American Journal of Scholarly Research and Innovation
The American Journal of Scholarly Research and Innovation (AJSRI) ONLINE ISSN (3067-2163) is a leading Peer-Reviewed, Open Access International Journal dedicated to fostering innovation and excellence across a wide array of academic disciplines. The journal provides a platform for scholars, researchers, and professionals to disseminate groundbreaking research that addresses real-world challenges through interdisciplinary collaboration. As a monthly publication, AJSRI releases 12 issues annually, maintaining a consistent schedule that ensures the timely distribution of high-quality, impactful research. The journal encompasses diverse fields, including Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering, making it a truly interdisciplinary platform. AJSRI collaborates with international and national conferences to publish extended versions of selected papers, furthering the visibility and impact of researchers’ work. The journal is committed to publishing original, high-quality research that strengthens academic knowledge and drives innovation.
AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency
This study examined why supply chains still face preventable execution errors and inconsistent KPI performance despite dashboards, because reporting visibility does not consistently translate exceptions into coordinated corrective action. The purpose was to test whether AI-enabled enterprise scorecard capability improves operational error reduction and supply chain consistency, and whether AI alert trust and actionability act as mechanisms in an enterprise case setting. A quantitative, cross-sectional, case-based design used a five-point Likert survey of N = 210 scorecard users from planning, procurement, warehouse, logistics, and BI or reporting functions. Key variables were Scorecard Capability (SC_CAP), Alert Trust (TRUST), Alert Actionability (ACT), Operational Error Reduction (OER), and Supply Chain Consistency (SCC). Analysis combined descriptive statistics, reliability testing, Pearson correlations, and multiple regression for hypotheses and mechanisms. Reliability was acceptable to excellent (α = .86–.91; SC_CAP α = .89; OER α = .91; SCC α = .90), supporting composite-index modeling. Respondents reported moderately high capability (SC_CAP M = 3.86, SD = 0.62) and broad availability of KPI dashboards (82%), drill-down traceability (74%), and automated exception alerts (69%). Correlations supported the expected direction: SC_CAP related to OER (r = .62, p < .001) and SCC (r = .58, p < .001), and OER related to SCC (r = .55, p < .001). In regression, SC_CAP predicted OER (β = .47, p < .001, R² = .43) and SCC (β = .34, p < .001, R² = .38), and OER predicted SCC (β = .29, p < .001, R² = .31). Adding TRUST and ACT increased explained variance (OER R² = .51; SCC R² = .49) and showed partial mediation: SC_CAP’s OER effect reduced to β = .36 while TRUST (β = .18, p = .004) and ACT (β = .21, p = .001) remained significant, and ACT was especially influential for SCC (β = .24, p < .001). Implications are that organizations should manage AI scorecards as operational control infrastructure by strengthening KPI governance and drill-down traceability and by improving alert credibility and usability through clear ownership and response playbooks, so exceptions lead to repeatable actions that reduce errors and stabilize execution.
Copyright (c) 2024 Md Khaled Hossain, Md. Morshedul Islam (Author)
Quantitative Evaluation of Snort IDS Rule Effectiveness against DDOS, Brute-Force, and MITM Attacks in a Controlled Lab Network
Intrusion detection systems play a critical role in protecting network infrastructures from increasingly sophisticated cyber threats. This study quantitatively evaluated the effectiveness of Snort intrusion detection system rules in detecting three common categories of cyberattacks, namely distributed denial-of-service attacks, brute-force authentication attempts, and man-in-the-middle interception attacks within a controlled laboratory network environment. A quantitative experimental design was implemented in which simulated attack scenarios were executed across multiple experimental runs while network traffic was monitored using Snort IDS. The dataset consisted of packet capture logs, IDS alert records, and rule activation events generated during 30 experimental attack sessions and 10 baseline monitoring sessions. Across all sessions, a total of approximately 1,245,600 network packets were captured and analyzed, producing 360 confirmed attack events used to evaluate IDS detection performance. Detection effectiveness was measured using several quantitative indicators including detection rate, alert frequency, rule trigger counts, false positive events, and false negative occurrences. The results indicated that detection performance differed significantly across the three attack categories. Distributed denial-of-service attacks achieved the highest detection rate at 94.2%, with 113 out of 120 attack events successfully identified by the IDS. Brute-force authentication attacks produced a detection rate of 88.3%, with 106 detected events out of 120 attempts. In contrast, man-in-the-middle attack simulations produced a lower detection rate of 76.7%, with 92 detected events and 28 undetected attack instances. The IDS generated a total of 1,128 alerts and 1,542 rule activation events during monitoring, with DDoS scenarios producing the highest alert frequency due to the high-volume traffic patterns associated with flooding attacks. Statistical analysis confirmed that the differences in detection performance across attack categories were statistically significant. Effect size analysis further demonstrated a large practical difference between DDoS and MITM detection performance. The findings indicated that rule-based intrusion detection systems were highly effective in identifying volumetric and repetitive attack patterns but showed comparatively lower performance in detecting subtle packet interception behaviors. These results contribute empirical evidence regarding the operational strengths and limitations of rule-based intrusion detection systems in controlled network security environments.
Copyright (c) 2024 Md. Morshedul Islam, MD Zahedul Islam (Author)
Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting
This study addresses the persistent problem that finance teams in cloud-enabled and ERP-integrated enterprises often lack decision-grade visibility into short-horizon cash flow and liquidity, because working-capital timing is driven by heterogeneous receivables and payables behaviors that are poorly captured by simple rules or baseline statistical forecasts. The purpose was to quantify, using a quantitative cross-sectional, case-based evidence map, how predictive analytics and machine learning improve cash flow and liquidity forecasting for working capital management across enterprise cases documented in the literature. The study design treated each documented application instance as a “case” and synthesized N = 35 cases spanning cloud and enterprise data ecosystems (ERP, treasury workflows, and banking-adjacent settings), with sector-identifiable enterprise contexts reported in 18 of 35 cases (51.4%). Key variables included forecasting target (AR cash-in, cash position, AP cash-out, liquidity shortfall), model family (boosting/ensembles, regression, SVM, deep sequence, hybrid), feature strategy (aging, calendar, behavioral history, bank-reconciliation signals), and implementation conditions (governance, explainability, workflow integration, monitoring). The analysis plan used frequency distributions, cross-tabulation-style comparisons, and hypothesis-aligned evidence-strength scoring, plus improvement-rate coding versus baselines. Headline findings show ML outperformed statistical or rule baselines in 24/35 studies (68.6%) with strongest support for H1 (4.2/5), while tree-based ensembles were most prevalent (57.1%) and had the highest within-family improvement rate (75.0%). Feature enrichment mattered: among cases using aging structures, 82.6% reported better performance when behavioral or process-state features were added (evidence strength 4.0/5). For liquidity stress handling, hybrids were tested in 40.0% of cases and outperformed single models under volatility proxies in 71.4% of that subset (evidence strength 3.6/5). Practical implications are that enterprises gain the most when ML forecasting is embedded into governance and workflows: governance detail appeared in 42.9% of cases with 80.0% uptake, and workflow integration appeared in 40.0% with 78.6% uptake, indicating that trust and integration convert accuracy gains into operational impact.
Quantitative Impact Assessment of Digital Payment Solutions on Small Business Revenue Panel Data Analysis From 1,200 U.S. SMES
This study assessed the revenue impact of Digital Payment Solutions (DPS) using a longitudinal panel dataset of 1,200 U.S. small and medium-sized enterprises (SMEs) observed over 14,400 firm-period observations. The sample included SMEs from retail (30.0%), food service (20.0%), personal services (17.5%), professional services (16.7%), and repair-based industries (15.8%), with 70.0% employing fewer than 20 employees. Descriptive results showed mean logged revenue of 10.42 (SD = 0.88) and an average digital sales share of 49.6% (SD = 24.3%). DPS adoption increased from 68.0% in the early period to 85.0% by the final period. Reliability analysis confirmed strong internal consistency for the main constructs, including DPS intensity (α = 0.86), breadth (α = 0.79), and integration (α = 0.82). Two-way fixed effects regression models controlling for firm size, age, sector, location, channel mix, and competitive density indicated that DPS adoption was positively associated with revenue (β = 0.082, p < 0.001), corresponding to an average revenue increase of approximately 8.2% relative to pre-adoption levels. Usage intensity demonstrated the strongest effect (β = 0.214, p < 0.001). Payment breadth was also significant (β = 0.036, p = 0.010), while integration showed a moderate positive effect (β = 0.051, p = 0.020). Dynamic models confirmed revenue persistence (lagged revenue β = 0.641, p < 0.001) and maintained coefficient stability. The baseline model explained 42% of within-firm revenue variation (within R² = 0.42). Overall, findings provided robust quantitative evidence that DPS adoption, particularly when used intensively and integrated operationally, was positively associated with SME revenue performance in the U.S. context.
