ASRC Procedia: Global Perspectives in Science and Scholarship
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.
Impact Of Random Forest and Ensemble Methods on Infection Trend Forecasting: A Quantitative Evaluation Using Global Post Covid-19 Data
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.
Impact of Zero Trust Architecture on Stock Exchange Network Security: A Quantitative Evaluation of Access Control and Incident Reduction
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 authentication accuracy, reducing incident frequency, and increasing operational responsiveness.
Impact of Automated Server and Database Monitoring Systems on ATM Network Uptime: A Quantitative Evaluation
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.
Copyright (c) 2025 Taru Binte Amin (Author)
AI-Assisted Medical Records Management and EHR Workflow Optimization for Community Health Centres Serving Immigrant Populations
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), and service delivery quality (r = 0.71, p < .001). Regression results further confirmed that AI-assisted medical records management significantly predicted EHR workflow optimization (β = 0.59, p < .001), while the overall model explained 46.2% of workflow variance (R² = 0.462, F(4,195) = 41.82, p < .001). The study implies that AI-enabled documentation systems can strengthen workflow efficiency, multilingual recordkeeping, and equitable service delivery in immigrant-focused community healthcare settings.
Copyright (c) 2025 Mst Kaniz Fatema (Author)
Advanced Engineering Materials Applications for Enhancing Durability and Lifecycle Performance of Steel Building Systems
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 = 0.680, p < .01), and durability and lifecycle performance (r = 0.740, p < .01). Regression results showed that AEM explained 50.4% of durability variance and 46.2% of lifecycle performance variance, while durability explained 54.8% of lifecycle performance variance. The combined model explained 61.8% of lifecycle performance, with durability emerging as the stronger predictor (β = 0.510, p < .001) than AEM (β = 0.290, p = .002). Advanced protective coatings ranked highest in perceived effectiveness (M = 4.27), followed by corrosion-resistant steel grades (M = 4.19). The findings imply that advanced materials should be treated as strategic lifecycle assets that preserve structural reliability, reduce maintenance burden, extend service life, and support sustainable steel construction decision-making.
Copyright (c) 2025 Md. Mainuddin Faisal (Author)
A Quantitative Assessment of AI-Driven Predictive Analytics for Economic Development Decision Support in U.S. Public Policy Centers
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 = .794, R² = .630, F = 91.84, p < .001, indicating that analytics capability explained 63.0% of the variance in decision-support effectiveness. Forecasting capability was the strongest predictor, β = .340, followed by data integration, β = .280. The study implies that U.S. public policy centers should strengthen predictive forecasting, integrated data infrastructures, and real-time insight systems to improve evidence-based economic development planning.
Impact of Digital Twin Technology on Predictive Maintenance and Asset Lifecycle Management in Energy Infrastructure: A Quantitative Evaluation
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 supports more efficient asset lifecycle management in energy infrastructure systems.
Copyright (c) 2025 Md Asif Ali Sheak Arju (Author)
Impact of Predictive Analytics and Ensemble Learning on Operational Efficiency and KPI Forecasting in U.S. Engineering Firms
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.
Copyright (c) 2025 Md Aminul Islam (Author)
Impact of SCADA-GIS Integration on Real-Time Water Distribution Monitoring: A Quantitative Evaluation of Smart Utility Infrastructure
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-operational visibility (r = 0.76, p = 0.000), and infrastructure response readiness (r = 0.64, p = 0.000). Regression analysis further showed that SCADA-GIS integration significantly predicted monitoring effectiveness (R² = 0.52, β = 0.72, F = 229.84, p = 0.000). The study therefore concludes that SCADA-GIS integration is a strategic digital capability that improves visibility, accuracy, and response readiness in modern water utility operations, with important implications for smart infrastructure investment, operational coordination, and digital transformation planning.
Copyright (c) 2025 Kazi Mohammad Khalid Ahsan (Author)
Machine Learning-Enabled Business Intelligence Dashboards for Strategic Decision Support
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.
Copyright (c) 2025 Khairum Nahar Pinky (Author)
AI-Enabled Decision Support Systems for Industrial Energy Optimization in U.S. Manufacturing
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 in industrial energy optimization (R² = 0.547; F = 95.140; p < .001), with real-time monitoring and automation emerging as the strongest predictor (β = 0.370), followed by AI-enabled DSS capability (β = 0.290) and predictive analytics capability (β = 0.240). The study implies that manufacturers can achieve better energy efficiency, cost control, and sustainability outcomes by integrating intelligent decision support, predictive analytics, and real-time automated monitoring into operational routines.
