American Journal of Data Science and Analytics
The American Journal of Data Science and Analytics (AJDSA) is a peer-reviewed, open-access international journal dedicated to advancing scholarly research, innovation, and practical applications in the rapidly evolving fields of data science, analytics, and artificial intelligence. As data-driven approaches increasingly shape decision-making across industries, governments, and scientific disciplines, AJDSA provides a global platform for disseminating high-quality research that integrates theoretical foundations with real-world problem solving. AJDSA serves academics, researchers, data scientists, engineers, industry professionals, and policymakers by publishing original contributions that explore how data, algorithms, and analytical frameworks can generate actionable insights and support intelligent systems. The journal emphasizes interdisciplinary research that bridges statistics, computer science, mathematics, engineering, and domain-specific applications, reflecting the central role of data science in contemporary innovation ecosystems. The journal is committed to fostering rigorous scientific inquiry while ensuring accessibility and transparency through its open-access publishing model. By removing subscription barriers, AJDSA enables the global research community to freely access, share, and build upon published work, accelerating knowledge diffusion and technological progress worldwide.
Secure Distributed Data Processing Using Privacy-Preserving Artificial Intelligence and Zero Trust Architecture for Enterprise Risk Identification and Performance Evaluation
This study examined the problem of increasing privacy, security, and governance risks in enterprise distributed data processing environments where sensitive operational, customer, compliance, and security data are processed across cloud platforms, enterprise applications, remote systems, and third-party infrastructures. The purpose of the study was to assess how privacy-preserving artificial intelligence and Zero Trust Architecture strengthen secure distributed data processing for enterprise risk identification and performance evaluation. A quantitative, cross-sectional, case-based research design was adopted, focusing on cloud and enterprise cases involving distributed data systems, cybersecurity tools, AI-supported analytics, compliance systems, and digital governance practices. The sample consisted of 214 valid respondents selected from IT and system professionals, cybersecurity professionals, data and analytics professionals, compliance and risk officers, managers, and digital transformation officers, with a usable response rate of 85.6%. The key variables included privacy-preserving artificial intelligence, Zero Trust Architecture, secure distributed data processing, enterprise risk identification, and enterprise performance evaluation. The analysis plan used descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, regression analysis, readiness index assessment, and a secure distributed data processing trustworthiness matrix. The findings showed high mean scores for all major constructs, including Zero Trust Architecture, M = 4.14, secure distributed data processing, M = 4.11, privacy-preserving AI, M = 4.08, enterprise risk identification, M = 4.06, and enterprise performance evaluation, M = 4.02. Reliability was strong, with Cronbach’s alpha values ranging from 0.854 to 0.891 and an overall scale reliability of 0.914. Correlation results showed that Zero Trust Architecture had a strong relationship with secure distributed data processing, r = 0.721, p < 0.001, while secure distributed data processing was strongly related to enterprise risk identification, r = 0.697, p < 0.001. Regression results confirmed that Zero Trust Architecture significantly predicted secure distributed data processing, β = 0.442, p < 0.001, and secure distributed data processing significantly predicted enterprise risk identification, β = 0.514, p < 0.001. The combined model explained 58.4% of enterprise security performance variance. The study implies that enterprises should integrate privacy-aware AI, Zero Trust controls, access governance, anomaly detection, and auditability into distributed data systems to improve risk visibility, compliance, resilience, and performance evaluation.
Privacy-preserving artificial intelligence
Copyright (c) 2024 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
Enhancing Financial Accountability and Cost Control in North American Outpatient Clinics Through Data Science: A Mixed-Methods Healthcare Administration Study
Rising operating costs, reimbursement complexity, and administrative inefficiencies continue to threaten the financial sustainability of outpatient clinics across North America, yet the accountability mechanisms intended to contain these pressures remain unevenly implemented. This study examines how four financial accountability mechanisms—budgetary control, internal auditing, expenditure monitoring, and financial reporting transparency—influence cost efficiency, administrative effectiveness, and financial sustainability at the clinic level. A quantitative, cross-sectional, case-based design was employed. Structured questionnaires were distributed to 300 healthcare administrators, clinic managers, finance officers, and operations supervisors across selected outpatient clinic cases, yielding 250 usable responses (response rate: 83.3%). Data were analyzed using descriptive statistics, Cronbach's alpha reliability testing, Pearson correlation, and multiple regression. Respondents reported consistently strong accountability practices, with the highest mean scores for expenditure monitoring (M = 4.14, SD = 0.68) and budgetary control (M = 4.08, SD = 0.71), followed by internal auditing (M = 3.96, SD = 0.76) and reporting transparency (M = 3.89, SD = 0.74). Outcome measures were similarly elevated: administrative effectiveness (M = 4.10, SD = 0.66), cost efficiency (M = 4.02, SD = 0.70), and financial sustainability (M = 3.94, SD = 0.73). All scales demonstrated acceptable reliability (α = 0.79–0.86; overall α = 0.88). Correlation analysis revealed significant positive associations across all variables, most notably between expenditure monitoring and cost efficiency (r = 0.66, p < 0.01). The regression model was significant, F(4, 245) = 39.82, p < 0.001, accounting for 39.4% of the variance in cost-control performance (R² = 0.394). Expenditure monitoring emerged as the strongest predictor (β = 0.34), followed by budgetary control (β = 0.28), internal auditing (β = 0.21), and reporting transparency (β = 0.17). The findings suggest that strengthening structured accountability systems—particularly expenditure monitoring and budgetary control—can meaningfully improve financial discipline, reduce waste, and reinforce administrative governance in outpatient healthcare settings. The study offers practical implications for clinic administrators and policymakers seeking to enhance fiscal resilience without compromising service continuity.