Copyright (c) 2024 Iftekhar Ahmed, Md Tohidul Islam (Author)
Cyber-Resilient Marketing Intelligence Systems for Fraud Detection and Data Integrity in Digital Campaigns
This quantitative study examined the role of cyber-resilient marketing intelligence system capability in shaping data integrity reliability and fraud detection performance within digital campaign analytics environments. Drawing on data collected from 300 organizations operating multi-platform digital marketing campaigns, the study employed multivariate regression and integrated modeling techniques to evaluate direct, indirect, and conditional relationships among cyber resilience capability, data integrity reliability, and fraud detection performance while accounting for marketing intelligence capability, platform complexity, and contextual controls. Descriptive findings indicated moderate-to-high levels across core constructs, with mean values of 3.78 for cyber-resilient marketing intelligence capability, 3.81 for data integrity reliability, and 3.59 for fraud detection performance on a five-point scale, suggesting relatively advanced analytics and resilience maturity within the sample. Correlation analysis showed significant positive associations between cyber resilience capability and data integrity reliability (r = 0.62), cyber resilience capability and fraud detection performance (r = 0.54), and data integrity reliability and fraud detection performance (r = 0.58), providing preliminary support for the proposed relationships without indicating problematic construct overlap. Regression results demonstrated that cyber resilience capability was the strongest predictor of data integrity reliability (β = 0.48, p < .001) and fraud detection performance (β = 0.29, p < .001), even after controlling for marketing intelligence capability and platform complexity. Mediation analysis revealed that data integrity reliability partially mediated the relationship between cyber resilience capability and fraud detection performance, with a statistically significant indirect effect (β = 0.17, p < .001) alongside a remaining direct effect. Moderation analysis further showed that platform complexity strengthened the positive effect of cyber resilience capability on fraud detection performance (interaction β = 0.11, p = .018), indicating greater resilience benefits in more complex, multi-platform environments. Overall, the findings demonstrated that cyber-resilient marketing intelligence systems were associated with more reliable data handling, reduced integrity degradation, and more stable fraud detection outcomes, supporting an integrated quantitative perspective on resilience, integrity, and analytical performance in digital marketing campaigns.
Copyright (c) 2023 Molla Al Rakib Hasan, Md. Towhidul Islam (Author)
AI-Driven Diagnostic Modelling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems
This study examined AI-Driven Diagnostic Modeling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems using a framework-level quantitative approach that integrated predictive performance, calibration reliability, and privacy-risk evaluation. Structured evidence mapping process reviewed 72 peer-reviewed papers to define constructs, metrics, and privacy threat considerations, after which a retrospective multi-site design was implemented to compare centralized non-private modeling, differentially private training, federated learning, and hybrid privacy configurations under standardized cohort rules and leakage-resistant validation. The analytic dataset included 48,620 adult patients contributing 162,904 encounters across three health systems, with a mean age of 57.8 years (SD = 16.4) and 52.6% female representation. Median encounter density was 3.0 encounters per patient (IQR = 2.0–5.0), and 18.9% of patients were classified as low-contact (≤1 encounter during the lookback window). Data completeness varied by domain, with missingness of 6.8% for vital signs, 18.4% for core laboratories, and 9.6% for medication indicators. Overall diagnostic outcome prevalence was 8.6%, ranging from 7.5% to 9.6% across sites. Correlation analysis indicated a strong relationship between encounter density and measurement frequency (r = 0.58) and a moderate association between comorbidity burden and outcome occurrence (r = 0.34). Collinearity diagnostics showed elevated redundancy among utilization predictors, including a variance inflation factor of 6.8 for total encounters and 5.9 for inpatient admissions, supporting composite consolidation before regression. Multivariable regression showed that differentially private training was associated with a modest reduction in discrimination (ΔAUC = −0.012) and increased calibration error (+0.021), while federated learning showed minimal average discrimination change (ΔAUC = −0.004) but greater cross-site dispersion. Privacy-risk evaluation indicated reduced membership inference leakage under privacy-preserving configurations, with leakage reductions of −0.083 for differentially private training and −0.071 for hybrid training relative to baseline. Overall, accuracy and privacy outcomes co-varied as system-level properties shaped by data quality, institutional heterogeneity, and framework design choices.
Copyright (c) 2026 Jinnat Ara, Samiha Binte Abdullah (Author)
Digital Ledger Optimization Techniques for Enhancing Transaction Speed and Reporting Accuracy in Accounting Systems
This quantitative study examined how digital ledger optimization techniques influenced transaction speed and reporting accuracy in accounting systems using archival operational data. A panel dataset comprising 360 unit–period observations from 30 accounting environments observed over 12 reporting periods was analyzed. Optimization intensity was measured across three layers—data, process, and control—while transaction speed was captured through posting latency, throughput, and queue waiting time, and reporting accuracy was measured using reconciliation mismatch rates, journal error rates, and correction entry frequency and value. Descriptive results showed that median posting latency was 9.6 seconds, while tail latency reached 44.2 seconds at the 95th percentile, confirming pronounced peak-load effects. Regression findings indicated that data-layer optimization was strongly associated with reduced posting latency (β = –0.41, p < .001), while process-layer optimization was most strongly associated with increased throughput (β = 0.39, p < .001). Control-layer optimization demonstrated the strongest relationships with reporting accuracy outcomes, including lower reconciliation mismatch rates (β = –0.51, p < .001) and reduced correction entry frequency (β = –0.53, p < .001). Moderation analysis revealed that validation strictness reduced the magnitude of batching-related speed gains (interaction β = 0.13, p = .020), while automation maturity strengthened the relationship between optimization intensity and reporting accuracy (interaction β = –0.15, p = .009). Robustness checks confirmed that results remained stable when alternative dependent-variable definitions were applied, including tail latency and mismatch value. Overall, the findings demonstrated that transaction speed improvements were most strongly associated with data- and process-layer optimization, while reporting accuracy improvements were primarily associated with control-layer practices. The study provided empirical evidence that layered digital ledger optimization contributed to faster processing and more reliable financial reporting under real operational conditions.
Copyright (c) 2022 Faysal Khan, Shamsunnahar Chadni (Author)
AI Based Quantitative Optimization Models for FMCG Supply Chain Efficiency in High-Demand Markets: A Linear Programming and Mixed-Integer Programming Approach
This study addressed a persistent supply chain performance gap in fast moving consumer goods environments where AI forecasting and visibility tools exist, yet efficiency declines under demand surges because replenishment and distribution decisions are not optimized under real capacity and lead time constraints. The purpose was to quantify how AI enabled analytics capability and related planning practices influence supply chain efficiency and to validate the statistical results with prescriptive optimization outcomes. A quantitative, cross sectional, case-based design was used, combining survey evidence from 168 supply chain professionals with cloud and enterprise planning cases modeled under normal and high demand conditions. Key variables included AI analytics capability, forecasting effectiveness, inventory optimization practice, logistics and distribution planning quality, supplier coordination and lead time reliability. The analysis plan applied reliability testing, descriptive statistics, Pearson correlation, and multiple regression, followed by linear programming and mixed integer programming scenario evaluation with feasibility checks and forecast noise sensitivity testing. Reliability was acceptable across constructs (Cronbach alpha 0.79 to 0.88). Descriptive results showed moderately high AI analytics capability (M 3.84, SD 0.62) and forecasting effectiveness (M 3.71, SD 0.66), while overall efficiency remained moderate (M 3.52, SD 0.67). Supply chain efficiency correlated strongly with AI analytics capability (r 0.62, p < 0.001). The regression model explained 54 percent of variance in efficiency (R2 0.54, p < 0.001), with AI analytics capability as the strongest predictor (beta 0.34, p < 0.001), followed by forecasting effectiveness (beta 0.21, p = 0.001), inventory optimization practice (beta 0.17, p = 0.005), and supplier coordination (beta 0.14, p = 0.017); logistics planning was positive but not statistically significant (p = 0.075). Optimization results reinforced these findings: under normal demand, total cost decreased 11.8 percent (1.72M to 1.52M), service level improved from 92.1 to 96.0 percent, and stockouts reduced 18.4 percent; under high demand, cost decreased 9.3 percent (2.05M to 1.86M), service improved from 88.4 to 93.2 percent, and stockouts reduced 15.1 percent, with a 0.8 percent optimality gap and solutions remaining feasible across scenarios. With plus or minus 10 percent forecast noise, cost rose only 2.1 percent and service declined 0.9 points, indicating robustness. The findings imply that enterprises should prioritize analytics and forecasting governance, enforce disciplined inventory policy execution, and embed constraint-based optimization into routine planning to sustain cost and service performance during demand peaks.
