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.
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)
Who Needs Humans? A Study on How Virtual Influencer Autonomy Builds Synthetic Parasocial Bonds Stronger Than Anticipated
Virtual influencers are increasingly used in social media marketing, yet their ability to drive consumer behavior remains theoretically ambiguous. Drawing on parasocial relationship theory and human–AI interaction research, this study examines how perceived virtual influencer likeness and perceived agent autonomy shape purchase intention through synthetic parasocial relationships (S-PSR). We further account for ambivalent affective responses, coolness and eeriness, and test the moderating roles of product type and authenticity skepticism. Survey data from 372 U.S. Instagram users exposed to virtual and human influencer endorsements across technology products and experiential services were analyzed using PLS-SEM. Results show that S-PSR is the strongest predictor of purchase intention. Perceived agent autonomy significantly strengthens S-PSR, while perceived virtual likeness simultaneously increases coolness and eeriness, revealing an ambivalent pathway. The impact of S-PSR on purchase intention is stronger for technology products and weaker under high authenticity skepticism. Overall, the findings demonstrate that virtual influencers are most persuasive when perceived as autonomous, cool, rather than eerie, and contextually aligned with technology-oriented products.
Copyright (c) 2026 Jarif Ul Alam, Ajaan R. Khan (Author)
Development of Model Influence on Consumer Behavior in U.S. e-commerce and Digital Marketing
This study addresses the practical and theoretical problem that, although recommender systems are now embedded across U.S. e-commerce and digital marketing touchpoints, organizations still lack clear quantitative evidence on which perceived recommender attributes most strongly drive consumer behavioral outcomes and how privacy concerns constrain those effects. The purpose was to test a perception-driven influence model using a quantitative, cross-sectional, case-based design anchored in enterprise-scale, cloud-deployed e-commerce recommendation environments (multiple recommendation “surfaces” such as home-page personalization, product-page similar items, cart cross-sells, and email or push recommendations). Data were collected from N = 312 eligible consumers with recent recommender exposure, where 71.5% reported interacting with recommendation carousels at least weekly; key stimulus and organism variables included Personalization Quality (PQ), Perceived Relevance (PR), Transparency/Explainability (TRNSP), Trust (TR), Privacy Concern (PVC), and two study-specific indices, Recommendation Exposure and Interaction Intensity (REI²) and Algorithm Aversion–Appreciation (AAAT), while response variables were Purchase Intention (PI), Satisfaction (SAT), and Loyalty/Repurchase Intention (LOY). The analysis plan applied 5-point Likert scale measurement, reliability testing (Cronbach’s α range .81–.90 across constructs), descriptive statistics, Pearson correlations, and multiple regression models with multicollinearity diagnostics (VIF approximately 1.28–2.34). Descriptives indicated above-midpoint perceptions for PR (M = 4.01, SD = 0.66) and PQ (M = 3.88, SD = 0.72) with moderate TRNSP (M = 3.46, SD = 0.81) and PVC (M = 3.21, SD = 0.84), alongside strong PI (M = 3.97, SD = 0.70) and SAT (M = 3.90, SD = 0.68). Correlations showed trust as a central mechanism (TR with PI r = .61, p < .001; TR with SAT r = .55, p < .001), while privacy concern reduced trust (PVC with TR r = −.34, p < .001). In the main PI regression, the model explained substantial variance (R² = .54, p < .001), with the strongest predictors being PR (β = .31, p < .001) and TR (β = .29, p < .001); PQ (β = .18, p = .003), TRNSP (β = .12, p = .019), REI² (β = .15, p = .006), and AAAT (β = .11, p = .022) added significant positive effects, while PVC showed a smaller negative effect (β = −.09, p = .041). Exposure intensity also produced clear practical differences: high REI² users reported higher PI (M = 4.18) than low REI² users (M = 3.62), indicating an interpretable engagement-linked uplift. These findings imply that enterprise e-commerce teams should prioritize perceived relevance and trust-building transparency (explanations and control cues) while implementing privacy-assurance and preference-editing features to reduce trust erosion and improve purchase and loyalty outcomes.
