Journal of Science, Innovation & Social Impact
type: channel
description: 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.
language: en
publication name: Journal of Science, Innovation & Social Impact
issn: 3105-501X
items: Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning
items: Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach
items: StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting
items: Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control
items: Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning
items: A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection
items: Theoretical Evolution and Frontier Trends of Corporate Strategic Management in the AI Era: A Literature Review
items: Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach
items: Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation
items: Integrated Fatigue and Vibration Design of Medical Welded Rotating Structures
items: A Study on Predicting Adolescent Cross-Sport Performance Using Shared Feature Space and Multi-Task Regression
items: Optimizing Transaction Matching Performance Using Hybrid Collaborative Filtering and Deep Learning: An Empirical Analysis of Feature Engineering and Similarity Metrics
items: Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness
items: Privacy-Utility Tradeoffs in Federated Financial Analytics: An Optimization Framework
items: Multimodal Deep Learning for Advertising Content Safety: A Comprehensive Study on Detection and Governance Strategies
items: Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases
items: Integrating Causal Inference with Multi-Channel Attribution for Equitable Pharmaceutical Marketing Effectiveness Evaluation
items: A Comparative Study of Forecasting Techniques for Reducing Food Waste in Retail Operations
items: Enhancing Transparency in Asset-Backed Securities: A Deep Learning Approach for Automated Risk Assessment and Regulatory Compliance
items: Adaptive Confidence-Weighted Feature Fusion for Robust Multimodal Autism Screening in Heterogeneous Pediatric Populations
items: Interpretable Early Detection of Adverse Drug Reactions: Integrating Robust Anomaly Scoring with Temporal Lag Analysis and Causal Verification
items: Integrating Graph Neural Networks into Computational Drug Design for Pediatric Diseases
items: Depth-Integrated Spatial Mapping for Enhanced Robotic Placement Accuracy
items: Research on the Improved Gray Wolf-Random Forest Hybrid Model in Credit Risk Assessment
items: Application of the Improved Whale Optimization Algorithm in Random Forest Parameter Tuning
items: The Effects of Soluble Dietary Fiber Intervention on Gut Microbiota Diversity and Short-Chain Fatty Acid Production in Diabetic Patients
items: Research on Digital Platform User Retention Strategies and Marketing Model Optimization from a Data-Driven Perspective
items: Classifying Tenant Legal Inquiries: A Comparative Study of Traditional and Deep Learning Approaches
items: Fairness-Aware Feature Attribution for Credit Scoring: A Causal Path Decomposition Approach
items: Enhanced Feature Fusion and Transfer Learning for Multi-Format Government Document Classification

Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: verbal operants
  topic: keywords
subject: autism spectrum disorder
  topic: keywords
subject: natural language processing
  topic: keywords
subject: context-aware classification
  topic: keywords
creator: Yaqing Bai
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 232
ending page: 243

Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: multimodal health data fusion
  topic: keywords
subject: algorithmic fairness
  topic: keywords
subject: temporal deep learning
  topic: keywords
subject: chronic disease prediction
  topic: keywords
creator: Xiaotong Shi
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 217
ending page: 231

StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting
type: item
description: 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.
subject: Articles
  topic: section
subject: statistical-neural fusion
  topic: keywords
subject: interpretable forecasting
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: conformal prediction
  topic: keywords
creator: Ye Lei
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 205
ending page: 216

Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control
type: item
description: 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.
subject: Articles
  topic: section
subject: clinical trial quality control
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: cross-center consistency
  topic: keywords
subject: evidence matrix
  topic: keywords
creator: Yisi Liu
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 190
ending page: 204

Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: cloud computing
  topic: keywords
subject: multi-source environmental data
  topic: keywords
subject: natural disaster prediction
  topic: keywords
subject: data integration
  topic: keywords
subject: probabilistic modeling
  topic: keywords
subject: feature engineering
  topic: keywords
creator: Guoli Ying
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 178
ending page: 189

A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: fraud detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: ensemble learning
  topic: keywords
subject: feature engineering
  topic: keywords
creator: Jin Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-26
publication date: 2026-02-26
starting page: 164
ending page: 177

Theoretical Evolution and Frontier Trends of Corporate Strategic Management in the AI Era: A Literature Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Strategic Management
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Literature Review
  topic: keywords
subject: Competitive Advantage
  topic: keywords
subject: Dynamic Capabilities
  topic: keywords
subject: Strategic Decision-Making
  topic: keywords
subject: AI Era
  topic: keywords
creator: Fiona C. McGregor
creator: Alistair J. Vance
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-25
publication date: 2026-02-25
starting page: 155
ending page: 163

Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach
type: item
description: 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 invest…
subject: Articles
  topic: section
subject: AI Startups
  topic: keywords
subject: Venture Capital
  topic: keywords
subject: Valuation
  topic: keywords
subject: Knowledge Graph
  topic: keywords
subject: Investment Strategy
  topic: keywords
subject: Due Diligence
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
creator: Julian S. Thorne
creator: Hao-Ran Chen
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-22
publication date: 2026-02-22
starting page: 146
ending page: 154

Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation
type: item
description: 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.
subject: Articles
  topic: section
subject: Stress testing
  topic: keywords
subject: consumer credit
  topic: keywords
subject: Macroeconomic simulation
  topic: keywords
subject: scenario analysis
  topic: keywords
subject: default forecasting
  topic: keywords
creator: Louis Bernard
creator: Manon Giroux
creator: Nicolas Faure
creator: Sophie Lambert
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-14
publication date: 2026-02-14
starting page: 139
ending page: 145

Integrated Fatigue and Vibration Design of Medical Welded Rotating Structures
type: item
description: 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.
subject: Articles
  topic: section
subject: medical welded structures
  topic: keywords
subject: fatigue-vibration coupling
  topic: keywords
subject: residual stress
  topic: keywords
subject: rotating components
  topic: keywords
subject: integrated design
  topic: keywords
creator: Rohan Mehta
creator: Priya Nair
creator: Amit Kumar
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-14
publication date: 2026-02-14
starting page: 132
ending page: 138

A Study on Predicting Adolescent Cross-Sport Performance Using Shared Feature Space and Multi-Task Regression
type: item
description: 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.
subject: Articles
  topic: section
subject: cross-sport prediction
  topic: keywords
subject: multi-task learning
  topic: keywords
subject: shared feature space
  topic: keywords
subject: athletic performance
  topic: keywords
subject: LightGBM
  topic: keywords
creator: Yue Hou
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 124
ending page: 131

Optimizing Transaction Matching Performance Using Hybrid Collaborative Filtering and Deep Learning: An Empirical Analysis of Feature Engineering and Similarity Metrics
type: item
description: 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.
subject: Articles
  topic: section
subject: transaction matching
  topic: keywords
subject: collaborative filtering
  topic: keywords
subject: deep learning
  topic: keywords
subject: similarity metrics
  topic: keywords
creator: Ziyi Wang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 111
ending page: 123

Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness
type: item
description: 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.
subject: Articles
  topic: section
subject: cash flow forecasting
  topic: keywords
subject: business intelligence tools
  topic: keywords
subject: manufacturing financial analysis
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Liya Ge
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 96
ending page: 110

Privacy-Utility Tradeoffs in Federated Financial Analytics: An Optimization Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: federated learning
  topic: keywords
subject: differential privacy
  topic: keywords
subject: secure multi-party computation
  topic: keywords
subject: financial privacy
  topic: keywords
creator: Yiyi Cai
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 80
ending page: 95

Multimodal Deep Learning for Advertising Content Safety: A Comprehensive Study on Detection and Governance Strategies
type: item
description: 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.
subject: Articles
  topic: section
subject: multimodal learning
  topic: keywords
subject: advertising safety
  topic: keywords
subject: content moderation
  topic: keywords
subject: deep learning
  topic: keywords
creator: Xin Lu
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 64
ending page: 79

Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases
type: item
description: 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.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: molecular pathway analysis
  topic: keywords
subject: biomarker discovery
  topic: keywords
subject: immune-related diseases
  topic: keywords
creator: Haofeng Ye
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 46
ending page: 63

Integrating Causal Inference with Multi-Channel Attribution for Equitable Pharmaceutical Marketing Effectiveness Evaluation
type: item
description: 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.
subject: Articles
  topic: section
subject: pharmaceutical marketing attribution
  topic: keywords
subject: causal inference
  topic: keywords
subject: health equity evaluation
  topic: keywords
subject: multi-channel effectiveness
  topic: keywords
creator: Zhenghao Pan
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 31
ending page: 45

A Comparative Study of Forecasting Techniques for Reducing Food Waste in Retail Operations
type: item
description: 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.
subject: Articles
  topic: section
subject: food waste reduction
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: machine learning
  topic: keywords
subject: retail operations
  topic: keywords
subject: perishable goods management
  topic: keywords
creator: Yi Wang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 18
ending page: 30

Enhancing Transparency in Asset-Backed Securities: A Deep Learning Approach for Automated Risk Assessment and Regulatory Compliance
type: item
description: 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.
subject: Articles
  topic: section
subject: explainable artificial intelligence
  topic: keywords
subject: asset-backed securities
  topic: keywords
subject: risk assessment
  topic: keywords
subject: regulatory compliance
  topic: keywords
creator: Jiahui Han
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-13
publication date: 2026-02-13
starting page: 1
ending page: 17

Adaptive Confidence-Weighted Feature Fusion for Robust Multimodal Autism Screening in Heterogeneous Pediatric Populations
type: item
description: 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.
subject: Articles
  topic: section
subject: autism spectrum disorder
  topic: keywords
subject: multimodal fusion
  topic: keywords
subject: adaptive weighting
  topic: keywords
subject: behavioral screening
  topic: keywords
subject: deep learning
  topic: keywords
creator: Yaqing Bai
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-02-11
publication date: 2026-02-11
starting page: 485
ending page: 498