Copyright (c) 2026 Md Shahab Uddin (Author)
GPU-Accelerated Physics-Informed Digital Twins for Real-Time State Estimation and Fault Localization in Distribution Grids
This study addresses a persistent operational problem in distribution-grid management: utilities need fast, trustworthy state estimation and fault localization to support restoration and switching decisions, yet conventional analytics are often too slow for real-time use and can be difficult to trust under changing grid conditions. The purpose of the study was to evaluate whether GPU-accelerated, physics-informed digital twins can measurably improve real-time state estimation, fault localization effectiveness, and operational decision support in cloud and enterprise deployment contexts. Using a quantitative, cross-sectional, case-based design, data were collected from a sample of N = 210 respondents drawn from cloud and enterprise distribution-grid digital-twin cases. The key independent variables were GPU Acceleration Capability, Physics-Informed Modeling Strength, Digital Twin Fidelity, and Data Integration Quality; the dependent variables were Real-Time State Estimation Performance, Fault Localization Effectiveness, and Operational Decision Support Value, with an additional trust indicator for physics-consistency confidence. The analysis plan employed reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling to estimate direct effects and the enabling pathway from acceleration and physics constraints to operational value. Reliability results indicated strong internal consistency across constructs (α = .83 to .91). Correlation results showed that GPU capability was positively associated with state estimation (r = .56, p < .001) and that physics-informed strength had an even stronger association with state estimation (r = .61, p < .001); state estimation was strongly related to fault localization (r = .63, p < .001). Regression findings confirmed that state estimation performance was significantly predicted by GPU capability (β = .29, p < .001) and physics-informed strength (β = .37, p < .001), explaining 54% of variance (R² = .54). Fault localization effectiveness was significantly predicted by state estimation (β = .41, p < .001), digital twin fidelity (β = .28, p < .001), and data integration quality (β = .19, p = .002), explaining 62% of variance (R² = .62). Operational decision support value was driven primarily by fault localization (β = .46, p < .001) and state estimation (β = .21, p = .004), with GPU providing incremental contribution (β = .14, p = .030), explaining 58% of variance (R² = .58). Trust results further indicated high physics-consistency confidence (M = 4.12, SD = 0.48). Overall, the study implies that utilities should prioritize physics-consistent modeling and GPU-ready architectures, while investing in data integration quality and twin fidelity to convert computational speed into reliable, actionable grid decisions.
Copyright (c) 2024 Zaheda Khatun, Md Hamidur Rahman (Author)
Machine Learning–Based Financial Forecasting and Valuation Models for Investment Decision-Making and Capital Allocation Efficiency
This study addresses a recurring enterprise problem: capital allocation decisions often rely on forecasts and valuation estimates that are inconsistent across tools and teams, leading to delayed approvals, mispriced projects, and inefficient resource deployment. The purpose of the study was to quantify how machine learning based forecasting and machine learning based valuation contribute to higher investment decision-making quality and improved capital allocation efficiency within cloud and enterprise decision contexts. A quantitative cross-sectional, case-based design was used, drawing on cloud and enterprise cases where ML dashboards and analytics were actively used in screening, budgeting, and portfolio governance. Data were collected using a structured 5-point Likert survey from N = 162 participants involved in the forecasting–valuation–allocation pipeline (analysts/associates 46.3%, managers/senior managers 32.1%, committee or strategic roles 21.6%). The key variables were ML Forecasting Effectiveness (FCAST), ML Valuation Effectiveness (VAL), Investment Decision-Making Quality (DMQ), and Capital Allocation Efficiency (CAE), with role and experience treated as controls; two additional indices were examined to clarify adoption conditions, Model Trust and Adoption Readiness (MTAI) and Forecasting–Valuation Alignment (FVAD). The analysis plan included reliability testing, descriptive statistics, Pearson correlations, multiple regression, and bootstrap mediation to test whether DMQ transmits the effects of FCAST and VAL to CAE. Reliability was strong (Cronbach’s alpha: FCAST = .88; VAL = .86; DMQ = .84; CAE = .87). Mean scores were above neutral (FCAST M = 3.94, SD = 0.61; VAL M = 3.88, SD = 0.64; DMQ M = 3.76, SD = 0.58; CAE M = 3.71, SD = 0.62), indicating generally favorable perceptions of ML support. Correlations were positive and significant (FCAST–DMQ r = .56; VAL–DMQ r = .52; DMQ–CAE r = .62; all p < .001). In regression, FCAST and VAL jointly predicted DMQ (R² = .41; FCAST β = .39, p < .001; VAL β = .31, p < .001), while CAE was explained by DMQ with additional direct effects (R² = .53; DMQ β = .45, p < .001; FCAST β = .18, p = .007; VAL β = .12, p = .049). Mediation results showed meaningful indirect effects via DMQ for forecasting (indirect = .18, 95% CI [ .10, .28]) and valuation (indirect = .14, 95% CI [ .07, .23]). Implementation implications are clear: enterprises should govern ML forecasting and valuation as an integrated decision system, strengthen model trust and adoption readiness (MTAI M = 3.82, SD = 0.55; 68.5% high readiness), and actively manage forecast–valuation alignment because misalignment is associated with lower allocation efficiency (FVAD–CAE r = −.34, p < .001).
Copyright (c) 2026 Shamsunnahar Chadni (Author)
AI-Driven Credit Scoring and Default Probability Modeling for Basel III Risk-Weighted Asset Optimization in Banking
This study addresses a problem: AI-driven credit scoring can strengthen probability of default (PD) estimation, but uneven data quality, governance controls, and explainability reduce model trust and constrain Basel III risk-weighted asset (RWA) optimization. The purpose was to quantify the pathway from AI capability to PD modeling effectiveness and from PD effectiveness to Basel III RWA optimization in a cloud-enabled enterprise case bank. A quantitative cross-sectional, case-based survey was administered; 300 questionnaires were distributed and 212 valid responses were analyzed (70.7% effective response rate). Key predictors were AI-driven credit scoring capability, data quality readiness, model governance maturity, and explainability readiness; PD modeling effectiveness served as the mechanism, and Basel III RWA optimization effectiveness was the outcome. Reliability was strong across constructs (Cronbach’s α = 0.84–0.90). The analysis plan combined statistics, Pearson correlations, and multiple regression with moderation testing. Mean ratings were positive (AI capability M = 3.82; PD effectiveness M = 3.91; RWA optimization M = 3.76 on a 1–5 scale). Correlations supported the framework, including an association between PD effectiveness and RWA optimization (r = 0.66, p < .001). In regression Model 1, PD effectiveness was predicted by AI capability (β = 0.34, p < .001), data quality (β = 0.21, p = .002), governance maturity (β = 0.25, p < .001), and explainability readiness (β = 0.14, p = .018), with R² = 0.57. In Model 2, PD effectiveness predicted RWA optimization (β = 0.51, p < .001) and governance retained a direct effect (β = 0.19, p = .004; R² = 0.49). Moderation results showed a significant AI × governance interaction (β = 0.11, p = .031; ΔR² = 0.02), indicating that stronger governance amplifies the benefits of AI capability for PD outcomes. Implications are that banks seeking Basel III capital efficiency via AI should invest not only in scoring capability, but also in data readiness, governance discipline, and explainability practices so that PD gains translate into defensible RWA optimization.