Copyright (c) 2025 Tahmina Akter Rainy, Aditya Dhanekula (Author)
AI-Assisted Underwriting Models for Improving Risk Assessment Accuracy in U.S. Insurance Markets
AI-assisted underwriting has gained prominence as insurers seek to improve risk assessment accuracy within increasingly complex U.S. insurance markets. This quantitative study evaluated the performance of AI-assisted underwriting models relative to conventional underwriting models using policy-level data from U.S. personal automobile and residential property insurance portfolios. The analytical sample comprised 48,620 underwriting observations across 18 U.S. states, with personal automobile insurance representing 64.7% of policies and residential property insurance accounting for 35.3%. Risk assessment accuracy was operationalized as a multi-dimensional construct encompassing discrimination, calibration alignment, loss sensitivity, and stability. Descriptive results showed that AI-assisted models achieved higher average discrimination (mean = 0.748, SD = 0.058) compared with conventional models (mean = 0.692, SD = 0.041), alongside improved loss sensitivity (0.721 versus 0.667). Calibration alignment increased from a mean of 0.914 under conventional models to 0.941 under AI-assisted models, while stability declined slightly from 0.884 to 0.861, indicating greater segment-level variability. Regression analysis confirmed statistically significant effects of AI-assisted models on discrimination (β = 0.056, p < 0.001), calibration alignment (β = 0.031, p < 0.001), and loss sensitivity (β = 0.049, p < 0.001), with adjusted R² values ranging from 0.32 to 0.41 across accuracy dimensions. Enriched data inputs produced additional gains in discrimination (β = 0.043, p < 0.001) and loss sensitivity (β = 0.038, p < 0.001), independent of model family. Reliability analysis demonstrated strong internal consistency for composite accuracy constructs, with Cronbach’s alpha values between 0.816 and 0.889. Overall, the findings provided quantitative evidence that AI-assisted underwriting models improved multiple dimensions of risk assessment accuracy in U.S. insurance markets, while introducing measurable trade-offs in performance stability across states and underwriting tiers.
Copyright (c) 2022 Tahmina Akter Bhuya Mita, Rebeka Sultana (Author)
Remote Sensing Based Integrity Assessment of Infrastructure Corridors Using Spectral Anomaly Detection and Material Degradation Signatures
This study developed and empirically evaluated a quantitative remote sensing–based framework for integrity assessment of infrastructure corridors by integrating spectral anomaly detection, material degradation signature engineering, multisensor fusion, and time-series change analytics. Corridor integrity was operationalized as a continuous, segment-level outcome derived from multispectral, thermal, and synthetic aperture radar observations constrained to corridor footprints. The analytical dataset consisted of 480 corridor segments, retained after quality screening from an initial extraction of 520 segments, and stratified across land-cover adjacency, terrain class, and exposure conditions. Spatially blocked partitions were applied to reduce spatial dependence bias, producing 336 development segments and 144 testing segments with comparable contextual and condition distributions. Descriptive analysis showed moderate right-skew in spectral anomaly intensity (mean = 0.42, SD = 0.21) and wider dispersion in degradation signatures (mean = 0.55, SD = 0.27), while fused integrity scores exhibited reduced variance (mean = 0.48, SD = 0.18), indicating stabilization through multisensor integration. Reliability analysis confirmed strong internal consistency for composite constructs, with Cronbach’s alpha values ranging from 0.79 to 0.88 across exposure strata. Multiple regression modeling demonstrated substantial explanatory power, with the final model achieving R² = 0.62 and remaining statistically significant (F = 153.4, p < 0.001). Material degradation signatures showed the strongest standardized effect on corridor integrity (β = 0.42, p < 0.001), followed by spectral anomaly intensity (β = 0.31, p < 0.001) and temporal change constructs (β = 0.19, p < 0.001), while contextual controls exhibited smaller but significant contributions. Diagnostic checks indicated low multicollinearity (maximum VIF = 2.14) and minimal residual autocorrelation after spatial blocking (Durbin–Watson = 1.96). Hypothesis testing results remained stable under conservative multiple-testing adjustment, confirming robustness of inferential conclusions. Overall, the findings demonstrated that physically interpretable, multisensor-derived indicators can be transformed into reliable and statistically defensible measures of corridor integrity, supporting quantitative monitoring across heterogeneous and spatially extensive infrastructure systems.