Copyright (c) 2025 K M Tanvir Anjum Anick (Author)
Quantitative Evaluation of Machine Learning Models for Project Risk Prediction and Resource Optimization in Business Operations
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 resource optimization (β = .690, R² = .476, p < .001), and machine learning model effectiveness, project risk prediction, and resource optimization jointly predicted operational efficiency (Adjusted R² = .455), while resource optimization significantly predicted decision-making quality (β = .630, R² = .397, p < .001). The study implies that organizations should treat machine learning not merely as a technical tool but as a strategic capability for improving predictive control, resource deployment, and operational performance in enterprise project settings.
Copyright (c) 2025 Kazi Rakib Hasan Saurav (Author)
Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility
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 accessibility (β = .66, p < .001), while the combined model explained 58.4% of the variance in platform performance. The study implies that predictive analytics in e-commerce should be designed not only for conversion and profitability, but also for inclusive access, payment readiness, and equitable participation in digital markets.
Copyright (c) 2025 MD Hasibur Rahman Murad (Author)
AI-Driven Real-Time Methane Emissions Monitoring and Predictive Leak Detection Using Lidar and IOT Sensor Fusion in Upstream Oil and Gas Operations
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 performance with leak response capability (r = .733, p < .001). Regression analysis further showed that the predictors explained 67.4% of the variance in real-time monitoring performance (R² = .674) and 71.6% of the variance in predictive leak detection effectiveness (R² = .716), with AI emerging as the strongest predictor of predictive leak detection (β = .389, p < .001). The study implies that integrating AI, LiDAR, and IoT sensor fusion can provide a more proactive, trustworthy, and operationally deployable methane surveillance architecture for upstream oil and gas operations.
Copyright (c) 2025 Albert Anokye (Author)
Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
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) = 74.3; p < .001), with DMO the strongest unique predictor. A supporting traceability analysis further clarified mechanisms: personalization/targeting analytics most strongly predicted DMO (β = .34, p < .001), while automation/decision-rule enablement most strongly predicted EWT (β = .33, p < .001).
Copyright (c) 2025 Md Khaled Hossain (Author)
Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
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 networks (β = .31) and machine learning (β = .27). The study implies that chemical plants should adopt integrated, data-driven safety architectures to strengthen hazard detection, emergency readiness, and reliability-oriented plant safety performance.
Copyright (c) 2025 Md Abubakar Siddique Akash, Bhanu Prakash Sah (Author)
Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
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 operational dependability remains constrained by lifecycle linkages. Monitoring maturity was strongest for service observability (p95/p99 latency and tracing, M = 3.9) but weaker for label-scarce performance degradation tracking (M = 3.4). The most severe gaps were interoperability fragmentation (M = 4.3; 75.0% ≥4) and unclear incident ownership (M = 4.2; 71.9% ≥4), implying that organizations should prioritize integrated platforms with traceability, governance linkage, and label-scarce monitoring proxies to translate scaling gains into stable operations.
Copyright (c) 2025 Mohammad Robel Miah (Author)
Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
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), indicating an average 22% reduction in GWP per service year for geopolymer pathways and a 17% reduction in cumulative emissions for composite rehabilitation versus full replacement, while decision-fit scores were 4.12/5 for composites and 3.89/5 for geopolymers. These findings imply that utilities should match material choice to exposure regime, require QA for joints and curing, and adopt impact-per-service-year criteria in procurement.
Copyright (c) 2025 Palash Chandra Das (Author)
Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
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-oriented dashboards outperformed descriptive dashboards (median cycle-time reduction 16% vs 9%). Practical implications are that organizations should treat predictive indicators as governed KPIs with clear ownership, refresh cadence, thresholds, and intervention playbooks, while addressing top barriers such as data-quality inconsistency (57.7% of cases; negative impact M = 4.35) and system integration gaps (50.0%; negative impact M = 4.21).
Copyright (c) 2025 Tanjina Binte Sohrab (Author)
QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
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 best predictive performance (Accuracy 0.89, F1 0.82, ROC-AUC 0.92) and the lowest false negative rate (10.8%), supporting risk-aware deployment. Implications are that enterprise defect prediction success depends not only on algorithms, but also on robust data governance, monitoring coverage, stable process windows, and explainable outputs that strengthen trust and workflow adoption.
Copyright (c) 2025 Md. Ashfaq Siddiquee, Ashraful Islam (Author)
Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
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 predictive validity, interpretability reliability, security resilience, and human decision performance within regulated systems.
