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.
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.
Copyright (c) 2026 Journal of Science, Innovation & Social Impact
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.
Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control
Multi-site clinical trials generate heterogeneous data requiring robust quality control mechanisms to ensure data integrity and regulatory compliance. This paper presents a comprehensive framework for automated anomaly detection and cross-center consistency assessment in distributed clinical trials. We propose a multi-layered detection approach combining rule-based thresholds, quantile drift analysis, and graph-structured consistency verification to identify protocol violations and data irregularities across trial sites. The methodology integrates dynamic threshold calibration with historical distributions, hierarchical relationship mapping between centers, investigators, and subjects, and ensemble aggregation techniques to construct audit-friendly evidence matrices. Experimental validation on multi-center trial datasets demonstrates superior detection accuracy, with sensitivity exceeding 87.3%, and early-warning capabilities that identify anomalies after analyzing only 23.5% of accumulated data, compared to 61.2% required by conventional approaches. The framework achieves 42.6% higher sensitivity compared to traditional monitoring approaches while maintaining computational efficiency suitable for real-time deployment. Implementation considerations address regulatory alignment with FDA and EMA guidelines, supporting repeatability, traceability, and auditability principles essential for clinical trial quality assurance.
Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning
Accurate natural disaster prediction requires the integration of heterogeneous environmental datasets and the ability to model uncertainty arising from sensor noise, incomplete observations, and inconsistent spatiotemporal coverage. This study proposes a cloud-based, uncertainty-aware machine learning framework designed to support large-scale prediction of hurricanes, floods, and wildfires in the United States. The framework incorporates distributed data ingestion, cloud-native preprocessing, multi-source feature engineering, and probabilistic learning to address the complexity and variability inherent in environmental data. Through a unified cloud workflow, heterogeneous datasets from NOAA, USGS, and NASA FIRMS are harmonized using temporal alignment, spatial normalization, and uncertainty-reduction strategies such as imputation, smoothing filters, and cross-source calibration. Model evaluation focuses on performance trends rather than fixed numerical benchmarks, examining how different learning algorithms respond to the characteristics of each disaster type. Results indicate that deep models and uncertainty-aware approaches are particularly effective for highly dynamic hazards such as hurricanes and wildfires, while tree-based models perform well for structured hydrological predictions. A real-world wildfire event is used to illustrate the practical applicability of the framework. Overall, the proposed approach enhances robustness, interpretability, and operational relevance, demonstrating the value of combining cloud computing with uncertainty-aware machine learning for multi-hazard disaster prediction.
A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection
The exponential growth of digital payment platforms has introduced unprecedented security challenges in detecting fraudulent transactions. This study presents a comprehensive comparative evaluation of deep learning architectures and ensemble learning algorithms for online payment fraud detection. We systematically assess Long Short-Term Memory networks, Recurrent Neural Networks, logistic regression, and gradient boosting methods across detection accuracy, precision-recall trade-offs, and computational efficiency. Through rigorous experimentation on real-world transaction datasets, we evaluate two feature engineering strategies: user behavior-based features from RFM analysis and transaction amount patterns. Our analysis reveals that ensemble methods achieve superior F1-scores of 0.876, while LSTM architectures demonstrate enhanced capability in capturing temporal dependencies. The study establishes quantitative guidelines for algorithm selection based on dataset characteristics and operational constraints.
