American Journal of Interdisciplinary Studies
The American Journal of Interdisciplinary Studies (AJIS) (ISSN 3067-5146) is a distinguished, peer-reviewed, open-access international journal dedicated to fostering a global platform for scholars, academicians, researchers, and professionals from diverse disciplines. The journal serves as a bridge for interdisciplinary research, encouraging the exchange of knowledge and innovative ideas that transcend traditional academic boundaries. By publishing high-quality, original research articles and critical reviews, AJIS contributes significantly to various domains, including but not limited to Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering. Recognizing the ever-evolving landscape of research and scholarship, AJIS maintains rigorous editorial and peer-review standards to ensure that every publication upholds the highest levels of academic integrity, originality, and scholarly contribution. The journal is committed to promoting intellectual discourse, evidence-based research, and theoretical advancements that have practical implications across multiple sectors.
Enterprise-Scale Risk Decision Intelligence Systems Using Large Language Models (LLMs) For Automated Governance, Compliance, and Policy Enforcement
This study investigated enterprise-scale risk decision intelligence systems using Large Language Models (LLMs) for automated governance, compliance, and policy enforcement within digitally integrated enterprise environments. The research examined the influence of LLM capability integration, governance automation intensity, compliance intelligence capability, explainable artificial intelligence implementation, cybersecurity readiness, and data governance maturity on governance performance, compliance accuracy, operational resilience, organizational transparency, decision-making efficiency, and institutional accountability. A quantitative cross-sectional research design was adopted, and data were collected from 400 governance professionals, compliance officers, cybersecurity analysts, enterprise risk managers, and operational executives employed in medium-sized and large enterprises across finance, healthcare, information technology, telecommunications, logistics, manufacturing, and insurance sectors. Statistical analysis was conducted using SPSS and SmartPLS software packages through descriptive statistics, reliability testing, correlation analysis, multiple regression analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The findings demonstrated that governance performance achieved the highest mean score (M = 4.31, SD = 0.63), followed by compliance accuracy (M = 4.27, SD = 0.66) and governance automation intensity (M = 4.26, SD = 0.65), indicating strong organizational adoption of AI-enabled governance infrastructures. Regression and Structural Equation Modeling findings revealed statistically significant positive relationships between governance variables and organizational performance outcomes. Cybersecurity readiness demonstrated the strongest predictive influence on operational resilience (β = 0.44, p < 0.001), while LLM capability integration significantly influenced governance performance (β = 0.41, p < 0.001). Compliance intelligence capability also positively predicted decision-making efficiency (β = 0.40, p < 0.001), whereas explainable AI implementation significantly strengthened organizational transparency (β = 0.35, p < 0.01). Reliability analysis further confirmed strong internal consistency across all constructs, with Cronbach’s alpha values ranging from 0.87 to 0.93. The findings demonstrated that enterprise-scale LLM governance systems significantly improved governance coordination, compliance reliability, institutional accountability, operational resilience, and predictive governance intelligence across digitally interconnected enterprise ecosystems.
Copyright (c) 2025 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
A Meta-Analysis of Deep Reinforcement Learning for Dynamic Project Scheduling in Engineering Systems
This study conducted a quantitative meta-analysis to evaluate the effectiveness of deep reinforcement learning (DRL) for dynamic project scheduling in engineering systems. The analysis synthesized data from 52 empirical studies across multiple domains, including manufacturing (38.5%), construction (23.1%), logistics (17.3%), and infrastructure systems (11.5%). The findings demonstrated that DRL-based scheduling models significantly outperformed traditional deterministic, heuristic, and classical reinforcement learning approaches across key performance indicators. The aggregated results indicated an average makespan reduction of 18.7%, resource utilization improvement of 14.2%, cost efficiency gain of 11.6%, tardiness reduction of 15.3%, and throughput improvement of 12.8%. Statistical analysis confirmed that these improvements were significant, with 84.6% of studies reporting p-values below 0.05. Effect size evaluation showed moderate to large effects, with makespan reduction achieving a standardized mean difference of 0.91 and resource utilization 0.84, indicating strong practical significance. Subgroup analysis revealed that hybrid DRL models achieved the highest overall improvement (21.5%), followed by Actor-Critic (16.8%), Deep Q-Network (17.0%), and Policy Gradient approaches (14.0%). Domain-specific results indicated more consistent improvements in manufacturing systems, while construction and infrastructure projects showed higher variability due to increased uncertainty. Heterogeneity analysis produced an I² value of 61.3%, reflecting moderate to high variability across studies, while meta-regression indicated that dataset size, domain, and algorithm type explained 47.8% of the variance in outcomes. Visual analysis supported these findings, showing consistent positive effect distributions and minimal publication bias. Overall, the study provided robust quantitative evidence that DRL-based scheduling models enhance efficiency, adaptability, and performance in complex engineering environments, particularly under dynamic and uncertain conditions.
Copyright (c) 2026 Chapal Barua (Author)
AI-Driven Accounts Payable and Receivable Automation for Operational Risk Mitigation in U.S. SMEs: A Systematic Review (2018–2026)
This study examined the problem of operational risk exposure in U.S. small and medium-sized enterprises caused by manual, fragmented, or weakly integrated accounts payable and accounts receivable workflows. The purpose was to determine whether AI-driven AP/AR automation reduces risks related to invoice errors, duplicate payments, delayed collections, reconciliation mismatches, fraud exposure, weak audit trails, and limited cash flow visibility. Using a quantitative, cross-sectional, case-based design, the study collected survey data from 220 valid respondents drawn from cloud-based and enterprise financial automation cases in U.S. SMEs, including SME owners, finance managers, accountants, bookkeepers, controllers, operations managers, and AP/AR staff. The key variables were accounts payable automation, accounts receivable automation, AI-enabled invoice processing, AI-enabled reconciliation, AI-based fraud detection, cash flow visibility, AI automation maturity, and operational risk mitigation. Data were analyzed using descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, multiple regression, and hypotheses testing at the 0.05 significance level. The findings showed high to very high agreement that AI automation improves financial control, with operational risk mitigation recording a mean of 4.27, AI-enabled reconciliation 4.31, fraud detection 4.24, invoice processing 4.20, AP automation 4.18, AR automation 4.12, cash flow visibility 4.16, and AI automation maturity 3.98. Reliability was strong, with an overall Cronbach’s Alpha of 0.91. Correlation results confirmed significant positive relationships between all automation variables and operational risk mitigation, with reconciliation showing the strongest association, r = 0.72, p < 0.001, followed by fraud detection, r = 0.70, p < 0.001. Regression analysis showed that the model explained 65.8% of the variance in operational risk mitigation, R² = 0.658, F (7, 212) = 58.47, p < 0.001. AI-enabled reconciliation was the strongest predictor, β = 0.24, followed by fraud detection, β = 0.21. The study implies that AI-driven AP/AR automation should be treated as a strategic financial control mechanism for improving accuracy, transparency, liquidity awareness, fraud prevention, and operational resilience in U.S. SMEs.
Copyright (c) 2026 Mst Shurovi Akter (Author)
Impact of IOT-Based Energy Monitoring Systems on Operational Efficiency in Industrial Facilities: A Qualitative Evaluation
This study examined the impact of IoT-based energy monitoring systems on operational efficiency in industrial facilities using a quantitative quasi-experimental and longitudinal design. The research analyzed data collected from 12 industrial facilities and 48 production units over a 12-month period, comparing pre-implementation and post-implementation performance indicators. Key variables included energy consumption, production output, equipment utilization, and downtime frequency. The findings revealed a significant reduction in average energy consumption from 15,300 kWh to 13,200 kWh, representing a decrease of approximately 13.7%. At the same time, production output increased from 8,450 units to 9,720 units, reflecting a growth of 15.0%. Equipment utilization improved from 68.5% to 79.2%, while downtime frequency declined from 14.3 hours to 8.7 hours per month, indicating a reduction of nearly 39%. Statistical analysis confirmed that these improvements were significant, with strong effect sizes observed across all variables. Regression results showed that IoT-related factors, including data frequency and system integration, accounted for over 60% of the variance in operational efficiency outcomes. Facilities with higher system integration demonstrated greater improvements, achieving up to 18.6% energy reduction and 19.8% production increase. Time-series analysis further indicated that efficiency gains were gradual and sustained rather than immediate, reflecting stable performance improvements over the study period. The results demonstrated that IoT-based energy monitoring systems contributed to enhanced operational efficiency by improving real-time visibility, enabling faster decision-making, and optimizing resource utilization. Variations across sub-groups highlighted the influence of contextual factors such as production scale and energy intensity, with high-energy-consuming processes showing greater efficiency gains. Overall, the study provided robust quantitative evidence that IoT-based monitoring systems significantly improved industrial performance and operational stability across multiple facilities.