Copyright (c) 2022 Ratul Debnath, Subrato Sarker (Author)
Quantitative Benchmarking of ERP Analytics Architectures: Evaluating Cloud vs On-Premises ERP Using Cost-Performance Metrics
This study addresses the problem that organizations choose between cloud and on-premises ERP analytics without workload-normalized evidence, materially increasing cost and performance risk. The purpose was to benchmark ERP analytics architectures and test how architecture type relates to analytics effectiveness. Using a quantitative, cross-sectional, case-based design, objective cost and performance indicators were extracted over a fixed four-week window and paired with a 5-point Likert survey from cloud and on-prem cases (n = 152 valid responses; cloud-exposed n = 79, on-prem-exposed n = 73). Key variables included architecture type (cloud = 1), total monthly analytics-related cost and cost per active user, latency (median and 95th percentile), throughput under 50-user concurrency, availability, incident rate and recovery time, and perceptual constructs: System Quality (SQ), Information Quality (IQ), Service Quality (ServQ), User Satisfaction (US), and Analytics Effectiveness (AE). The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha: SQ = .89, IQ = .86, ServQ = .84, US = .88, AE = .90), Pearson correlations, and multiple regression with controls (usage frequency, experience, role). Results show that cloud delivered lower cost ($48,200 vs $61,750 per month; $214 vs $289 per user) and stronger performance (median latency 2.3 s vs 3.7 s; 95th percentile 6.9 s vs 10.8 s; throughput 1,420 vs 1,050 queries/hour; availability 99.91% vs 99.62%; incidents 3 vs 6 per month; mean time to recovery 38 vs 64 minutes). Perceptions aligned, with higher AE for cloud (M = 4.14, SD = 0.60) than on-prem (M = 3.49, SD = 0.73). AE correlated most strongly with US (r = .71) and measured performance (r = .55), and regression explained substantial variance (Adjusted R² = .49), with performance (β = .34, p < .001), architecture (β = .21, p = .002), and cost efficiency (β = .17, p = .009) as significant predictors. Implications are that ERP analytics selection should use repeatable workload definitions, percentile-based SLAs, and TCO accounting that includes support and downtime, because performance stability and cost efficiency jointly drive decision value.
Copyright (c) 2020 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
Explainable AI Models for Transparent Grammar Instruction and Automated Language Assessment
This study addresses the problem that cloud-hosted AI grammar feedback and automated scoring tools are often experienced as opaque, which can weaken transparency, trust, and perceived fairness and ultimately reduce learning value and adoption in real institutional, enterprise-managed deployments. The purpose was to quantify how explainability features shape user outcomes and to test whether explanation clarity, actionability, and consistency predict perceived transparency, trust, fairness, perceived learning effectiveness, and acceptance or intention to use within a quantitative, cross-sectional, case-based design using a five-point Likert instrument and hypothesis testing through associations and prediction models. The sample comprised N = 210 end users from a single case setting with meaningful system exposure (2–4 weeks: 29.5%; 5–8 weeks: 44.8%; 9+ weeks: 25.7%), providing a realistic cloud or enterprise usage context for perceptions of explainable feedback and scoring. Key variables were operationalized as Explanation Clarity, Explanation Actionability, Explanation Consistency, Perceived Transparency, Trust in AI Outputs, Perceived Fairness, Perceived Learning Effectiveness, and Acceptance or Intention. The analysis plan applied descriptive statistics to profile construct levels, internal consistency reliability testing, Pearson correlations to evaluate hypothesized relationships, and multiple regression to estimate unique predictor effects while controlling overlap among constructs. Headline findings showed consistently positive perceptions above the neutral midpoint, including Clarity (M = 3.98, SD = 0.62), Actionability (M = 3.87, SD = 0.66), Transparency (M = 3.81, SD = 0.64), Trust (M = 3.76, SD = 0.68), Fairness (M = 3.69, SD = 0.73), Learning Effectiveness (M = 3.85, SD = 0.65), and Acceptance (M = 3.90, SD = 0.63). Reliability was strong across constructs (α range .83 to .90). Correlations supported the mechanism that clearer explanations strengthen transparency and that transparency supports trust, for example Clarity–Transparency r = .62 and Transparency–Trust r = .63 (p < .001), while Actionability–Learning Effectiveness r = .58 and Trust–Acceptance r = .59 (p < .001). In regression, the learning model achieved R² = .56 with Actionability as the strongest predictor (β = .36, p < .001), followed by Transparency (β = .21, p = .002) and Clarity (β = .17, p = .009); the acceptance model achieved R² = .59, led by Trust (β = .29, p < .001) and Fairness (β = .22, p = .001), with Transparency and Actionability also contributing. These findings imply that cloud and enterprise deployments should prioritize explanation designs that are not only understandable but concretely actionable, while governance and communication features that enhance transparency and fairness are central to calibrated trust and sustained adoption.