Theoretical Evolution and Frontier Trends of Corporate Strategic Management in the AI Era: A Literature Review
This literature review explores the theoretical evolution and emerging trends in corporate strategic management within the context of the artificial intelligence (AI) era. The rapid advancements in AI technologies are fundamentally reshaping the business landscape, necessitating a re-evaluation of traditional strategic management frameworks. This review traces the historical development of strategic management theories, highlighting key milestones and paradigm shifts. It then delves into two core themes: the integration of AI into strategic decision-making processes and the impact of AI on competitive advantage and dynamic capabilities. Furthermore, the review compares and contrasts existing strategic management approaches in light of AI’s transformative potential, identifying key challenges and limitations. Finally, it proposes future research directions, focusing on the development of new theoretical models and empirical investigations that can better capture the complexities of corporate strategic management in the AI era. This review contributes to a deeper understanding of how organizations can effectively leverage AI to achieve sustainable competitive advantage and navigate the challenges of a rapidly evolving technological environment. It synthesizes existing knowledge, identifies research gaps, and offers insights for both academics and practitioners seeking to understand the strategic implications of AI.
Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach
This review paper explores the intersection of valuation logic and venture capital (VC) investment strategies within the domain of Artificial Intelligence (AI) startups, employing a knowledge graph-based approach. Traditional valuation techniques often fall short in capturing the unique characteristics and inherent uncertainties associated with AI ventures, including rapidly evolving technologies, data dependencies, and talent scarcity. A knowledge graph provides a structured framework for integrating diverse data sources, representing complex relationships between entities (e.g., technology, teams, market trends), and facilitating nuanced valuation assessments. The paper synthesizes existing literature on AI startup valuation, VC decision-making, and knowledge graph applications, identifying key valuation drivers and investment criteria. We examine how knowledge graphs can enhance due diligence processes, support dynamic risk assessment, and improve the accuracy of financial projections. Specifically, we investigate applications to forecasting AI’s impact on different sectors. We explore the strategic implications for VC firms seeking to capitalize on the growth potential of AI, discussing how they can leverage knowledge graphs to identify promising startups, optimize investment portfolios, and generate superior returns. The review addresses the challenges and limitations of adopting a knowledge graph-based approach, including data quality issues, computational complexity, and the need for specialized expertise. Finally, it highlights future research directions, such as the development of automated valuation models and the integration of explainable AI (XAI) techniques to enhance transparency and trust in VC investment decisions. Ultimately, this review argues that a knowledge graph-based approach offers a powerful tool for navigating the complexities of AI startup valuation and informing more effective VC investment strategies, creating win-win outcomes for investors, entrepreneurs and society.
Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation
This study proposes a dynamic stress-testing framework for consumer-finance portfolios by generating forward-looking macroeconomic scenarios using a vector autoregression model combined with Monte Carlo simulation. The scenarios are fed into a panel logistic-regression engine trained on 4.2 million loan accounts from 2015-2024. Using 1,000 simulated macro paths, the model quantifies stressed default probabilities up to 12 months ahead. Results show that under the 5% most adverse scenarios, 90-day delinquency rates rise by 28.9-46.2%, depending on product type. The model achieves an ROC-AUC of 0.82 in out-of-sample stress periods and captures 72.5% of actual default surges during the 2020 downturn. This approach offers a systematic tool for scenario-driven risk assessment in consumer lending.
Integrated Fatigue and Vibration Design of Medical Welded Rotating Structures
An integrated design framework combining fatigue life prediction and vibration analysis is developed for medical welded rotating structures. Residual stress fields obtained from welding simulations were coupled with harmonic vibration analysis under rotational excitation. A case study involving a welded rotating component subjected to 600 rpm operation showed that regions with high residual tensile stress also experienced amplified dynamic stress responses. Design optimization reduced combined fatigue-vibration damage by 29.7%, highlighting the necessity of joint consideration of welding and dynamic effects in medical device design.
A Study on Predicting Adolescent Cross-Sport Performance Using Shared Feature Space and Multi-Task Regression
Common transfer patterns exist across different sports disciplines in adolescents regarding abilities such as speed, explosiveness, and rhythm control. This study constructs a cross-discipline prediction framework based on shared feature space, targeting throwing velocity, sprint performance, and jumping ability. The framework comprises a general ability feature layer, discipline-specific feature layer, and multi-task regression head. General features include motion rhythm statistics, velocity distribution, acceleration trends, and training load indicators, while the discipline layer incorporates discipline-specific motion information. Predictions are performed using multi-head LightGBM. Experiments on 2.1 million sequence data points from 619 athletes demonstrate an average MAPE of 6.5%, representing a 22.7% improvement over single-task models. The framework maintains stable advantages even under sparse data conditions, indicating that the shared feature structure effectively enhances cross-sport prediction capabilities for adolescent athletic performance.