Copyright (c) 2026 Sazzadul Islam (Author)
A Hybrid Lean-Six Sigma Model with Automated Kaizen for Real-Time Quality Improvement
This study addresses the problem that many enterprise quality improvement programs generate dashboards and alerts yet still fail to convert real-time deviations into owned corrective actions and verified closure, which sustains defect recurrence and process instability. The purpose was to test whether a Hybrid Lean Six Sigma (LSS) capability base, strengthened by Automated Kaizen execution workflows, predicts Real-Time Quality Improvement (RQI) in a quantitative, cross-sectional, case-based model. The sample comprised n = 210 valid responses collected from operational roles across cloud-enabled enterprise quality cases, including operators/technicians (35.2%), QA/QC staff (21.0%), supervisors (19.5%), engineers (15.2%), and CI or Lean Six Sigma team members (9.0%). Key variables were LSS capability, Automated Kaizen effectiveness, signal-to-action actionability, Kaizen closure confidence, DMAIC-stage functioning under automation, and RQI, measured on 5-point Likert scales and aggregated as composite indices. The analysis plan applied reliability testing (Cronbach’s alpha), descriptive statistics, Pearson correlations, and multiple regression models to estimate both baseline effects and incremental explanatory power. Measurement reliability was strong (α = .85–.90), including LSS α = .88, Automated Kaizen α = .90, and RQI α = .87. Descriptively, respondents reported high LSS capability (M = 3.94) and moderate-to-high Automated Kaizen (M = 3.78) and RQI (M = 3.83). Correlations supported the hypothesized relationships: LSS correlated with RQI (r = .62, p < .001) and with Automated Kaizen (r = .58, p < .001), while Automated Kaizen correlated more strongly with RQI (r = .67, p < .001). Regression results showed LSS predicting RQI (β = .62; R² = .38); when Automated Kaizen was added, it became the stronger predictor (β = .49) while LSS remained significant but reduced (β = .33), increasing explained variance to R² = .55, consistent with Automated Kaizen serving as a practical execution pathway. Mechanism evidence highlighted that signal-to-action actionability strongly related to RQI (r = .69; β = .41, p < .001) and automation’s highest perceived leverage appeared in the DMAIC Measure (M = 3.96) and Control (M = 3.91) stages. Implications indicate that enterprises seeking real-time quality gains should prioritize alert relevance, workflow routing integrity, clear ownership, and control-plan updates so that digital signals reliably trigger, track, verify, and standardize Kaizen actions at scale.
Copyright (c) 2023 Shofiul Azam Tarapder, Md. Al Amin Khan (Author)
A Quantitative Data-Driven Evaluation of Cost Efficiency in Cloud and Distributed Computing for Machine Learning Pipelines
This study presents a quantitative evaluation of cost efficiency, performance behavior, and resource utilization in machine learning pipelines executed under cloud-based and distributed computing environments. Using a consolidated dataset that integrated pipeline execution logs, fine-grained telemetry, and infrastructure billing records, the analysis examined 120 replicated pipeline runs spanning training-dominant and inference-dominant workloads. Each run was decomposed into five pipeline stages—ETL, preprocessing, training, evaluation, and serving—enabling both run-level and stage-level assessment of cost and runtime behavior. Descriptive results indicated that cloud executions exhibited higher median total cost per run (USD 42.80, IQR 31.20–66.50) compared to distributed executions (USD 38.10, IQR 29.40–52.30), alongside greater cost dispersion driven by autoscaling, orchestration overhead, and network egress. Distributed runs demonstrated higher average compute utilization (median 74.8% vs. 61.2%) and lower idle time shares (11.6% vs. 18.9%), contributing to more stable cost behavior. Multivariate regression models explained a substantial proportion of cost variability (R² = 0.68 for total cost; R² = 0.54 for normalized cost efficiency). Job completion time (β = 0.018, p < 0.001) and network egress (β = 0.031, p = 0.001) emerged as the strongest positive predictors of total cost, while higher average compute utilization was associated with lower cost after controlling for runtime (β = −0.007, p < 0.001). In the cost-efficiency model, higher training throughput (β = 0.00042, p < 0.001) and higher utilization intensity improved efficiency, whereas orchestration overhead (β = −0.091, p = 0.015), higher inference concurrency (β = −0.033, p = 0.026), and increased tail latency reduced efficiency. Hypothesis testing confirmed statistically significant differences favoring distributed environments for normalized cost efficiency (Cohen’s d = 0.44) and training completion time, while cloud environments achieved lower inference tail latency (p95 difference ≈ −65 ms). Overall, the findings demonstrate that cost efficiency in machine learning pipelines is driven less by median throughput differences than by utilization stability, orchestration behavior, and network-driven variability. By empirically linking telemetry-derived predictors to monetary outcomes, this study provides robust quantitative evidence for infrastructure-aware cost optimization in modern machine learning systems.
Copyright (c) 2023 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
COMPUTATIONAL MODELING AND SIMULATION TECHNIQUES FOR MANAGING RAIL–URBAN INTERFACE CONSTRAINTS IN METROPOLITAN TRANSPORTATION SYSTEMS
Rail–urban interface constraints in metropolitan transportation systems reduce reliability and safety because dense stations, corridor conflict points, and community exposure can turn small disruptions into network-wide delays and crowding. This study examined whether computational modeling and simulation capability (CMSC) improves constraint-management effectiveness (CME) in an enterprise-scale metro rail case were cloud and enterprise analytics support scenario testing and decisions. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered to N = 312 professionals from operations/control (26.3%), planning/timetabling (20.5%), station management (17.9%), engineering/maintenance (19.9%), and safety/risk (15.4%); 41.0% were direct model users and 37.8% indirect users. Key variables were rail–urban interface constraint severity (RICS), CMSC, decision integration (DI), and CME. Data screening showed mean missingness of 1.8% and Harman single-factor variance of 32.6%. Reliability was strong (α: RICS .88, CMSC .91, DI .87, CME .90). The most severe constraints were station crowding/circulation (M = 4.21, SD = 0.62) and peak dwell-time variability (M = 4.08, SD = 0.67). CMSC was moderate-high (M = 3.78, SD = 0.64), with scenario analysis strongest (M = 3.92) and validation weakest (M = 3.49). CMSC and DI correlated positively with CME (r = .62 and .58; p < .001), while RICS correlated negatively (r = −.41; p < .001). Regression was significant (R² = .51): CMSC (β = .38) and DI (β = .29) increased CME, and RICS reduced it (β = −.17); CMSC benefits were stronger at higher DI (ΔR² = .03). Implications emphasize station-area actions, stronger validation governance, and institutionalized use of simulation outputs in routine decisions.
