International Journal of Scientific Interdisciplinary Research
The International Journal of Scientific Interdisciplinary Research (IJSIR) is a prestigious, peer-reviewed, open-access international journal devoted to advancing the frontiers of interdisciplinary scholarship. IJSIR provides a dynamic platform for researchers, academicians, scientists, practitioners, and policymakers from diverse fields to collaborate and share innovative ideas that transcend conventional disciplinary boundaries. The journal is dedicated to publishing cutting-edge, original research articles, critical reviews, and case studies that offer novel insights and practical relevance in a rapidly evolving global research environment. With a focus on fostering integrative and cross-disciplinary discourse, International Journal of Scientific Interdisciplinary Research (IJSIR) covers a broad spectrum of scientific domains, including but not limited to Physical and Life Sciences, Engineering, Environmental Science, Information Technology, Social Sciences, Economics, Education, and Healthcare. The journal encourages submissions that explore the convergence of knowledge across disciplines, offering transformative solutions to complex scientific, technological, and societal challenges.
AI-Enabled Predictive Asset Integrity Monitoring for Industrial Equipment in Oil & Gas and Energy Sector Facilities
The increasing complexity of industrial equipment within oil and gas and energy sector facilities has intensified the need for advanced asset integrity management approaches capable of improving reliability, reducing operational disruptions, and enhancing maintenance efficiency. This study examined the influence of Artificial Intelligence (AI)-enabled predictive asset integrity monitoring on equipment reliability, operational availability, maintenance efficiency, downtime reduction, and overall asset performance. A quantitative cross-sectional research design was employed, drawing on data collected from 312 maintenance engineers, reliability engineers, asset integrity specialists, operations managers, and technical professionals working across oil and gas production, refinery, power generation, and pipeline facilities. Data were gathered using a structured questionnaire and analyzed using descriptive statistics, Pearson correlation analysis, multiple regression analysis, reliability testing, and analysis of variance (ANOVA) through SPSS version 29. The findings revealed strong positive relationships between AI-enabled monitoring capability and key operational performance indicators. AI Monitoring Capability demonstrated significant correlations with Maintenance Efficiency (r = 0.806), Equipment Reliability (r = 0.781), Predictive Maintenance Effectiveness (r = 0.742), Operational Availability (r = 0.724), and Downtime Reduction (r = 0.687), all significant at p < 0.001. Multiple regression analysis indicated that AI Monitoring Capability (β = 0.521, p < 0.001) and Predictive Maintenance Effectiveness (β = 0.356, p < 0.001) significantly predicted overall operational performance. The regression model explained 70.9% of the variance in operational outcomes (R² = 0.709), demonstrating substantial explanatory power. Descriptive findings further showed high levels of perceived effectiveness, with mean scores of 4.21 for Maintenance Efficiency, 4.18 for AI Monitoring Capability, 4.15 for Operational Availability, 4.12 for Predictive Maintenance Effectiveness, and 4.09 for Equipment Reliability. Subgroup analyses revealed that organizations with more than five years of AI implementation experience achieved the highest operational performance score (M = 4.45), compared with organizations having less than two years of adoption (M = 3.84). The study concluded that AI-enabled predictive asset integrity monitoring significantly enhanced industrial asset performance by improving reliability, maintenance effectiveness, operational continuity, and downtime management. The findings provide empirical evidence supporting the integration of predictive analytics, intelligent monitoring systems, and AI-driven maintenance strategies within modern oil and gas and energy sector facilities.
Copyright (c) 2024 Mukut Kanti Barua (Author)
A Meta-Analysis of Incident Response Frameworks for Banking Network Operations Centers in Emerging Economies
This study addresses the growing problem of ineffective cybersecurity incident handling in banking Network Operations Centers within emerging economies, where rapid digital transformation has not always been matched by equally mature incident response capabilities. The purpose of this research is to quantitatively evaluate the effectiveness and maturity of incident response frameworks by examining how key organizational variables influence operational outcomes. The study adopts a quantitative cross-sectional, case-based design using data collected from 248 valid respondents working in cloud-enabled and enterprise banking environments, including NOC staff, cybersecurity analysts, IT operations personnel, and compliance professionals. The key variables include incident response preparedness, response speed, framework standardization, and team coordination and training as independent predictors, with incident response effectiveness as the dependent variable measured through service continuity, containment, recovery efficiency, and cybersecurity performance. The analysis plan incorporates descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. The findings reveal strong quantitative evidence, with mean scores above 3.87 across all variables and a high effectiveness index of 4.03, while regression results show that the model explains 64.2% of variance in effectiveness (R² = 0.642, p < 0.001). Team coordination and training emerged as the strongest predictor (β = 0.34), followed by response speed (β = 0.24), preparedness (β = 0.21), and framework standardization (β = 0.17). Correlation results further confirm significant positive relationships (r ranging from 0.58 to 0.72, p < 0.01). Additionally, 71% of institutions were classified as high effectiveness, indicating strong operational capability. These findings highlight that incident response effectiveness is primarily driven by socio-technical alignment rather than isolated technical controls. The study implies that banking institutions must prioritize human coordination, rapid response mechanisms, and structured frameworks to enhance cyber resilience, particularly in emerging economy contexts where institutional maturity varies.
Copyright (c) 2023 Md. Shakhawat Hossen, Taru Binte Amin, Binayan Dey (Author)
Predictive Cash Flow Forecasting Using Deep Learning and ERP Transaction Data in Mid-Market Manufacturing Firms
Mid-market manufacturing firms occupy an awkward middle ground in corporate finance. They are large enough to generate transaction volumes that exceed the capacity of spreadsheet-driven treasury practices, yet too small to maintain the dedicated quantitative finance teams that large enterprises deploy for liquidity management. The result is a persistent gap between the cash-flow visibility these firms need and the forecasting tools they actually use. This paper develops a conceptual and methodological framework for closing that gap by combining deep learning models with the granular transaction data already captured inside enterprise resource planning (ERP) systems. We argue that the ERP layer—accounts receivable, accounts payable, sales orders, production schedules, inventory movements, and the general ledger—constitutes an underexploited, high-frequency data source that is naturally suited to sequence-modeling architectures such as long short-term memory (LSTM) networks and attention-based transformers. Drawing on a structured review of more than sixty sources spanning financial forecasting, machine learning, working-capital theory, ERP analytics, and SME technology adoption, we propose a forecasting pipeline that ingests ERP transaction streams, engineers liquidity-relevant features, trains a hybrid recurrent–attention model, and surfaces explainable, decision-ready forecasts through a treasury dashboard. Because no proprietary firm dataset was available for this study, the empirical section presents a clearly labeled illustrative simulation rather than results from real operating data. In that simulation, deep learning architectures reduce forecasting error relative to a seasonal ARIMA baseline by margins consistent with the comparative literature. We discuss why interpretability, data governance, and modest implementation cost are decisive for adoption in resource-constrained firms, and we close with recommendations and an explicit account of the study’s limitations. The contribution is a deployable blueprint and research agenda rather than a validated empirical claim.