Optimizing Transaction Matching Performance Using Hybrid Collaborative Filtering and Deep Learning: An Empirical Analysis of Feature Engineering and Similarity Metrics
This paper presents a comprehensive empirical analysis of transaction-matching optimization in commercial real estate markets by integrating collaborative filtering and deep learning techniques. We address critical challenges in buyer-seller matching by developing a hybrid framework that combines matrix factorization-based collaborative filtering with attention-enhanced deep neural networks. Our approach introduces novel feature engineering methodologies designed explicitly for transaction data, incorporating both technical market indicators and behavioral patterns derived from historical transactions. Through extensive experimentation on a dataset of 50,000 commercial real estate transactions, we systematically compare multiple similarity metrics, including cosine similarity, Euclidean distance, and hybrid combinations. The proposed framework achieves 87.3% matching accuracy (Precision@10) and reduces computational latency to 45ms per query, representing significant improvements over baseline methods. Ablation studies reveal that attention mechanisms contribute a 12.4% performance gain, while proper feature engineering accounts for an 18.7% improvement in matching quality.
Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness
Manufacturing enterprises face mounting pressure to enhance cash flow forecasting accuracy amid increasingly volatile market conditions. This study presents a systematic comparative evaluation of traditional Excel-based methods against AI-powered business intelligence platforms, specifically Power BI and Tableau, for cash flow forecasting in manufacturing contexts. Through empirical analysis of 18 months of transaction data from a mid-sized manufacturing enterprise processing $750,000 weekly cash flows, the research quantifies performance differences across three critical dimensions: forecasting accuracy, operational usability, and cost-effectiveness. Results demonstrate that AI-enabled tools improve forecast accuracy by up to ~33% (Excel 12.5% → Power BI 8.3%) and ~27% (Tableau 9.1%), as measured by Mean Absolute Percentage Error, reduce ongoing analytical time requirements by 57-66%, and deliver a positive return on investment within 14-16 months despite higher initial implementation costs. The findings establish an evidence-based decision framework for manufacturing financial managers evaluating the adoption of business intelligence tools.
Privacy-Utility Tradeoffs in Federated Financial Analytics: An Optimization Framework
Cross-institutional financial analytics face fundamental challenges balancing privacy protection, model utility, and computational efficiency. This paper presents a comprehensive optimization framework addressing privacy-utility tradeoffs in federated learning for financial services. We propose adaptive privacy budget allocation mechanisms combined with a hybrid Trusted Execution Environment and Secure Multi-Party Computation protocols. Our framework targets KYC/AML workflows where regulatory compliance demands stringent data protection without sacrificing analytical AUC‑ROC. Experimental evaluation demonstrates superior performance across multiple financial datasets, achieving AUC-ROC = 0.867 at ε=2.0, while reducing per-round bandwidth costs by ~94% via gradient compression; TEE-assisted aggregation reduces compute/round-trip overhead rather than bandwidth. (achieving 3.21× speedup over a pure MPC-based secure aggregation baseline and reducing round time from 847s to 264s). The proposed approach ensures algorithmic fairness through demographic parity constraints and provides quantifiable privacy risk metrics aligned with commonly used industry thresholds and internal policy targets. Metric Convention: Unless otherwise specified, all performance metrics reported in this paper are AUC‑ROC; any occurrences labeled as 'AUC‑ROC' in results refer to AUC‑ROC for binary classification.