Copyright (c) 2022 Md Aminul Islam, Mst Shamima Akter (Author)
Proactive Server Monitoring and Threat Assessment on Uptime in Financial Trading Systems: A Qualitative Evaluation
This study examined the impact of proactive server monitoring and threat assessment on uptime performance in financial trading systems, addressing the problem that many trading platforms still experience service disruption due to weak server visibility, delayed threat detection, poor alerting, and limited response readiness. The purpose was to quantify how preventive infrastructure and cybersecurity practices support continuous availability in cloud-based and enterprise financial trading environments. A quantitative, cross-sectional, case-based design was used, with data collected from 200 professionals across banks, brokerage firms, fintech companies, investment/capital market institutions, and technology service providers. The key variables were proactive server monitoring, threat assessment, real-time alerting, incident response readiness, and uptime performance. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple regression, and an Uptime Resilience Index. The findings showed high agreement for all major constructs: proactive server monitoring recorded a mean of 4.18, threat assessment 4.11, real-time alerting 4.23, incident response readiness 4.05, and uptime performance 4.20. Reliability was strong, with Cronbach’s alpha values ranging from 0.82 to 0.88 and an overall scale reliability of 0.91. Correlation results confirmed significant positive relationships between uptime performance and proactive server monitoring (r = 0.68), threat assessment (r = 0.63), real-time alerting (r = 0.71), and incident response readiness (r = 0.59), all at p < 0.01. Regression analysis showed that the model explained 62.8% of the variance in uptime performance, R² = 0.628, F (4,195) = 82.46, p < 0.001. Real-time alerting was the strongest predictor (β = 0.31), followed by proactive monitoring (β = 0.27), threat assessment (β = 0.22), and incident response readiness (β = 0.18). The Uptime Resilience Index was 4.15, indicating high resilience. The study implies that financial institutions should integrate monitoring dashboards, automated alerts, threat intelligence, vulnerability assessment, and response playbooks to reduce downtime and strengthen operational resilience.
Copyright (c) 2022 Binayan Dey, Md. Shakhawat Hossen (Author)
DinSAR AND Remote Sensing–Based Predictive Modeling of Ground Subsidence Induced by Mineral Extraction: Implications for Environmental Risk Mitigation and Land-Use Planning
This study examined how DInSAR and remote sensing based predictive modeling can improve the assessment of ground subsidence induced by mineral extraction and strengthen environmental risk mitigation and land use planning in mining affected areas. The problem addressed in this research is that although mining related subsidence creates serious threats to infrastructure, ecosystems, settlements, and planning systems, its monitoring and predictive use in decision making are often fragmented and insufficiently integrated. The purpose of the study was therefore to test whether stronger DInSAR and remote sensing capability improves predictive modeling accuracy and whether such predictive strength contributes to better environmental mitigation and planning effectiveness. The study adopted a quantitative, cross sectional, case-based design using cloud and enterprise oriented geospatial and institutional case contexts in mineral extraction environments, with a purposive sample of 162 professional respondents drawn from mining engineering, geology, GIS and remote sensing, environmental management, planning, and regulatory fields. The key variables were DInSAR and Remote Sensing Capability, Predictive Modeling Accuracy, Environmental Risk Mitigation, and Land Use Planning Effectiveness. Data were collected through a 5-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression analysis in SPSS. The findings showed high mean scores across all constructs, including DInSAR and Remote Sensing Capability (M = 4.18, SD = 0.61), Predictive Modeling Accuracy (M = 4.09, SD = 0.66), Environmental Risk Mitigation (M = 4.14, SD = 0.58), and Land Use Planning Effectiveness (M = 4.11, SD = 0.63). Regression results revealed that DInSAR capability significantly predicted predictive modeling accuracy (β = 0.652, R² = 0.425, p < .001), predictive modeling significantly predicted environmental risk mitigation (β = 0.718, R² = 0.516, p < .001) and land use planning effectiveness (β = 0.681, R² = 0.478, p < .001), while environmental risk mitigation also significantly predicted land use planning effectiveness (β = 0.603, p < .001). The study implies that integrating satellite-based deformation monitoring with predictive analytics can provide a stronger evidence base for safer environmental governance, hazard prioritization, infrastructure protection, and sustainable land use planning in subsidence prone mining regions.
Copyright (c) 2022 Taufiqur Rahaman, Albert Anokye (Author)
AI-Enabled Predictive Maintenance for Fire Alarm and Smoke Management Systems: A Systematic Review of Models
This study presented a quantitative systematic review of AI-enabled predictive maintenance models applied to fire alarm and smoke management systems, with the objective of evaluating their effectiveness in improving system reliability, operational efficiency, and fault detection accuracy. The analysis synthesized data from 62 peer-reviewed studies covering diverse infrastructure environments, including hospitals, airports, commercial buildings, industrial facilities, and high-rise structures. The findings indicated that predictive maintenance models achieved a high overall mean accuracy of 91.6%, with deep learning and ensemble models outperforming traditional approaches by reaching accuracy levels of 94.2% and 93.5%, respectively. The results further demonstrated a significant reduction in false alarm rates by an average of 37.4%, alongside improvements in detection sensitivity, with recall values increasing to 90.8%. The study also revealed that dataset size and sensor integration significantly influenced model performance, as large datasets exceeding 200,000 observations achieved mean accuracy levels of 93.8%, compared to 87.2% in smaller datasets. Multi-sensor IoT-based systems demonstrated superior anomaly detection performance at 92.9%, highlighting the importance of integrated data environments. Detection latency improved by approximately 24.3%, indicating faster system responsiveness, while overall system reliability scores increased by 28.6% following predictive maintenance implementation. Statistical analysis confirmed that these improvements were significant, with large effect sizes observed in false alarm reduction and fault detection performance. Subgroup analysis showed variability across infrastructure types, with commercial buildings achieving the highest accuracy at 94.1%, while more complex environments such as industrial facilities reported lower performance at 88.9%. Hybrid models demonstrated consistent improvements across all environments, particularly in dynamic conditions. The findings also indicated strong positive correlations between dataset size, sensor integration, and predictive accuracy. Overall, the study provided comprehensive quantitative evidence that AI-enabled predictive maintenance significantly enhances the performance and reliability of fire safety systems, supporting its application as an effective and scalable solution for safety-critical infrastructure management.
Blockchain-Enabled Security Protocols Combined with AI For Securing Next-Generation Internet of Things (IOT) Networks
This study examined the effectiveness of integrating blockchain-enabled security protocols with artificial intelligence for securing next-generation Internet of Things (IoT) networks through a quantitative experimental approach. The research addressed critical security challenges associated with distributed IoT environments, including data integrity, authentication reliability, and threat detection accuracy. A total of 52,480 network observations were analyzed, comprising 31,200 normal traffic instances and 21,280 labeled cyberattack records, including distributed denial-of-service, spoofing, and data tampering scenarios. The proposed integrated framework was evaluated against traditional and standalone AI-based security models using multiple performance indicators, including accuracy, precision, recall, F1-score, latency, throughput, and data integrity metrics. The findings demonstrated that the integrated blockchain-AI model achieved a detection accuracy of 96.8%, significantly outperforming the standalone AI model at 91.4% and the traditional model at 82.3%. Precision and recall values reached 95.9% and 96.2%, respectively, resulting in an F1-score of 96.0%, indicating balanced and reliable detection performance. The framework also reduced unauthorized access incidents from 124 cases in the baseline model to 77 cases, representing a 38% improvement in authentication security. Data integrity violations decreased from 98 to 52 instances, confirming the effectiveness of blockchain in ensuring secure data transactions. Despite the additional computational processes introduced by blockchain and AI integration, latency increased only marginally from 32.6 ms to 34.7 ms, while throughput remained stable at approximately 1.78 Mbps. Statistical analysis confirmed that these improvements were significant at p < 0.05, with large effect sizes observed for key performance indicators. The results indicated that the integrated framework maintained consistent performance across varying network conditions and device densities. Overall, the study established that the combination of blockchain and artificial intelligence provides a robust, scalable, and efficient solution for enhancing IoT security, offering substantial improvements in detection capability, system reliability, and data protection.