Copyright (c) 2022 Samia Hossain Swarnali, H M Mahir Uddin (Author)
Impact of SQL-Driven Financial Data Pipelines on Audit-Readiness and Reporting Cycles in State Government Accounting
This study investigated the impact of SQL-driven financial data pipelines on audit-readiness and reporting-cycle performance in state government accounting through a comprehensive quantitative meta-analysis of 74 empirical studies comprising a cumulative sample of approximately 19,820 organizational observations. The research was grounded in information processing theory, internal control frameworks, and data governance perspectives, aiming to quantify the relationship between structured query language (SQL) based data integration and key public-sector accountability indicators, including audit-readiness, reporting timeliness, reconciliation efficiency, and financial data accuracy. The findings revealed a strong and statistically significant positive relationship, with a pooled effect size of r = 0.59 (p < 0.001), indicating that SQL-driven pipelines substantially enhanced audit-readiness and reporting performance across government accounting environments. Specifically, audit-readiness improved by an average of 22.6%, while reporting-cycle time was reduced by 27.9%, demonstrating notable gains in administrative efficiency. Automated reconciliation processes contributed further by reducing reconciliation time by 31.2%, supporting faster financial close cycles. Subgroup analyses indicated that the strength of this relationship varied across contexts, with higher effect sizes observed in state treasury and finance functions (r = 0.66) and tax and revenue agencies (r = 0.63), as well as in large agencies (r = 0.64) compared to smaller entities. Differences in information technology maturity showed stronger effects in high-maturity agencies (r = 0.65) relative to low-maturity agencies (r = 0.53), reflecting differences in digital infrastructure readiness. Moderating factors such as data quality (r = 0.63), schema standardization (r = 0.65), and extract-transform-load (ETL) automation (r = 0.62) were found to significantly enhance audit-readiness outcomes. Heterogeneity analysis indicated moderate variability (I² = 41.3%), while publication bias tests confirmed the robustness of the results. Overall, the study demonstrated that SQL-driven financial data pipelines are critical enablers of accurate, timely, and auditable financial reporting in state government accounting, providing strong empirical support for their adoption across diverse public-sector contexts.
Copyright (c) 2024 S M Arif Al Sany (Author)
Cloud and Distributed Computing for Project Analytics: A Meta-Analysis of Decision-Making Performance
This study investigated the impact of cloud and distributed computing on decision-making performance in project analytics through a comprehensive quantitative meta-analysis of 68 empirical studies comprising a cumulative sample of approximately 24,350 observations. The research was grounded in data-driven decision-making theory and information processing perspectives, aiming to quantify the relationship between advanced computational infrastructures and key decision performance indicators, including accuracy, speed, and consistency. The findings revealed a strong and statistically significant positive relationship, with a pooled effect size of r = 0.61 (p < 0.001), indicating that cloud and distributed computing substantially enhanced decision-making effectiveness in project environments. Specifically, decision accuracy improved by 18.7%, while decision response time was reduced by 21.4%, demonstrating notable gains in operational efficiency. Distributed computing frameworks further contributed to analytical performance by reducing processing time by 24.3%, supporting faster and more responsive decision cycles. Subgroup analyses indicated that the strength of this relationship varied across contexts, with higher effect sizes observed in information technology (r = 0.68) and construction (r = 0.64) sectors, as well as in large organizations (r = 0.66) compared to smaller firms. Geographical differences showed slightly stronger effects in developed regions (r = 0.63) relative to emerging economies (r = 0.58), reflecting differences in digital maturity. Moderating factors such as data quality (r = 0.62), system integration (r = 0.64), and user experience (r = 0.60) were found to significantly enhance decision-making outcomes. Heterogeneity analysis indicated moderate variability (I² = 38.6%), while publication bias tests confirmed the robustness of the results. Overall, the study demonstrated that cloud and distributed computing are critical enablers of efficient, accurate, and consistent decision-making in modern project analytics, providing strong empirical support for their adoption across diverse organizational contexts.
Copyright (c) 2023 Kazi Rakib Hasan Saurav, Chapal Barua (Author)
Digital Twin Modeling of Fiber Optic Telecom Infrastructure for Proactive SCADA Network Resilience in Power Transmission Utilities
This study investigated the effectiveness of digital twin modeling in enhancing the resilience of fiber optic telecom infrastructure supporting SCADA systems in power transmission utilities through a quantitative, simulation-based approach. A digital twin framework was developed to replicate real-time network conditions and evaluate performance under both baseline and optimized scenarios. The dataset consisted of 120 simulated network segments characterized by key performance indicators including latency, packet loss, bandwidth utilization, signal attenuation, and recovery time. Descriptive analysis revealed a baseline mean latency of 48.6 ms and packet loss of 2.1%, with recovery time averaging 18.9 minutes. Following digital twin-based optimization, latency was reduced to 34.2 ms, packet loss declined to 0.9%, and recovery time improved to 9.7 minutes, representing improvements of 29.6%, 57.1%, and 48.7%, respectively. Bandwidth utilization showed improved stability with reduced variance, indicating more efficient load balancing across network segments. Inferential statistical analysis confirmed that these improvements were statistically significant at p < 0.05, with effect sizes indicating moderate to large practical impact, particularly for recovery time (η² = 0.52) and packet loss (η² = 0.46). Regression analysis demonstrated strong relationships between latency, packet loss, and network reliability, with packet loss exhibiting the highest influence (β = -0.67). Sub-group analysis further revealed that high-load segments experienced the greatest performance gains, with latency decreasing from 62.7 ms to 41.3 ms and packet loss from 3.4% to 1.5%. Multi-point failure scenarios showed substantial resilience improvement, with recovery time reduced from 26.4 minutes to 13.2 minutes. The findings confirmed that digital twin integration significantly enhanced SCADA communication performance by enabling real-time monitoring, predictive analytics, and adaptive network optimization. This study provided strong quantitative evidence that digital twin modeling is an effective approach for improving telecom network reliability, operational stability, and resilience in power transmission utilities.
Copyright (c) 2023 Abu Naser Md Golam Mosharraf, Amir Razaq (Author)
A Meta-Analysis of AI-Driven Geospatial Analytics for Predictive Maintenance of Critical Infrastructure in Developing Economies
This study conducted a quantitative meta-analysis to evaluate the effectiveness of AI-driven geospatial analytics in predictive maintenance of critical infrastructure within developing economies. The research synthesized data from 52 empirical studies encompassing 18,745 infrastructure units across transportation, energy, water systems, and telecommunications sectors. The analysis aimed to assess improvements in predictive accuracy, maintenance efficiency, cost reduction, and system reliability achieved through the integration of artificial intelligence and geospatial data. The findings indicated that AI models incorporating spatial-temporal data significantly outperformed non-geospatial models, achieving a mean prediction accuracy of 91.2% compared to 78.8%, reflecting an average improvement of 12.4%. Regression-based models demonstrated an error reduction of 18.7%, while classification models improved failure detection accuracy by 14.2%. The overall pooled effect size was 0.68, indicating a moderate to strong impact on predictive maintenance performance. Sectoral analysis revealed that transportation and energy systems achieved higher predictive accuracies of 92.4% and 90.8%, respectively, compared to water systems at 86.3% and telecommunications at 84.7%. The results further showed that predictive maintenance approaches contributed to a 24.5% reduction in maintenance costs and a 19.2% decrease in system downtime. Subgroup analysis confirmed that deep learning and ensemble methods achieved higher effect sizes, ranging from 0.72 to 0.77, particularly when applied to integrated datasets combining satellite imagery and sensor data, which achieved predictive accuracy up to 93.3%. Additionally, 84.6% of the included studies reported statistically significant results, reinforcing the robustness of the findings. The study highlighted the critical role of data integration, algorithm selection, and contextual factors such as data availability and infrastructure type in influencing model performance. Overall, the results demonstrated that AI-driven geospatial analytics provides a highly effective and scalable approach for enhancing predictive maintenance strategies in infrastructure systems, particularly within resource-constrained environments.