Multimodal Deep Learning for Advertising Content Safety: A Comprehensive Study on Detection and Governance Strategies
The proliferation of digital advertising across multiple platforms has created unprecedented challenges for content safety and brand protection. This paper presents a comprehensive study on multimodal deep learning approaches for detecting unsafe advertising content, addressing both explicit violations and implicit misleading information. We propose a novel framework that integrates visual, textual, and cross-modal features through advanced fusion architectures to achieve robust detection performance. Our methodology combines pre-trained language models, vision transformers, and optical character recognition systems with attention-based fusion mechanisms for comprehensive content analysis. Experimental results on a dataset of 45,000 advertising samples demonstrate that our approach achieves 92.3% accuracy in detecting policy violations, outperforming single-modality baselines by consistent gains. The framework shows particular strength in identifying implicit misleading content with an 89.1% F1-score and maintains balanced precision-recall trade-offs suitable for production deployment. This research contributes practical governance strategies for human-AI collaboration in content moderation workflows, addressing the critical need for scalable and accurate advertising safety systems in the digital ecosystem. Our method outperforms the best single-modality baseline by 15.5 percentage points and a strong late-fusion baseline by 8.6 percentage points.
Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases
Immune-related diseases pose significant diagnostic challenges due to complex molecular mechanisms and heterogeneous clinical presentations. Machine learning approaches have emerged as powerful tools for molecular pathway identification and biomarker discovery. This comparative study evaluates five machine learning algorithms using transcriptomic datasets from rheumatoid arthritis, systemic lupus erythematosus, and inflammatory bowel disease. We assess algorithm performance across accuracy, computational efficiency, biological relevance, and clinical validity. Graph neural networks achieved superior disease classification performance (AUC: 0.847) and identified 21 significantly enriched pathways, compared to traditional clustering methods (classification AUC: 0.762, 17 pathways identified). Results establish practical guidelines for algorithm selection, advancing personalized diagnostic development.
Integrating Causal Inference with Multi-Channel Attribution for Equitable Pharmaceutical Marketing Effectiveness Evaluation
The pharmaceutical industry confronts significant challenges measuring marketing effectiveness across digital and traditional channels while meeting health equity mandates. Traditional attribution approaches inadequately address confounding variables in observational data, yielding suboptimal budget allocation and inequitable demographic reach. This research proposes an integrated framework combining causal inference with multi-channel attribution modeling for pharmaceutical marketing evaluation. The framework incorporates propensity score matching, Bayesian marketing mix modeling with adstock effects, and equity-aware stratification. Empirical validation demonstrates 23.4% higher predictive accuracy versus last-touch attribution while identifying effectiveness disparities across demographic subgroups. Findings provide actionable guidance for pharmaceutical marketers optimizing resource allocation while ensuring equitable medical information access across patient populations.
A Comparative Study of Forecasting Techniques for Reducing Food Waste in Retail Operations
Food waste represents a critical challenge in retail supply chains, with approximately 30-40% of the food supply being discarded annually, resulting in substantial economic and environmental consequences. This study presents a systematic evaluation of demand forecasting techniques designed to mitigate food waste in retail operations. We compare statistical methods including ARIMA and SARIMA, machine learning approaches such as Random Forest, XGBoost, and LightGBM, alongside deep learning architectures including LSTM and Bidirectional LSTM networks. The evaluation framework encompasses multiple dimensions: forecasting accuracy across different product categories, computational efficiency, scalability considerations, and potential waste reduction impacts. Results demonstrate that machine learning techniques achieve superior performance in capturing complex demand patterns, with XGBoost and LightGBM delivering optimal accuracy-complexity tradeoffs. Deep learning models exhibit particular strength in handling long-term dependencies and seasonal variations. The findings provide actionable guidance for retail practitioners seeking to implement data-driven forecasting systems for waste reduction initiatives.