Interpretable Early Detection of Adverse Drug Reactions: Integrating Robust Anomaly Scoring with Temporal Lag Analysis and Causal Verification
type: item
description: 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.
subject: Articles
  topic: section
subject: adverse drug reaction detection
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
subject: causal inference
  topic: keywords
subject: pharmacovigilance
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subject: temporal analysis
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creator: Yisi Liu
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-02-11
publication date: 2026-02-11
starting page: 471
ending page: 484

Integrating Graph Neural Networks into Computational Drug Design for Pediatric Diseases
type: item
description: 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.
subject: Articles
  topic: section
subject: Graph Neural Networks (GNNs)
  topic: keywords
subject: computational drug design (CDD)
  topic: keywords
subject: pediatric diseases
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subject: drug discovery
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subject: Deep Learning
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subject: drug-target interaction
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subject: molecular property prediction
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creator: Lina Zhang
creator: Wei Zhou
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-31
publication date: 2026-01-31
starting page: 175
ending page: 182

Depth-Integrated Spatial Mapping for Enhanced Robotic Placement Accuracy
type: item
description: 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.
subject: Articles
  topic: section
subject: spatial mapping
  topic: keywords
subject: depth reconstruction
  topic: keywords
subject: industrial placement
  topic: keywords
subject: geometry optimization
  topic: keywords
subject: robotic assembly
  topic: keywords
creator: Daniel A. Levin
creator: Maya R. Shapira
creator: Eitan Goldfarb
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-20
publication date: 2026-01-20
starting page: 168
ending page: 174

Research on the Improved Gray Wolf-Random Forest Hybrid Model in Credit Risk Assessment
type: item
description: 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.
subject: Articles
  topic: section
subject: credit risk
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Grey Wolf Optimization
  topic: keywords
subject: default prediction
  topic: keywords
subject: imbalanced data
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subject: F1-score
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subject: model tuning
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creator: James R. Coleman
creator: Michael S. Bradford
creator: Sarah K. Foster
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-20
publication date: 2026-01-20
starting page: 161
ending page: 167

Application of the Improved Whale Optimization Algorithm in Random Forest Parameter Tuning
type: item
description: 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.
subject: Articles
  topic: section
subject: Improved Whale Optimization Algorithm
  topic: keywords
subject: Random Forest
  topic: keywords
subject: hyperparameter search
  topic: keywords
subject: UCI datasets
  topic: keywords
subject: inertia weight
  topic: keywords
subject: step-size update
  topic: keywords
subject: classification accuracy
  topic: keywords
creator: Oliver Bennett
creator: Hannah Collins
creator: Samuel Turner
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-20
publication date: 2026-01-20
starting page: 155
ending page: 160

The Effects of Soluble Dietary Fiber Intervention on Gut Microbiota Diversity and Short-Chain Fatty Acid Production in Diabetic Patients
type: item
description: 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.
subject: Articles
  topic: section
subject: inulin
  topic: keywords
subject: soluble fiber
  topic: keywords
subject: gut bacteria
  topic: keywords
subject: short-chain fatty acids
  topic: keywords
subject: HbA1c
  topic: keywords
subject: inflammation
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subject: type 2 diabetes
  topic: keywords
creator: Yuting Zhao
creator: Daniel R. Collins
creator: Hannah M. Nguyen
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-20
publication date: 2026-01-20
starting page: 149
ending page: 154

Research on Digital Platform User Retention Strategies and Marketing Model Optimization from a Data-Driven Perspective
type: item
description: 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.
subject: Articles
  topic: section
subject: digital platforms
  topic: keywords
subject: user retention
  topic: keywords
subject: data-driven marketing
  topic: keywords
subject: marketing model optimization
  topic: keywords
subject: personalization
  topic: keywords
subject: behavioral targeting
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subject: algorithmic bias
  topic: keywords
creator: Robert L. Chen
creator: Simon J. Richards
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-20
publication date: 2026-01-20
starting page: 463
ending page: 470

Classifying Tenant Legal Inquiries: A Comparative Study of Traditional and Deep Learning Approaches
type: item
description: 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.
subject: Articles
  topic: section
subject: legal text classification
  topic: keywords
subject: tenant rights
  topic: keywords
subject: BERT
  topic: keywords
subject: low-resource NLP
  topic: keywords
creator: Hanfei Zhang
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-19
publication date: 2026-01-19
starting page: 452
ending page: 462

Fairness-Aware Feature Attribution for Credit Scoring: A Causal Path Decomposition Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: algorithmic fairness
  topic: keywords
subject: credit scoring
  topic: keywords
subject: causal inference
  topic: keywords
subject: feature attribution
  topic: keywords
creator: Minju Zhong
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-18
publication date: 2026-01-18
starting page: 442
ending page: 451

Enhanced Feature Fusion and Transfer Learning for Multi-Format Government Document Classification
type: item
description: 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.
subject: Articles
  topic: section
subject: document classification
  topic: keywords
subject: feature fusion
  topic: keywords
subject: transfer learning
  topic: keywords
subject: government digitization
  topic: keywords
creator: Qiaomu Zhang
rights: Copyright (c) 2025 Journal of Science, Innovation & Social Impact
date: 2026-01-18
publication date: 2026-01-18
starting page: 427
ending page: 441