Copyright (c) 2023 Risha Alam, Kazi Mohammad Khalid Ahsan (Author)
A Quantitative Assessment of Cybersecurity Frameworks for Industrial Control Systems in Critical Energy Infrastructure
This study investigates the effectiveness of cybersecurity frameworks in enhancing Industrial Control System security within critical energy infrastructure, addressing the problem that many organizations adopt frameworks formally without achieving consistent operational protection. The purpose is to provide a quantitative evaluation of how cybersecurity framework adoption influences measurable security outcomes across ICS environments. A quantitative, cross-sectional, case-study–based design was applied using a structured Likert-scale survey, with a final valid sample of 210 professionals from cloud-integrated and enterprise energy environments including SCADA, OT networks, and smart grid systems. Key variables included cybersecurity framework adoption, governance and compliance, risk management practices, threat detection capability, incident response effectiveness, operational resilience, and overall ICS cybersecurity performance. Data analysis was conducted using descriptive statistics, reliability testing, Pearson correlation, and multiple regression modeling. Findings reveal that cybersecurity framework adoption achieved a high mean score of 4.12 and significantly predicted ICS cybersecurity performance, with a strong correlation (r = 0.71, p < 0.001) and the highest regression effect (β = 0.31, p < 0.001). The regression model explained 64.3% of the variance (R² = 0.643), confirming strong explanatory power. Governance and compliance (β = 0.24), threat detection capability (β = 0.21), risk management (β = 0.18), incident response (β = 0.16), and operational resilience (β = 0.14) were all significant predictors. The ICS maturity index indicated a managed level (M = 4.03), while readiness gap analysis highlighted weaknesses in recovery capability (gap = -0.18) and IT/OT segmentation. These results imply that cybersecurity frameworks significantly enhance ICS security when implemented with strong governance, detection, and resilience practices rather than as compliance tools. The study contributes practical and theoretical insights by validating a TOE-based model and emphasizing the need for improved recovery planning and operational resilience in energy-sector cybersecurity.
Copyright (c) 2023 Md Aminul Islam, Md Asif Ali Sheak Arju (Author)
Language Access and Health Equity: Role of Multilingual Administrative Staff in Reducing Healthcare Disparities Among South Asian Immigrant Communities in The U.S.
This study examined the role of multilingual administrative staff in reducing healthcare disparities and improving health equity among South Asian immigrant communities in the United States using a quantitative, cross-sectional design. A total of 412 participants were included in the final analysis after data validation, representing diverse linguistic, socioeconomic, and migration backgrounds. The study assessed the impact of language-concordant administrative interaction on key outcomes including healthcare access efficiency, patient satisfaction, communication clarity, and healthcare utilization patterns. Descriptive findings indicated that 58.7% of participants reported access to multilingual administrative staff, while 35.9% had limited English proficiency, highlighting the relevance of language access in this population. Comparative analysis revealed that participants with multilingual administrative support demonstrated significantly higher healthcare access scores (M = 4.12, SD = 0.68) compared to those without support (M = 3.21, SD = 0.81). Patient satisfaction scores were also higher in the multilingual group (4.25 vs. 3.34), while missed appointment rates were substantially lower (12.8% vs. 28.9%). Preventive care utilization was greater among participants with language-concordant support (67.4% vs. 48.2%), indicating improved engagement with healthcare services. Multivariable regression analysis confirmed that multilingual administrative staffing was a significant predictor of patient satisfaction (β = 0.41, p < 0.001) and access efficiency (β = 0.36, p < 0.001) after controlling for demographic and socioeconomic variables. Subgroup analysis showed stronger effects among individuals with limited English proficiency, recent immigrants, and those with lower education levels, with satisfaction improvements exceeding +1.0 mean difference in these groups. Effect size analysis indicated large effects for patient satisfaction (d = 0.91) and communication clarity (d = 0.84), and moderate effects for access efficiency (d = 0.68). These findings demonstrated that multilingual administrative staff significantly improved both operational and patient-centered outcomes, providing strong empirical evidence that administrative-level language access plays a critical role in reducing healthcare disparities and enhancing equitable healthcare delivery among South Asian immigrant populations.
Copyright (c) 2022 Mst Kaniz Fatema (Author)
Blockchain-Enabled Identity Management Systems for Privacy and Cybersecurity in Enterprise IT
This study examined the effectiveness of blockchain-enabled identity management systems in enhancing privacy and cybersecurity within enterprise IT environments through a quantitative research approach. A quasi-experimental and cross-sectional design was adopted to compare blockchain-based identity systems with traditional identity and access management frameworks. The final sample consisted of 312 participants, including IT professionals, system administrators, and end users, equally distributed across traditional and blockchain identity systems. Data were collected using structured survey instruments and system-generated performance logs, and analyzed using statistical techniques including independent sample tests, regression analysis, and correlation analysis at a significance level of p < 0.05. The findings revealed statistically significant improvements in key performance indicators for blockchain-enabled systems. Authentication reliability increased from a mean score of 3.71 in traditional systems to 4.32 in blockchain systems, while unauthorized access incidents decreased from 2.94 to 1.85. Data exposure levels were significantly reduced from 3.88 to 2.41, indicating enhanced privacy preservation. System integrity also improved from 3.65 to 4.21, demonstrating stronger data consistency and protection against tampering. Regression analysis showed that system type had a strong positive effect on user trust (β = 0.48) and perceived security (β = 0.52), confirming that blockchain adoption significantly influenced user perception and confidence. Subgroup analysis indicated that system administrators and IT professionals reported higher trust levels (mean = 4.41 and 4.28, respectively) compared to end users (mean = 3.76), highlighting the role of technical familiarity in adoption. Organizations with higher digital maturity demonstrated better integration efficiency (mean = 4.35) and scalability (mean = 4.29) compared to less mature environments. Effect size analysis revealed large to very large effects in areas such as data exposure reduction (d = 1.35) and unauthorized access reduction (d = 1.12), confirming the practical significance of the findings. Overall, the study demonstrated that blockchain-enabled identity management systems provide measurable improvements in privacy, cybersecurity, and user trust, offering a robust alternative to traditional identity management approaches in enterprise IT environments.
Copyright (c) 2023 MD Hasibur Rahman Murad, Atif Khan (Author)
Data-Driven Optimization of Reverse Osmosis Treatment Systems for Industrial Wastewater: A Machine Learning Approach to Effluent Compliance and Energy Reduction
This study investigated how data-driven optimization improves the performance of reverse osmosis treatment systems in industrial wastewater operations, with particular focus on effluent compliance and energy reduction. The problem addressed was the persistent difficulty industries face in maintaining compliant effluent quality while controlling the high energy burden of reverse osmosis under variable feedwater conditions, fouling risk, and reactive monitoring practices. The purpose of the study was to evaluate whether machine learning integration, data-driven monitoring capability, predictive control efficiency, and process optimization significantly enhance treatment outcomes in real industrial settings. Using a quantitative, cross-sectional, case-based design, data were collected through structured questionnaires from professionals involved in cloud-enabled and enterprise industrial wastewater treatment cases, including plant engineers, wastewater operators, maintenance staff, supervisors, and compliance officers. Of 252 distributed questionnaires, 214 valid responses were analyzed, yielding an effective response rate of 84.9%. The key variables included machine learning integration, data-driven monitoring capability, predictive control efficiency, process optimization, effluent compliance performance, energy reduction performance, and overall operational efficiency. Data were analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed strong positive perceptions across all major constructs, with mean scores of 4.27 for effluent compliance, 4.21 for process optimization, 4.18 for operational efficiency, and 3.94 for energy reduction. Reliability was satisfactory, with Cronbach’s alpha ranging from 0.81 to 0.89. Correlation results indicated significant positive relationships, including process optimization with effluent compliance (r = 0.72, p < .01) and operational efficiency (r = 0.74, p < .01). Regression analysis showed that the predictors explained 47.3% of variance in effluent compliance, 37.4% in energy reduction, and 50.4% in operational efficiency. Process optimization was the strongest predictor of compliance (β = 0.31, p < .001) and operational efficiency (β = 0.34, p < .001), while predictive control efficiency most strongly predicted energy reduction (β = 0.29, p < .001). The study implies that integrated digital optimization can strengthen environmental compliance, improve operational stability, and support more sustainable industrial wastewater management.