Copyright (c) 2022 Atif Khan, MD Hasibur Rahman Murad (Author)
Impact Of GIS-Based Spatial Decision Support Systems on Urban Water Supply Network Optimization: A Qualitative Evaluation
This study examined the impact of GIS-based Spatial Decision Support Systems on the optimization of urban water supply networks using a quantitative cross-sectional case study design. Data were collected from 120 professionals, including engineers, GIS analysts, planners, and maintenance supervisors, alongside operational and spatial data from 15 urban water network zones. Descriptive and inferential statistical analyses were conducted to evaluate relationships between 3SDSS utilization and key performance indicators such as network efficiency, pressure stability, leakage detection, service coverage, and maintenance prioritization. The findings revealed strong positive correlations between SDSS usage and network optimization outcomes, with correlation coefficients ranging from 0.55 to 0.72, all statistically significant at p < 0.05. Regression analysis indicated that GIS-based SDSS variables explained 62% of the variance in network performance (R² = 0.62), demonstrating substantial predictive power. Leakage rates were significantly lower in high SDSS integration zones (17.9%) compared to low integration zones (27.1%), while pressure stability scores improved from 3.42 to 4.25 on a five-point scale. Sub-group analysis showed that GIS analysts reported higher effectiveness scores (mean = 4.32) compared to maintenance personnel (mean = 3.61), highlighting the influence of user expertise. Reliability testing confirmed strong internal consistency (Cronbach’s alpha = 0.82). The results demonstrated that GIS-based SDSS significantly enhanced decision-making accuracy and operational efficiency, particularly in complex and high-demand network environments. Overall, the study provided empirical evidence supporting the effectiveness of GIS-based spatial decision tools in improving urban water supply system performance and infrastructure management.
Copyright (c) 2022 Taufiqur Rahaman, Kazi Mohammad Khalid Ahsan (Author)
Strengthening United States Economic Competitiveness Through AI-Driven Workforce Transformation and Organizational Innovation Systems
This study examined how AI-driven workforce transformation and organizational innovation systems strengthen United States economic competitiveness. The problem addressed was that many organizations adopt AI technologies without sufficient workforce readiness, employee reskilling, leadership support, digital infrastructure, or innovation systems, limiting the economic value of AI implementation. The purpose was to assess whether AI adoption, workforce reskilling, employee AI readiness, leadership support, and digital infrastructure significantly influence organizational innovation systems and competitiveness outcomes. A quantitative, cross-sectional, case-based design was applied using structured five-point Likert-scale data from 250 respondents representing cloud-enabled and enterprise organizations across technology, finance, healthcare, manufacturing, logistics, retail, and professional services. The key variables included AI adoption, workforce reskilling and upskilling, employee AI readiness, leadership support, digital infrastructure, organizational innovation systems, and economic competitiveness. Data analysis involved descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, regression modeling, readiness index scoring, and mediation/pathway analysis. The findings showed high levels of AI adoption (M = 4.12, SD = 0.61), organizational innovation systems (M = 4.08, SD = 0.59), and economic competitiveness (M = 4.10, SD = 0.62). Reliability was strong, with Cronbach’s Alpha ranging from 0.80 to 0.89 and an overall instrument reliability of 0.91. Correlation analysis showed that organizational innovation systems had the strongest relationship with economic competitiveness (r = 0.72, p < 0.01). Regression results revealed that AI adoption, reskilling, leadership support, and digital infrastructure explained 58% of the variance in AI-driven workforce transformation, while the full AI innovation competitiveness model explained 64% of competitiveness variance. The mediation result confirmed that organizational innovation systems partially mediated the relationship between AI-driven workforce transformation and competitiveness, with an indirect effect of 0.21. The study implies that U.S. enterprises should manage AI as a combined technological, human-capital, and innovation strategy to improve productivity, adaptability, efficiency, and long-term organizational growth.
Copyright (c) 2025 Hisham Mahmood, Risha Alam (Author)
Pharmaceutical Manufacturing Practices and Antimicrobial Resistance Mitigation: A Quantitative Case-Based Assessment
Antimicrobial resistance (AMR) remains one of the most pressing threats to global public health, yet scholarly and policy attention has largely concentrated on clinical prescribing behavior and patient-level misuse, leaving the upstream contribution of pharmaceutical manufacturing comparatively underexamined. This study addresses that gap by investigating how deficiencies in production discipline, quality control, waste handling, and compliance infrastructure may elevate resistance-related risks through the circulation of substandard antimicrobial products and the unsafe discharge of pharmaceutical effluents into the environment. Accordingly, the study evaluates the effects of four manufacturing dimensions, Good Manufacturing Practice (GMP), Quality Control Systems, Waste Management and Environmental Safety, and Regulatory Compliance, on Antimicrobial Resistance Mitigation across selected pharmaceutical enterprise case settings. A quantitative, cross-sectional, case-based design was adopted, with primary data collected through structured questionnaires administered to personnel drawn from production, quality assurance, quality control, regulatory compliance, and environmental health and safety functions across four manufacturing sites. Of 230 instruments distributed, 214 valid responses were retained, yielding a response rate of 93.0%. The data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis.Descriptive findings revealed uniformly high ratings across the study constructs: GMP (M = 4.18, SD = 0.64), Quality Control Systems (M = 4.11, SD = 0.69), Regulatory Compliance (M = 4.06, SD = 0.67), Waste Management and Environmental Safety (M = 3.97, SD = 0.73), and Antimicrobial Resistance Mitigation (M = 4.14, SD = 0.61). Correlation analysis indicated that GMP exhibited the strongest association with mitigation outcomes (r = .68, p < .001), followed by Regulatory Compliance (r = .66), Quality Control Systems (r = .63), and Waste Management and Environmental Safety (r = .59). Regression results corroborated these relationships, identifying GMP as the strongest predictor (β = .31, p < .001), followed by Regulatory Compliance (β = .27, p < .001), Quality Control Systems (β = .24, p = .002), and Waste Management and Environmental Safety (β = .19, p = .008). Collectively, the four predictors accounted for 47.3% of the variance in mitigation outcomes (R² = .473; F = 46.82, p < .001).The findings underscore that pharmaceutical manufacturing should be conceptualized not merely as an industrial activity but as a critical public health control system, one that plays a decisive role in preserving antimicrobial efficacy and curbing the emergence and spread of resistance.
Copyright (c) 2023 Beatrice Onyinyechi Mkpa (Author)
Computational Thermo-Mechanical Modeling for Energy-Efficient Solid-State Metal Manufacturing Processes
This study examined computational thermo-mechanical modeling for energy-efficient solid-state metal manufacturing processes using a quantitative quasi-experimental simulation design. A total of 120 validated simulation cases were analyzed, covering a wide range of process parameters including tool velocity (200–1200 rpm), applied force (5–25 kN), and varying thermal boundary conditions. The results showed that peak temperature varied significantly from 420.00 K to 980.00 K, with a mean value of 685.40 K (SD = 112.35), while stress ranged from 85.00 MPa to 410.00 MPa, averaging 236.70 MPa (SD = 68.45). Strain values exhibited a mean of 0.42 (SD = 0.15), and energy consumption ranged between 1.85 J/mm³ and 6.75 J/mm³, with a mean of 3.92 J/mm³ (SD = 1.12). Statistical analysis confirmed that tool velocity (F = 18.45, η² = 0.42), applied force (F = 15.72, η² = 0.38), and thermal conditions (F = 12.89, η² = 0.34) had significant effects on thermo-mechanical outputs (p < 0.05). Regression modeling demonstrated strong predictive capability, with an R² value of 0.81 for energy consumption and low standard error (0.42), indicating high model accuracy. Subgroup analysis revealed that friction-based processes generated higher peak temperatures (765.30 K) and energy consumption (4.35 J/mm³) compared to diffusion-based processes (612.85 K and 3.25 J/mm³). High-speed conditions further increased temperature to 825.60 K and stress to 285.90 MPa, while low-speed conditions maintained lower energy demand at 2.95 J/mm³. Optimal performance was observed at moderate parameter ranges, where energy consumption remained below 3.50 J/mm³ and stress levels were controlled below 240 MPa. These findings confirmed that thermo-mechanical coupling significantly influenced process efficiency, and precise parameter optimization was essential for achieving balanced energy-efficient manufacturing performance.