Copyright (c) 2024 Masud Rana, Hammad Sadiq (Author)
THE IMPACT OF DEEP LEARNING AND SPEAKER DIARIZATION ON ACCURACY OF DATA-DRIVEN VOICE-TO-TEXT TRANSCRIPTION IN NOISY ENVIRONMENTS
This quantitative, cross-sectional, case-based study investigated why cloud and enterprise voice-to-text deployments still produce variable transcription accuracy in noisy, multi-speaker settings and whether Deep Learning ASR Capability (DL) and Speaker Diarization Quality (SD) function as complementary drivers of perceived Transcription Accuracy in Noise (TA). Using a one-time Likert-scale survey (1 = strongly disagree to 5 = strongly agree), the study retained N = 156 usable responses from users and reviewers embedded in operational enterprise and cloud transcription cases. The research problem centers on the persistent gap between real-world acoustic difficulty and dependable transcript quality for analytics, compliance, and decision support. In the model, DL and SD were the core independent variables, TA was the dependent variable, and Noise Severity and Overlap Frequency were included as contextual controls to isolate technical effects from environmental difficulty. Descriptively, the case context was genuinely adverse, with Noise Severity M = 3.94 (SD = 0.71) and Overlap Frequency M = 3.52 (SD = 0.82), while outcomes remained only moderate (TA M = 3.46, SD = 0.64); DL was rated moderately high (M = 3.62, SD = 0.59) and SD moderate (M = 3.38, SD = 0.67). Measurement quality supported inferential testing, with strong internal consistency (DL α = 0.88, SD α = 0.85, TA α = 0.90). The analysis plan applied composite scoring, reliability testing, Pearson correlations, and multiple regression. Correlation results showed strong positive relationships between DL and TA (r = 0.61, p < .001) and SD and TA (r = 0.55, p < .001), alongside negative associations between TA and noise (r = −0.31, p < .001) and TA and overlap (r = −0.28, p < .01). Regression findings confirmed joint predictive power: the model was significant (F(4,151) = 39.18, p < .001) and explained 51% of TA variance (R² = 0.51; Adjusted R² = 0.49); DL was the strongest positive predictor (B = 0.47, β = 0.43, p < .001) and SD added an independent positive contribution (B = 0.34, β = 0.31, p < .001), while noise (B = −0.11, p = .046) and overlap (B = −0.10, p = .021) reduced accuracy. A robustness check using TA groups further reinforced the pattern: 29.5% low TA (≤ 3.0), 52.6% moderate TA, and 17.9% high TA (> 3.75), with monotonic increases in DL and SD means across groups (DL 3.18 → 3.63 → 4.12; SD 2.97 → 3.36 → 3.98). Overall, the findings imply that enterprise teams should optimize ASR and diarization together, prioritize overlap-aware diarization improvements, and treat noise and overlap profiling as first-class deployment controls to improve transcript trust and downstream usability.
Copyright (c) 2023 Abdulla Mamun, Alifa Majumder Nijhum (Author)
AI-DRIVEN PREDICTIVE ANALYTICS FRAMEWORK FOR ELECTRONIC FUNDS TRANSFER, LOAN ORIGINATION, AND AML COMPLIANCE IN DIGITAL BANKING
This study addresses the problem that electronic funds transfer (EFT) monitoring, loan origination decisioning, and anti-money laundering (AML) compliance are often governed as separate control silos in digital banking, which limits risk visibility and reduces audit ready decision defensibility. The purpose was to validate an AI driven predictive analytics framework and quantify how Predictive Analytics Capability (PAC) influences EFT monitoring effectiveness, loan origination decision quality, AML monitoring effectiveness, and overall digital banking risk control performance (DBRCP). A quantitative cross sectional, case-based survey was administered across a cloud enabled digital banking environment, yielding 268 responses from EFT operations (31.7%), lending or underwriting (27.6%), AML or compliance (24.3%), and risk, analytics, or IT (16.4%). PAC (20 items) operationalized capability maturity across data integration, data quality, model development and validation, model governance and documentation, and user competence; outcome constructs were measured as Likert 1 to 5 composites. The analysis plan combined descriptive profiling, internal consistency testing, Pearson correlations, and hypothesis driven regression models. Reliability was adequate (Cronbach’s alpha: PAC 0.91, EFT_EFF 0.88, LOAN_QUAL 0.90, AML_EFF 0.89, DBRCP 0.92). Descriptively, respondents rated PAC at M = 3.84 (SD = 0.56), with governance and documentation the lowest dimension (M = 3.68), while EFT_EFF (M = 3.79), LOAN_QUAL (M = 3.73), AML_EFF (M = 3.76), and DBRCP (M = 3.76) were all above the scale midpoint. PAC correlated positively and significantly with EFT_EFF (r = 0.56), LOAN_QUAL (r = 0.52), AML_EFF (r = 0.59), and DBRCP (r = 0.63) at p < .001. Regression results showed that PAC predicted EFT_EFF (beta = 0.48, R2 = 0.31), LOAN_QUAL (beta = 0.44, R2 = 0.27), and AML_EFF (beta = 0.51, R2 = 0.35), all p < .001, indicating the strongest capability to outcome contribution in AML. In the integrated model, EFT_EFF (beta = 0.26), LOAN_QUAL (beta = 0.21), and AML_EFF (beta = 0.37) jointly explained DBRCP (R2 = 0.58), underscoring that coordinated improvements across payments, credit, and compliance drive risk control. Implications are that banks should invest in PAC foundations, particularly governance and documentation, to translate predictive models into consistent operational decisions and demonstrable compliance outcomes.
Copyright (c) 2025 Md Nahid Hossain (Author)
PRIVACY-PRESERVING SECURITY MODEL FOR EARLY CANCER DIAGNOSIS, POPULATION-LEVEL EPIDEMIOLOGY, AND SECURE INTEGRATION INTO U.S. HEALTHCARE SYSTEMS
The increasing use of data-intensive analytics for early cancer diagnosis and population-level epidemiology has heightened concerns regarding patient privacy, data security, and analytical reliability within healthcare systems. Early detection and cancer surveillance depend on large-scale integration of clinical, imaging, and epidemiological data across institutional boundaries, creating complex environments with elevated risks of disclosure, re-identification, and system vulnerability. This study investigates the effectiveness of a privacy-preserving security model for supporting early cancer diagnosis and population-level epidemiological analysis within regulated healthcare data environments in the United States. Using a quantitative, cross-sectional design, the study analyzed 120 oncology-related analytic units, including healthcare systems, cancer registries, and public health platforms, to assess the implementation of privacy controls, security architectures, and analytical performance outcomes. Results indicate a mean Privacy Control Index score of 68.4 (SD = 11.7), reflecting moderate to high adoption of privacy-preserving mechanisms with notable variability across institutions. Security architectures were more consistently implemented, with a mean Security Control Score of 72.9 (SD = 9.6), and were found to be a significant positive predictor of analytical performance. Analytical outputs remained stable under privacy constraints, with an overall performance stability score of 0.84 (SD = 0.06). The relationship between privacy strength and analytical performance was weak and non-linear (r = −0.21, p < .05), indicating that stronger privacy controls did not substantially degrade analytic utility. At the population level, secure aggregation mechanisms achieved high consistency, with a mean Aggregation Consistency Index of 88.1 (SD = 6.4), supporting reliable epidemiological analysis while limiting disclosure risk. Overall, the findings provide empirical evidence that privacy-preserving security models can be effectively integrated into cancer analytics systems, demonstrating that privacy, security, and analytical performance can function as complementary components within modern healthcare data infrastructures.
Healthcare Data Security And Interoperability
Copyright (c) 2022 Md Fokhrul Alam, Md Ashraful Alam, Md Fardaus Alam (Author)
ASSESSING ORGANIZATIONAL AI READINESS IN CRITICAL INFRASTRUCTURE: AN INTEGRATED MATURITY FRAMEWORK FOR HEALTHCARE SYSTEMS AND SUPPLY CHAIN MANAGEMENT
Critical infrastructure sectors face unprecedented challenges requiring artificial intelligence (AI) integration to enhance operational resilience and national security. However, AI adoption remains limited due to inadequate organizational readiness assessment frameworks. This research develops and validates a comprehensive AI readiness maturity model adapted from the Capability Maturity Model Integration (CMMI) framework for healthcare systems and supply chain management. Drawing on Technology-Organization-Environment (TOE) theory, Resource-Based View (RBV), and Dynamic Capabilities Theory, we propose an integrated assessment framework encompassing six dimensions: Technical Infrastructure, Data Capabilities, Organizational Capabilities, Strategic Readiness, Governance and Ethics, and Ecosystem Integration. Through multi-method validation involving survey data from 312 organizations, 45 semi-structured interviews, and four longitudinal case studies, we demonstrate strong psychometric properties and significant positive relationships between AI readiness maturity and infrastructure resilience outcomes. Results reveal that only 23% of healthcare organizations and 18% of supply chain organizations achieve optimized maturity levels, with significant gaps in data capabilities and governance structures. Our findings provide actionable frameworks for practitioners, inform policy development for critical infrastructure protection, and establish theoretical foundations for future research on AI-enabled infrastructure resilience.