Copyright (c) 2023 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
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 smaller but significant contribution (β = .14, p = .018). Multicollinearity was not problematic (VIF range = 1.31–1.58). Overall, the findings confirmed that operational performance outcomes were significantly associated with the effectiveness of monitoring, diagnostic accuracy, predictive maintenance capability, and integration quality, providing quantitative evidence that structured machine learning–driven condition monitoring systems contribute meaningfully to measurable operational performance improvement in electrical power and control environments.
Copyright (c) 2025 Shamsul Arifeen (Author)
Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
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 < .001) and AQWII (β = 0.23, p < .001), alongside meaningful contributions from ATA (β = 0.18, p = .002), DQF (β = 0.15, p = .007), SIA (β = 0.14, p = .015), MET (β = 0.12, p = .029), EARMGE (β = 0.13, p = .018), and TCAR (β = 0.11, p = .042).
Copyright (c) 2025 Sazzadul Islam (Author)
AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
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 = .55), and ENV_SUPPORT (r = .32), and a negative association with CONSTRAINTS (r = −.41). The regression model was significant and explanatory (R² = .45; Adj. R² = .44; F (5,306) = 49.8, p < .001; VIF 1.22–2.05), with ORG_READY as the strongest predictor (β = .29), followed by AIPC (β = .24) and DQI (β = .18). Results indicate that AI-enabled planning can improve access, but realized gains depend on data integration, organizational embedding, and targeted interventions to narrow subarea gaps under persistent constraints.
Data Quality and Organizational Readiness
Copyright (c) 2025 Md Shahab Uddin (Author)
Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
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) and faster decisions (DTS M = 3.66, SD = 0.69; r = 0.58, p < .001). The findings imply that organizations should prioritize governed, stable definitions and timely integration, then reinforce automation and interpretability, and institutionalize BRPRI-based maturity tracking to sustain risk-informed portfolio management.
Copyright (c) 2025 Shamsunnahar Chadni (Author)
Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk
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.
Copyright (c) 2025 Mostafa Kamal (Author)
UAV-Based Hyperspectral and Thermal Signature Analytics for Early Detection of Soil Moisture Stress, Erosion Hotspots, and Flood Susceptibility
This study addresses the problem that soil moisture stress, erosion initiation, and localized flood exposure emerge at micro-scales that routine monitoring misses, delaying mitigation. The purpose was to test whether UAV-based hyperspectral and thermal signature analytics can deliver defensible early detection of soil moisture stress and improve erosion hotspot and flood susceptibility mapping using a quantitative, cross-sectional, case-based design. Across cloud and enterprise GIS deployment cases, the sample included 210 spatial zones within the case-study area, with five-point Likert composites for Soil Moisture Stress (SMS), Erosion Hotspot Concern (EHC), and Flood Susceptibility Perception (FSP). Key predictors were a hyperspectral moisture proxy (HSI_MoistureIndex), local thermal anomaly (TH_Anomaly), and terrain and hydrologic controls (slope, flow accumulation, distance to channel, and elevation). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, multivariate regression with sensor-fusion model comparison, risk-class summaries, hotspot agreement (Jaccard), and robustness checks. Results indicated moderate-to-high perceived risk (SMS M=3.62, SD=0.71; EHC M=3.48, SD=0.76; FSP M=3.55, SD=0.74) and strong construct reliability (alpha=0.86, 0.83, and 0.88). SMS increased with TH_Anomaly (r=0.58, p<.001) and decreased with HSI_MoistureIndex (r=-0.52, p<.001). Regression confirmed a fusion gain: the fused stress model achieved Adj. R²=0.58 and RMSE=0.44, outperforming hyperspectral-only (Adj. R²=0.42, RMSE=0.56) and thermal-only (Adj. R²=0.46, RMSE=0.53), with significant effects for TH_Anomaly (beta=0.41) and HSI_MoistureIndex (beta=-0.36). Erosion modeling achieved Adj. R²=0.54 with flow accumulation and slope as primary drivers, and flood susceptibility improved from Adj. R²=0.55 (terrain only) to 0.62 when thermal information was added. Spatial validation showed strong agreement for stress versus thermal hotspots (68% overlap; J=0.52) and flood susceptibility versus flow corridors (71% overlap; J=0.55), while sensitivity tests reduced the fused model from Adj. R²=0.58 to 0.49 without TH_Anomaly and to 0.50 without HSI_MoistureIndex. High-risk classes covered 22 to 28% of zones and consistently aligned with Likert means above 4.0. Implications are that sensor-fusion UAV analytics, operationalized through cloud and enterprise workflows, can support earlier irrigation triage, prioritized erosion control in convergent high-slope zones, and more defensible flood preparedness zoning with auditable quantitative evidence.