Copyright (c) 2022 Manam Ahmed, Md. Ashfaq Siddiquee (Author)
Development of an AI-Driven Predictive Decision Support Model for Multi-Domain Business Analytics and Infrastructure Optimization
This study addresses the persistent organizational problem that many firms possess growing volumes of business and infrastructure data but still lack an integrated AI-driven predictive decision support model capable of converting those data into high-quality decisions and optimization outcomes across multiple domains. Its purpose was to develop and empirically examine a unified model linking predictive analytics, business analytics capability, data integration, organizational readiness, decision quality, and infrastructure optimization within a quantitative, cross-sectional, case-based research design. Using survey data from 210 respondents drawn from cloud-enabled and enterprise-oriented operational contexts across operations, IT/data systems, finance/planning, infrastructure or asset management, and strategy or administration, the study measured six core variables: AI-Driven Predictive Analytics, Business Analytics Capability, Data Integration Capability, AI Decision Readiness, Decision-Making Quality, and Infrastructure Optimization. The analysis plan combined descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. Findings showed strong construct reliability with Cronbach’s alpha values ranging from 0.83 to 0.89, while all major variables recorded high mean scores, including AI-Driven Predictive Analytics at 4.18, Business Analytics Capability at 4.09, AI Decision Readiness at 4.05, Decision-Making Quality at 4.21, and Infrastructure Optimization at 4.12. Correlation results were positive and significant, with the strongest association found between Decision-Making Quality and Infrastructure Optimization at r = .71, p < .001. Regression results indicated that the model explained 56% of the variance in Decision-Making Quality and 62% of the variance in Infrastructure Optimization. AI-Driven Predictive Analytics significantly improved Decision-Making Quality (beta = .34, p < .001), while Decision-Making Quality was the strongest predictor of Infrastructure Optimization (beta = .38, p < .001). Overall, the study concludes that AI-driven predictive decision support becomes most effective when analytical capability, integrated data, and organizational readiness are aligned, producing measurable gains in decision quality and infrastructure performance. The implications are that organizations should treat AI decision support as an integrated capability system rather than as a standalone technical tool.
A Comparative Analysis of Monitoring and Observability Tools for Machine Learning and Data Science Pipelines
This study investigates the growing problem of limited visibility, delayed fault diagnosis, and inconsistent operational control in machine learning and data science pipelines, where traditional monitoring tools often provide only surface-level metrics while failing to explain complex cross-stage failures. The purpose of the research was to comparatively evaluate monitoring and observability tools and determine how their core capabilities influence overall pipeline effectiveness in real cloud and enterprise analytical environments. Using a quantitative, cross-sectional, case-based design, the study collected data from 210 valid respondents drawn from cloud and enterprise pipeline cases involving data scientists, ML engineers, data engineers, MLOps and DevOps engineers, and technical managers. The key independent variables were monitoring capability, observability capability, integration capability, scalability, and information interpretability, while the dependent variable was overall pipeline effectiveness, measured through reliability, issue detection efficiency, operational efficiency, and user satisfaction. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression. The results showed that observability capability recorded the highest mean score (M = 4.12, SD = 0.68), followed by overall pipeline effectiveness (M = 4.08, SD = 0.66) and information interpretability (M = 4.05, SD = 0.69), while monitoring capability remained positive but lower (M = 3.89, SD = 0.71). Reliability was strong across all constructs, with Cronbach’s alpha ranging from 0.79 to 0.88. Correlation analysis revealed that observability capability had the strongest relationship with pipeline effectiveness (r = 0.710, p < .001), followed by information interpretability (r = 0.670, p < .001). The regression model was statistically significant, F (5, 204) = 42.63, p < .001, explaining 51.1% of the variance in pipeline effectiveness (R² = 0.511). Observability capability emerged as the strongest predictor (β = 0.31, p < .001), followed by information interpretability (β = 0.27, p < .001). The study implies that organizations should prioritize observability-rich, interpretable, and scalable tools to strengthen pipeline governance, reliability, and troubleshooting performance in modern ML operations.
Copyright (c) 2022 Aditya Dhanekula, Mohammad Robel Miah (Author)
Large-Scale Data-Driven Intelligence Frameworks for Predictive Healthcare Analytics, Early Clinical Intervention, and Sustainable Chronic Disease Management in the United States
This study addresses the problem that many U.S. healthcare organizations can build predictive models but struggle to scale them into reliable, workflow-embedded, and governable intelligence frameworks that consistently enable early clinical intervention and sustainable chronic disease management. The purpose was to quantify, across published enterprise and cloud enabled implementations, which end-to-end design elements (data integration, predictive evaluation discipline, workflow actionability, and governance) are most strongly associated with measurable operational and patient-impact signals. Using a quantitative cross-sectional, case-based synthesis, each included study was treated as a “case” drawn from enterprise health systems, multi-site networks, payer programs, and cloud or app-platform deployments (for example, standards-based interoperable apps), and coded for key variables: data readiness (EHR, claims, device and patient-generated integration), model and information quality (validation and utility reporting), workflow fit (CDS embedding and routing), governance (monitoring, privacy, equity), and sustainability indicators (utilization, mortality, continuity). The analysis plan combined descriptive statistics (frequencies and proportions), cross-case comparison matrices, and a light numeric evidence-rating procedure (Likert 1–5) to summarize strength and consistency of effects. Headline findings show that workflow-embedded decision support most consistently improved care processes: across 148 trials, 86% assessed process outcomes and significant improvements were reported for preventive services (OR = 1.42, 95% CI 1.27–1.58), ordering clinical studies (OR = 1.72, 95% CI 1.47–2.00), and prescribing therapies (OR = 1.57, 95% CI 1.35–1.82), while only 20% assessed clinical outcomes and 15% assessed costs. For chronic disease sustainability, remote monitoring and structured support in heart failure reduced admissions by 21% (95% CI 11%–31%) and all-cause mortality by 20% (95% CI 8%–31%), and diabetes self-management apps improved HbA1c by a median 0.4%. Implications are that organizations should treat predictive analytics as a governed data-to-action capability, prioritizing interoperable pipelines, decision-utility evaluation, alert-fatigue controls, subgroup equity checks, and lifecycle monitoring to achieve scalable and durable impact.
