ASRC Procedia: Global Perspectives in Science and Scholarship
type: channel
description: ASRC Procedia is a peer-reviewed, open-access conference proceedings journal that serves as the official publication outlet of the American Scholarly Research Conference (ASRC). Published from Florida, USA, the journal is dedicated to disseminating original research presented at academic and industry symposia held under the ASRC umbrella, including the flagship Global Research and Innovation Summit (GRIS). The journal promotes cross-disciplinary knowledge exchange by capturing diverse scholarly outputs across the domains of science, engineering, technology, health, environment, education, business, economics, public policy, and the social sciences.
language: en
publication name: ASRC Procedia: Global Perspectives in Science and Scholarship
items: Impact of ERP Integration and Workflow Automation on General Ledger Accuracy and Reconciliation Efficiency in Public-Sector Finance
items: AI-Enhanced ERP Financial Intelligence Systems for Real-Time Business Analytics and Risk Decision Support
items: Impact of Predictive Analytics on Sales Forecasting Accuracy in B2B Financial Services: A Quantitative Evaluation
items: AI-Augmented VAPT and SecureAI Controls: A Quantitative Compliance Study Under ISO 27001, ISO/IEC 42001, SWIFT CSP, and PCI DSS
items: Impact Of Random Forest and Ensemble Methods on Infection Trend Forecasting: A Quantitative Evaluation Using Global Post Covid-19 Data
items: Impact of Zero Trust Architecture on Stock Exchange Network Security: A Quantitative Evaluation of Access Control and Incident Reduction
items: Impact of Automated Server and Database Monitoring Systems on ATM Network Uptime: A Quantitative Evaluation
items: AI-Assisted Medical Records Management and EHR Workflow Optimization for Community Health Centres Serving Immigrant Populations
items: Advanced Engineering Materials Applications for Enhancing Durability and Lifecycle Performance of Steel Building Systems
items: A Quantitative Assessment of AI-Driven Predictive Analytics for Economic Development Decision Support in U.S. Public Policy Centers
items: Impact of Digital Twin Technology on Predictive Maintenance and Asset Lifecycle Management in Energy Infrastructure: A Quantitative Evaluation
items: Impact of Predictive Analytics and Ensemble Learning on Operational Efficiency and KPI Forecasting in U.S. Engineering Firms
items: Impact of SCADA-GIS Integration on Real-Time Water Distribution Monitoring: A Quantitative Evaluation of Smart Utility Infrastructure
items: Machine Learning-Enabled Business Intelligence Dashboards for Strategic Decision Support
items: AI-Enabled Decision Support Systems for Industrial Energy Optimization in U.S. Manufacturing
items: Quantitative Evaluation of Machine Learning Models for Project Risk Prediction and Resource Optimization in Business Operations
items: Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility
items: AI-Driven Real-Time Methane Emissions Monitoring and Predictive Leak Detection Using Lidar and IOT Sensor Fusion in Upstream Oil and Gas Operations
items: Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
items: Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
items: Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
items: Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
items: Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
items: QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
items: Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
items: AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
items: Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
items: AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
items: Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
items: Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk

Impact of ERP Integration and Workflow Automation on General Ledger Accuracy and Reconciliation Efficiency in Public-Sector Finance
type: item
description: This study examined the impact of ERP integration and workflow automation on general ledger accuracy and reconciliation efficiency in public-sector finance institutions. The study employed a quantitative, cross-sectional, explanatory research design grounded in systems theory and organizational information processing theory. Data were collected from 286 finance professionals working in accounting, treasury, procurement, payroll, budgeting, and audit departments within ERP-enabled public-sector organizations. Descriptive statistical findings showed that 47.9% of participating institutions operated fully integrated ERP systems, while 44.1% reported high levels of workflow automation. The majority of respondents possessed more than four years of ERP experience, representing 67.2% of the total sample population. Data were analyzed using SPSS through descriptive statistics, reliability analysis, Pearson correlation, and multiple regression analysis. The findings revealed high mean scores for ERP integration (M = 4.18, SD = 0.61), workflow automation (M = 4.11, SD = 0.65), general ledger accuracy (M = 4.26, SD = 0.58), and reconciliation efficiency (M = 4.21, SD = 0.60), indicating strong agreement regarding improvements in accounting performance outcomes. Reliability analysis confirmed excellent internal consistency, with Cronbach’s alpha values ranging from 0.807 to 0.913. Pearson correlation analysis demonstrated statistically significant positive relationships among all study variables, with ERP integration strongly associated with general ledger accuracy (r = 0.781, p < 0.01) and workflow automation strongly associated with reconciliation efficiency (r = 0.809, p < 0.01). Multiple regression analysis further indicated that ERP integration significantly predicted general ledger accuracy (β = 0.521, t = 9.884, p < 0.001), while workflow automation significantly predicted reconciliation efficiency (β = 0.589, t = 11.246, p < 0.001). The regression models explained 64.8%…
subject: Articles
  topic: section
subject: ERP Integration
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: General Ledger Accuracy
  topic: keywords
subject: Reconciliation Efficiency
  topic: keywords
subject: Public-Sector Finance
  topic: keywords
creator: S M Arif Al Sany
rights: Copyright (c) 2025 S M Arif Al Sany (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2742
ending page: 2794
doi: 10.63125/jsfzhg72

AI-Enhanced ERP Financial Intelligence Systems for Real-Time Business Analytics and Risk Decision Support
type: item
description: This study examines the problem that many enterprises use ERP systems for transaction recording and financial reporting but still struggle to transform ERP-based financial data into real-time analytics, predictive intelligence, and proactive risk decision support. The purpose of the research is to assess how AI-enhanced ERP financial intelligence systems improve real-time business analytics, risk decision support, and financial decision-making effectiveness in cloud-based and enterprise ERP cases. The study used a quantitative, cross-sectional, case-based design with a valid sample of 150 ERP users, accountants, finance officers, business analysts, IT managers, internal auditors, risk officers, compliance personnel, and financial decision-makers drawn from organizations using ERP platforms such as SAP, Oracle ERP, Microsoft Dynamics, NetSuite, or similar enterprise systems. The key variables included AI-enhanced ERP financial intelligence, financial data integration, predictive analytics capability, AI-driven automation, real-time business analytics, risk decision support, and financial decision-making effectiveness. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression modeling. The findings showed high agreement across all major constructs, with real-time business analytics recording the highest mean score of 4.15, followed by AI-enhanced ERP financial intelligence at 4.12, financial decision-making effectiveness at 4.10, financial data integration at 4.08, AI-driven automation at 4.05, risk decision support at 4.02, and predictive analytics capability at 3.96. Reliability was strong, with Cronbach’s alpha values ranging from 0.84 to 0.90 and an overall instrument reliability of 0.92. Correlation results confirmed significant positive relationships, including AI-enhanced ERP financial intelligence with real-time business analytics, …
subject: Articles
  topic: section
subject: AI-enhanced ERP
  topic: keywords
subject: Financial Intelligence
  topic: keywords
subject: Real-Time Business Analytics
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Risk Decision Support
  topic: keywords
creator: Samia Hossain Swarnali
rights: Copyright (c) 2025 Samia Hossain Swarnali (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2703
ending page: 2741
doi: 10.63125/t1zw3w09

Impact of Predictive Analytics on Sales Forecasting Accuracy in B2B Financial Services: A Quantitative Evaluation
type: item
description: This quantitative study examined the impact of predictive analytics on sales forecasting accuracy within business-to-business financial service organizations operating in highly competitive and data-driven enterprise environments. The study investigated the influence of machine learning forecasting systems, customer analytics platforms, business intelligence infrastructures, and automated forecasting coordination mechanisms on forecasting precision, revenue estimation consistency, customer acquisition forecasting, and operational forecasting efficiency. A quantitative correlational research design was employed using a purposive sample of 210 participants drawn from banking institutions, insurance organizations, investment management firms, enterprise financing companies, and financial consulting enterprises. Participants included sales managers, forecasting analysts, business intelligence specialists, and customer relationship management personnel directly involved in enterprise forecasting operations. Data were collected using structured survey instruments and operational forecasting datasets obtained from participating financial organizations. Statistical analysis was conducted using SPSS, R statistical software, and Python analytical libraries through descriptive statistics, Pearson correlation analysis, multiple regression analysis, ANOVA, and structural equation modeling. The findings revealed statistically significant positive relationships between predictive analytical integration and sales forecasting performance across participating organizations. Machine learning forecasting systems demonstrated the strongest predictive influence on forecasting accuracy with a regression coefficient of β = 0.481 and p = 0.001. Organizations implementing integrated predictive analytical systems achieved forecasting accuracy of 91.8% compared with 72.6% among organizations using conventional forecasting systems, representing a 26.4% improvement. Forecasting responsiveness im…
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Sales Forecasting
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: H M Mahir Uddin
rights: Copyright (c) 2025 H M Mahir Uddin (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2653
ending page: 2702
doi: 10.63125/g9fvv860