Copyright (c) 2023 Albert Anokye, Md Rashedul Islam (Author)
Safeguarding Financial Records: A Cybersecurity-Driven Management Framework
This study addressed the persistent problem that financial records stored and processed across cloud and enterprise platforms remain vulnerable to confidentiality, integrity, availability, and auditability failures because preventive controls are often implemented without equally strong monitoring, incident response readiness, and user compliance discipline. The purpose was to validate a cybersecurity-driven management framework that explains Financial Records Safeguarding Effectiveness (FRSE) within a quantitative, cross-sectional, case-based design using survey evidence from enterprise roles working with cloud and on-premises record workflows. Data were collected from 210 valid respondents drawn from finance and accounting (52.4%), audit and compliance (23.8%), and IT and security (23.8%), with 80.0% handling financial records weekly or more. Key variables included four independent constructs, Access Control and Authentication (ACA), Data Protection and Recovery Readiness (DPR), Awareness and Compliance Discipline (ACD, PMT-informed), and Monitoring and Incident Response Preparedness (MIRP), and the dependent construct FRSE, all measured on 5-point Likert scales. The analysis plan applied reliability testing (Cronbach’s alpha), descriptive statistics, Pearson correlations, and multiple regression with multicollinearity checks. Findings showed moderate to high safeguarding maturity, with mean scores of ACA 3.82 (SD 0.64), DPR 3.71 (SD 0.66), ACD 3.58 (SD 0.70), MIRP 3.49 (SD 0.73), and FRSE 3.67 (SD 0.61); reliability was strong (α = 0.84–0.90 across constructs). FRSE correlated positively with all predictors, strongest with MIRP (r = 0.71, p < .001) and ACD (r = 0.66, p < .001). Regression results confirmed the integrated model was significant (F(4,205) = 74.6, p < .001) and explained substantial variance (R² = 0.593; Adjusted R² = 0.585), with MIRP the strongest predictor (β = 0.36, p < .001), followed by ACD (β = 0.27, p < .001), ACA (β = 0.19, p = .002), and DPR (β = 0.12, p = .041); VIF values (1.42–2.18) indicated no multicollinearity concern. A derived Threat Exposure Index averaged 0.266 (SD 0.122), with 18.6% low exposure, 62.4% moderate, and 19.0% high. Implications are that organizations should prioritize evidence-producing controls, especially monitoring and incident readiness, alongside PMT-aligned compliance strengthening, because these domains deliver the highest measurable gains in safeguarding effectiveness in cloud and enterprise financial record environments.
Copyright (c) 2026 Mahmudul Hasan, Sadia Zaman (Author)
Explainable Data Analytics in Financial Decision Systems: Enhancing Transparency in Big Data–Driven Credit Risk and Loan Approval Models
This study examined explainable data analytics in big data–driven credit risk and loan approval decision systems using a quantitative, model-comparison research design. A cross-sectional dataset consisting of 1,248 loan application records was analyzed within a retail lending context to evaluate how explainability-related attributes functioned alongside conventional predictive performance indicators. The empirical analysis incorporated applicant demographic and financial profile variables engineered behavioral features, and multiple explainability constructs, including explanation stability, sparsity, and governance-related traceability measures. The observed loan approval rate was 63.8%, while the default or delinquency rate within the performance window was 11.9%, indicating a moderately imbalanced but analytically viable outcome distribution. Descriptive results showed that predicted risk scores were concentrated in lower-to-moderate ranges, with a mean score of 0.284 and limited out-of-time drift, reflected by a score stability index mean of 0.087. Explainability summaries indicated concentrated attribution structures, with the top three explanatory drivers accounting for an average of 64.8% of total contribution magnitude and an explanation sparsity mean of 4.2 features per decision. Reliability testing demonstrated acceptable to excellent internal consistency across composite constructs, with Cronbach’s alpha values ranging from 0.81 to 0.92. Regression analysis revealed that explainability stability (β = 0.842, p < 0.001) and governance traceability (β = 0.311, p < 0.001) were statistically significant predictors of loan approval outcomes after controlling for applicant risk and financial variables, improving model fit from a pseudo-R² of 0.241 to 0.293. A secondary regression model showed that higher explanation stability and sparsity were associated with lower error concentration, with the explainability stability index exhibiting a negative relationship with misclassification clustering (β = −0.118, p < 0.001). Overall, the findings demonstrated that explainability, when operationalized through measurable stability, sparsity, and governance indicators, functioned as a statistically observable dimension of model quality and decision consistency within credit risk and loan approval systems.
Copyright (c) 2025 Sazzadul Islam (Author)
MITRE ATT&CK–Driven Threat Hunting in A SOC Environment: a Real-World Case Study Using SPLUNK Correlation Rules
This study investigates the effectiveness of MITRE ATT&CK driven threat hunting in a Security Operations Center environment through the use of Splunk correlation rules, with the central problem being that modern SOCs often struggle with excessive alert volume, false positives, incomplete adversary visibility, and delayed response in complex cloud and enterprise security environments. The purpose of the study was to determine whether ATT&CK aligned threat hunting and better engineered Splunk correlation rules improve detection effectiveness, triage efficiency, response efficiency, and overall threat hunting success. The research adopted a quantitative, cross sectional, case-based design using a structured five-point Likert scale questionnaire administered to 120 cybersecurity professionals drawn from cloud and enterprise SOC cases, including SOC analysts, threat hunters, incident responders, detection engineers, and SOC managers. The key independent variables were MITRE ATT&CK alignment, Splunk correlation rule quality, technique coverage adequacy, and precision noise balance, while the dependent variables were threat detection effectiveness, triage efficiency, response efficiency, and overall threat hunting success. Data analysis was conducted using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression. The findings showed that all major constructs recorded positive mean scores above 3.00, including MITRE ATT&CK alignment (M = 4.18, SD = 0.61), Splunk rule quality (M = 4.09, SD = 0.66), threat detection effectiveness (M = 4.16, SD = 0.58), and overall threat hunting success (M = 4.12, SD = 0.60). Reliability values were satisfactory, with Cronbach’s alpha ranging from 0.78 to 0.86. Significant positive relationships were found between ATT&CK alignment and threat detection effectiveness (r = .68, p < .001), Splunk rule quality and triage efficiency (r = .63, p < .001), technique coverage adequacy and SOC performance (r = .59, p < .001), and precision noise balance and response efficiency (r = .61, p < .001). Regression results showed that the model explained 64.0% of the variance in threat hunting success (R² = .640, F = 42.37, p < .001), with ATT&CK alignment emerging as the strongest predictor (β = .31, p = .002). The study implies that organizations can strengthen SOC performance by aligning detection logic with adversary behavior, improving rule precision, and closing technique coverage gaps, particularly in lateral movement and exfiltration.
Copyright (c) 2024 MD Zahedul Islam, Aditya Dhanekula (Author)
Integrating Psychometric and Neurocognitive Biomarkers in Computational Models to Predict Cognitive Behavioral Therapy Outcomes in Adolescents with Anxiety and Depression
This study investigated the predictive relationships between psychometric indicators and neurocognitive biomarkers in determining Cognitive Behavioral Therapy (CBT) outcomes among adolescents diagnosed with anxiety and depressive disorders. A quantitative prospective longitudinal research design was employed to examine how psychological and cognitive factors contributed to variability in treatment response. The study sample consisted of 120 adolescents aged between 12 and 18 years who were receiving structured CBT interventions in outpatient clinical settings. Data were collected at pretreatment, midpoint, and posttreatment stages using standardized psychometric scales and neurocognitive assessment tasks. Psychometric variables included anxiety severity, depressive symptoms, cognitive distortions, behavioral avoidance, emotional regulation difficulties, and resilience, while neurocognitive variables included attention bias, executive control, working memory performance, cognitive flexibility, emotional reactivity, and reward sensitivity. Descriptive analysis indicated substantial reductions in symptom severity over the course of treatment, with mean anxiety scores decreasing from 31.42 (SD = 6.85) at pretreatment to 17.63 (SD = 5.27) at posttreatment, while depressive symptoms declined from 28.73 (SD = 7.11) to 16.84 (SD = 5.89). Approximately 65% of participants were classified as treatment responders, demonstrating clinically significant improvement following CBT. Correlation analysis revealed significant relationships between psychological variables and treatment outcomes. Behavioral avoidance (r = -0.37, p < 0.01) and emotional regulation difficulties (r = -0.33, p < 0.01) were negatively associated with treatment improvement, whereas resilience demonstrated a positive correlation with treatment outcomes (r = 0.36, p < 0.01). Hierarchical regression analysis indicated that psychometric variables explained 39% of the variance in CBT outcomes (R² = 0.39, p < 0.001). When neurocognitive predictors were incorporated, the explanatory power of the model increased to 52% of the variance (R² = 0.52, p < 0.001). Executive control (β = 0.34, p < 0.01) and cognitive flexibility (β = 0.28, p < 0.05) emerged as significant positive predictors of treatment improvement, while emotional reactivity (β = -0.22, p < 0.05) was negatively associated with therapy outcomes. These findings demonstrated that integrating psychometric and neurocognitive indicators improved the prediction of CBT effectiveness. The study highlighted the importance of considering cognitive functioning, emotional regulation capacity, and behavioral coping mechanisms when evaluating treatment outcomes in adolescent psychotherapy.