Copyright (c) 2022 Md Fardaus Alam, Md Fokhrul Alam, Md Ashraful Alam (Author)
Material Degradation and Durability Assessment of Pipelines and Sanitation Structures Under Aggressive Environmental Conditions
This study investigates the problem of accelerating material degradation and declining durability in pipelines and sanitation structures exposed to aggressive environmental conditions, where moisture intrusion, corrosive soils, chemical attack, salinity, microbial activity, and temperature fluctuation progressively weaken structural integrity and serviceability. The purpose of the study was to quantitatively assess how these environmental stressors influence degradation patterns and durability outcomes across selected infrastructure cases. A quantitative, cross-sectional, case-based research design was adopted, using structured questionnaire data collected from 186 valid respondents out of 214 distributed instruments, yielding an 86.9% valid response rate. The sample reflected clouded enterprise-style infrastructure case contexts through pipelines, sewer chambers, wastewater conduits, and sanitation tanks/manholes assessed by engineers, maintenance officers, utility managers, and sanitation specialists. The key variables were aggressive environmental conditions as the independent variable, material degradation indicators as the mediating variable, and durability and serviceability performance as the dependent variable. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. The findings showed a high presence of aggressive exposure conditions with an aggregate mean of 4.18, led by moisture intrusion at 4.36, corrosive soil exposure at 4.27, and chemical attack at 4.21. Material degradation was also severe with an aggregate mean of 4.09, with corrosion intensity recording the highest mean of 4.33, followed by cracking severity at 4.17 and wall thinning at 4.09. Durability concern was substantial with an aggregate mean of 3.88, especially increased maintenance frequency at 4.12 and reduced expected service life at 4.07. Correlation analysis revealed a strong positive relationship between aggressive environmental conditions and material degradation (r = .742, p < .001), while degradation was strongly negatively related to durability performance (r = -.711, p < .001). Regression results further showed that environmental conditions explained 55.1% of degradation variance (R² = .551), and together with degradation explained 61.7% of durability loss (R² = .617). The study implies that durability management should shift toward risk-based, condition-focused, and exposure-sensitive maintenance planning for long-term infrastructure resilience.
Copyright (c) 2022 Aditya Dhanekula, Palash Chandra Das (Author)
A Panel Data Econometric Analysis on the Impact of Digital Payment Adoption on Small Business Revenue Growth in Global Business
This study addresses the persistent problem that many small businesses adopt digital payments, yet the revenue impact remains uneven because adoption is often shallow and not operationally integrated. The purpose was to quantify how digital payment adoption and adoption maturity influence small business revenue growth and to test which adoption drivers strengthen or constrain this relationship. Using a quantitative, cross-sectional, case-based design, data were collected from N = 240 small businesses as enterprise-style operational cases embedded in cloud-enabled and platform-mediated payment ecosystems (for example, wallet, QR, POS gateway, and settlement services). The sample covered retail (38%), food/service (34%), and other transaction-intensive firms (28%), with most respondents being owners or general managers (71%) who manage payment decisions. Key variables included Digital Payment Adoption (ADOPT), Digital Payment Adoption Maturity Index (DPAMI), perceived ease of use (PEOU), trust/security perception (TR), perceived transaction cost (COST), customer preference for digital payment (CUSTPREF), and a triangulated Revenue Growth Composite (REVCOMP), with controls for firm size, firm age, sector, location, and sales-channel mix. The analysis plan applied scale reliability tests (Cronbach’s alpha), descriptive statistics, Pearson correlations, interaction and subgroup tests, and diagnostics (VIF). Measurement reliability was strong (for example, REVCOMP α = .88, TR α = .86, PEOU α = .83). Descriptives showed high adoption (ADOPT M = 3.74, SD = 0.71) and positive revenue outcomes (REVCOMP M = 3.49, SD = 0.69). Revenue growth correlated with adoption (r = .46, p < .001) and more strongly with maturity (DPAMI r = .54, p < .001). In regression, adding adoption increased model fit (Adj. R² = .27) with a significant effect (β = .31, p < .001), while adding maturity improved fit further (Adj. R² = .34) with a stronger effect (DPAMI β = .39, p < .001). Trust and usability promoted adoption (TR β = .33, p < .001; PEOU β = .28, p < .001), while cost constrained it (COST β = −.21, p = .002); the adoption–growth link strengthened when customers preferred digital payments (Adoption × CUSTPREF β = .17, p = .011). Implications suggest that SMEs should prioritize maturity practices (integration, staff readiness, reconciliation discipline), providers should reduce friction and strengthen trust signals, and policymakers should support interoperability and transparent pricing to help firms convert adoption into measurable growth.
Copyright (c) 2022 Istiaq Ahmed, Nusrat Jahan (Author)
Fabrication-Driven Structural Optimization Techniques for Cost-Efficient Steel Construction Using CNC-Based Design Workflows
This study investigated how fabrication-driven structural optimization improves cost efficiency in steel construction when delivered through CNC-based, BIM-enabled design workflows, addressing the problem that many “optimized” steel designs lose their economic advantage due to traceability gaps, revision churn, and incomplete fabrication constraint alignment across multi-tool, multi-organization pipelines. The purpose was to quantify the relationships among Fabrication-Driven Structural Optimization (FDSO), CNC Workflow Traceability and Data Integrity (CWTDI), Fabrication Constraint Compliance Index (FCCI), and Cost-Efficiency outcomes (CE) using a quantitative cross-sectional, case-based design. Data were collected from a purposive sample of 162 professionals embedded in enterprise steel delivery cases that used CNC fabrication and cloud or enterprise digital collaboration tools for model-to-detail-to-machine handoffs (structural engineers 24.1%, BIM/modeling 18.5%, detailers 21.0%, shop supervisors 16.7%, CNC/QA 9.3%, project managers 10.5%; mean experience 7.8 years, SD 4.6; 72.2% frequent CNC exposure). Reliability was strong (α: FDSO 0.88; CWTDI 0.90; FCCI 0.86; CE 0.89). Descriptively, respondents reported above-mid maturity (FDSO M 3.92, SD 0.62; CWTDI M 3.74, SD 0.71; FCCI M 0.77, SD 0.12; CE M 3.85, SD 0.66 on 1–5 scales). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with controls (role, experience, project complexity). Headline findings showed strong positive associations with CE (FDSO r 0.63; CWTDI r 0.69; FCCI r 0.58; all p < .001) and a high explanatory regression model (F(6,155)=33.9, p<.001; R²=0.57; Adj R²=0.55) where CWTDI was the strongest predictor (β=0.41, p<.001), followed by FDSO (β=0.29, p<.001) and FCCI (β=0.18, p=.006; VIFs < 2.1). Case validation aligned higher traceability with fewer major revision cycles (2.1 vs 3.6) and fewer CNC regeneration events (1.4 vs 2.6), and cost-impact decomposition attributed perceived savings mainly to rework reduction (31%), then labor-hours reduction (24%), waste reduction (19%), coordination-cycle reduction (15%), and machine-time gains (11%). Implications suggest that organizations should prioritize digital-thread governance (release gates, revision propagation, audit trails) alongside fabrication-aware standardization to realize reliable cost efficiency from CNC-integrated optimization.
Copyright (c) 2022 Md. Mainuddin Faisal, Palash Chandra Das (Author)
Advanced Computing, IT Strategy, and Network-Optimized Frameworks for Retail Business Intelligence
This study addresses a problem in retail business intelligence (BI) modernization: retailers upgrade compute and analytics, yet dashboards still suffer from stale data, inconsistent KPIs, and unreliable Realtime performance because computing, governance, and network engineering are implemented in silos. The purpose is to test, using a quantitative cross sectional, case-based synthesis, whether advanced computing capability (AC), IT strategy alignment (ITSA), and network optimized frameworks (NOF) jointly improve BI service quality and decision usefulness in retail deployments. The sample comprises literature derived cloud and BI cases, with each publication treated as a case unit representing an architecture or evaluated deployment. Key variables are AC (cloud elasticity, distributed processing, streaming ingestion, modular orchestration), ITSA (data governance and stewardship, BI operating model, KPI ownership), NOF (WAN traffic engineering, observability, resiliency, edge placement), and BI outcomes (data freshness, dashboard latency, uptime, KPI consistency, adoption intensity). The analysis plan applies a standardized 1 to 5 evidence scoring rubric per case, vote counting of hypothesis support, and an Integrated Capability Index computed as the mean of AC, ITSA, and NOF. Findings show strong support for AC with a composite score of 4.2/5 (SD 0.6; 76% support), led by cloud elasticity (M 4.4; SD 0.6; 78% support) and distributed processing (M 4.3; SD 0.7; 74% support). ITSA scores 3.9/5 (SD 0.6; 70% support), driven by governance and stewardship (M 4.2; SD 0.7). NOF shows moderate to strong support at 3.7/5 (SD 0.6; 62% support), with observability highest (M 4.0; SD 0.7). Integrated configurations outperform siloed upgrades: high integrated cases report BI_Perf of 4.4/5 versus 3.1/5 in low integrated cases. Implications are that retailers should manage BI as an end-to-end service by coordinating cloud scale, KPI governance, and network reliability with explicit latency, freshness, and uptime targets to sustain secure decision support across omnichannel operations.