Copyright (c) 2025 Tonay Pal, Md Maruf Islam (Author)
REINFORCEMENT LEARNING APPROACHES TO OPTIMIZE IT SERVICE MANAGEMENT UNDER DATA SECURITY CONSTRAINTS
This quantitative study evaluated reinforcement learning (RL) approaches to optimize IT Service Management (ITSM) decision-making under explicit data security constraints using a retrospective observational design and historical operational logs. The analytic dataset was extracted from a 12-month observation window and contained 128,450 raw incident and service-request tickets. After deterministic preprocessing, including timestamp normalization, deduplication, linkage resolution, and censoring under pre-specified rules, 112,680 tickets were retained for analysis, representing 46.3% incidents and 53.7% service requests across 10 service lines and 5 regions. Mandatory ITSM fields were complete for 96.8% of retained records, and 78.4% of tickets were linked to usable telemetry context for state construction. Descriptive statistics indicated distributional asymmetry typical of service systems: the median time-to-acknowledge was 0.42 hours and the median time-to-resolve was 14.6 hours, with substantial tail behavior (P90 resolution time 63.4 hours; P95 resolution time 97.6 hours). Governance indicators were consistently recorded, with audit-log completeness averaging 98.9%. Correlational analysis showed that time-to-resolve was positively associated with congestion and workflow friction, including relationships with backlog intensity (0.46), ticket aging (0.52), reassignment count (0.41), and escalation occurrence (0.35), motivating multivariable modeling with collinearity screening. Adjusted regression models controlling for ticket severity, category, region, service line, workload context, and major-incident regime indicated that the RL policy was associated with improved performance relative to baseline handling, including reduced time-to-acknowledge (β = −0.08 hours; p < .001) and improved resolution performance (log-linear β = −0.12; p < .001), alongside improved percentile-oriented SLA attainment (P90 threshold OR = 1.28, p < .001; P95 threshold OR = 1.19, p = .001). Process-quality outcomes also improved, including reduced reopen occurrence (OR = 0.86; p < .001) and reduced reassignment occurrence (OR = 0.89; p < .001). Constraint-related models showed no statistically significant increases in privileged-action events (IRR = 0.99; p = .61), exception approvals (IRR = 0.98; p = .44), or restricted-field access (IRR = 1.01; p = .69), and audit completeness remained statistically unchanged, supporting admissibility of security constraints while service outcomes improved.
Copyright (c) 2023 Md Mohaiminul Hasan, Md Muzahidul Islam (Author)
NEUROBIOTECHNOLOGY-DRIVEN REGENERATIVE THERAPY FRAMEWORKS FOR POST-TRAUMATIC NEURAL RECOVERY
This study addresses the problem that neurobiotechnology-driven regenerative therapies for post-traumatic neural injury often yield inconsistent recovery across organizations because framework implementation strength and its operational drivers are rarely measured and compared systematically. The purpose was to quantify Neurobiotechnology-Driven Regenerative Therapy Framework Strength (NDRTF) and test its association with Post-Traumatic Neural Recovery Outcomes (PNRO), and to identify which framework dimensions most strongly predict outcomes. Using a quantitative, cross-sectional, case-study-based design, a five-point Likert questionnaire was administered in three enterprise-scale case settings with technology-enabled monitoring and protocol tracking (Case A, B, C). The sample comprised 180 professionals (60 per case) spanning clinicians/therapists (52.8%), biomedical or neurotech staff (30.0%), and program coordinators (17.2%). Measurement quality was verified via reliability testing (α = 0.88 for the 25-item NDRTF scale; α = 0.86 for the 10-item PNRO scale). The analysis plan combined descriptive profiling, Pearson correlations, multiple regression, and case-specific regressions. Descriptively, overall NDRTF was moderate to high (M = 3.62, SD = 0.54) and PNRO was similar (M = 3.58, SD = 0.57), with Case A highest (NDRTF M = 3.74; PNRO M = 3.70) and Case C lowest (NDRTF M = 3.52; PNRO M = 3.49). Overall framework strength correlated strongly with recovery (r = 0.71, p < .001). In the multivariate model, the five NDRTF dimensions explained 59% of PNRO variance (R² = 0.59; F(5,174) = 49.30, p < .001), with Monitoring and Feedback Quality (β = 0.29, p < .001), Multidisciplinary Coordination (β = 0.23, p = .001), and Bio-Neurotech Integration (β = 0.19, p = .004) as the strongest predictors. Case models showed R² = 0.64 (A), 0.57 (B), and 0.49 (C). Implications include prioritizing monitoring feedback loops, cross-team coordination, and integrated biological plus neurotechnology delivery, supported by secure cloud data governance to sustain fidelity and scale outcomes.
Copyright (c) 2022 Md. Akbar Hossain, Sharmin Ara (Author)
DATA-DRIVEN FRAMEWORKS FOR IMPROVING DECISION-MAKING IN U.S. PUBLIC AND PRIVATE ORGANIZATIONS
This study examined the statistical relationships between data-driven framework maturity and decision-making effectiveness across U.S. public and private organizations using a quantitative, explanatory, cross-sectional comparative design. Analysis was conducted at the organizational level for 140 organizations, including 68 public-sector and 72 private-sector organizations. Data-driven framework maturity was operationalized through five dimensions—governance maturity, data quality management, architecture and integration depth, analytics capability, and decision integration—while decision-making effectiveness was assessed using five outcome families: decision timeliness, decision consistency, accuracy/error sensitivity, compliance-adjusted outcomes, and performance attainment. Descriptive results showed higher mean digital maturity in private organizations (M = 3.62, SD = 0.68) than public organizations (M = 3.28, SD = 0.74), while regulatory intensity was higher in public organizations (M = 4.01, SD = 0.63) than private organizations (M = 3.42, SD = 0.77). Correlation analysis indicated positive associations among maturity dimensions, with the strongest inter-correlation between analytics capability and decision integration (r = 0.71). Reliability results were acceptable to strong across constructs, with Cronbach’s alpha ranging from 0.80 to 0.90 and composite reliability from 0.83 to 0.91; confirmatory factor analysis supported the measurement model (CFI = 0.94, TLI = 0.93, RMSEA = 0.061, SRMR = 0.049). Collinearity diagnostics were within acceptable limits (VIF range: 1.20–2.56). Regression results showed that maturity dimensions improved explanatory power beyond controls, with adjusted R² reaching 0.41 for performance attainment and 0.36 for decision timeliness. Decision integration was positively associated with decision timeliness (β = 0.31, 95% CI [0.14, 0.46]), accuracy/error sensitivity (β = 0.28, 95% CI [0.11, 0.43]), and performance attainment (β = 0.30, 95% CI [0.13, 0.45]). Analytics capability was also positively related to performance attainment (β = 0.26, 95% CI [0.10, 0.40]), while governance maturity aligned most strongly with compliance-adjusted outcomes (β = 0.29, 95% CI [0.12, 0.44]).
Copyright (c) 2022 Md. Wahid Zaman Raj, Sai Praveen Kudapa (Author)
AI APPLICATIONS IN EMERGING TECH SECTORS: A REVIEW OF AI USE CASES ACROSS HEALTHCARE, RETAIL, AND CYBERSECURITY
Organizations increasingly deploy AI use cases to improve decisions, yet many implementations underperform because data, people, and governance readiness are uneven and the pathway from readiness to outcomes is rarely quantified. This study tested a readiness to adoption intensity to outcomes model using a cross-sectional, case-based survey (Likert 1 to 5) across three enterprise case settings in real operational contexts (healthcare, retail, cybersecurity). From 500 invitations, 342 responses were received (68.4%), and 318 usable cases were retained (63.6%), split across healthcare (n=108), retail (n=110), and cybersecurity (n=100). Key variables were data readiness, human capability, governance readiness, AI adoption intensity, and performance outcomes, with organization size, role group, and years of AI exposure as controls. Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression (standardized β, R²), plus mediation interpretation via the combined regression pattern. Construct means were moderate-to-positive (data readiness 3.62, human capability 3.55, governance readiness 3.48, adoption intensity 3.58, outcomes 3.67), and cybersecurity reported the highest governance (3.60) and outcomes (3.73). Reliability was strong (α=0.82 to 0.90). Adoption intensity correlated with outcomes (r=0.62, p<.01) and with data readiness (r=0.54), human capability (r=0.49), and governance readiness (r=0.46), all p<.01. In regression, readiness explained substantial variance in adoption (R²=0.48), led by data readiness (β=0.33, p<.001), followed by human capability (β=0.24, p<.001) and governance readiness (β=0.19, p=.002). Outcomes were explained (R²=0.52) by adoption intensity (β=0.45, p<.001) and governance readiness (β=0.21, p=.004), with a smaller direct data effect (β=0.12, p=.041) and a non-significant direct human capability effect once adoption was included (p=.180), indicating that skills primarily improve outcomes by increasing routine AI use. Implications are that organizations should prioritize data integration and quality, invest in workforce capability to sustain adoption, and strengthen governance to translate AI deployments into measurable gains.