Copyright (c) 2024 Amena Begum Sumi, Mst Kaniz Fatema (Author)
AI-Supported Agricultural Information Systems for National Yield Forecasting and Food Price Stabilization in the United States: A Mixed-Methods Investigation
AI-supported agricultural information systems have emerged as critical tools for improving crop yield forecasting and strengthening food price monitoring in modern agricultural economies. This study investigated the effectiveness of artificial intelligence–driven predictive analytics in enhancing national yield forecasting accuracy and examining its relationship with food price stability in the United States. A quantitative longitudinal design was applied using integrated datasets consisting of historical crop yield records, climatic indicators, satellite-derived vegetation indices, and national food price series. Machine learning models including Random Forest, Gradient Boosting, and Support Vector Regression were compared with conventional regression-based forecasting models. The analysis incorporated cross-validation and out-of-sample testing to evaluate predictive reliability and model generalization across multiple production seasons. The findings demonstrated that AI-supported forecasting models significantly outperformed traditional statistical approaches. Regression models produced prediction errors ranging between 4.0% and 5.0% across the analyzed years, whereas machine learning models reduced prediction errors to approximately 1.1%–1.2%, indicating a substantial improvement in forecasting precision. Model comparison results showed that Gradient Boosting achieved the highest predictive performance with an R² value of 0.88 and the lowest root mean square error (RMSE) of 4.02 compared with 6.84 in linear regression models. Environmental predictors played a critical role in forecasting performance, with satellite-derived vegetation indices demonstrating the strongest standardized effect size (β = 0.47, p < 0.001), followed by rainfall variability (β = 0.32, p < 0.001) and temperature variability (β = 0.21, p < 0.05). Regional analysis further indicated that AI-based forecasting systems improved predictive accuracy by approximately 25%–34% across major agricultural regions, with the largest gains observed in the Midwest and Great Plains. In addition to improving yield prediction, the results revealed measurable statistical relationships between predicted crop supply levels and food price dynamics. Years with reduced crop supply indices, such as 2019 and 2022, corresponded with higher food price volatility scores of 0.29 and 0.31 respectively, whereas years with higher predicted supply exhibited lower volatility levels near 0.19–0.22. These findings indicate that AI-supported agricultural intelligence systems can provide early signals of supply fluctuations that influence commodity market behavior. Overall, the study demonstrates that integrating machine learning algorithms, remote sensing technologies, and large-scale agricultural datasets significantly improves the predictive capacity of national agricultural monitoring systems while providing valuable insights into the interaction between agricultural production variability and food price stability.
Copyright (c) 2025 Md. Mehedi Hasan (Author)
AI-Driven Customer Behavior Modeling for Performance-Based Digital Marketing Systems
This study addresses a practical problem in performance-based digital marketing: organizations increasingly deploy AI models to predict and influence customer behavior, yet reported improvements in conversion efficiency and ROI are inconsistent because data signals, activation decisions, and measurement logic vary widely across cloud marketing stacks and enterprise platforms. The purpose of this research is to quantify, compare, and explain what AI-driven customer behavior modeling approaches work best, under what data conditions, and with which evaluation designs, using a quantitative cross-sectional, case-based synthesis. The sample comprises N = 45 peer-reviewed cloud and enterprise cases (2005–2023) drawn from marketing systems implemented across contexts such as e-commerce conversion optimization, subscription and SaaS retention, mobile funnels, and omnichannel operations. Key variables include AI technique family (supervised ML, deep learning or sequence models, recommenders, causal or uplift models, bandits or reinforcement learning), signal strategy (RFM and value features, clickstream and session features, exposure intensity, context and creative features, multi-source integration), activation lever (targeting, bidding and budget allocation, creative selection, timing and frequency control, lifecycle messaging), and measurement approach (observational attribution vs quasi-experimental incrementality vs experimental lift tests). The analysis plan applies descriptive frequency statistics, structured vote-counting of KPI direction, and a 5-point Likert evidence-support scoring to test hypotheses about performance lift, multi-source advantage, incrementality alignment, and governance effects. Headline findings show supervised ML as the most prevalent technique (31/45, 68.9%), while deep learning or sequence models appear in 19/45 (42.2%) and causal or uplift modeling in 12/45 (26.7%); overall, 33/45 studies (73.3%) report positive KPI movement attributable to AI-informed modeling, with H1 receiving strong support (M = 4.08, SD = 0.71). Multi-source data integration demonstrates stronger consistency, with 17/20 (85.0%) multi-source studies reporting positive impact versus 16/25 (64.0%) single-source studies, supporting H2 (M = 3.89, SD = 0.77). Measurement rigor is the most decisive moderator: incrementality-oriented studies report positive conclusions 24/27 (88.9%) versus 9/18 (50.0%) for attribution-only studies, yielding the strongest hypothesis support for H3 (M = 4.22, SD = 0.64). Finally, governance-explicit cases show more stable gains (15/18, 83.3%) than governance-implicit cases (18/27, 66.7%), supporting H4 (M = 3.76, SD = 0.80). These results imply that enterprises should prioritize consent-aware multi-source signal integration, operationalize model outputs into clear activation levers, and validate impact through incrementality-based measurement to avoid optimizing toward credited but non-incremental outcomes.
Copyright (c) 2026 Khairum Nahar Pinky (Author)
Implementation of Explainable AI-Driven Framework for Supply Chain Optimization: A Practical Case Study in Industrial Engineering
Supply chain optimization remains one of the most complex and data-intensive challenges in contemporary industrial engineering, requiring decision-making frameworks capable of processing high-dimensional operational data while maintaining the transparency and interpretability necessary for practitioner trust and organizational adoption. This empirical study presents the design, implementation, and evaluation of an Explainable Artificial Intelligence (XAI)-driven framework for supply chain performance optimization within a real-world industrial engineering context, addressing the critical gap between advanced predictive model accuracy and actionable operational decision support. A case study methodology was employed within a mid-sized manufacturing organization operating across multiple supply chain tiers, where historical operational data comprising 24 months of procurement, inventory, logistics, and demand records were collected, preprocessed, and used to train a hybrid machine learning model combining XGBoost and Random Forest algorithms for supply chain performance prediction. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) techniques were systematically integrated into the predictive framework to generate transparent, human-understandable explanations of model outputs, enabling supply chain managers and industrial engineers to identify the operational variables most significantly influencing inventory inefficiency, delivery delays, and procurement cost overruns. The implemented framework achieved a supply chain disruption prediction accuracy of 94.3 percent, a demand forecasting mean absolute percentage error of 6.7 percent, and an inventory optimization improvement of 28.4 percent compared to the organization's existing rule-based planning system, demonstrating the substantial operational performance benefits of XAI-driven analytical approaches in industrial supply chain environments. Qualitative evaluation through structured interviews with 14 supply chain practitioners confirmed that SHAP-based explanation interfaces significantly enhanced decision-maker confidence, model trust, and willingness to act upon AI-generated recommendations, with 87 percent of interviewed practitioners reporting that explanation transparency was a decisive factor in their acceptance of the framework for operational use. The findings demonstrate that the integration of explainability infrastructure into supply chain ML systems bridges the critical gap between predictive model capability and realized operational improvement, confirming that XAI represents not merely a technical enhancement but a fundamental organizational enabler of AI-driven supply chain excellence in industrial engineering practice.