Copyright (c) 2022 Tanjina Binte Sohrab, Md. Hasan Or Rashid (Author)
Anticipatory Intelligence Systems: How Data Analytics Reshape Organizational Preparedness and Action Timing
This study addresses the persistent problem that many organizations deploy advanced analytics yet still respond too late to emerging threats and opportunities because signals are not converted into governed decisions and executable actions in time. The purpose is to quantify how Anticipatory Intelligence Systems (AIS) reshape organizational preparedness and action timing in cloud and enterprise operating contexts. Using a quantitative, cross-sectional, case-based design, evidence from N = 40 documented organizational cases and empirical studies was coded on 5-point Likert rubrics to operationalize three AIS capability layers (data-to-signal, signal-to-decision, decision-to-action), an Organizational Preparedness Index (OP), and Action Timing Advantage (ATA) as proportional decision-latency reduction. The sample covered cloud and enterprise implementations across manufacturing and asset maintenance (30%), supply chain and logistics (25%), finance and risk (20%), healthcare operations (15%), and cybersecurity (10%). Key variables included preparedness markers (governance clarity, resource mobilization readiness and timing stages (alert-to-triage, decision commitment, execution initiation, lead-time advantage). The analysis plan computed descriptive statistics (means, SDs, and percent of cases scoring ≥4) and compared “high-enabler” versus “lower-enabler” subsets based on data maturity and governance strength. Headline findings show strong preparedness gains (OP = 3.92/5, SD = 0.63; 80% of cases ≥4) with the highest single element in cross-functional coordination (mean = 4.06; 82% ≥4). Action timing improved meaningfully, with ATA = 0.28 (SD = 0.12), indicating an average 28% reduction in decision latency, and 75% of cases reporting measurable timing gains; stage-wise reductions were strongest for alert-to-triage (ATA = 0.32; baseline 6.2 days to 4.2 days) and weakest for commitment-to-execution (ATA = 0.20; 5.1 to 4.1 days). Stability and resilience effects were moderate (composite = 3.66/5, SD = 0.70; 65% with ≥4 on at least one dimension). Enabler analysis indicates stronger outcomes when governance and data maturity were high (OP = 4.26; ATA = 0.34) versus lower-enabler contexts (OP = 3.64; ATA = 0.23). Implications are that cloud and enterprise leaders should treat AIS as end-to-end decision infrastructures by prioritizing semantic data integration, explicit decision rights, explainability, and pre-approved response playbooks to close the loop from sensing to execution and capture timing advantage at scale.
Copyright (c) 2022 Rukaiya Khatun Moury, Md. Morshedul Islam (Author)
Enhancing U.S. Manufacturing Competitiveness through Lean Manufacturing: A Framework for Supply Chain Resilience and Risk Mitigation
This study addresses a persistent problem in U.S.-oriented manufacturing supply chains: firms pursue lean to stabilize cost and flow, yet disruptions expose gaps in preparedness, response, recovery, and risk mitigation, creating uncertainty about which lean bundles most reliably strengthen supply chain resilience. The purpose was to quantify and synthesize how lean practice clusters relate to resilience capabilities and risk-mitigation outcomes across enterprise case evidence using a quantitative, cross-sectional, case-based research design. Using a structured evidence-synthesis dataset of 42 enterprise manufacturing cases reported in peer-reviewed studies, lean was operationalized as practice clusters (LP1–LP9) such as standardized work and visual management, continuous improvement, quality at the source, TPM, pull routines, supplier development, and visibility dashboards; resilience was measured as four capability variables (Readiness RC1, Response RC2, Recovery RC3, Adaptation RC4) and an overall Resilience Capability Score (RCS); risk mitigation was captured via an overall Risk Mitigation Index (RMI) across risk categories. The analysis plan applied a five-point Likert evidence-strength scoring for each construct link and aggregated means, standard deviations, and study counts, complemented by cross-case comparisons by manufacturing segment. Headline findings show strong overall support for a positive lean–resilience relationship (RCS M = 4.1, SD = 0.6, n = 42), with the strongest capability evidence in Response (RC2 M = 4.3, SD = 0.6, n = 41) and Recovery (RC3 M = 4.2, SD = 0.6, n = 40), while Adaptation was comparatively lower (RC4 M = 3.8, SD = 0.8, n = 33) . Lean practice evidence was highest for standardized work and visual management (LP1 M = 4.4, SD = 0.6, n = 42) and continuous improvement (LP2 M = 4.2, SD = 0.7, n = 40), and overall risk mitigation was strong (RMI M = 3.9, SD = 0.7, n = 42). Cross-case patterns indicate higher resilience in discrete, coordination-intensive segments such as automotive (RCS M = 4.4, SD = 0.5) than process industries (RCS M = 3.9, SD = 0.7), and supplier-facing lean yielded stronger supplier-risk mitigation (M ≈ 4.1) than internal-only lean (M ≈ 3.6). Implications are that managers should treat lean as a capability-building system, prioritizing visibility, disciplined escalation, and joint problem-solving across supplier interfaces, and should pair flow-focused tools with selective buffers and segment-appropriate continuity strategies where external logistics constraints dominate.
Copyright (c) 2025 Kazi Saiful Islam (Author)
Empirical Validation of a Hybrid Deep Learning Architecture for Real-Time Fault Detection and Cyber-Threat Classification in SCADA-Based Smart Grid Environments
This study addresses a critical operational gap in smart grid infrastructure management: while hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have been theoretically proposed for simultaneous fault detection and cyber-threat classification in SCADA-based environments, no empirically validated quantitative framework exists to determine which hybrid architecture capability dimensions most strongly predict operational reliability, threat response effectiveness, and detection latency reduction in real-world deployment contexts. The purpose was to empirically validate a hybrid CNN-LSTM deep learning framework that links core architectural capabilities to smart grid operational outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 218 smart grid security and operations professionals including SCADA engineers, network security analysts, data scientists, grid operations managers, and protection relay engineers working in operational environments where AI-driven monitoring and anomaly detection platforms are actively deployed. Key independent variables were CNN Feature Extraction Capability (C), LSTM Temporal Sequence Modeling Capability (L), and Hybrid Architecture Integration Depth (H), alongside two domain-specific indices: the Hybrid Architecture Performance Index (HAPI) and the Real-Time Threat Detection Alignment (RTDA); key dependent variables were Fault Detection Accuracy (Y1), Cyber-Threat Classification Effectiveness (Y2), and Operational Continuity Performance (Y3). The analysis applied descriptive statistics, reliability and validity testing (Cronbach's alpha, EFA with KMO and Bartlett's test), Pearson correlations, and multiple regression models. Headline findings demonstrate strong measurement quality (alpha = .83-.89; KMO = .91; Bartlett's chi-squared = 2318.7, p < .001) and moderately high capability levels (CNN Capability M = 3.97, SD = 0.61; LSTM Capability M = 4.11, SD = 0.56; Hybrid Integration M = 3.84, SD = 0.67). All core relationships were positive and significant (p < .001), including Fault Detection Accuracy with HAPI (r = .66) and LSTM Capability (r = .63), Cyber-Threat Classification with RTDA (r = .68), and Operational Continuity with Hybrid Integration (r = .64). Regression results indicate substantial explained variance for Fault Detection Accuracy (R2 = .63; F(5,212) = 72.4, p < .001), with HAPI as the strongest predictor (beta = .37, p < .001), followed by LSTM Capability (beta = .25, p = .001).