Copyright (c) 2023 Fahimul Habib (Author)
ADVANCING TRAUMA-INFORMED PSYCHOTHERAPY AND CRISIS INTERVENTION FOR ADULT MENTAL HEALTH IN COMMUNITY-BASED CARE: INTEGRATING NEURO-LINGUISTIC PROGRAMMING
This quantitative study examined the effectiveness of an integrated trauma-informed psychotherapy and crisis intervention approach, incorporating structured communication-based techniques consistent with Neuro-Linguistic Programming–oriented delivery, within a community-based adult mental health setting. A quasi-experimental pretest–posttest design was applied to data collected from 180 adult service users receiving routine outpatient psychotherapy and crisis stabilization services. Standardized instruments were administered at baseline and post-intervention to assess trauma-related symptom severity, acute psychological distress, emotional regulation capacity, distress tolerance, perceived control, coping efficacy, psychosocial functioning, and quality of life. Internal consistency reliability across all multi-item scales was acceptable to excellent, with Cronbach’s alpha values ranging from .81 to .94 at baseline and .83 to .94 post-intervention. Descriptive analyses indicated substantial baseline clinical burden, with 62.2% of participants scoring above clinical thresholds for trauma-related symptoms and 67.2% exhibiting elevated emotional regulation difficulties. Post-intervention results demonstrated consistent improvement across all constructs, including reductions in mean trauma symptom severity from 48.6 to 34.2 and acute psychological distress from 21.8 to 14.1, alongside increases in distress tolerance from 31.6 to 39.8 and quality-of-life scores from 46.8 to 58.4. Multivariate regression analyses showed that therapy session dosage was a significant positive predictor of improvement across outcomes, explaining up to 46% of the variance in trauma symptom change and 38% of the variance in psychosocial functioning change. Higher crises contact frequency was negatively associated with outcome improvement and positively associated with crisis recurrence. Mechanism-consistent patterns were observed, with changes in emotional regulation, perceived control, and coping efficacy significantly associated with reductions in trauma symptoms and gains in functional recovery. Subgroup analyses indicated differential improvement based on baseline trauma severity, comorbidity status, and socioeconomic indicators. Overall, the findings provide empirical support for integrated trauma-informed and crisis-oriented care models in community adult mental health services, demonstrating multidimensional recovery patterns across psychological, emotional, functional, and quality-of-life domains under routine clinical conditions.
Copyright (c) 2025 Amena Begum Sumi (Author)
EHS Analytics for Improving Hazard Communication, Training Effectiveness, and Incident Reporting in Industrial Workplaces
This study addresses a persistent problem in industrial enterprises: EHS programs generate substantial data, yet many workplaces struggle to translate those signals into consistently clear hazard communication, effective safety training, and high-quality incident reporting. The purpose was to test whether stronger EHS analytics capability predicts these upstream EHS process outcomes within an enterprise case workplace that uses routine EHS logs, dashboards, and trend reviews. This responds to growing interest in leading indicators and data-driven safety governance. A quantitative, cross-sectional, case-based survey was administered to employees across operational roles. After data-quality screening, 210 valid responses were retained from 228 submissions, with frontline operators representing 52.4%, supervisors 27.6%, and EHS or support staff 20.0%; mean experience was 6.8 years (SD = 4.9). The independent variable was EHS Analytics Capability (EHSAC, 8 items; M = 3.62, SD = 0.67; α = 0.88) and the dependent variables were Hazard Communication Quality (HCQ, M = 3.71, SD = 0.63; α = 0.86), Training Effectiveness (TE, M = 3.68, SD = 0.61; α = 0.89), and Incident Reporting Quality (IRQ, M = 3.55, SD = 0.70; α = 0.87). The analysis plan combined descriptive statistics, reliability testing, Pearson correlations, and ordinary least squares regressions, followed by robustness models controlling for role and experience. EHSAC showed moderate-to-strong correlations with HCQ (r = 0.56), TE (r = 0.52), and IRQ (r = 0.49), all p < .001. Regression results provided convergent evidence, with EHSAC significantly predicting HCQ (β = 0.56, t = 10.42, R² = 0.34), TE (β = 0.52, t = 9.32, R² = 0.29), and IRQ (β = 0.49, t = 8.36, R² = 0.24), and remaining significant with controls (β = 0.51, 0.47, 0.43; adjusted R² = 0.38, 0.32, 0.27). Item diagnostics indicated that timely feedback after reporting (M = 3.21) and post-training reinforcement (M = 3.39) were key gaps. Findings imply that investing in analytics capability and strengthening feedback loops can measurably improve communication, learn transfer, and report quality in enterprise EHS systems.