AI-Augmented VAPT and SecureAI Controls: A Quantitative Compliance Study Under ISO 27001, ISO/IEC 42001, SWIFT CSP, and PCI DSS
type: item
description: This study quantitatively examined the impact of AI-augmented Vulnerability Assessment and Penetration Testing (VAPT) and SecureAI control mechanisms on cybersecurity compliance performance across internationally recognized frameworks, including ISO 27001, ISO/IEC 42001, SWIFT Customer Security Programme (CSP), and PCI DSS. A cross-sectional explanatory research design was employed, utilizing data collected from 210 cybersecurity and compliance professionals across financial and enterprise IT sectors. The findings revealed that AI integration significantly enhanced compliance performance, with a strong positive correlation identified between AI-augmented VAPT and compliance outcomes (r = 0.68, p < 0.05). SecureAI control maturity demonstrated an even stronger relationship with compliance performance (r = 0.72, p < 0.05), indicating the critical role of governance mechanisms in achieving regulatory alignment. Multiple regression analysis showed that AI-augmented VAPT (β = 0.41, p < 0.01) and SecureAI controls (β = 0.46, p < 0.01) were significant predictors of compliance performance, collectively explaining 58% of the variance (R² = 0.58). Continuous monitoring practices also contributed significantly (β = 0.29, p < 0.01), reinforcing the importance of real-time security assessment. Sectoral analysis indicated that financial institutions achieved higher compliance scores (M = 4.28) compared to non-financial sectors (M = 3.76), while organizations with advanced cybersecurity maturity reported superior detection accuracy (M = 4.22) and audit readiness (M = 4.30). The results further showed that high AI integration levels corresponded with increased compliance performance (M = 4.32), compared to low AI adoption (M = 3.42). Overall, the study provided strong empirical evidence that AI-driven cybersecurity practices significantly improve compliance effectiveness, operational efficiency, and governance consistency across multi-framework environments, highlighting the impor…
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: VAPT
  topic: keywords
subject: SecureAI
  topic: keywords
subject: Compliance
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Customer Security Programme (CSP)
  topic: keywords
creator: Md. Shakhawat Hossen
rights: Copyright (c) 2025 Md. Shakhawat Hossen (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2611
ending page: 2652
doi: 10.63125/ep9s9k54

Impact Of Random Forest and Ensemble Methods on Infection Trend Forecasting: A Quantitative Evaluation Using Global Post Covid-19 Data
type: item
description: This study quantitatively evaluated the impact of Random Forest and ensemble methods on infection trend forecasting using global post-COVID-19 datasets. A longitudinal analytical framework was applied to 18,720 time-series observations across 52 countries, incorporating key variables such as daily infection cases, vaccination rates (mean = 64.2%), mobility indices (mean deviation = −18.5%), and policy stringency scores (mean = 57.3). The findings indicated that both Random Forest and ensemble methods significantly outperformed baseline models in predictive accuracy. Random Forest achieved a root mean square error of 2,145, mean absolute error of 1,620, mean absolute percentage error of 12.8%, and coefficient of determination of 0.87. Ensemble methods demonstrated superior performance with lower error values, including a root mean square error of 1,980, mean absolute error of 1,480, mean absolute percentage error of 11.3%, and a higher coefficient of determination of 0.91. Regional analysis showed that ensemble methods consistently produced lower mean absolute error values, ranging from 1,290 in Oceania to 1,710 in Africa, compared to Random Forest values ranging from 1,430 to 1,890. Temporal analysis revealed that forecasting accuracy improved by approximately 15% during stable transmission phases, with root mean square error decreasing from 2,480 during outbreak periods to 1,720 under stable conditions. Statistical testing confirmed significant differences between models (p < 0.05), with effect sizes ranging from 0.49 to 0.68, indicating moderate to strong practical significance. Overall, the results demonstrated that ensemble methods provided enhanced stability, reduced prediction variance by approximately 11.6%, and improved generalizability across heterogeneous datasets, while Random Forest maintained strong adaptability in capturing complex nonlinear relationships in global infection trends.
subject: Articles
  topic: section
subject: Random Forest
  topic: keywords
subject: Ensemble Methods
  topic: keywords
subject: Infection Forecasting
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Post-COVID Data
  topic: keywords
creator: Risha Alam
rights: Copyright (c) 2025 Risha Alam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2570
ending page: 2610
doi: 10.63125/b0yw2q91

Impact of Zero Trust Architecture on Stock Exchange Network Security: A Quantitative Evaluation of Access Control and Incident Reduction
type: item
description: This study examined the impact of Zero Trust Architecture on stock exchange network security through a quantitative quasiexperimental design, focusing on access control performance and incident reduction. The analysis was based on a comprehensive dataset of 48,600 network events collected across preimplementation and postimplementation phases, ensuring balanced and comparable conditions. The findings revealed substantial improvements in key security indicators following the adoption of Zero Trust controls. Authentication success rates increased from 82.4% to 91.7%, while unauthorized access attempts decreased by 58.4%, demonstrating enhanced identity verification and access governance. Additionally, total security incidents declined from 312 to 138 events, representing a reduction of 55.8%, indicating improved resilience against cyber threats. Operational efficiency also improved significantly, with mean time to detect decreasing from 48.2 seconds to 27.6 seconds and mean time to respond decreasing from 95.4 seconds to 52.1 seconds. Statistical analysis confirmed that these differences were highly significant, with p-values below 0.001 across all major indicators, and effect size analysis revealed moderate to large impacts, including a Cohen’s d of 1.21 for incident reduction. Regression analysis further demonstrated that Zero Trust implementation explained approximately 64% of the variance in incident frequency, confirming its strong predictive influence on security outcomes. Sub-group analysis indicated that privileged accounts and external access points experienced the most significant improvements, with unauthorized access attempts reduced by 66.1% and 57.2%, respectively. Systems with higher transaction volumes also showed greater enhancements in detection and response efficiency. Overall, the results provided strong empirical evidence that Zero Trust Architecture significantly improves network security performance in stock exchange environments by enhancing au…
subject: Articles
  topic: section
subject: Zero Trust
  topic: keywords
subject: Network Security
  topic: keywords
subject: Stock Exchange
  topic: keywords
subject: Access Control
  topic: keywords
subject: Incident Reduction
  topic: keywords
creator: Binayan Dey
rights: Copyright (c) 2025 Binayan Dey (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2528
ending page: 2569
doi: 10.63125/dcvska73

Impact of Automated Server and Database Monitoring Systems on ATM Network Uptime: A Quantitative Evaluation
type: item
description: This study quantitatively evaluated the impact of automated server and database monitoring systems on ATM network uptime and operational performance. A quasi-experimental design was applied using data collected from 48 ATM network clusters over a 12-month period, comprising automated monitoring environments (n = 24) and conventional monitoring environments (n = 24). Descriptive and inferential statistical analyses were conducted to assess differences in key performance indicators. The findings revealed that automated monitoring systems significantly improved ATM network uptime, increasing from a mean of 95.9% in conventional environments to 98.1% in automated environments, representing a 2.2% improvement. Mean time to repair decreased substantially from 3.6 hours to 1.8 hours, reflecting a 50% reduction in system recovery time. Transaction success rates improved from 93.8% to 97.5%, while incident frequency declined from 12.9 to 9.6 incidents per month, indicating a 25.6% reduction in operational disruptions. Statistical testing confirmed that these differences were significant (t = 4.87, p < 0.001 for uptime; t = -5.12, p < 0.001 for MTTR), with large effect sizes observed (Cohen’s d = 0.85 for uptime and 0.92 for MTTR). Regression analysis showed that alert response time (β = -0.48, p < 0.001) and server performance stability (β = 0.41, p < 0.01) were strong predictors of uptime performance. Subgroup analysis further indicated that high-traffic ATM clusters achieved uptime levels as high as 98.6% under automated monitoring, compared to 95.4% in conventional systems. These findings provided strong empirical evidence that automated monitoring systems significantly enhance ATM network uptime, operational efficiency, and service reliability.
subject: Articles
  topic: section
subject: ATM uptime
  topic: keywords
subject: Automated Monitoring
  topic: keywords
subject: Database Monitoring Systems
  topic: keywords
subject: Performance
  topic: keywords
subject: Reliability
  topic: keywords
creator: Taru Binte Amin
rights: Copyright (c) 2025 Taru Binte Amin (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2486
ending page: 2527
doi: 10.63125/qcr55n60