Copyright (c) 2025 Sayeda Sufia Sumi (Author)
Artificial Intelligence Enabled Predictive Safety Analytics for Proactive Hazard Identification in Industrial Manufacturing Environments
This study quantitatively evaluated the effectiveness of an artificial intelligence–enabled predictive safety analytics system for proactive hazard identification in an industrial manufacturing environment. A quasi-experimental design combining interrupted time-series analysis, difference-in-differences estimation, hierarchical regression, and multilevel modeling was employed to assess whether AI-generated predictive risk scores contributed to measurable improvements in both leading and lagging safety outcomes. The analytic dataset included 2,184 line–shift observations and 312 workforce respondents across intervention and comparison production units. Descriptive results indicated that AI-generated risk scores decreased from a pre-implementation mean of 0.52 (SD = 0.16) to 0.41 (SD = 0.15) post-implementation (t = 8.74, p < .001). Exposure duration in high-risk zones declined from 81.2 to 69.3 minutes per shift (p < .001), and anomaly frequency decreased from 3.91 to 2.96 events per shift (p < .001). Near-miss frequency was reduced from 2.94 to 2.21 per 200,000 exposure hours (p = .002), while recordable incident rates declined from 3.81 to 3.05 (p = .015). Hierarchical regression analysis demonstrated that AI-generated risk scores significantly increased explained variance in leading safety indicators from R² = .055 to R² = .269. Multilevel modeling further showed that exposure duration (β = .29, p < .001) and anomaly frequency (β = .34, p < .001) significantly predicted near-miss outcomes. Interrupted time-series analysis revealed an immediate post-intervention incident reduction of −0.68 incidents (p = .004), with a sustained downward trend thereafter. Reliability testing confirmed strong internal consistency across safety constructs (Cronbach’s alpha range = .81–.89). Collectively, these findings provided statistically robust evidence that AI-enabled predictive safety analytics significantly enhanced proactive hazard detection and contributed to measurable reductions in industrial safety incidents within a complex manufacturing setting.
Copyright (c) 2026 Md Abubakar Siddique Akash (Author)
The Impact of Explainable AI On EHR-Based Clinical Risk Prediction: A Quantitative Evaluation of Transparency and Diagnostic Accuracy
This study quantitatively evaluated the impact of explainable artificial intelligence (XAI) on electronic health record (EHR)-based clinical risk prediction by comparing diagnostic accuracy, probability reliability, transparency stability, and clinician-centered utility within a controlled paired design. A retrospective cohort of 12,480 index patient-encounter observations was constructed from a hospital-based EHR system, with 3,744 encounters allocated to the independent test set. Identical feature engineering pipelines, cohort definitions, and data splits were applied to both non-explainable baseline models and explainable models augmented with structured explanation mechanisms. Diagnostic performance was assessed using discrimination, precision-oriented metrics, calibration summaries, and threshold-based error profiles. Transparency was evaluated using global and local explanation concentration indices and stability measures across 20 repeated training runs and controlled perturbations. The explainable model demonstrated higher discrimination performance (mean 0.826, SD 0.016) compared to the baseline model (mean 0.812, SD 0.018), along with improved precision-sensitive performance (0.447 vs. 0.421). Calibration slope improved from 0.91 to 0.97, and false-negative rate decreased from 18.6% to 16.8% at the prespecified operating threshold. Explanation stability was high, with a mean rank correlation of 0.91 across repeated runs. Clinician-centered evaluation (n = 64) showed strong internal reliability (Cronbach’s alpha range: 0.86–0.91) and high comprehension accuracy (84.3%, SD 6.7). Regression analysis indicated that the explainable condition significantly predicted improved discrimination (B = 0.014, 95% CI 0.006–0.022, p = 0.001) and increased odds of correct classification (OR = 1.28, p = 0.002). Explanation clarity significantly predicted clinician adoption proxy scores (B = 0.37, p < 0.001). Overall, findings demonstrated that explainability integration was associated with modest yet consistent improvements in technical performance, probability reliability, explanation stability, and clinician-facing interpretive outcomes under a rigorously controlled evaluation framework.
Copyright (c) 2024 Hisham Mahmood, Khairum Nahar Pinky (Author)
Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity
This study examined how climate-induced water stress and migration pressures jointly influenced state capacity, and whether water governance quality and resilience capacity buffered institutional strain. A longitudinal panel dataset covering 148 sovereign states over 20 years (N = 2,960 country-year observations) was analyzed using fixed-effects regression models with cluster-robust standard errors. Climate-induced water stress was found to have a statistically significant negative association with state capacity (β = −0.21, p < .001), particularly within fiscal and service delivery domains. Migration pressure also demonstrated a significant negative effect (β = −0.17, p = .005), with stronger impacts observed in administrative coordination indicators. The interaction between water stress and migration was negative and significant (β = −0.11, p = .006), indicating that institutional strain intensified under compound stress conditions. Marginal effects analysis showed that the impact of water stress increased from −0.18 at low migration levels to −0.29 at high migration levels. Moderation analysis revealed that stronger water governance reduced the magnitude of the water stress effect (β = 0.09, p = .025), while resilience capacity significantly buffered compound pressure (β = 0.12, p = .003). The within-model R² increased from 0.47 in the baseline model to 0.57 in moderation models. Findings demonstrated that environmental scarcity and demographic redistribution operated both independently and interactively to challenge state capacity, while governance quality and resilience functioned as measurable institutional buffers.
Copyright (c) 2026 FNU Zulqarnain (Author)
Artificial Intelligence Based Predictive Modeling for Structural Health Monitoring and Failure Risk Assessment in Water and Sanitation Infrastructure
Artificial intelligence–based predictive modeling has increasingly been adopted to enhance structural health monitoring and failure risk assessment in water and sanitation infrastructure systems characterized by aging assets, heterogeneous environmental exposure, and rare-event failure dynamics. This study developed and evaluated an integrated quantitative framework incorporating asset registry attributes, historical failure indicators, telemetry-derived operational metrics, inspection-based condition severity measures, and environmental exposure covariates. The analytical dataset included 1,248 infrastructure assets observed over a 10-year period across multiple operational districts. Hierarchical regression modeling demonstrated that baseline registry variables explained 28.6% of the variance in failure likelihood, while the inclusion of historical break frequency increased explanatory power to 52.4%. The addition of telemetry variability metrics and inspection-based severity indices further improved model performance, resulting in a final integrated model explaining 68.2% of outcome variance. Asset age and material class showed significant standardized effects of 0.31 and 0.27, respectively, while historical recurrence exhibited the strongest influence with a coefficient of 0.39. Telemetry-derived pressure variability improved predictive discrimination by 11.5% in monitored zones. Composite risk prioritization based on integrated modeling captured 74% of observed failures within the top 20% of ranked assets, representing a 31.4% improvement over age-based heuristics. Reliability analysis confirmed internal consistency of multi-item constructs, with Cronbach’s alpha values exceeding 0.80 for telemetry and inspection indices. The findings demonstrate that integrated AI-driven predictive modeling substantially enhances structural condition estimation accuracy and failure risk prioritization efficiency within complex water and sanitation infrastructure systems.
