery Scheduling
The exponential growth of e-commerce has intensified the need for efficient real-time delivery-scheduling algorithms that can handle dynamic urban logistics environments. This study presents a comprehensive comparative analysis of reinforcement learning and metaheuristic algorithms for last-mile delivery optimization under time-sensitive constraints. We implement and evaluate Adaptive Large Neighborhood Search (ALNS) and Deep Q-Network (DQN) approaches on benchmark instances representing realistic urban delivery scenarios with 50-200 customer nodes. The experimental results demonstrate that ALNS achieves superior solution quality with an average optimality gap of 3.2% under an approximately 5-second (≤6 s) operational time budget, while DQN exhibits better runtime scalability for networks exceeding 150 customers (in the stress-test setting). The sensitivity analysis reveals critical trade-offs between computational efficiency and solution robustness under varying traffic conditions. This research provides empirical guidelines for logistics practitioners to select appropriate algorithms based on operational constraints, thereby contributing to sustainable urban transportation systems.
FraudGuardian: Self-Supervised and Adversarial Learning for Robust Financial Fraud Detection
Financial fraud detection is challenged by severe class imbalance, evolving adversarial tactics, and the demand for explainable decisions. To address these issues, we propose FraudGuardian, a novel deep learning framework for robust and interpretable fraud detection. FraudGuardian integrates two synergistic learning mechanisms: Self-supervised Consistency Learning (SCL) captures intrinsic normal patterns at the local event level to improve sensitivity to subtle anomalies, while Adversarial Feature Mining (AFM) actively synthesizes challenging samples to learn a more generalized decision boundary. These components are dynamically balanced through an adaptive multi-task optimization scheme, effectively mitigating data imbalance. Extensive experiments on real-world financial transaction datasets show that FraudGuardian significantly outperforms state-of-the-art methods, achieving 97.9% AUC, 90.6% PR-AUC, and 86.2% F1-Score on a challenging credit card fraud dataset, representing a 3.1% PR-AUC improvement over the best baseline. Ablation studies validate the contribution of each component. Moreover, FraudGuardian demonstrates strong generalization in cross-dataset and cross-attack-type evaluations, with a 9.1% F1Score improvement over baselines when detecting novel fraud strategies. The framework also provides interpretability by highlighting suspicious local patterns, offering a powerful and generalizable solution for enhancing secure transaction systems.
Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning
Verbal operant assessment plays a critical role in autism spectrum disorder intervention planning, yet current manual evaluation methods suffer from subjectivity and time constraints. This study presents a context-aware deep learning framework for automatic classification of verbal operants (mand, tact, echoic, and intraverbal) in therapeutic speech recordings of children with ASD. The proposed multi-task learning architecture integrates contextual features including antecedent stimuli, functional consequences, and prosodic patterns through attention mechanisms. Experiments on 1,847 annotated speech samples from 52 children demonstrate classification accuracy of 83.7% for operant type identification and 89.2% for spontaneous versus prompted language discrimination. The framework successfully identifies atypical language patterns including delayed echolalia and scripted language with 81.4% precision. Results indicate that contextual feature integration improves classification performance by 12.3% compared to text-only baselines, providing objective support for language assessment and intervention planning in clinical practice.
Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach
Chronic diseases constitute a significant public health challenge, with early detection enabling effective preventive interventions. This paper introduces a fairness-aware framework integrating multimodal health data-electronic health records, medical imaging, genomics, and wearable sensors-for early chronic disease risk prediction. The approach addresses three critical challenges: cross-modal feature harmonization across heterogeneous data types, algorithmic bias mitigation through fairness-constrained learning, and temporal pattern extraction for disease progression modeling. Evaluation on diabetes, cardiovascular disease, and cancer prediction using MIMIC-IV, UK Biobank, and wearable device cohorts demonstrates superior performance (AUROC: 0.892-0.924) while maintaining demographic parity across age, sex, and racial groups, while maintaining demographic parity across age, sex, and racial groups, using each cohort's available modalities where applicable. Fairness metrics improve by 76.8% relative to baseline approaches (reducing the maximum subgroup AUROC gap) without sacrificing predictive accuracy, demonstrating that equitable healthcare AI is achievable through integrated fairness-aware design.
StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting
The integration of traditional statistical methods with modern deep learning architectures offers opportunities to develop prediction frameworks that balance accuracy and interpretability. This paper introduces StatFuse, a hybrid approach synthesizing statistical decomposition with neural prediction while maintaining rigorous uncertainty quantification. By combining time-series analysis principles with neural architectures, the framework achieves strong and competitive performance across benchmark datasets. The methodology incorporates conformal prediction intervals for distribution-free coverage guarantees and employs statistical diagnostics and perturbation-based attribution for feature importance. Experimental validation on economic forecasting and public health monitoring demonstrates that StatFuse improves performance on two of four benchmarks and remains close to strong baselines on the others, while offering enhanced interpretability.