Erosion And Flood Susceptibility Mapping
Copyright (c) 2025 Ratul Debnath (Author)
Quantitative Assessment of Predictive Analytics for Risk Management in U.S. Healthcare Finance Systems
This study addresses the problem that U.S. healthcare finance workflows on cloud and enterprise platforms face denial, improper payment, and audit exposure risk, while predictive analytics is not always embedded into control decisions. The purpose was to test whether Predictive Analytics Capability (PAC) improves Risk Management Effectiveness (RME) and how Data Quality and Integration (DQI), Governance and Compliance Readiness (GCR), and Actionability (ACT) contribute. A quantitative cross sectional, case-based design surveyed an enterprise revenue cycle and payment integrity environment, producing N = 214 usable responses (89.2% completion). Measures were 1 to 5 Likert composites with strong reliability (Cronbach’s alpha: PAC .88, DQI .84, GCR .86, ACT .85, RME .90). Descriptives indicated high PAC (M = 3.74) and high RME (M = 3.81), alongside moderate data integration readiness (DQI M = 3.46). The analysis plan used descriptive statistics, Pearson correlations, and hierarchical multiple regression with role controls. All predictors were positively associated with RME (PAC r = .62; DQI r = .49; GCR r = .53; ACT r = .58; all p < .001). In regression, adding PAC increased explained variance by ΔR² = .33 (total R² = .39) with a large effect (β = .58, p < .001). The final model explained 51% of variance (R² = .51) and retained independent effects for PAC (β = .31, p < .001), ACT (β = .29, p < .001), GCR (β = .18, p = .006), and DQI (β = .12, p = .041). A risk value map showed the strongest analytics impact in denials and rework (M = 3.94) and fraud or waste or abuse screening (M = 3.86). Pipeline reliability was moderate (PRR M = 3.44) and separated outcomes (mean RME 3.28 at low PRR versus 4.09 at high PRR). Implications are that risk leaders should prioritize workflow actionability, governance traceability, and cross system linkage, and strengthen outcome tracking so predictive signals translate into sustained risk reduction in practice.
Copyright (c) 2025 Rifat Chowdhury (Author)
Integrating Smart Sensor Systems and Digital Safety Dashboards for Real-Time Hazard Monitoring in High-Risk Industrial Facilities
Copyright (c) 2025 Jahangir Shekh (Author)
Computational Modeling of Failure Mechanisms in Mechanical Systems: Applications For Energy and Industrial Sectors
This study examined computational modeling of failure mechanisms in mechanical systems operating within energy and industrial sector applications using a quantitative, explanatory case study design. A panel dataset comprising 412 component-level units embedded within 168 mechanical assets was analyzed across 9,214 time-indexed observation windows spanning an average observation horizon of 17.2 months. Failure severity was operationalized as a continuous composite outcome to preserve degradation gradients rather than binary failure states. Descriptive analysis showed that failure severity exhibited strong right-skewness, with a median value of 0.61 and a 90th percentile of 1.82, indicating concentration of degradation within a limited subset of operating windows. Multivariable mixed-effects regression was applied to quantify the influence of operational exposure and degradation constructs while accounting for asset-level heterogeneity. The final model achieved a marginal R² of 0.48 and a conditional R² of 0.62, confirming substantial explanatory power from both fixed effects and asset-level random effects. Condition monitoring indicators demonstrated the strongest association with failure severity (β = 0.356, p < 0.001), followed by cyclic loading (β = 0.287, p < 0.001) and thermal exposure intensity (β = 0.214, p < 0.001). Contact stress (β = 0.169, p < 0.001) and time-under-load (β = 0.132, p < 0.001) also retained statistically significant effects. Operational regime analysis showed that continuous-duty components exhibited higher severity than intermittent-duty units (β = 0.118, p < 0.001), while cyclic-duty operation also increased severity (β = 0.064, p = 0.040). Interaction testing revealed amplification effects for combined thermal exposure and cyclic loading (β = 0.072, p = 0.013), indicating non-additive degradation behavior. Robustness checks demonstrated coefficient stability within ±5% under alternative specifications, and time-based holdout validation resulted in a modest 6.2% increase in prediction error. Overall, the findings established a statistically stable and interpretable framework linking monitoring-derived degradation signals and core exposure mechanisms to continuous failure severity within operational mechanical systems.
Copyright (c) 2023 Efat Ara Haque (Author)