Copyright (c) 2023 Jahangir Shekh, Muhammad Mohiul Islam (Author)
A Hybrid Genetic Algorithm–Neural Network Framework for Optimizing Thermal and Mechanical Properties of 3D-Printed Components: An Empirical Study
Additive manufacturing, particularly fused deposition modeling (FDM), enables the fabrication of complex polymer components; however, achieving simultaneous optimization of mechanical and thermal performance remains challenging due to the nonlinear and interdependent nature of process parameters. This study presents an empirical hybrid optimization framework integrating an Artificial Neural Network (ANN) with a Genetic Algorithm (GA) to optimize the mechanical and thermal properties of FDM-fabricated components. A quantitative experimental design was employed, generating an empirical dataset from 120 printed specimens produced across 40 experimental runs with systematic variation of layer thickness (0.12–0.28 mm), raster angle (0–90°), infill density (25–100%), print speed (35–75 mm/s), and extrusion temperature (195–235 °C). Mechanical testing results showed tensile strength values ranging from 43.7 ± 2.9 MPa to 52.8 ± 3.1 MPa across build orientations, flexural strength from 76.8 ± 4.7 MPa to 88.2 ± 4.5 MPa, and elastic modulus from 2.05 ± 0.07 GPa to 2.42 ± 0.08 GPa. Thermal measurements indicated thermal conductivity values between 0.26 ± 0.02 W/m·K and 0.31 ± 0.02 W/m·K, with warpage ranging from 0.42 ± 0.06 mm to 0.61 ± 0.07 mm. The ANN achieved high predictive accuracy, with R² values of 0.94 for mechanical properties and 0.92 for thermal properties. GA-based optimization identified parameter configurations that improved tensile strength by 8.4 MPa, flexural strength by 11.2 MPa, elastic modulus by 0.29 GPa, and thermal conductivity by 0.06 W/m·K, while reducing warpage by 0.17 mm and dimensional change by 0.28% relative to baseline conditions. Empirical validation confirmed close agreement between predicted and measured results, demonstrating the effectiveness of the hybrid GA–ANN framework for data-driven optimization of FDM process parameters.
Copyright (c) 2025 Md Tawfiqul Islam (Author)
AN EMPIRICAL EVALUATION OF MACHINE LEARNING TECHNIQUES FOR FINANCIAL FRAUD DETECTION IN TRANSACTION-LEVEL DATA
Financial institutions increasingly rely on cloud hosted, data driven transaction monitoring, yet many fraud programs still struggle to balance fraud capture with false alert workload. This study tested how readiness determinants shape fraud detection effectiveness and compared machine learning classifiers on transaction level data in a quantitative, cross sectional, case-based design. Survey data were collected from 180 practitioners in one enterprise fraud platform case, spanning fraud and risk analysts, compliance and audit, IT and data engineering, and operations management, complemented by transaction records from the same environment. Independent variables were Data Quality, System Integration, Analytics Competency, Model Interpretability, Management Support, and Compliance Readiness; the dependent variable was Fraud Detection Effectiveness. Analysis included reliability testing, descriptive statistics, Pearson correlations, multiple regression, and an ML benchmark using precision, recall, F1, and ROC AUC with cross validation and threshold sensitivity. All constructs were reliable (Cronbach alpha .81 to .89). Fraud Detection Effectiveness was moderately high (M 3.74, SD 0.62), while System Integration was the weakest area (M 3.41, SD 0.71). Correlations were positive, strongest for Data Quality (r .62) and Model Interpretability (r .49). The regression model explained 53 percent of variance (F (6,173) 32.41, R2 .53); Data Quality (beta .36, p < .001), Model Interpretability (beta .19, p .002), Management Support (beta .17, p .004), and Analytics Competency (beta .14, p .017) were significant, while System Integration was not (beta .07, p .192). On transaction evaluation, XGBoost achieved the best balance (Precision 0.84, Recall 0.79, F1 0.81, ROC AUC 0.93) and remained stable (F1 0.80 ± 0.03). Profiling showed higher fraud rates at night (2.8 percent) and in high velocity bursts (4.6 percent). Compliance Readiness showed borderline influence (beta .09, p .052), and mid-range amounts of $120 to $500 contained 46.9 percent of fraud cases. Implications are that data governance and explainability should be treated as core controls alongside model selection, improving performance, auditability, and threshold tuning to match investigation capacity.