Enhancing Transparency in Asset-Backed Securities: A Deep Learning Approach for Automated Risk Assessment and Regulatory Compliance
The 2008 financial crisis exposed critical transparency deficiencies in asset-backed securities markets, prompting regulatory reforms mandating asset-level disclosure. This research develops an automated risk assessment framework combining deep neural networks with SHAP explainability techniques to address the regulatory technology gap in processing large-scale securitization data. The framework processes Schedule AL disclosures from SEC Electronic Data Gathering, Analysis, and Retrieval (EDGAR) filings, extracting loan-level and pool-level features to predict default risk while providing interpretable explanations for each assessment. Empirical validation on 450,382 mortgages from 50 residential mortgage-backed securities transactions demonstrates superior performance with an AUC-ROC of 0.883, outperforming XGBoost by 2.7 percentage points while maintaining complete transparency through feature attribution. Case studies illustrate the practical applications of detecting underwriting quality deterioration and geographic risk concentration, thereby supporting regulatory compliance monitoring and investor protection objectives.
Adaptive Confidence-Weighted Feature Fusion for Robust Multimodal Autism Screening in Heterogeneous Pediatric Populations
Autism Spectrum Disorder early detection encounters significant challenges from heterogeneous manifestations and inconsistent data quality during behavioral assessment. This paper introduces an adaptive confidence-weighted feature fusion algorithm that dynamically adjusts the importance of modalities based on quality metrics and individual characteristics. The framework integrates facial expressions, speech patterns, and eye-tracking through meta-learning-driven weighting strategies. Unlike fixed-weight approaches, the algorithm estimates real-time confidence scores and computes instance-specific weights via cross-modal attention mechanisms. Validation on naturalistic behavioral datasets (video, audio, eye-tracking) from children aged 2-8 years demonstrates 4.9% accuracy improvement over conventional methods, achieving 91.2% accuracy and 88.6% sensitivity. The adaptive mechanism proves particularly effective in scenarios involving low-quality data and diverse age groups.
Copyright (c) 2025 Journal of Science, Innovation & Social Impact
Interpretable Early Detection of Adverse Drug Reactions: Integrating Robust Anomaly Scoring with Temporal Lag Analysis and Causal Verification
Adverse drug reactions impose substantial clinical burdens, yet conventional pharmacovigilance approaches suffer from prolonged detection latencies and elevated false positive rates. This paper presents an integrated framework combining robust statistical anomaly scoring, temporal lag pattern mining, and causal inference methodologies to enable early ADR identification while maintaining interpretability. The three-stage pipeline employs median absolute deviation-based robust scoring, applies Cumulative Sum Control Charts for temporal pattern analysis, and utilizes propensity score matching with Bradford Hill criteria for causal verification. SHAP-based feature attribution generates clinically actionable evidence chains. Validation on FAERS and VigiBase datasets demonstrates 87% AUC with 73% sensitivity at 89% specificity, achieving a median time-to-signal of 4.2 months. The framework exhibits robust generalization across demographic subgroups, establishing a paradigm for trustworthy AI deployment in pharmacovigilance applications.
Integrating Graph Neural Networks into Computational Drug Design for Pediatric Diseases
Computational drug design (CDD) has emerged as a powerful tool for accelerating the discovery of novel therapeutics, particularly for diseases affecting pediatric populations. However, traditional CDD methods often struggle with the complexities of pediatric diseases, including unique biological targets and developmental considerations. Graph Neural Networks (GNNs), a class of deep learning models capable of processing graph-structured data, have shown remarkable promise in various CDD tasks, such as drug-target interaction prediction, molecular property prediction, and de novo drug design. This review provides a comprehensive overview of the integration of GNNs into CDD for pediatric diseases. We begin with a historical overview of CDD and its applications in pediatrics, highlighting the limitations of traditional approaches. We then delve into the core concepts of GNNs and their specific adaptations for CDD. We discuss the application of GNNs across diverse pediatric diseases, including cancers, genetic disorders, and infectious diseases, focusing on how GNNs can address specific challenges such as data scarcity and target heterogeneity. A comparative analysis of different GNN architectures and their performance in pediatric CDD is presented, along with a discussion of current challenges and limitations, such as the need for improved interpretability and validation. Finally, we explore future perspectives and opportunities for GNN-driven CDD in pediatrics, including the integration of multi-omics data, the development of personalized medicine approaches, and the application of explainable AI techniques. This review aims to provide a valuable resource for researchers and practitioners interested in leveraging GNNs to accelerate the development of safe and effective treatments for pediatric diseases.