Copyright (c) 2023 Rakibul Hasan, Alifa Majumder Nijhum (Author)
RELIABILITY-CENTERED MAINTENANCE OPTIMIZATION USING MULTI-OBJECTIVE AI ALGORITHMS IN REFINERY EQUIPMENT
This quantitative study on Reliability-Centered Maintenance Optimization Using Multi-Objective AI Algorithms in Refinery Equipment had examined how reliability-centered maintenance (RCM) logic could be translated into measurable decision variables and optimized under refinery constraints using multi-objective AI. A structured review of 62 peer-reviewed papers had informed objective selection, constraint definition, and algorithm benchmarking practices. The empirical analysis had used a retrospective refinery case dataset comprising 420 assets observed over 60 months (190 rotating, 170 static, 60 instrumentation/control) with 17,980,000 operating exposure hours. The dataset had included 612 functional failure events, 9,284 work orders (59.0% preventive, 41.0% corrective), and 4,116 inspection observations. Downtime severity had been right-skewed, with median downtime per event of 6.2 hours (IQR 2.1–18.4) and a 95th percentile of 92.0 hours, with static equipment exhibiting longer restoration burdens. Composite indices for operating severity, monitoring coverage, execution burden, degradation evidence, and risk exposure had demonstrated acceptable internal consistency (0.77–0.85). Collinearity screening reduced model predictors and improved stability, lowering maximum VIF from 9.4 to 2.8 in rotating equipment models. Regression results showed that operating severity increased functional failure hazard (HR 1.29, 95% CI 1.18–1.41, p < 0.001) and degradation evidence increased hazard (HR 1.33, 95% CI 1.22–1.46, p < 0.001), while monitoring coverage reduced hazard (HR 0.83, 95% CI 0.75–0.92, p = 0.001). Preventive intensity reduced corrective recurrence (IRR 0.79, 95% CI 0.70–0.90, p < 0.001) but increased direct cost (+5.4%, p = 0.001). Turnaround proximity reduced unavailability (RR 0.86, p = 0.001). Safety-critical assets showed higher inspection adherence (96.1% vs 88.7%; OR 2.72, p < 0.001). The findings had clarified the trade-off structure motivating Pareto-efficient maintenance portfolios that remained interpretable and constraint-feasible in refinery operations.
Copyright (c) 2023 Saikat Sarkar, Aditya Dhanekula (Author)
GLOBAL SOURCING, CYBERSECURITY VULNERABILITIES, AND U.S. RETAIL MARKET OUTCOMES: A REVIEW OF PRICING IMPACTS AND CONSUMER TRENDS
This quantitative review synthesizes evidence on how global sourcing and emerging cybersecurity vulnerabilities jointly influence U.S. retail pricing structures and consumer market outcomes. Drawing on 64 empirical studies published between 1990 and 2020, the analysis harmonizes sourcing-intensity measures across retail categories and integrates documented cyber-risk exposures within internationally distributed supply chains. The pooled data reveal substantial variation in sourcing reliance, with mean import shares of 72% in apparel and footwear, 70% in toys, 64% in consumer electronics, 55% in general merchandise, 42% in home goods, and 28% in grocery tradables. Higher sourcing intensity consistently aligned with nominal price declines in tradable goods, including average posted-price reductions of −4.8% in apparel, −4.1% in toys, and −3.5% in electronics, while grocery tradables experienced a modest −0.8% decrease. Transaction-price effects were even larger, reaching −6.2% in apparel and −5.4% in toys, indicating strong promotional pass-through. Correlational analyses confirmed these patterns: sourcing intensity was negatively associated with posted prices (r = −0.62) and price-change magnitudes (r = −0.58), and positively associated with imported-variety expansion (r = 0.66) and quality-adjusted affordability (r = 0.49). Random-effects meta-regressions further demonstrated significant price-reducing effects of sourcing intensity on both posted (β = −0.032, p < .001) and transaction prices (β = −0.041, p < .001). Exchange-rate cost shocks generated additional price decreases (β = −0.021), whereas tariff shocks produced modest increases (β = 0.012). Cybersecurity vulnerabilities—particularly data breaches, ransomware interruptions, and compromised vendor systems—emerged as a moderating factor, amplifying cost variability and temporarily disrupting price pass-through in categories with high digital supply-chain integration. Despite these risks, non-price outcomes remained substantial: sourcing intensity predicted imported-variety growth (β = 0.058) and stronger substitution patterns (β = 0.026). Overall, the evidence shows that global sourcing lowers U.S. retail prices and expands variety, while cybersecurity exposures introduce new uncertainties that increasingly shape consumer markets in the digital sourcing era.
Copyright (c) 2021 Md. Hasan Imam, Shaikat Biswas (Author)
QUANTITATIVE ASSESSMENT OF AI-ENABLED CONSTRUCTION PLANNING TOOLS FOR REDUCING DELAYS IN U.S. INFRASTRUCTURE PROJECTS
This study addresses schedule overruns in U.S. infrastructure projects and the limited quantitative evidence on whether AI-enabled construction planning tools reduce delays. The purpose is to quantify relationships between AI-based planning adoption and schedule performance using project-level data. A quantitative cross-sectional, case-based design used a Likert five-point survey of practitioners covering 198 infrastructure cases across public agencies and enterprise contractors and consultants. Key variables included AI-enabled planning tool adoption, planning quality, coordination effectiveness, project size, complexity, contract type, and a Schedule Delay Index (SDI) from planned and actual durations. Reliability was high for all multi-item scales (α = 0.84-0.91). The analysis plan combined descriptive statistics, Pearson correlations, and multiple regression with moderation tests. Projects showed moderate AI adoption (M = 3.47, SD = 0.78) and an average 11% schedule overrun (SDI M = 0.11, SD = 0.09). AI adoption correlated negatively with SDI (r = −0.41, p < .001) and remained a significant predictor of lower delay after controlling for size, complexity, and contract type; a one-point increase in adoption was associated with a 2.8 percentage point reduction in SDI. Adding planning quality and coordination effectiveness increased explained variance in SDI from 25% to 41% and partially mediated the AI-delay relationship, with effects strongest on highly complex projects. The headline finding is that AI-enabled planning tools contribute meaningfully to delay reduction when embedded in robust planning and coordination practices. The study implies that infrastructure owners should treat AI-enhanced planning as a strategic capability for improving delivery reliability across the sample.
IMPACT OF HIGH-PERFORMANCE COMPUTING IN THE DEVELOPMENT OF RESILIENT CYBER DEFENSE ARCHITECTURES
This study addresses the growing problem that many cloud and enterprise environments are investing in artificial intelligence and advanced computing for cyber defense without clear evidence of how these capabilities translate into resilient performance under attack. The purpose is to empirically examine how AI capability and advanced computing capacity contribute to cyber defense resilience in real organizational settings. A quantitative, cross sectional, case-based survey was conducted with organizations that operate AI enabled security solutions on cloud, high performance, and edge or fog infrastructures, yielding 220 usable responses from 310 distributed questionnaires (70.9 percent) from cybersecurity and IT professionals. Core variables included AI capability, advanced computing capacity, cybersecurity readiness, and a composite cyber defense resilience index. Data were analyzed using reliability and factor analysis, descriptive statistics, Pearson correlations, and multiple regression. Results show moderately high AI capability (M = 3.72, SD = 0.71) and advanced computing capacity (M = 3.85, SD = 0.66), but only moderate resilience (M = 3.61, SD = 0.69). AI capability (β = 0.39, p < .001) and advanced computing capacity (β = 0.27, p < .001) significantly predict resilience in a model that explains 61 percent of variance (R² = 0.61), with strong correlations between AI and resilience (r = .62) and computing and resilience (r = .55). These findings indicate that resilient cyber defense in cloud and enterprise contexts depends on the integrated development of AI analytics, scalable computing infrastructures, and cybersecurity readiness, offering actionable guidance for CISOs and security architects.