Digital-Twin-Based Quantitative Frameworks for Modeling, Monitoring, and Optimization of Electrical Power Infrastructure
This study addresses a critical problem in electrical power infrastructure operations: many organizations deploy digital-twin technologies, yet lack a validated, quantitative way to determine which digital-twin capabilities actually drive measurable improvements in reliability, response effectiveness, and operational efficiency. The purpose was to test a digital-twin-based quantitative framework that links core capabilities to infrastructure outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 212 power-infrastructure professionals working in enterprise operational contexts where monitoring and decision-support platforms are used. Key independent variables were Digital Twin Modeling Capability (M), Monitoring Capability (N), and Optimization Capability (O), alongside two domain indices: Digital Twin Fidelity and Synchronization Readiness Index (DT-FSRI) and Event Detection and Response Alignment (EDRA); key dependent variables were Infrastructure Reliability/Continuity (Y₁), Response Effectiveness (Y₂), and Operational Efficiency (Y₃). The analysis plan applied descriptive statistics, reliability and validity testing (Cronbach’s alpha, EFA with KMO and Bartlett’s test), Pearson correlations, and multiple regression models. Headline findings show strong measurement quality (α = .82–.88; KMO = .89; Bartlett’s χ² = 2146.3, p < .001) and moderately high capability levels (Modeling M = 3.94, SD = 0.63; Monitoring M = 4.07, SD = 0.58; Optimization M = 3.76, SD = 0.69). All core relationships were positive and significant (p < .001), including Reliability with DT-FSRI (r = .63) and Monitoring (r = .61), Response Effectiveness with EDRA (r = .65), and Efficiency with Optimization (r = .62). Regression results indicate substantial explained variance for Reliability (R² = .60; F(5,206) = 62.7, p < .001), with DT-FSRI as the strongest predictor (β = .34, p < .001), followed by Monitoring (β = .23, p = .002). Implications suggest that utilities should prioritize synchronization discipline and event-to-response alignment before scaling advanced optimization, because these coherence mechanisms most strongly predict operational performance.
Event Detection and Response Alignment (EDRA)
Copyright (c) 2022 Md. Shahinur Islam, Md. Sultan Mahamud (Author)
A Systematic Review of AI-Driven Credit Risk Assessment Models in Commercial Banking (2018–2026)
This systematic review examines the evolution, methodological advancements, and governance implications of AI-driven credit risk assessment models in commercial banking from 2018 to 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review synthesizes evidence from 27 peer-reviewed studies, representing a diverse body of research exploring machine learning and deep learning applications in probability of default estimation, loss and exposure modeling, early-warning systems, portfolio monitoring, and automated credit decisioning. The findings show that AI models consistently outperform traditional statistical approaches in predictive accuracy and behavioral sensitivity, particularly when leveraging ensemble architectures and temporal or transactional features. However, despite these technical advantages, the review identifies substantial constraints related to data quality, model generalizability, explainability, fairness, drift vulnerability, and regulatory acceptability. Many studies highlight persistent challenges in achieving transparency and stability under stress scenarios, indicating that current AI systems often struggle to meet prudential and consumer-protection expectations. The review also notes a widening gap between research innovation and real-world deployment, as operational requirements such as continuous monitoring, documentation, and interoperability create significant barriers to adoption. Overall, this study provides a comprehensive evidence base demonstrating both the promise and limitations of AI credit risk models, emphasizing the need for more interpretable model architectures, standardized validation frameworks, privacy-preserving data ecosystems, and cross-institutional benchmarking. These insights contribute to shaping the next generation of trustworthy, governable, and regulator-aligned AI systems for commercial banking.
Copyright (c) 2026 Rajib Sarkar (Author)
A Systematic Review of AI-Enabled Predictive Quality Control in Advanced Metal Manufacturing Systems
This study addresses the persistent problem that quality control in advanced metal manufacturing remains largely reactive, while defect formation is nonlinear, high-dimensional, and costly to detect late, creating scrap, rework, warranty risk, and inspection bottlenecks. The purpose was to quantify and synthesize how AI-enabled predictive quality control (PQC) performs and how deployment-ready it is across manufacturing settings, using a quantitative cross-sectional, case-based design in which each eligible publication is treated as a comparable “case.” The sample comprised 52 peer-reviewed cases (2005–2023) spanning industrial and laboratory environments, including enterprise production cases (real factory datasets) and digitally integrated settings where analytics are typically deployed through cloud and enterprise workflows. Key variables included (1) model family (deep learning, classical ML, hybrid), (2) data modality (machine vision, sensor time-series, NDT-based data), (3) validation rigor (internal vs external), (4) performance metrics (F1, AUC, precision, recall), and (5) implementation readiness (1–5 rubric). The analysis plan used structured coding, frequency distributions, cross-tabulation by context and modality, and comparative summary statistics (means, SD). Headline findings show that deep learning dominated 61.5% (32/52) of cases versus 34.6% (18/52) classical ML and 3.8% (2/52) hybrid approaches; machine vision was the most common input (44.2%, 23/52), followed by sensor time-series (36.5%, 19/52) and NDT-driven data (19.2%, 10/52). Multi-modal fusion appeared in 30.8% (16/52) of cases and 75.0% (12/16) reported performance gains over single-modality baselines. Reporting rigor was uneven: while 86.5% (45/52) reported standard predictive metrics, only 21.2% (11/52) reported external validation. Industrial enterprise datasets showed lower average performance (mean F1 = 0.84, SD = 0.07) than lab datasets (mean F1 = 0.91, SD = 0.05), yet more often reported operational impact (63.2% vs 21.7%). Implications are that PQC benefits most when embedded in enterprise-quality data ecosystems, prioritized for drift-aware validation, and paired with explainability and monitoring to support auditability, risk-based inspection, and scalable deployment.
Copyright (c) 2026 Tahmina Akter Rainy, Md. Ashfaq Siddiquee (Author)
AI-Assisted Credit Evaluation Models for Improving Risk Assessment Accuracy in U.S. Banking Systems
This study investigated a persistent problem in U.S. banking credit decisioning: traditional scorecards and manual underwriting can produce inconsistent judgments and avoidable misclassification, especially when borrower profiles are complex and decision speed is high. The purpose was to quantify how AI-assisted credit evaluation, deployed within enterprise banking environments, improves perceived risk assessment accuracy and which enabling conditions most strongly drive those gains. Using a quantitative, cross-sectional, case-based design, data were collected via a structured 5-point Likert survey from n = 214 eligible banking professionals (usable response rate 71.3%) across underwriting (38.8%), credit analysis (27.1%), risk management (22.0%), and model risk/compliance (12.1%), representing enterprise-grade, cloud-supported decision workflows in the case banks. Key variables included Risk Assessment Accuracy Improvement (dependent) and five predictors: AI Model Capability, Data Quality and Availability, Explainability/Transparency, Governance and Compliance Alignment, and Monitoring and Drift Management. The analysis plan applied reliability testing (Cronbach’s α), descriptive statistics, Pearson correlations, and multiple regression. Measurement reliability was strong (α = .81–.90; DV α = .90). Descriptively, respondents agreed that AI improved accuracy (DV M = 3.97, SD = 0.63), with high ratings for data quality (M = 4.05) and governance (M = 3.94), while monitoring was lower (M = 3.72). Accuracy improvement correlated significantly with all predictors (r = .39–.56, p < .001), strongest for data quality (r = .56) and governance (r = .51). In regression, the model explained substantial variance (R² = .46; Adj. R² = .44; F(5,208) = 35.4, p < .001), with Data Quality (β = .29, p < .001), Governance (β = .22, p = .002), and AI Capability (β = .18, p = .006) as significant drivers; explainability was marginal (β = .11, p = .071) and monitoring was not significant after controls (β = .09, p = .104). Practically, the strongest perceived operational gain was improved underwriter consistency (M = 4.06), alongside reduced false approvals (M = 3.84), implying that banks realize the largest accuracy benefits when enterprise AI is paired with disciplined data pipelines and governance controls rather than model sophistication alone.