Depth-Integrated Spatial Mapping for Enhanced Robotic Placement Accuracy
Accurate robotic placement of small industrial components requires stable depth interpretation under noisy factory conditions. We present a depth-integrated spatial mapping framework that reconstructs volumetric geometric fields using denoised depth cues and surface continuity priors. A geometry-consistency optimizer corrects depth-induced inconsistencies, enhancing spatial reliability. Tests on AssemblyDepth-2025 and RoboPlacement datasets show reductions of 29.1% in spatial variance and 17.4% in depth distortion. Real-factory deployment improves placement accuracy by 22.5% and reduces misalignment events by 19.1%. The framework demonstrates strong repeatability over 10,000+ cycles.
Research on the Improved Gray Wolf-Random Forest Hybrid Model in Credit Risk Assessment
Credit risk models must deal with imbalanced data and mixed borrower features. In this study, a Random Forest model tuned with Grey Wolf Optimization is used to raise the accuracy of default prediction on two UCI credit datasets. The optimizer adjusts the number of trees and the depth of each tree under an F1-based rule. After cleaning and encoding 2,000 records, the tuned model reaches an F1-score of 0.78, higher than the 0.74 achieved by the grid-search RF. Test accuracy increases from 0.83 to 0.85, and AUC rises from 0.89 to 0.91. Recall for default cases improves from 0.71 to 0.77, while precision stays near 0.79. These results show that a small change in model settings can reduce missed defaults without raising false alarms. The method is simple to train and can be used in regular scoring tasks. The study is limited by the use of public datasets with few variables and by the focus on one model type. Future work should include richer financial data and test multi-period predictions for broader use in lending systems.
Application of the Improved Whale Optimization Algorithm in Random Forest Parameter Tuning
Random Forest models rely on well-chosen hyperparameters, but common search methods often run slowly and give uneven results across datasets. This study introduces an Improved Whale Optimization Algorithm (IWOA) that updates inertia weight and step size during the search. The method was tested on twelve UCI classification datasets and compared with standard WOA-RF, PSO-RF, and DE-RF. IWOA-RF increased mean accuracy by 3.8% over WOA-RF and lowered training time by 11%. It also showed steadier results across repeated runs. The convergence curves showed that IWOA reached good solutions in fewer iterations. These results suggest that small changes in the update rules can make WOA more suitable for tuning RF models. The study also notes that only classification tasks were tested, and future work should include regression datasets and more complex RF settings.
The Effects of Soluble Dietary Fiber Intervention on Gut Microbiota Diversity and Short-Chain Fatty Acid Production in Diabetic Patients
This study explored how soluble fiber affects gut bacteria and blood sugar in adults with type 2 diabetes. Eighty participants were randomly divided into an inulin group (15 g/day) and a control group for 12 weeks. Gut bacterial diversity and short-chain fatty acids (SCFAs) were tested using 16S rRNA sequencing and gas chromatography. The inulin group showed a 28% rise in Shannon index and higher levels of Bifidobacterium and Faecalibacterium compared with the start of the study. Fecal butyrate and propionate increased by 49% and 35%, while HbA1c fell by 1.2 ± 0.3%. Blood C-reactive protein dropped by about 22%. These findings show that inulin may help lower blood sugar by increasing helpful gut bacteria and their fermentation products. The study supports the use of soluble fiber as a simple and safe way to aid blood sugar control in type 2 diabetes.