Copyright (c) 2023 Md Jamil Ahmmed, Mostafa Kamal (Author)
INTELLIGENT CONDITION MONITORING AND FAULT DIAGNOSIS OF ELECTRICAL POWER AND CONTROL SYSTEMS USING MACHINE LEARNING–BASED PREDICTIVE ANALYTICS
Intelligent condition monitoring and fault diagnosis of electrical power and control systems using machine learning–based predictive analytics was quantitatively investigated to evaluate diagnostic reliability, robustness across operating regimes, and explanatory value under realistic data conditions. The study analyzed a large multi-asset dataset consisting of 211,200 time-window observations derived from electrical, thermal, mechanical, insulation-related, and control-residual measurements, including 312 documented fault events. Descriptive analysis showed strong class imbalance, with healthy operation representing 88.2% of observations, degraded states 7.1%, and fault states 4.7%, and fault behavior occurring in temporally clustered events with a median duration of 18 minutes. Correlation and collinearity analyses indicated moderate within-domain feature dependence but low cross-domain redundancy, with mean variance-based collinearity indices remaining below conservative thresholds across predictor groups, supporting multivariate modeling. Reliability analysis demonstrated acceptable to strong internal consistency across indicator domains, with internal consistency coefficients ranging from 0.76 to 0.87 and temporal stability coefficients exceeding 0.86 under stable operating regimes. Multivariate regression and hypothesis testing results showed that multi-domain predictive models explained substantially more variance in fault outcomes than baseline context-only models, with adjusted fit indices improving from approximately 0.27 to 0.59 after integration of regime-aware normalization and multi-domain features. Hypothesis testing confirmed statistically significant and practically meaningful improvements in diagnostic performance, with effect sizes exceeding 0.50 for multi-domain versus baseline comparisons. Prognostic-oriented analyses further demonstrated statistically meaningful associations between degradation indicators and time-to-event proxies, indicating systematic risk escalation prior to documented intervention events. Overall, the findings provided quantitative evidence that regime-aware, multi-domain machine learning–based predictive analytics improved fault detection reliability, reduced confounding from operating variability, and supported interpretable diagnostic inference in electrical power and control systems.
Copyright (c) 2026 Tasnim Kabir (Author)
AI-DRIVEN PREDICTIVE MAINTENANCE FOR MOTOR DRIVES IN SMART MANUFACTURING A SCADA-TO-EDGE DEPLOYMENT STUDY
This quantitative study evaluated AI-driven predictive maintenance for motor drives in smart manufacturing by comparing three deployment architectures: SCADA-only, edge-only, and hybrid SCADA-to-edge fusion. A longitudinal multi-asset dataset was analyzed from 48 motor drives monitored over 16 weeks, representing 18,720 operating hours, 12,614,380 SCADA tag records, and 3,456,000 edge analysis windows. Outcomes were defined using tiered event labels, including 37 Tier-1 confirmed failures, 64 Tier-2 verified defect findings, and 142 Tier-3 operational abnormality episodes. Time-based evaluation and motor-drive clustering controls were applied, and performance was assessed under a fixed alert-budget policy using event detection, precision, false alarm density, and lead time outcomes, alongside deployment feasibility metrics. Compared with SCADA-only, edge-only deployment improved event detection (odds ratio = 1.62, p = .014) and increased precision from 0.41 to 0.53 (p = .006), while reducing false alarms from 1.48 to 1.31 per 100 operating hours (p = .041). Hybrid fusion produced the strongest predictive outcomes, increasing event detection (odds ratio = 2.08, p < .001), raising precision to 0.58 (p < .001), and lowering false alarms to 1.27 per 100 operating hours (p = .018). Median lead time increased from 18.6 hours (SCADA-only) to 31.4 hours (edge-only) and 37.9 hours (hybrid) (p < .01). Deployment tradeoffs were quantified: inference latency increased from 18 ms per window (SCADA-only) to 122 ms (edge-only) and 141 ms (hybrid), while bandwidth use was reduced by 96.1%–96.8% in edge and hybrid configurations through feature-level reporting. Overall, SCADA-to-edge fusion yielded the most stable and effective predictive maintenance performance across operating regimes with manageable system overhead.
Copyright (c) 2025 Zaheda Khatun (Author)