AI-Assisted Medical Records Management and EHR Workflow Optimization for Community Health Centres Serving Immigrant Populations
type: item
description: This study investigates the persistent problem of documentation burden, incomplete patient records, multilingual communication challenges, and workflow inefficiencies in community health centres serving immigrant populations, where conventional EHR systems often struggle to capture and manage complex patient information effectively. The purpose of the research was to examine whether AI-assisted medical records management improves EHR workflow optimization, record completeness, multilingual documentation performance, service delivery quality, and overall operational effectiveness in these settings. The study adopted a quantitative, cross-sectional, case-based design and collected data from 200 respondents drawn from selected cloud-enabled and enterprise-style EHR case contexts within community health centres, including physicians, nurses, medical records officers, administrative staff, and IT/EHR support personnel. The key variables included AI-assisted medical records management, automated documentation support, intelligent record retrieval, error detection and validation, multilingual documentation support, EHR workflow optimization, record accuracy and completeness, service delivery quality, and operational effectiveness. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong positive perceptions of AI-supported records management, with mean scores of 3.98 for AI-assisted medical records management, 4.07 for EHR workflow optimization, 4.12 for record accuracy and completeness, 4.15 for multilingual documentation support, and 4.18 for service delivery quality. Reliability values were high, with Cronbach’s alpha ranging from 0.821 to 0.914. Correlation analysis revealed significant positive relationships between AI-assisted medical records management and EHR workflow optimization (r = 0.71, p < .001), record accuracy and completeness (r = 0.69, p < .001…
subject: Articles
  topic: section
subject: AI-Assisted Medical Records Management
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Workflow Optimization
  topic: keywords
subject: Multilingual Documentation Support
  topic: keywords
subject: Community Health Centres
  topic: keywords
creator: Mst Kaniz Fatema
rights: Copyright (c) 2025 Mst Kaniz Fatema (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2446
ending page: 2485
doi: 10.63125/nvn1yr86

Advanced Engineering Materials Applications for Enhancing Durability and Lifecycle Performance of Steel Building Systems
type: item
description: This study investigates advanced engineering materials applications for enhancing durability and lifecycle performance in steel building systems, addressing the problem that conventional steel materials and protection methods often fail to prevent corrosion, fatigue, coating degradation, moisture exposure, and maintenance-related performance decline over long service periods. The purpose of the study was to examine whether advanced engineering materials, including advanced protective coatings, corrosion-resistant steel grades, high-performance steel sections, composite reinforcement systems, and smart protective materials, significantly improve durability and lifecycle performance in practical steel-building project contexts. A quantitative, cross-sectional, case-based design was adopted, using structured five-point Likert-scale survey data collected from professional respondents involved in steel construction, design, materials selection, project management, and maintenance. Out of 240 distributed questionnaires, 218 were returned and 210 were usable, giving an effective response rate of 87.5%. The sample included structural engineers, civil engineers, architects, project managers, materials specialists, and maintenance professionals across industrial, commercial, institutional, high-rise, and mixed-use steel building cases. The analysis plan included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, simple regression, multiple regression, material effectiveness ranking, and case-based mean comparison. Findings showed high mean scores for advanced engineering materials applications (M = 4.18, SD = 0.61), durability (M = 4.11, SD = 0.57), and lifecycle performance (M = 4.09, SD = 0.63). Reliability was strong, with Cronbach’s alpha values of 0.86, 0.83, and 0.88 respectively. Correlation results confirmed significant positive relationships between AEM and durability (r = 0.710, p < .01), AEM and lifecycle performance (r = 0.…
subject: Articles
  topic: section
subject: Advanced Engineering Materials
  topic: keywords
subject: Steel Building Systems
  topic: keywords
subject: Durability
  topic: keywords
subject: Lifecycle Performance
  topic: keywords
subject: Protective Coatings
  topic: keywords
creator: Md. Mainuddin Faisal
rights: Copyright (c) 2025 Md. Mainuddin Faisal (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2406
ending page: 2445
doi: 10.63125/t9xvg986

A Quantitative Assessment of AI-Driven Predictive Analytics for Economic Development Decision Support in U.S. Public Policy Centers
type: item
description: This study examined the problem of limited empirical evidence on whether AI-driven predictive analytics meaningfully improves economic development decision support in U.S. public policy centers. Although these institutions increasingly use data integration, forecasting systems, analytical automation, and real-time insight tools, the measurable value of these capabilities for policy decision quality, efficiency, forecasting accuracy, and responsiveness has remained insufficiently established. The purpose of the study was to quantitatively assess how AI-driven predictive analytics capability influences economic development decision-support effectiveness in selected U.S. public policy centers. A quantitative, cross-sectional, case-based research design was adopted, using cloud-enabled and enterprise-oriented public policy cases involving policy analysts, economists, research officers, program managers, strategic planners, and administrative professionals. From 260 distributed questionnaires, 228 were returned and 220 valid responses were analyzed, producing a usable response rate of 84.6%. The key independent variables were data integration capability, forecasting capability, analytical automation, and real-time insight generation, while the dependent variables were decision quality, decision-making efficiency, forecasting accuracy, policy responsiveness, and overall decision-support effectiveness. The analysis plan included descriptive statistics, Pearson correlation analysis, multiple regression modeling, and hypothesis testing using SPSS. The findings showed a high overall level of AI-driven predictive analytics capability, with a mean of 3.96, and high decision-support effectiveness, with a mean of 4.01. Predictive analytics was significantly correlated with forecasting accuracy, r = .760, decision quality, r = .710, policy responsiveness, r = .680, and decision-making efficiency, r = .640, all at p < .001. The regression model was statistically significant, R = .7…
subject: Articles
  topic: section
subject: AI-Driven Predictive Analytics
  topic: keywords
subject: Economic Development
  topic: keywords
subject: Decision Support Effectiveness
  topic: keywords
subject: U.S. Public Policy Centers
  topic: keywords
subject: Forecasting Capability
  topic: keywords
creator: Atif Khan
rights: Copyright (c) 2025 Atif Khan (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2364
ending page: 2405
doi: 10.63125/0n7av251

Impact of Digital Twin Technology on Predictive Maintenance and Asset Lifecycle Management in Energy Infrastructure: A Quantitative Evaluation
type: item
description: This study examined the impact of digital twin technology on predictive maintenance and asset lifecycle management within energy infrastructure through a comprehensive quantitative evaluation. A quasi-experimental, comparative, and longitudinal research design was adopted to analyze operational data collected from 180 energy assets, including power generation units, substations, and grid-connected systems. The study compared traditional maintenance strategies with digital twin-enabled predictive maintenance using key performance indicators such as downtime, failure frequency, maintenance cost, repair duration, and asset availability. Descriptive statistics, analysis of variance, and multivariate regression techniques were applied to assess performance differences and relationships among variables. The findings demonstrated that digital twin-enabled systems significantly outperformed traditional approaches, with average downtime reduced from 18.4 hours to 9.7 hours per month, representing a 47.3% improvement. Asset availability increased from 82.3% to 91.8%, while mean time between failures improved from 320 hours to 510 hours, indicating enhanced reliability. Maintenance costs decreased from an average of USD 4,950 to USD 3,420 per month, reflecting a reduction of approximately 30.9%. Statistical analysis confirmed that these differences were significant at the 0.05 level, with strong effect sizes observed across multiple indicators. Regression results further indicated that digital twin functionalities, including real-time monitoring and predictive alert accuracy, accounted for 68% of the variance in maintenance performance outcomes. Subgroup analysis revealed that high-criticality and older assets experienced the greatest benefits, with downtime reductions exceeding 50% in optimized environments. Overall, the study provided robust empirical evidence that digital twin technology enhances predictive maintenance effectiveness, improves operational reliability, and su…
subject: Articles
  topic: section
subject: Digital
  topic: keywords
subject: Twin
  topic: keywords
subject: Predictive
  topic: keywords
subject: Maintenance
  topic: keywords
subject: Asset Lifecycle Management
  topic: keywords
subject: Energy Infrastructure
  topic: keywords
creator: Md Asif Ali Sheak Arju
rights: Copyright (c) 2025 Md Asif Ali Sheak Arju (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2323
ending page: 2363
doi: 10.63125/tg461a54