Copyright (c) 2026 Palash Chandra Das (Author)
AI-Driven Cybercrime Forensics for Predictive Threat Detection and Investigative Intelligence
AI-Driven Cybercrime Forensics has become increasingly important for predictive threat detection and investigative intelligence due to the rising complexity and volume of digital evidence. This quantitative study examined how AI-driven forensic capabilities predicted key investigative outcomes, including investigative efficiency, decision accuracy, and case documentation quality. Data were obtained from 210 valid respondents working in cybercrime-related roles. The sample included digital forensics analysts (24.8%), incident response specialists (21.0%), SOC analysts or threat hunters (18.1%), and cybersecurity managers (13.3%). Most respondents reported high familiarity with AI-based security tools (43.8%) and high exposure to multi-source forensic evidence (45.7%). Descriptive results showed high agreement for predictive threat detection effectiveness (M = 4.12, SD = 0.61) and investigative intelligence quality (M = 4.05, SD = 0.64). Workflow efficiency also scored strongly (M = 3.98, SD = 0.69). Explain ability and trust calibration produced the lowest mean (M = 3.74, SD = 0.78), while evidence traceability and documentation integrity remained moderate-high (M = 3.81, SD = 0.73). Reliability analysis confirmed acceptable-to-strong internal consistency, with Cronbach’s alpha values ranging from 0.78 to 0.89. Multiple regression results indicated that investigative intelligence quality was the strongest predictor of investigative efficiency (β = .41, t = 7.82, p < .001), decision accuracy (β = .34, t = 6.11, p < .001), and documentation quality (β = .29, t = 5.02, p < .001). Workflow efficiency significantly predicted investigative efficiency (β = .33, p = .001) and decision accuracy (β = .21, p = .018). The regression models explained 62% of the variance in investigative efficiency (R² = .62), 55% in decision accuracy (R² = .55), and 49% in documentation quality (R² = .49). Overall, findings confirmed that AI-supported correlation and intelligence generation most strongly improved investigative outcomes.
Design and Optimization of End-To-End Artificial Intelligence and Machine Learning Pipelines in CI/CD-Enabled Cloud Infrastructures
This quantitative study examined the design and optimization of end-to-end artificial intelligence and machine learning (AI/ML) pipelines deployed in CI/CD-enabled cloud infrastructures by evaluating measurable relationships between pipeline engineering maturity and operational outcomes. A total of 214 valid responses from AI/ML, DevOps/Mops, and cloud engineering professionals were analyzed across technology, finance, healthcare, manufacturing, and logistics sectors. Descriptive results showed high maturity in component separation (M = 4.12, SD = 0.63), schema validation (M = 4.18, SD = 0.54), and automated test coverage (M = 4.05, SD = 0.58), while greater variability was observed in dependency complexity (M = 3.41, SD = 0.72) and drift detection (M = 3.78, SD = 0.75). Operational indicators revealed an average training runtime of 142.6 minutes (SD = 38.4), GPU utilization of 81.7% (SD = 6.8), scaling efficiency of 78.3% (SD = 9.5), and inference latency of 84.2 MS (SD = 15.6). Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.83 to 0.91. Regression results indicated that modularity, automation depth, and validation effectiveness significantly reduced pipeline runtime (R² = 0.528), while monitoring readiness, test coverage, and gate pass rate significantly reduced change failure rate (R² = 0.496). Autoscaling responsiveness, caching maturity, and serving optimization significantly improved tail latency stability (R² = 0.551). Cost models showed scaling efficiency and GPU utilization significantly reduced training cost per run (R² = 0.467), while caching maturity reduced inference cost per 1,000 predictions (R² = 0.419). Overall, the findings demonstrated that measurable pipeline optimization outcomes were strongly associated with architectural modularity, CI/CD automation depth, validation rigor, and observability maturity in cloud-native AI/ML delivery systems.
Copyright (c) 2026 Mohammad Robel Miah (Author)
Privacy-Preserving Federated Learning for Critical Infrastructure: A Systematic Review of Security Threat Models, Deployment Patterns, And Governance
This study presented a quantitative systematic synthesis of privacy-preserving federated learning research in critical infrastructure contexts by examining how security threat models, privacy mechanisms, deployment architectures, and governance controls were jointly represented and evaluated across the empirical literature. A total of 120 peer-reviewed and high-quality technical studies were coded and analyzed using structured extraction protocols that transformed heterogeneous experimental reports into comparable variables. Descriptive analysis showed that malicious-client and colluding-client threat models dominated the evidence base (approximately 55% and 32%, respectively), while availability-focused threats were reported in fewer than 20% of studies. Passive inference attacks were evaluated more frequently (67.5%) than active attacks (40.8%), with confidentiality emerging as the most frequently targeted security property (65.8%). In terms of privacy mechanisms, secure aggregation (50.8%) and differential privacy (45.0%) were the most prevalent, whereas cryptographic computation approaches (21.7%) and trusted execution techniques (15.8%) appeared less often and were associated with higher reported overhead. Deployment analysis indicated strong dominance of centralized aggregation (70.8%) and cross-silo regimes (60.0%), reflecting institutional coordination patterns typical of critical infrastructure systems. Governance reporting was uneven, with role definitions (55.8%) and access rules (59.2%) appearing more frequently than consent artifacts (27.5%) and accountability procedures (17.5%). Regression analysis demonstrated that active threat exposure and cryptographic or hybrid privacy mechanisms were positively associated with standardized overhead burden, while cross-silo deployment and higher governance maturity scores were negatively associated with both overhead and utility loss. Governance maturity and auditability indices achieved acceptable-to-strong internal consistency (Cronbach’s α ranging from 0.72 to 0.86) and showed statistically significant relationships with outcome stability. Configuration prevalence analysis revealed that a limited set of design patterns dominated the literature, most commonly combining client-centric threat models, baseline privacy mechanisms, centralized architectures, and limited governance instrumentation. Overall, the study provided a variable-driven quantitative account of prevailing configurations, measured trade-offs, and reporting gaps in privacy-preserving federated learning for critical infrastructure, offering an empirical foundation for structured comparison across technical and institutional dimensions.
Copyright (c) 2026 Istiaq Ahmed (Author)
Machine Learning Based Data Optimization of Merchant Onboarding Processes in Fintech Ecosystems: A Time-Series and Process-Mining Study
This study examined how machine learning–based data optimization and process analytics influence merchant onboarding performance within a FinTech ecosystem by integrating supervised modeling, process mining, and time-series evaluation. Using a retrospective dataset of 3,200 onboarding cases observed across 26 operational weeks, the analysis reconstructed event-driven workflows and quantified relationships between data quality, workflow behavior, and decision outcomes. Descriptive results showed moderate data missingness (mean = 7.8%), high median document completeness (92%), and substantial operational heterogeneity reflected in manual-touch intensity (41.7%) and escalation rate (9.6%). Median onboarding cycle time was 24.7 hours, although the mean reached 38.4 hours due to delay-heavy exception cases, and waiting time concentration averaged 63.2%, indicating that queue-related idle time constituted the majority of total duration. Logistic regression results demonstrated that the Data Quality Optimization Index significantly increased approval likelihood (OR = 1.65, p < .001), while Document Risk Intensity (OR = 0.72, p < .001) and Workflow Friction (OR = 0.66, p < .001) significantly reduced approval probability. Linear regression analysis further showed that Workflow Friction (+12.1 hours, p < .001) and Operational Effort (+9.3 hours, p < .001) substantially increased onboarding cycle time, whereas improved data quality reduced processing duration (−6.8 hours, p < .001). The final approval model achieved a McFadden pseudo R² of 0.29, and the cycle-time model explained 42% of duration variance (R² = 0.42; RMSE = 28.9 hours), with coefficient stability confirmed across rolling time-window robustness checks. Overall, the findings demonstrated that onboarding performance was systematically shaped by measurable data conditions and trace-level workflow characteristics rather than solely by merchant risk segmentation. By linking temporal KPI shifts with process variants and predictive constructs, this study provided an integrated quantitative framework for evaluating data-driven optimization in digital merchant onboarding systems.