Copyright (c) 2026 Mahfuj Ahmed Ruzel (Author)
AI-Enabled Intrusion Detection in Enterprise Networks: A Systematic Review of Methods, Datasets, and Evaluation Metrics (2018–2026)
This study addresses the problem that AI-enabled intrusion detection systems (IDS) often report strong benchmark performance yet struggle to deliver consistently actionable, trusted alerts in real enterprise and hybrid cloud environments where traffic is heterogeneous, class imbalance is extreme, and false-positive workload can overwhelm security operations centers. The purpose was to quantify which enterprise-relevant factors most strongly predict perceived AI-IDS effectiveness and operational suitability, while linking evaluation evidence to workload impact. Using a quantitative, cross-sectional, case-based design, data were collected from enterprise and cloud-facing security stakeholders (N = 162 valid responses) involved in IDS monitoring and triage. Key variables were measured on 5-point Likert scales, including Dataset Representativeness (DREP), Evaluation Rigor (ERIG), Model Robustness (MROB), Deployment Readiness (DREADY), Explainability and Trust (TRUST), and the dependent outcome Perceived AI-IDS Effectiveness (EFFECT); construct reliability was acceptable to strong (Cronbach’s α = 0.78–0.88). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with diagnostics (VIF = 1.28–2.05). Headline findings showed moderately high perceived effectiveness (EFFECT M = 3.73, SD = 0.66) and trust (TRUST M = 3.69, SD = 0.68), with EFFECT positively correlated with TRUST (r = .52, p < .001), ERIG (r = .46, p < .001), DREP (r = .42, p < .001), MROB (r = .39, p < .001), and DREADY (r = .34, p < .001). The regression model explained substantial variance in effectiveness (R² = .51; adjusted R² = .49; F(5,156) = 32.45, p < .001), with TRUST the strongest predictor (β = .33, p < .001), followed by ERIG (β = .22, p = .002), DREP (β = .18, p = .008), MROB (β = .15, p = .019), and DREADY (β = .11, p = .041). Operational implications were quantified using a False-Positive Burden Index: at 420 alerts/day, FPR = 0.07, and 6.5 minutes triage time, false positives consumed 191.1 minutes/day (3.19 hours/day), while reducing FPR to 0.04 lowered burden to 109.2 minutes/day (1.82 hours/day), a 42.8% reduction. Overall, the results imply that enterprises gain the most adoption-ready value when AI-IDS is explainable, evaluated rigorously with enterprise-representative data, and tuned to reduce workload through threshold governance and imbalance-aware metrics.
Copyright (c) 2026 Md Zahedul Islam (Author)
Business Intelligence for Service Optimization in U.S. Education and Customer-Facing Enterprises: A Prisma Systematic Review of KPI Frameworks
This study examined how Business Intelligence (BI) capability was empirically associated with service optimization outcomes through the quality of Key Performance Indicator (KPI) frameworks in U.S. education and customer-facing enterprises using a PRISMA-guided quantitative systematic review design. A total of 92 peer-reviewed quantitative studies met the inclusion criteria and were coded as evidence records for structured synthesis. BI capability was most frequently operationalized through information quality (66.3% of studies), system quality (58.7%), and BI usage intensity (51.1%), while analytics maturity appeared in 42.4% of studies. KPI frameworks were distributed across strategic scorecard-based models (31.5%), service-quality KPI systems (27.2%), operations KPI systems (22.8%), and commercial outcome KPI systems (18.5%). Baseline regression synthesis indicated that BI capability demonstrated positive associations with all harmonized service outcome dimensions. The strongest mean standardized effects were observed for satisfaction (β̄ = 0.33) and efficiency (β̄ = 0.31), followed by retention or loyalty (β̄ = 0.29) and responsiveness (β̄ = 0.28). Equity or access outcomes showed weaker but still positive associations (β̄ = 0.22) and appeared predominantly in education studies. When KPI framework quality was included in extended models, the average BI capability coefficient declined from β̄ = 0.30 to β̄ = 0.22, while KPI framework quality demonstrated an independent positive association (β̄ = 0.27) and increased explanatory power by an average ΔR² of 0.09. Models incorporating governance maturity and organizational enablers produced further explanatory gains, with cumulative ΔR² increases reaching 0.16 in the most comprehensive specifications. Sector-based synthesis showed that education studies emphasized retention, progression, and equity outcomes, whereas customer-facing enterprise studies emphasized satisfaction and loyalty outcomes. Reliability reporting was concentrated in SEM and PLS-SEM studies, with mean Cronbach’s alpha values ranging from 0.82 to 0.88 across major construct categories. Overall, the findings demonstrated that BI capability was most strongly associated with service optimization when analytics outputs were embedded within coherent, well-governed KPI frameworks that translated data into standardized and actionable performance indicators.
AI-Enabled Financial Information Systems for Credit Risk Forecasting to Support Small Business Growth
This quantitative study examined how AI-enabled financial information systems (AI-FIS) influenced credit risk forecasting performance, credit allocation outcomes, and small business growth indicators in SME lending. Using a cross-sectional dataset of 420 SME borrower records, the analysis measured AI-FIS adoption intensity through integrated data sources, underwriting automation, model update frequency, real-time monitoring capability, and explain ability module availability. Descriptive results showed that institutions integrated an average of 4.21 data sources (SD = 1.37), automated 62.40% (SD = 18.55) of underwriting decisions, and updated forecasting models 3.10 times per year (SD = 1.25). Real-time monitoring capability averaged 3.88/5 (SD = 0.92), and explain ability modules were present in 73% of institutions. Credit allocation outcomes showed moderate approval probability (M = 3.41/5, SD = 0.86) and strong pricing consistency (M = 3.92/5, SD = 0.74). SME outcomes indicated high survival (89%) and positive average revenue growth (M = 3.62/5, SD = 0.83), with working capital improvement (M = 3.58/5, SD = 0.82). Regression results indicated that AI-FIS adoption intensity significantly predicted forecasting performance (β = 0.54, p < .001), explaining 41% of the variance (R² = 0.41). Forecasting performance significantly predicted approval probability (β = 0.38, p < .001; R² = 0.29), pricing consistency (β = 0.42, p < .001; R² = 0.33), loan size alignment (β = 0.31, p < .001; R² = 0.25), and monitoring intensity (β = 0.35, p < .001; R² = 0.27). Credit allocation outcomes significantly predicted SME growth. Approval probability and loan size alignment predicted revenue growth (β = 0.29 and β = 0.24, p < .001; R² = 0.26) and employee growth (β = 0.21 and β = 0.19, p < .001; R² = 0.19). Pricing consistency predicted working capital improvement (β = 0.27, p < .001; R² = 0.31). Logistic regression showed monitoring intensity increased survival odds (OR = 1.48, p < .001), while approval probability increased survival odds (OR = 1.36, p = .003). Mediation analysis confirmed forecasting performance mediated the adoption-to-allocation relationship (p < .01), and loan approval partially mediated forecasting-to-growth outcomes (p < .05). Overall, findings supported a measurable mechanism in which AI-FIS adoption improved forecasting quality, strengthened credit allocation efficiency, and was associated with higher SME growth and survival outcomes.
Copyright (c) 2026 Md Mehedi Hasan (Author)
Artificial Intelligence Assisted Power Flow Control, Fault Classification, and Adaptive Protection in Utility-Scale Electrical Power Grids
The increasing penetration of renewable energy resources, power electronics-based devices, and distributed generation has significantly increased the operational complexity of utility-scale electrical power grids. Conventional power flow control, fault detection, and protection schemes that rely on static models and fixed thresholds are often insufficient for managing the nonlinear, dynamic, and data-intensive behavior of modern power systems. This study examines the application of Artificial Intelligence (AI) techniques to enhance power flow control, fault classification, and adaptive protection in utility-scale grids. Machine learning and deep learning models are utilized to support real-time grid monitoring, predictive power flow optimization, and rapid fault identification under diverse operating conditions. The proposed AI-assisted framework leverages historical and real-time measurements obtained from phasor measurement units, intelligent electronic devices, and supervisory control and data acquisition systems to improve situational awareness and decision-making. In addition, adaptive protection strategies are designed to dynamically adjust relay settings in response to changes in network topology, load profiles, and fault characteristics, thereby improving system reliability, selectivity, and resilience. Simulation-based results indicate that AI-driven approaches achieve higher fault classification accuracy, faster response times, and greater robustness under uncertainty compared to conventional protection methods. The findings demonstrate the effectiveness of AI-assisted control and protection solutions in supporting secure and efficient operation of future utility-scale electrical power grids.
Copyright (c) 2026 Md Shahinur Islam (Author)