Impact of Predictive Analytics and Ensemble Learning on Operational Efficiency and KPI Forecasting in U.S. Engineering Firms
type: item
description: This study examined the impact of predictive analytics and ensemble learning on operational efficiency and KPI forecasting accuracy in U.S. engineering firms using a quantitative, cross-sectional design. Data were collected from 236 professionals across construction, manufacturing, and infrastructure sectors. The findings revealed strong and statistically significant relationships among the key variables. Correlation analysis indicated that predictive analytics adoption was highly associated with operational efficiency (r = 0.72) and KPI forecasting accuracy (r = 0.70), while ensemble learning integration showed the strongest relationship with forecasting accuracy (r = 0.74). Multiple regression results demonstrated that predictive analytics significantly influenced operational efficiency (β = 0.45, p < 0.001), whereas ensemble learning had a greater impact on KPI forecasting accuracy (β = 0.51, p < 0.001). The models explained 56% of the variance in operational efficiency and 62% in KPI forecasting accuracy, indicating substantial explanatory power. Sector-based analysis showed that manufacturing firms achieved the highest efficiency (M = 4.28), while construction firms reported the highest forecasting accuracy (M = 4.31). Additionally, firms with high analytics adoption demonstrated significantly better performance outcomes (efficiency M = 4.42; forecasting M = 4.39) compared to those with low adoption levels. The results confirmed that the integration of predictive analytics and ensemble learning enhanced both operational processes and forecasting reliability. Overall, the study provided strong empirical evidence that advanced analytics capabilities play a critical role in improving performance outcomes in engineering firms.
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: KPI Forecasting
  topic: keywords
subject: Engineering Firms
  topic: keywords
creator: Md Aminul Islam
rights: Copyright (c) 2025 Md Aminul Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2280
ending page: 2322
doi: 10.63125/r5s10176

Impact of SCADA-GIS Integration on Real-Time Water Distribution Monitoring: A Quantitative Evaluation of Smart Utility Infrastructure
type: item
description: This study investigates the impact of SCADA-GIS integration on real-time water distribution monitoring in smart utility infrastructure by addressing the problem of fragmented monitoring environments in which Supervisory Control and Data Acquisition systems and Geographic Information Systems often operate separately, thereby limiting timely event localization, monitoring accuracy, and coordinated response. The purpose of the research was to quantitatively evaluate whether integrating operational and geospatial intelligence improves monitoring effectiveness in water utility settings. Using a quantitative, cross-sectional, case-based design, the study collected primary survey data from 214 technically relevant respondents drawn from smart utility and enterprise-like operational cases, including SCADA engineers, GIS analysts, control-room operators, maintenance supervisors, utility managers, and technical officers. The key independent variable was SCADA-GIS integration, measured through data synchronization, spatial integration, asset visibility, interoperability and information connectivity, and control coordination, while the dependent variable was real-time monitoring effectiveness, measured through monitoring accuracy, fault-detection efficiency, spatial-operational visibility, infrastructure response readiness, and overall monitoring effectiveness. The analysis plan used descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and linear regression. Findings showed high perceived SCADA-GIS integration (M = 4.18, SD = 0.61) and high real-time monitoring effectiveness (M = 4.12, SD = 0.58). Reliability was strong, with Cronbach’s alpha of 0.89 for SCADA-GIS integration and 0.91 for real-time monitoring effectiveness. Correlation results revealed significant positive relationships with monitoring effectiveness (r = 0.72, p = 0.000), monitoring accuracy (r = 0.68, p = 0.000), fault-detection efficiency (r = 0.70, p = 0.000), spatial-operationa…
subject: Articles
  topic: section
subject: SCADA-GIS integration
  topic: keywords
subject: Real-time water monitoring
  topic: keywords
subject: Smart utility infrastructure
  topic: keywords
subject: Spatial-operational visibility
  topic: keywords
subject: Infrastructure response readiness
  topic: keywords
creator: Kazi Mohammad Khalid Ahsan
rights: Copyright (c) 2025 Kazi Mohammad Khalid Ahsan (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2239
ending page: 2279
doi: 10.63125/sp44qz29

Machine Learning-Enabled Business Intelligence Dashboards for Strategic Decision Support
type: item
description: This study investigated the impact of machine learning-enabled business intelligence dashboards on strategic decision support and organizational performance using a quantitative quasi-experimental design. Data were collected from a sample of 400 professionals, with 38.5% from finance, 24.3% from healthcare, 21.7% from retail, and 15.5% from manufacturing sectors. Among the participants, 57.4% reported using ML-enabled dashboards, while 42.6% relied on traditional BI systems. Descriptive analysis indicated an average professional experience of 6.8 years and a mean dashboard session duration of 18.6 minutes. Regression analysis revealed significant positive relationships between dashboard usage and decision accuracy (β = 0.46, t = 8.72, p < 0.001), decision speed (β = 0.39, t = 7.11, p < 0.001), and perceived effectiveness (β = 0.51, t = 9.34, p < 0.001), with R² values ranging from 0.52 to 0.61. Analysis of variance showed that high-exposure users achieved a mean decision performance score of 4.35 compared to 3.62 for low-exposure users (F = 27.48, η² = 0.29, p < 0.001). Correlation results indicated that dashboard usage frequency was positively associated with decision quality (r = 0.48, p < 0.001) and operational efficiency (r = 0.41, p < 0.001). Structural equation modeling confirmed that perceived usefulness (β = 0.42) and user engagement (β = 0.37) significantly influenced decision quality. Reliability testing showed strong internal consistency with Cronbach’s alpha values between 0.82 and 0.91. These findings demonstrated that ML-enabled dashboards significantly improved decision-making performance and organizational outcomes.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Dashboards
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Analytics
  topic: keywords
creator: Khairum Nahar Pinky
rights: Copyright (c) 2025 Khairum Nahar Pinky (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2202
ending page: 2238
doi: 10.63125/wz608k80

AI-Enabled Decision Support Systems for Industrial Energy Optimization in U.S. Manufacturing
type: item
description: This study addresses the persistent problem of inefficient and reactive energy management in U.S. manufacturing, where fragmented monitoring, delayed reporting, and weak coordination often lead to avoidable energy waste, higher operating costs, and weaker sustainability performance. The purpose of the research was to examine whether AI-enabled decision support systems improve industrial energy optimization by strengthening data-driven decision quality across manufacturing operations. Using a quantitative, cross-sectional, case-based design, the study collected structured questionnaire data from 240 respondents drawn from cloud-enabled and enterprise-oriented manufacturing cases, including plant managers, production supervisors, maintenance engineers, sustainability officers, and data or operations analysts in medium and large firms. The key independent variables were AI-enabled decision support systems capability, predictive analytics capability, and real-time monitoring and automation, while industrial energy optimization served as the main dependent variable, with operational cost reduction and sustainability performance treated as linked outcome variables. Data were analyzed using descriptive statistics, reliability analysis, Pearson correlation, and multiple regression. Findings showed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.81 to 0.88. Descriptive results were highly positive, including mean scores of 4.12 for AI-enabled DSS capability, 4.06 for predictive analytics capability, 4.18 for real-time monitoring and automation, and 4.21 for industrial energy optimization. Correlation analysis indicated significant positive relationships with industrial energy optimization for AI-enabled DSS capability (r = 0.61, p < .01), predictive analytics capability (r = 0.58, p < .01), and real-time monitoring and automation (r = 0.66, p < .01). Regression results further revealed that the model explained 54.7% of the variance …
subject: Articles
  topic: section
subject: AI-Enabled Decision Support Systems
  topic: keywords
subject: Industrial Energy Optimization
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Real-Time Monitoring And Automation
  topic: keywords
subject: U.S. Manufacturing
  topic: keywords
creator: K M Tanvir Anjum Anick
rights: Copyright (c) 2025 K M Tanvir Anjum Anick (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2160
ending page: 2201
doi: 10.63125/8vyhwm46