Copyright (c) 2025 Iftekhar Ahmed (Author)
Machine Learning–Based Transaction Risk Scoring Models for Financial Compliance Monitoring in Foreign Exchange Operations
This study provided a quantitative cross-study synthesis of machine learning–based transaction risk scoring models for financial compliance monitoring in foreign exchange operations, with emphasis on measurable modeling practices, evaluation rigor, and governance instrumentation. A total of 124 analytic records derived from 89 publications were coded using a structured extraction protocol that converted heterogeneous reporting into standardized variables across model family, feature construction, validation design, labeling strategy, evaluation metrics, and governance controls. Descriptive results showed that ensemble models were the most frequently evaluated approach (44.4%), followed by logistic regression and generalized linear models (37.1%), decision tree models (33.9%), neural architectures (29.8%), and unsupervised or semi-supervised methods (26.6%). Customer-profile variables (69.4%), geographic corridor indicators (62.1%), and temporal aggregation features (57.3%) were the most commonly engineered feature groups, while network-based variables appeared in 41.9% of records. Evaluation practices were dominated by discrimination metrics (82.3%), with lower reporting of ranking metrics (61.3%), calibration measures (34.7%), and cost-sensitive analyses (28.2%). Governance and auditability constructs were underreported, with access control indicators documented in 29.8% of records and traceability artifacts in 22.6%. Reliability testing demonstrated strong internal consistency for governance maturity (α = 0.86) and documentation completeness (α = 0.84) indices. Logistic regression analysis showed that ensemble models (OR = 2.27, p = 0.008), neural models (OR = 1.99, p = 0.041), and out-of-time validation (OR = 2.83, p = 0.004) were significantly associated with high predictive performance reporting. Linear regression indicated that operational studies were strongly associated with higher governance maturity scores (β = 1.12, p < 0.001). Overall, the findings indicated that methodological rigor in validation design and feature construction was more consistently associated with reported performance gains than model family alone, while governance instrumentation and operational alignment remained uneven across the FX compliance risk scoring literature.
Copyright (c) 2026 Rukaiya Khatun Moury (Author)
AI-Enabled Decision Support Systems for Service Operations an Analytical Modeling and IT Strategy Framework
This study examined the quantitative relationships among AI-enabled decision support system (AI-DSS) capability, IT strategy alignment, and measurable service operations performance using unit-level data from a data-intensive service organization. A cross-sectional explanatory design was applied with structured retrospective extraction of operational KPIs and AI-DSS system log indicators, combined with survey-based measurement of IT strategy alignment dimensions. The final dataset included 52 operational service units and 198 valid survey responses retained after screening from an initial pool of 214 responses (92.5% retention). AI-DSS capability was operationalized as a composite index (0–100) supported by indicators such as system-use frequency, recommendation viewing rate, decision latency, and forecasting accuracy. IT strategy alignment was measured using a composite index (1–5) based on data integration maturity, interoperability, governance strength, and workflow embedding. Service operations performance was measured through unit-level KPIs including average waiting time, service-level attainment, cost per transaction, and abandonment rate. Descriptive results indicated relatively high AI-DSS capability (M = 71.6, SD = 11.4) and moderate-to-high IT strategy alignment (M = 3.84, SD = 0.52), with the greatest dispersion observed in decision latency (M = 26.4 minutes, SD = 13.2). Regression analysis showed that AI-DSS capability was significantly associated with lower waiting time (B = -0.021, p = 0.001), higher service-level attainment (B = 0.142, p = 0.005), lower cost per transaction (B = -0.016, p = 0.002), and lower abandonment rate (B = -0.071, p = 0.001). IT strategy alignment demonstrated significant direct effects on waiting time (B = -0.118, p = 0.025), service-level attainment (B = 1.87, p = 0.004), and cost per transaction (B = -0.091, p = 0.044). Moderation analysis indicated significant interaction effects for waiting time (B = -0.0062, p = 0.005), service-level attainment (B = 0.041, p = 0.020), and cost per transaction (B = -0.0049, p = 0.009), confirming that IT strategy alignment strengthened the operational impact of AI-DSS capability. Overall, the findings supported an integrated analytical modeling and IT strategy framework in which AI-DSS capability functioned as a direct performance driver and IT strategy alignment acted as both an independent predictor and an amplifying condition for operational outcomes.
Copyright (c) 2026 Tanjina Binte Sohrab (Author)
Quantitative AI-Based Load-Flow and Fault-Prediction Modeling for Reliability Enhancement in Electrical Distribution Networks
This study examined an integrated AI-based framework for load-flow estimation and fault prediction to enhance reliability in electrical distribution networks. A quantitative design was applied using operational data from 12 medium-voltage feeders over an 18-month period, comprising 104,832 operating snapshots collected at 15-minute resolution and 286 confirmed fault events. Feeder sizes ranged from 48 to 137 buses, with topology depth varying from 6 to 18 levels. Descriptive results showed an overall mean voltage magnitude of 0.984 p.u. (SD = 0.031), with a minimum observed value of 0.901 p.u. and a maximum of 1.067 p.u. Mean feeder loading was 62.8% of rated capacity, with peak loading reaching 118.4%. Outage duration averaged 58.6 minutes (SD = 49.8) and customer interruptions ranged from 14 to 4,320 per event. Load-flow regression models indicated that measurement density significantly reduced voltage prediction error (β = –0.41, p < 0.001), while topology depth increased estimation error (β = 0.28, p = 0.002), producing adjusted R² values of 0.64 for voltage error and 0.58 for branch current error. Fault occurrence modeling demonstrated that environmental disturbance severity (β = 0.44, p < 0.001), electrical stress exposure (β = 0.37, p < 0.001), and asset vulnerability (β = 0.29, p = 0.004) significantly increased fault probability, with Nagelkerke R² = 0.52. Fault location accuracy improved significantly when load-flow-derived stress features were included (β = 0.35, p = 0.001). Reliability-linked regression results showed that improved fault localization reduced restoration time (β = –0.38, p < 0.001), and higher predicted fault probability increased interruption duration (β = 0.26, p = 0.009). Robustness testing under measurement perturbation reduced model explanatory power by less than 6%, confirming stability. Overall, the integrated framework demonstrated statistically significant relationships between state estimation accuracy, fault prediction performance, and reliability outcomes.
Copyright (c) 2026 Md. Sultan Mahamud (Author)
Electrical Engineering Approaches for AI-Enabled Autonomous Robotics in U.S. Industrial and Defense Applications
This quantitative study examined how electrical engineering approaches influenced the performance of AI-enabled autonomous robotics in U.S. industrial and defense applications. A total of 220 valid responses were analyzed, including 112 industrial (50.9%) and 108 defense (49.1%) system cases, ensuring balanced cross-domain representation. Six electrical engineering constructs were evaluated as predictors of autonomy performance: sensor system performance, embedded computing efficiency, power electronics and energy stability, control and actuation accuracy, communication system reliability, and safety and reliability engineering effectiveness. Descriptive results indicated that safety and reliability engineering demonstrated the highest consistency (M = 4.12, SD = 0.49), followed by control and actuation accuracy (M = 3.94, SD = 0.55) and sensor system performance (M = 3.82, SD = 0.61). Embedded computing efficiency (M = 3.56, SD = 0.74) and power stability (M = 3.41, SD = 0.79) showed the greatest variability across systems. Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha coefficients ranging from 0.842 to 0.926. Multiple regression analysis demonstrated that the combined model significantly predicted autonomy performance (R² = 0.659, Adjusted R² = 0.647, F = 67.83, p < .001). Embedded computing efficiency (β = 0.338, p < .001) and sensor system performance (β = 0.301, p < .001) were the strongest predictors. Power stability (β = 0.256, p < .001) and control accuracy (β = 0.213, p < .001) also significantly contributed. Communication reliability showed a moderate effect (β = 0.158, p = .001) and was stronger in defense contexts, while safety engineering provided a smaller but significant stabilizing influence (β = 0.109, p = .014). Findings confirmed that electrical subsystem performance significantly explained variation in AI-enabled autonomy outcomes across both domains.
Copyright (c) 2026 Shamsul Arifeen (Author)