Copyright (c) 2021 Zamal Haider Shish, Mst. Shahrin Sultana (Author)
IMPACT OF BIG DATA AND PREDICTIVE ANALYTICS ON FINANCIAL FORECASTING ACCURACY AND DECISION-MAKING IN GLOBAL CAPITAL MARKETS
This quantitative study examined how big data intensity and predictive-analytics capability influenced financial forecasting accuracy and decision-making quality in global capital markets. The study was grounded in an extensive review of 112 peer-reviewed empirical and methodological papers spanning econometric forecasting, alternative data applications, machine learning in asset pricing, and decision-value testing across international markets. Using a cross-sectional time-series panel of market–asset–time observations, the analysis integrated developed and emerging markets and covered equities, fixed income, foreign exchange, and derivatives. Big data intensity was operationalized as a composite index reflecting data volume, variety, frequency, latency, dimensionality, and coverage breadth, while predictive-analytics capability was measured as a ranked model-class indicator ranging from classical econometrics to hybrid econometric–machine-learning systems. Forecasting accuracy served as the primary outcome and was evaluated through rolling out-of-sample errors; decision-making quality was assessed through standardized portfolio, trading, and risk-performance indicators. Descriptive results showed substantial dispersion across markets, with mean big data intensity at 0.56 (SD = 0.19) and predictive-analytics capability at 3.12 (SD = 1.09) on a five-point scale. Developed markets exhibited higher data intensity (0.63) and sophistication (3.54) than emerging markets (0.48 and 2.66, respectively). Correlation analysis indicated that big data intensity and predictive-analytics capability were each inversely associated with forecasting error (r = −0.46 and r = −0.52), and forecasting error was inversely related to decision-making quality (r = −0.48). Fixed-effects panel regressions confirmed significant direct effects: big data intensity reduced forecasting error (β = −0.021, p < .001) and predictive-analytics capability reduced forecasting error (β = −0.018, p < .001). The interaction term was also negative (β = −0.007, p < .01), indicating complementarity such that analytics benefits strengthened under higher data intensity. In the decision model, forecasting error significantly predicted decision quality (β = −28.6, p < .001), demonstrating that statistical accuracy gains translated into improved economic outcomes. Overall, the study provided integrated evidence that richer data environments and advanced predictive analytics jointly enhanced forecasting precision and decision performance across global capital markets.
Copyright (c) 2024 Md. Mosheur Rahman, Md Arman Hossain (Author)
ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PREDICTING RENEWABLE-ENERGY DEMAND UNDER CLIMATE VARIABILITY
This quantitative, cross-sectional, case-study–based research investigates how artificial intelligence (AI) applications can improve renewable-energy demand forecasting under climate variability while being adopted in real operational settings. The study addresses the problem that utilities and renewable-intensive enterprises still underuse climate-variability indicators in AI models, limiting the accuracy and decision value of demand forecasts. Using a structured Likert five-point questionnaire and secondary operational and climate data from renewable-energy systems, 280 questionnaires were distributed and 214 valid responses were obtained (76.4% usable rate). Key constructs included organizational analytics capability, data quality and integration, climate-variability integration in AI models, AI model transparency, perceived forecast accuracy, trust in AI outputs, and intention to use AI-based forecasts. Reliability analysis showed Cronbach’s alpha values between 0.82 and 0.91, and multiple regression and correlation analyses were used alongside benchmarking of traditional regression and AI models. Compared with a traditional multiple regression model (MAPE 7.8%, RMSE 18.4 MW) and an AI model without explicit climate indicators (MAPE 6.1%, RMSE 15.2 MW), the climate-enhanced AI model achieved substantially lower error (MAPE 4.3%, RMSE 11.6 MW). Survey results indicated moderately high perceived forecast accuracy (mean 3.92) and trust (3.88), with intention to use AI forecasts averaging 4.03. Data quality and integration and climate-variability integration were the strongest predictors of perceived accuracy, while perceived accuracy and trust primarily drove intention to use. The findings imply that climate-aware AI forecasting delivers measurable accuracy gains but requires robust data pipelines and analytics capability to be trusted and embedded in renewable-energy planning and operations.
Copyright (c) 2024 Dr. Zulqarnain, Zayadul Hasan (Author)
INTEGRATION OF COMMUNICATIONS-BASED TRAIN CONTROL (CBTC) INTO CIVIL ENGINEERING DESIGN FOR SAFER AND CYBER-SECURE RAIL SYSTEMS
This study addresses the practical problem that Communications-Based Train Control (CBTC) technologies are often engineered separately from civil infrastructure, leaving unclear to what extent integrated design actually improves safety and cyber-secure performance in operating rail enterprises. The purpose of the research is to quantify how CBTC-civil engineering integration relates to perceived safety performance, cybersecurity resilience, and overall system performance in real CBTC-enabled corridors. A quantitative, cross-sectional, case-study based survey design was adopted, using a structured five-point Likert questionnaire administered to 220 experienced professionals in CBTC-equipped rail infrastructure managers, operators, consultants, and suppliers, yielding a 71.0% usable response rate. Key latent variables were CBTC-civil integration quality, safety performance, cybersecurity resilience capability, and overall system performance, all measured with reliable scales (Cronbach’s alpha 0.87-0.91). The analysis plan combined descriptive statistics, reliability and validity checks, Pearson correlations, and multiple regression models. Results show that integration quality is strongly associated with safety (r = 0.62) and cybersecurity resilience (r = 0.57), explaining 39% and 33% of their variance respectively, while integration, safety, and cybersecurity together explain 65% of the variance in overall performance. Safety and cybersecurity emerge as the strongest direct predictors of performance, with CBTC-civil integration exerting an additional indirect effect through these constructs, indicating that integrated civil-signaling design is an upstream driver of safer and more cyber-resilient CBTC enterprises. The findings imply that rail authorities and CISOs should institutionalize CBTC-civil co-design, security zoning, and safety-security co-engineering from the earliest alignment and station-planning stages to realize the full performance benefits of CBTC deployment.
Communications-Based Train Control (CBTC)
Copyright (c) 2023 Syed Zaki Uddin, Md Sarwar Hossain Shuvo (Author)
ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL TRANSFORMATION MODELS FOR ENHANCING ORGANIZATIONAL COMMUNICATION AND DECISION-MAKING EFFICIENCY
This quantitative study investigated how artificial intelligence (AI) capability and digital transformation (DT) maturity influenced organizational communication quality and decision-making efficiency, with communication quality tested as a mediator and DT maturity as a moderator. The literature review synthesized evidence from 68 prior quantitative papers to refine construct definitions, measurement logic, and empirical pathways. A cross-sectional survey was conducted with 412 respondents from AI-adopting organizations across multiple sectors. Descriptive results indicated moderate-to-high levels of AI capability (M = 3.71, SD = 0.64) and DT maturity (M = 3.62, SD = 0.61). Communication quality recorded the highest mean (M = 3.84, SD = 0.59), followed by decision-making efficiency (M = 3.68, SD = 0.62), and distributional diagnostics supported parametric modeling. Measurement quality was strong (Cronbach’s α = .86–.93; CR = .88–.94; AVE = .60–.70), and CFA fit was acceptable (CFI = .95, TLI = .94, RMSEA = .05, SRMR = .04). Correlations among principal constructs were positive and significant, with no multicollinearity risk (VIFs < 2.10). Structural modeling confirmed all hypothesized direct effects: AI capability positively predicted communication quality (β = .58, p < .001) and decision-making efficiency (β = .33, p < .001), and communication quality positively predicted decision-making efficiency (β = .49, p < .001). Mediation testing showed a significant indirect effect of AI capability on decision efficiency via communication quality (β_indirect = .28, p < .001), indicating partial mediation. Moderation analysis demonstrated that DT maturity strengthened the AI-to-decision efficiency relationship (β_interaction = .14, p = .001). Overall, the findings supported an integrated mediated–moderated model explaining how AI-driven digital transformation enhances communication and decision efficiency in organizational settings.
Copyright (c) 2025 Alifa Majumder Nijhum (Author)