Quantitative Evaluation of Machine Learning Models for Project Risk Prediction and Resource Optimization in Business Operations
type: item
description: This study addresses the persistent problem that many business projects continue to suffer from schedule delays, budget overruns, resource conflicts, and weak decision quality because organizations often rely on fragmented forecasting methods and subjective judgment instead of data-driven predictive systems. Guided by this problem, the purpose of the study was to quantitatively evaluate whether machine learning model effectiveness improves project risk prediction and resource optimization in business operations, and whether these effects extend to operational efficiency and decision-making quality. The study adopted a quantitative, cross-sectional, case-based design grounded in enterprise project environments, drawing on 250 valid responses from professionals such as project managers, operations managers, business analysts, risk officers, and IT or analytics staff working across IT and digital transformation, operations improvement, construction, finance, and mixed project portfolios. Data were collected using a five-point Likert-scale questionnaire and analyzed through descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression in SPSS. The key variables were machine learning model effectiveness, project risk prediction, resource optimization, operational efficiency, and decision-making quality. The findings showed consistently positive perceptions across all constructs, with mean scores ranging from 3.98 to 4.12, including machine learning model effectiveness (M = 4.12, SD = 0.64), project risk prediction (M = 4.05, SD = 0.68), resource optimization (M = 3.98, SD = 0.71), operational efficiency (M = 4.09, SD = 0.66), and decision-making quality (M = 4.01, SD = 0.69). Reliability was strong, with Cronbach’s alpha values from 0.81 to 0.89. Inferential results confirmed that machine learning model effectiveness significantly predicted project risk prediction (β = .720, R² = .518, p < .001), project risk prediction significantly predicted r…
subject: Articles
  topic: section
subject: Machine Learning Model Effectiveness
  topic: keywords
subject: Project Risk Prediction
  topic: keywords
subject: Resource Optimization
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Decision-Making Quality
  topic: keywords
creator: Kazi Rakib Hasan Saurav
rights: Copyright (c) 2025 Kazi Rakib Hasan Saurav (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2119
ending page: 2159
doi: 10.63125/01bg6n62

Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility
type: item
description: This study examines the growing problem that e-commerce platforms increasingly rely on machine learning based consumer behavior prediction models, yet limited empirical evidence explains whether these systems improve only commercial efficiency or also strengthen digital financial inclusion and market accessibility for consumers. The purpose of the study was therefore to investigate how machine learning based prediction models influence digital financial inclusion, market accessibility, and overall e-commerce platform performance within a quantitative, cross-sectional, case-based research design. Using purposive sampling, data were collected from 312 active online consumers drawn from cloud based and enterprise style e-commerce platform cases who had prior experience with personalized recommendations and digital payment systems. The key variables were machine learning based prediction models as the independent variable, digital financial inclusion and market accessibility as explanatory variables, and e-commerce platform performance as the dependent variable. Data were analyzed through descriptive statistics, reliability testing, correlation analysis, and multiple regression using SPSS. The findings showed consistently positive perceptions across all constructs, with mean scores of 3.94 for machine learning prediction models, 3.87 for digital financial inclusion, 4.01 for market accessibility, and 4.08 for platform performance. Reliability was strong, with Cronbach’s alpha ranging from 0.84 to 0.89 and 0.91 for the full instrument. Correlation analysis revealed significant positive relationships between machine learning prediction and digital financial inclusion (r = .61, p < .001), market accessibility (r = .66, p < .001), and platform performance (r = .72, p < .001). Regression results further showed that machine learning prediction significantly explained 37.2% of the variance in digital financial inclusion (β = .61, p < .001) and 43.6% of the variance in market a…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Consumer Behavior Prediction
  topic: keywords
subject: Digital Financial Inclusion
  topic: keywords
subject: Market Accessibility
  topic: keywords
subject: E-Commerce Platform Performance
  topic: keywords
creator: MD Hasibur Rahman Murad
rights: Copyright (c) 2025 MD Hasibur Rahman Murad (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2078
ending page: 2118
doi: 10.63125/pnz32s94

AI-Driven Real-Time Methane Emissions Monitoring and Predictive Leak Detection Using Lidar and IOT Sensor Fusion in Upstream Oil and Gas Operations
type: item
description: This study addresses the persistent problem of delayed, fragmented, and insufficiently integrated methane leak detection in upstream oil and gas operations, where conventional inspection-based approaches often fail to capture spatially dispersed, intermittent, and high-risk emission events. The purpose of the research was to examine whether an AI-driven real-time methane monitoring framework using LiDAR and IoT sensor fusion can improve monitoring performance, predictive leak detection, trustworthiness, and deployment readiness in upstream environments. The study adopted a quantitative, cross-sectional, case-based design focused on cloud-enabled enterprise monitoring contexts in upstream oil and gas operations, using a purposive sample of 214 valid professional respondents drawn from methane monitoring, operations, compliance, maintenance, and intelligent systems roles. Key variables included LiDAR monitoring capability, IoT sensor fusion efficiency, AI predictive analytics capability, real-time methane monitoring performance, predictive leak detection effectiveness, leak response capability, sensor fusion readiness and trustworthiness, leak detection timeline advantage, and high-risk scenario confidence. Data were collected through a structured 5-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression modeling. The findings showed strong positive evaluations across all core constructs, including AI predictive analytics capability (M = 4.23, SD = 0.58), predictive leak detection effectiveness (M = 4.20, SD = 0.60), LiDAR monitoring capability (M = 4.18, SD = 0.61), and real-time methane monitoring performance (M = 4.15, SD = 0.63). Correlation results indicated significant positive relationships, including LiDAR with real-time monitoring performance (r = .742, p < .001), AI capability with predictive leak detection effectiveness (r = .768, p < .001), and real-time monitoring performa…
subject: Articles
  topic: section
subject: Methane Emissions Monitoring
  topic: keywords
subject: Predictive Leak Detection
  topic: keywords
subject: Lidar
  topic: keywords
subject: IOT Sensor Fusion
  topic: keywords
subject: Upstream Oil and Gas Operations
  topic: keywords
creator: Albert Anokye
rights: Copyright (c) 2025 Albert Anokye (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2035
ending page: 2077
doi: 10.63125/yavd2f86

Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
type: item
description: This study addressed the problem that many organizations deploy AI-driven analytics but still struggle to demonstrate measurable, cross-functional value beyond isolated functional gains, especially across strategic market-entry decisions, digital marketing optimization, and enterprise workflow transformation. The purpose was to test whether AI-Analytics Intensity (AAI), defined as the embeddedness of analytics in routine decision checkpoints and execution controls, predicts (1) Market Entry Strategy Effectiveness (MES), (2) Digital Marketing Optimization (DMO), and (3) Enterprise Workflow Transformation (EWT), and whether these domain outcomes jointly explain overall Performance Impact (PI) within an enterprise case setting. Using a quantitative, cross-sectional, case-study–based design with purposive sampling of analytics-exposed staff across strategy, marketing, operations, and information systems functions, the study analyzed N = 210 valid responses. Key variables were operationalized as five-point Likert composite constructs with strong reliability (AAI α = 0.88; MES α = 0.86; DMO α = 0.84; EWT α = 0.87; PI α = 0.89), supporting construct consistency for hypothesis testing. The analysis plan combined descriptives, Pearson correlations, and multiple regression modeling. Descriptively, respondents reported moderately high analytics embeddedness and outcomes (AAI M = 3.88, SD = 0.62; MES M = 3.74, SD = 0.66; DMO M = 3.92, SD = 0.58; EWT M = 3.81, SD = 0.60; PI M = 3.86, SD = 0.57). Correlations showed strong positive associations between AAI and MES (r = .58), DMO (r = .62), EWT (r = .60), and PI (r = .64), all p < .001, indicating that higher analytics intensity aligns with stronger strategic, marketing, operational, and overall performance outcomes. In the integrated regression predicting performance impact, MES (β = .24, p = .001), DMO (β = .31, p < .001), and EWT (β = .27, p < .001) all remained significant, explaining 52% of PI variance (R² = .52; F (3,206) = …
subject: Articles
  topic: section
subject: AI-Driven Analytics Intensity
  topic: keywords
subject: Market Entry Strategy Effectiveness
  topic: keywords
subject: Digital Marketing Optimization
  topic: keywords
subject: Enterprise Workflow Transformation
  topic: keywords
subject: Performance Impact
  topic: keywords
creator: Md Khaled Hossain
rights: Copyright (c) 2025 Md Khaled Hossain (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1999
ending page: 2034
doi: 10.63125/82gn4y08

Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
type: item
description: This study addresses the persistent problem that conventional occupational safety systems in chemical processing plants often rely on periodic inspection, fixed alarms, and post-incident review, which are insufficient for detecting rapidly evolving hazards in high-risk environments. The purpose of the research was to examine whether smart occupational safety management can be improved through the integration of IoT sensor networks, machine learning, and real-time risk assessment in chemical processing plants using a quantitative, cross-sectional, case-based design. Data were collected from 200 valid respondents drawn from enterprise plant cases, including operations, maintenance, safety and compliance, process control, and supervisory roles, out of 220 distributed questionnaires, yielding a 90.9% valid response rate. The key independent variables were IoT sensor networks, machine learning, and real-time risk assessment, while smart occupational safety management was the dependent variable. Analysis was conducted using SPSS through descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression. The findings showed high mean scores for all core constructs, including IoT sensor networks (M = 4.18, SD = 0.61), machine learning (M = 4.06, SD = 0.67), real-time risk assessment (M = 4.24, SD = 0.58), and smart occupational safety management (M = 4.29, SD = 0.55). Reliability was strong, with Cronbach’s alpha ranging from 0.84 to 0.90 and an overall instrument reliability of 0.91. Correlation results indicated significant positive associations with smart occupational safety management for IoT sensor networks (r = .71, p < .001), machine learning (r = .68, p < .001), and real-time risk assessment (r = .76, p < .001). The regression model was highly significant, F (3,196) = 145.27, p < .001, explaining 69% of the variance (R² = .69), with real-time risk assessment emerging as the strongest predictor (β = .38), followed by IoT sensor…
subject: Articles
  topic: section
subject: IoT Sensor Networks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Real-Time Risk Assessment
  topic: keywords
subject: Occupational Safety Management
  topic: keywords
subject: Chemical Processing Plants
  topic: keywords
creator: Md Abubakar Siddique Akash
creator: Bhanu Prakash Sah
rights: Copyright (c) 2025 Md Abubakar Siddique Akash, Bhanu Prakash Sah (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1958
ending page: 1998
doi: 10.63125/dynnzy25

Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
type: item
description: This study addresses a persistent operational problem in distributed AI and ML: many organizations can scale model training, but still experience reliability and governance breakdowns because training, deployment, and monitoring are implemented as fragmented toolchains rather than an end-to-end lifecycle system. The purpose was to synthesize and compare advanced computing frameworks that support distributed training, deployment, and monitoring, and to quantify which framework patterns most strongly align with dependable production operations. A quantitative, cross-sectional, case-based design was applied via a structured literature review in which each eligible publication was treated as a case instance, spanning cloud and enterprise environments including cloud, on-premises clusters, and hybrid deployments. The sample comprised 32 case instances (n = 32). Key variables included framework category prevalence and Lifecycle Integration Score (LIS), dominant architectural patterns and evidence scores, effectiveness outcomes (scalability, deployment reliability, monitoring, traceability), monitoring-maturity indicators, and unresolved-gap severity. The analysis plan combined deductive and inductive coding with frequency, mean, and cross-tabulation summaries (reported as means, SDs, and shares), supported by spreadsheet tools and SPSS. Headline findings show that training-centric frameworks were most prevalent (11/32, 34.4%) but had lower integration (LIS M = 2.9), while end-to-end lifecycle platforms (8/32, 25.0%) achieved the highest integration (LIS M = 4.3) with 87.5% of cases scoring ≥4. The most common architecture was data-parallel training with collective all-reduce (62.5%), followed by orchestration-first deployment (56.3%) and observability-by-design (50.0%). Cross-sectionally, training scalability effectiveness scored highest (M = 4.1; 65.6% ≥4), while deployment reliability control (M = 3.7) and monitoring effectiveness (M = 3.6) lagged, indicating that opera…
subject: Articles
  topic: section
subject: Distributed AI/ML Frameworks
  topic: keywords
subject: Mlops Lifecycle Integration
  topic: keywords
subject: Distributed Training Scalability
  topic: keywords
subject: Model Monitoring And Observability
  topic: keywords
subject: Governance And Traceability
  topic: keywords
creator: Mohammad Robel Miah
rights: Copyright (c) 2025 Mohammad Robel Miah (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1922
ending page: 1957
doi: 10.63125/rxb2cb66

Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
type: item
description: This study addresses the problem that water supply and sanitation (WASH) assets fail under coupled pressure, corrosion, and sewer environments, while sustainability appraisals often ignore service life and intervention frequency. The purpose was to benchmark, in a quantitative cross-sectional, case-based evidence synthesis, how advanced composite systems and geopolymer or alkali-activated materials compare on functional performance, durability, and lifecycle sustainability for WASH infrastructure decisions. The sample comprised 92 studies spanning water supply assets (n=38), sanitation assets (n=34), and cross-cutting lifecycle or durability assessments (n=20). Key variables were composite water-performance indicators (pressure/hoop integrity, leakage-risk proxy, ring stiffness, joint reliability, and creep resilience), geopolymer sanitation-durability indicators (acid resistance, sulfate resistance, MICC proxy, abrasion tolerance, transport resistance, and strength retention), and sustainability/implementation indicators (GWP per service year, boundary completeness, and decision-fit feasibility). The analysis plan coded each study on a 5-point Likert support scale and summarized results using descriptive statistics, frequency counts, and service-life normalized impact metrics. Headline findings show strong support for composites in water contexts: 78.9% of water-focused studies were supportive (mean=4.21/5, SD=0.73), with average pressure-capacity or stiffness gains of 18–32% and a 45% mean reduction in corrosion-related degradation rates versus conventional comparators. Sanitation evidence favored geopolymers/AAMs: 73.5% supportive (mean=4.08/5, SD=0.81), with 28–41% lower mass loss under acid or sulfate exposure, 72–85% retained compressive strength after acid conditioning (vs 55–68% for OPC), and about 30% lower surface recession in biogenic corrosion simulations. Lifecycle evidence was supportive but boundary-sensitive (70.0% supportive; mean=3.95/5), indicatin…
subject: Articles
  topic: section
subject: Advanced composites
  topic: keywords
subject: Geopolymer
  topic: keywords
subject: Alkali-activated materials
  topic: keywords
subject: Lifecycle assessment
  topic: keywords
subject: WASH infrastructure
  topic: keywords
creator: Palash Chandra Das
rights: Copyright (c) 2025 Palash Chandra Das (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1887
ending page: 1921
doi: 10.63125/z64akr77

Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
type: item
description: This study addressed the persistent problem that many enterprises deploy predictive models and KPI dashboards in parallel, yet fail to convert model outputs into consistent, measurable workflow improvements because predictions are not embedded into governed, actionable decision routines. The purpose was to quantify, across applied enterprise cases, how integrating predictive analytics into KPI dashboards influences workflow performance, and which implementation conditions strengthen or weaken that impact. Using a quantitative, cross-sectional, case-based design, the study synthesized evidence from 52 eligible empirical and applied studies/cases (N = 52) and coded each case on adoption mechanisms, integration pathways, and outcome strength using a five-point Likert evidence scale to support comparable aggregation without overstating causality. Key variables included predictive analytics capabilities (e.g., delay and SLA-risk prediction), dashboard usage and KPI architecture, integration mechanisms (leading-indicator KPIs, threshold alerts, and routing or prioritization), governance conditions, and workflow outcomes (cycle time, SLA adherence, throughput, rework, decision timeliness). The analysis plan combined frequency statistics (cases and percentages), median and range synthesis for outcome deltas, and group comparisons of evidence scores by integration and governance quality. Overall workflow improvement evidence averaged M = 4.12 (SD = 0.71) across outcome-reporting cases (n = 47). Headline findings showed the clearest gains in time and compliance outcomes: cycle time decreased by a median 14% (typical range 8%–25%) and SLA adherence improved by a median +9 percentage points (range +4 to +18), while throughput improved by a median 11% (range 5%–20%) and rework decreased by a median 10% (range 6%–19%). Integration mattered: cases with explicit dashboard integration scored higher for performance improvement (M = 4.28) than cases without it (M = 3.41), and action-o…
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: KPI dashboards
  topic: keywords
subject: Workflow optimization
  topic: keywords
subject: SLA risk prediction
  topic: keywords
subject: KPI governance
  topic: keywords
creator: Tanjina Binte Sohrab
rights: Copyright (c) 2025 Tanjina Binte Sohrab (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1848
ending page: 1886
doi: 10.63125/5x0d1702

QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
type: item
description: This study addresses the persistent challenge of predicting and controlling defects in metal additive manufacturing within an enterprise production environment, where porosity, lack of fusion, cracking, and geometric anomalies can increase scrap, rework, and qualification cost. The purpose was to quantify how technical and socio-technical factors jointly explain defect prediction effectiveness and deployment success, and to benchmark machine-learning models using risk-relevant performance measures. Using a quantitative, cross-sectional, case-based design, the sample combined (a) 162 usable survey responses from an enterprise AM facility workforce (quality engineering 32.1%, process engineering 26.5%, operations 23.5%, automation/data 17.9%; mean experience 6.8 years, SD 3.4) and (b) 312 inspected build or part cases verified via CT and metallography. Key variables included Data Quality, Feature Richness/Monitoring Capability, Process Window Stability, Technology Readiness, Interpretability/Trust, and Technology Acceptance Model constructs (Perceived Usefulness and Perceived Ease of Use), with the outcome measured as Defect Prediction Effectiveness and Deployment Success (Likert 1–5 composites). The analysis plan used descriptive statistics, reliability testing (Cronbach’s α = 0.82–0.90), Pearson correlations, and multiple regression, alongside build-wise model validation comparing Logistic Regression, SVM, Random Forest, and Gradient Boosting. Headline findings showed defects were dominated by porosity (46.8%) and lack of fusion (28.5%), with 12.9% classified as critical; drift-zone builds had higher defect prevalence (19.6%) than stable-zone builds (8.4%). Regression explained substantial outcome variance (R² = 0.61, Adj. R² = 0.59; F(7,154) = 34.78, p < .001), with significant effects for Data Quality (β = 0.19, p = .001), Interpretability/Trust (β = 0.21, p < .001), Perceived Usefulness (β = 0.29, p < .001), and other predictors. Gradient Boosting achieved the be…
subject: Articles
  topic: section
subject: Metal additive manufacturing
  topic: keywords
subject: Defect prediction
  topic: keywords
subject: Technology acceptance model
  topic: keywords
subject: Interpretability and trust
  topic: keywords
subject: Gradient boosting
  topic: keywords
creator: Md. Ashfaq Siddiquee
creator: Ashraful Islam
rights: Copyright (c) 2025 Md. Ashfaq Siddiquee, Ashraful Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1810
ending page: 1847
doi: 10.63125/3fkkwg05

Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
type: item
description: Machine learning systems are increasingly deployed in U.S. regulated decision-support environments where predictive outputs must be accurate, interpretable, secure, privacy-preserving, and auditable. This study advanced an integrated quantitative assurance framework for evaluating explainable and secure machine learning in regulated contexts. Guided by a structured review of 118 peer-reviewed studies, a multi-phase experimental design was implemented incorporating predictive benchmarking, explanation fidelity and stability testing, adversarial robustness assessment, privacy inference evaluation, and human-in-the-loop experimentation. Baseline predictive accuracy across model families averaged 0.84 (SD = 0.04), with ensemble models reaching 0.86. Calibration error decreased from 0.041 in unconstrained models to 0.028 in constrained configurations. Under adversarial simulation, baseline models experienced a 14.2 percentage-point degradation in performance, whereas robustness-enhanced models limited degradation to 6.5 percentage points. Privacy controls reduced membership inference attack success rates from 0.64 (SD = 0.05) to 0.52 (SD = 0.04), with a modest 1.7% reduction in discrimination performance. Explanation fidelity reached 0.92 (SD = 0.02) for intrinsically interpretable models compared to 0.85 (SD = 0.04) for post hoc methods, and explanation stability variance decreased from 0.08 to 0.03 under enhanced configurations. In human-in-the-loop evaluation (N = 312; 6,240 trials), structured explanations increased decision accuracy from 0.72 (SD = 0.09) to 0.79 (SD = 0.07), improved confidence calibration from 0.74 to 0.82, and increased selective override behavior from 18.4% to 24.7%, while response time rose from 34.1 to 40.8 seconds. Regression models explained 34% of variance in decision accuracy and 38% in confidence calibration. Findings demonstrated that integrated evaluation of explain ability, robustness, and privacy produced measurable improvements in pre…
subject: Articles
  topic: section
subject: Explain ability
  topic: keywords
subject: Security
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Robustness
  topic: keywords
subject: Privacy
  topic: keywords
creator: Md Arifur Rahman
creator: B. M. Taslimul Haque
rights: Copyright (c) 2023 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
date: 2023-05-29
publication date: 2023-05-29
starting page: 231
ending page: 273
doi: 10.63125/fmp86e72

AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
type: item
description: This study examined intelligent condition monitoring and fault diagnosis in electrical power and control systems using a quantitative predictive analytics framework grounded in machine learning and construct-based modeling. A total of 268 responses were collected from professionals working in power distribution, industrial control, and power electronics environments, and after data screening 241 responses were retained for analysis, representing an 89.9% retention rate. The final sample was dominated by engineers (38.6%) and maintenance specialists (27.0%), with 73.9% reporting daily interaction with condition monitoring tools. Five constructs were evaluated: Condition Monitoring Effectiveness, Fault Detection Accuracy, Predictive Maintenance Capability, System Integration Quality, and Operational Performance Impact. Descriptive results indicated generally high perceptions of monitoring and diagnostic performance, with construct means ranging from 3.62 to 4.08 on a five-point scale and moderate dispersion (SD range = 0.58–0.72). Distribution diagnostics supported normality, with skewness values between −0.48 and −0.21 and kurtosis between −0.37 and 0.42. Internal consistency reliability was strong across constructs, with Cronbach’s alpha values ranging from .81 to .88 and two items removed during refinement due to weak item-total contribution. Multiple regression analysis was performed to evaluate the predictive influence of the independent constructs on Operational Performance Impact. The regression model was statistically significant, F(4, 236) = 67.84, p < .001, and explained 53.5% of the variance in the dependent construct (R² = .535; adjusted R² = .527), with independence of errors supported (Durbin–Watson = 1.94). Predictive Maintenance Capability demonstrated the strongest effect (β = .34, p < .001), followed by Condition Monitoring Effectiveness (β = .26, p < .001) and System Integration Quality (β = .19, p = .002), while Fault Detection Accuracy produced a …
subject: Articles
  topic: section
subject: Intelligent Monitoring
  topic: keywords
subject: Fault Diagnosis
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Power Systems
  topic: keywords
creator: Shamsul Arifeen
rights: Copyright (c) 2025 Shamsul Arifeen (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1778
ending page: 1809
doi: 10.63125/csjs7238

Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
type: item
description: This study addresses the persistent problem in digital banking that traditional rule-based AML/KYC transaction monitoring generates high alert noise and inconsistent investigative outcomes, increasing compliance cost and residual regulatory exposure. The purpose was to quantify how machine learning enabled transaction monitoring capabilities contribute to suspicious activity detection and perceived compliance risk reduction in a cloud-enabled enterprise digital banking case context. Using a quantitative cross-sectional, case-based design, data were collected via a structured 5-point Likert survey from professionals embedded in AML operations, compliance oversight, KYC/CDD, risk, and AML technology functions (N = 168 valid responses; mean AML experience = 6.2 years, SD = 2.9; role distribution led by transaction monitoring analysts at 42.3%). Key variables included ML Detection Effectiveness (MDE), Data Quality and Feature Readiness (DQF), , Analyst Trust and Adoption (ATA), and three domain indices: Alert Quality and Workload Impact (AQWII), Explainability Audit Readiness and Model Governance Evidence (EARMGE), and Typology Coverage and Adaptability Results (TCAR), with Compliance Risk Reduction (CRR) as the dependent variable. The analysis plan applied descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression to estimate unique predictors of CRR. Results showed strong overall agreement that ML monitoring improved outcomes (CRR M = 4.14, SD = 0.52; MDE M = 4.18, SD = 0.54; AQWII M = 4.12, SD = 0.55), and all constructs met good to excellent internal consistency (α = 0.83–0.91). CRR correlated most strongly with MDE (r = 0.72, p < .001) and AQWII (r = 0.69, p < .001), indicating that detection strength and workload-relevant alert quality moved together with compliance benefit. The regression model was significant and explanatory (R² = 0.68; F(8,159) = 42.11, p < .001), with headline effects from MDE (β = 0.31, p < .…
subject: Articles
  topic: section
subject: AML/KYC
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Transaction Monitoring
  topic: keywords
subject: Suspicious Activity Detection
  topic: keywords
subject: Compliance Risk Reduction
  topic: keywords
creator: Sazzadul Islam
rights: Copyright (c) 2025 Sazzadul Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1740
ending page: 1775
doi: 10.63125/r9c8q813

AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
type: item
description: This study examined how underserved communities experience moderate to low and uneven access to essential goods due to last-mile distribution planning that is constrained by unreliable data, partial workflow embedding, and volatile operating conditions. The purpose was to quantify how AI-driven distribution planning capability improves access optimization within cloud and enterprise distribution contexts, while accounting for technology, organizational, and environmental conditions. Using a quantitative, cross-sectional, case-based design, a structured five-point Likert survey captured system-level perceptions from 312 valid respondents (78.0% usable) out of 400 invited and 320 completed, spanning planners/coordinators (22.4%), field and delivery staff (27.6%), retail or community distribution points (18.9%), NGO or partner actors (11.5%), and beneficiaries (19.6%). Access Optimization (AO) was modeled as the outcome, with AI Planning Capability (AIPC), Data Quality and Integration (DQI), Organizational Readiness and Embeddedness (ORG_READY), Environmental Support (ENV_SUPPORT), and Constraint Severity (CONSTRAINTS) as predictors. The analysis included reliability testing, descriptive statistics, Pearson correlations, and multiple regression with collinearity diagnostics. Measurement reliability was strong across constructs (AO α = .91; AIPC α = .88; DQI α = .86; ORG_READY α = .89; ENV_SUPPORT α = .82; CONSTRAINTS α = .79). Baseline access was constrained (AO M = 2.84, SD = 0.76) alongside high operational constraints (M = 3.74, SD = 0.62), moderate AI capability (AIPC M = 3.27, SD = 0.71), and moderate organizational readiness (M = 3.12, SD = 0.69). Equity profiling revealed meaningful disparity, with the lowest access cluster reporting AO M = 2.41 versus 3.18 in the highest access cluster (gap = 0.77; inequity index = 0.21). Bivariate results aligned with the conceptual model, showing positive associations between AO and AIPC (r = .52), DQI (r = .47), ORG_READY (r…
subject: Articles
  topic: section
subject: AI-Driven Distribution Planning
  topic: keywords
subject: Access Optimization
  topic: keywords
subject: Underserved Communities
  topic: keywords
subject: TOE Framework
  topic: keywords
subject: Data Quality and Organizational Readiness
  topic: keywords
creator: Md Shahab Uddin
rights: Copyright (c) 2025 Md Shahab Uddin (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1700
ending page: 1739
doi: 10.63125/chv6qf37

Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
type: item
description: This study addresses a persistent challenge in financial and investment organizations where fragmented reporting, delayed reconciliation, and inconsistent risk definitions weaken portfolio oversight and slow decision-making. The purpose was to test whether business intelligence driven risk assessment capability (BIRA) improves portfolio performance analytics effectiveness (PPAE) and decision value in enterprise cloud analytics settings. A quantitative cross-sectional, case-based design surveyed N = 180 professionals across portfolio, risk, BI, and governance functions in an institutional portfolio case (mean experience 7.6 years, SD = 3.4). The dependent construct was PPAE, while five BIRA dimensions served as predictors: Integration and Timeliness (INT), Governance and Definition Stability (GOV), Automation and Consistency (AUTO), Usability and Interpretability (USE), and Cross-risk Coverage (XRISK). Decision outcomes were captured via the BI Risk-Portfolio Readiness Index (BRPRI), Decision Confidence Score (DCS), and Decision Timeliness Score (DTS). The analysis plan used data screening and descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling. Results indicated moderate-to-high levels (BIRA M = 3.78, SD = 0.62; PPAE M = 3.71, SD = 0.65) with strong reliability (α = 0.89 for BIRA; α = 0.87 for PPAE). BIRA was strongly associated with PPAE (r = 0.68, p < .001), and the regression model explained 52% of PPAE variance (R² = 0.52; Adjusted R² = 0.50; F (5,174) = 38.1; p < .001). GOV (β = 0.29, p < .001) and INT (β = 0.25, p < .001) were the strongest predictors, followed by AUTO (β = 0.19, p = .002) and USE (β = 0.12, p = .041), while XRISK was not statistically decisive (β = 0.08, p = .118). Readiness results showed BRPRI M = 3.77 (SD = 0.58) with 55.0% high readiness, 38.9% moderate, and 6.1% low. Higher capability aligned with stronger decision confidence (DCS M = 3.74, SD = 0.67; r = 0.63, p < .001) …
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Risk Assessment Capability
  topic: keywords
subject: Portfolio Performance Analytics
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Decision Confidence and Timeliness
  topic: keywords
creator: Shamsunnahar Chadni
rights: Copyright (c) 2025 Shamsunnahar Chadni (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1668
ending page: 1699
doi: 10.63125/827e2c29

Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk
type: item
description: Financial vulnerability embedded within global supply chains represents a significant source of trade instability and investment risk in highly interconnected economies. This study quantitatively examined how exposure-weighted financial vulnerability across global supply chain networks was associated with U.S. trade stability and sector-level investment risk. The study was informed by a comprehensive review of more than 120 peer-reviewed articles and empirical studies, which guided construct development and analytical design. The empirical analysis utilized 1,000 sector-level panel observations, of which 61.2% were drawn from manufacturing sectors and 38.8% from non-manufacturing sectors, spanning multiple consecutive years. Descriptive results showed a mean financial vulnerability index of 0.47 (SD = 0.18), a mean trade stability index of 0.62 (SD = 0.21), and a mean investment risk index of 0.53 (SD = 0.24), indicating substantial dispersion across sectors. Correlation analysis revealed a negative association between financial vulnerability and trade stability (r = −0.41) and a positive association between financial vulnerability and investment risk (r = 0.48). Panel regression results demonstrated that exposure-weighted financial vulnerability was significantly associated with lower trade stability (β = −0.372, p < 0.001) and higher investment risk (β = 0.418, p < 0.001), controlling for industry size, trade intensity, and time effects. The trade stability model explained 46% of the variance in trade flow variability, while the investment risk model explained 49% of performance variability. Reliability analysis confirmed strong internal consistency for all composite constructs, with Cronbach’s alpha values ranging from 0.81 to 0.86. Overall, the findings quantitatively demonstrated that financial vulnerability embedded within global supply chains is a measurable and influential determinant of U.S. trade stability and investment risk.
subject: Articles
  topic: section
subject: Financial Vulnerability
  topic: keywords
subject: Global Supply Chains
  topic: keywords
subject: Trade Stability
  topic: keywords
subject: Investment Risk
  topic: keywords
creator: Mostafa Kamal
rights: Copyright (c) 2025 Mostafa Kamal (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1636
ending page: 1667
doi: 10.63125/42rd4x66