Research on Digital Platform User Retention Strategies and Marketing Model Optimization from a Data-Driven Perspective
This review paper synthesizes existing research on digital platform user retention strategies and marketing model optimization from a data-driven perspective. It examines various approaches employed by platforms to enhance user engagement and loyalty, leveraging data analytics to personalize experiences and improve marketing effectiveness. The review encompasses an historical overview of user retention techniques, delving into the evolution of marketing models in the digital age. Core themes explored include data-driven personalization, behavioral targeting, and dynamic pricing strategies. A comparative analysis of different retention models is presented, highlighting their strengths and weaknesses, and addressing the challenges associated with data privacy and algorithmic bias. Furthermore, the paper explores future research directions, anticipating the impact of emerging technologies such as AI and blockchain on user retention and marketing practices. This review aims to provide a comprehensive understanding of the data-driven landscape of user retention, offering insights for both academics and practitioners.
Classifying Tenant Legal Inquiries: A Comparative Study of Traditional and Deep Learning Approaches
Legal aid organizations face increasing demand for tenant protection services amid limited resources. Accurate classification of tenant inquiries enables efficient case routing and volunteer attorney matching. This study compares traditional machine learning methods (Naive Bayes, Support Vector Machines) with deep learning approaches (BERT fine-tuning) for classifying tenant legal inquiries across four categories: illegal eviction, housing repairs, security deposit disputes, and rent disagreements. Experiments on 450 de-identified tenant assistance requests reveal that sample size critically impacts method selection. Traditional approaches demonstrate robust performance with fewer than 150 samples, while BERT achieves superior accuracy (F1-score 0.89 vs 0.81) with datasets exceeding 300 samples. Mixed-issue cases involving multiple complaint types pose consistent challenges across all methods. Results inform practical deployment strategies for legal aid intake workflows.
Fairness-Aware Feature Attribution for Credit Scoring: A Causal Path Decomposition Approach
Credit scoring algorithms increasingly influence financial inclusion outcomes, yet traditional approaches often encode discriminatory patterns that disadvantage protected groups. This paper presents a fairness-aware feature attribution framework that leverages causal path decomposition to distinguish legitimate predictive factors from discriminatory proxies in credit assessment. The proposed approach integrates Shapley Additive Explanations with causal directed acyclic graphs to quantify the fair and unfair contributions of each feature to credit decisions. Experimental validation on two benchmark datasets demonstrates that the framework improves the disparate impact (DI) ratio while retaining 94.2% of baseline predictive performance (AUC). The causal feature filtering mechanism identifies features whose contributions are dominated by proxy-discrimination effects. It mitigates such influence through targeted feature filtering, enabling financial institutions to develop credit-scoring algorithms that satisfy both regulatory compliance requirements and business performance objectives. This research provides practical guidance on integrating alternative data sources while preserving fairness constraints, thereby directly supporting the Consumer Financial Protection Bureau's algorithmic discrimination-prevention goals and the Community Reinvestment Act's financial-inclusion mandates.
Enhanced Feature Fusion and Transfer Learning for Multi-Format Government Document Classification
Government document digitization faces significant challenges due to diverse formats, degraded quality, and limited annotated data. This paper presents an enhanced feature fusion framework combining convolutional neural networks and transformer architectures for multi-format government document classification. The proposed approach integrates hierarchical visual features with contextual text embeddings via a cross-modal attention mechanism, leveraging progressive transfer learning from general document corpora to specialized government domains. Experimental results on real-world administrative datasets demonstrate classification accuracy improvements of 5.6-8.3 percentage points (pp) over baseline methods, with particular robustness on degraded historical documents. The framework achieves 94.7% accuracy across multiple document formats while maintaining computational efficiency suitable for large-scale deployment in federal and state digitization initiatives.