Copyright (c) 2026 Mst Shamima Akter (Author)
AI-Driven Predictive Supply Chain Resilience Framework for U.S. Manufacturing Systems: An Empirical Analysis
This study develops and empirically evaluates an AI-driven predictive supply chain resilience framework tailored for U.S. manufacturing systems, addressing the increasing vulnerability of traditional supply chains to global disruptions. The research is grounded in the transition from reactive, Just-in-Time models to proactive, data-driven resilience strategies aligned with Industry 4.0 principles. A comprehensive manufacturing and supply chain dataset was constructed by integrating operational variables, logistics performance metrics, and external disruption indicators. Advanced machine learning techniques, specifically the Random Forest algorithm, were applied to predict supply chain disruptions, their severity, and recovery timelines. The model demonstrated high predictive performance, achieving an overall accuracy of 91.8%, with precision of 89.6%, recall of 92.3%, and an F1-score of 90.9%. The area under the ROC curve (AUC) reached 0.94, confirming strong classification capability in identifying disruption events. The results further revealed significant operational improvements following the implementation of the predictive framework. Disruption response time was reduced by approximately 35%, while inventory optimization led to a 22% decrease in stockouts and a 15% reduction in excess inventory costs. Logistics efficiency improved, with transportation delays reduced by 18% and service levels increasing to an average of 96%. Additionally, the framework contributed to an estimated cost savings of 12–18% across supply chain operations. These findings highlight the effectiveness of integrating predictive analytics with real-time data for enhancing supply chain visibility and responsiveness. Overall, this study demonstrates that AI-driven predictive models significantly outperform traditional reactive approaches, offering a scalable and data-driven solution for disruption management. The proposed framework provides both theoretical and practical contributions by bridging the gap in real-time predictive resilience modeling and supporting strategic decision-making in complex manufacturing environments.
Data Privacy and Compliance in Hybrid Cloud Systems: Challenges and Opportunities for U.S. Enterprises
This study addresses the growing problem that U.S. enterprises increasingly depend on hybrid cloud systems for flexibility and scalability, yet often struggle to maintain consistent data privacy protections and compliance readiness across mixed private and public cloud environments. The purpose of the research was to examine the major privacy and compliance challenges and opportunities associated with hybrid cloud adoption and to determine how privacy controls, compliance governance, and hybrid cloud opportunities influence enterprise readiness and trust. Using a quantitative, cross-sectional, case-based design, data were collected through a structured five-point Likert-scale questionnaire from 200 valid respondents drawn from cloud, compliance, cybersecurity, and governance professionals across enterprise cases in finance, healthcare, retail, manufacturing, logistics, and education-related settings. The key variables included privacy risk exposure, compliance governance, hybrid cloud opportunities, compliance readiness, and enterprise trust, and the analysis plan combined descriptive statistics, reliability testing, Pearson correlation, and multiple regression modeling. The findings showed that privacy risk exposure remained high with a mean of 3.94, driven especially by cross-environment data visibility gaps (M = 4.18), third-party vendor handling risk (M = 4.07), inconsistent access control (M = 3.96), and audit-trail difficulty (M = 3.91). At the same time, hybrid cloud opportunities were strongly recognized (M = 4.11), while compliance governance (M = 3.88), compliance readiness (M = 3.83), and enterprise trust (M = 3.76) were moderately high. Correlation analysis revealed significant positive relationships among the major constructs, including compliance governance with compliance readiness (r = .67, p < .01), privacy controls with compliance readiness (r = .61, p < .01), and privacy controls with enterprise trust (r = .58, p < .01). Regression results showed that privacy controls, compliance governance, and hybrid cloud opportunities jointly explained 56.1% of the variance in compliance readiness (R² = .561, F (3,196) = 42.31, p < .001), with compliance governance emerging as the strongest predictor (β = .38). The study implies that enterprises can convert hybrid cloud benefits into secure and trusted value only when privacy and compliance are managed through integrated governance frameworks.
Copyright (c) 2024 MD Hasibur Rahman Murad, Atif Khan (Author)
GIS-Integrated Digital Twin Framework for Dynamic Environmental Site Assessment and Contaminated Plume Delineation in Petroleum Hydrocarbon Spill Zones
This study addresses the persistent problem of uncertainty in dynamic environmental site assessment and contaminated plume delineation in petroleum hydrocarbon spill zones, where static and fragmented assessment methods often limit the accuracy of contamination boundary identification, monitoring continuity, and remediation planning. The purpose of the research was to examine whether a GIS-integrated digital twin framework could improve spatial interpretation, dynamic environmental monitoring, and decision-support reliability in petroleum hydrocarbon spill contexts. The study adopted a quantitative, cross-sectional, case-based design and collected data from 210 professionals drawn from cloud-enabled and enterprise-oriented environmental assessment cases, including environmental engineers, GIS specialists, hydrogeologists, remediation consultants, and petroleum site managers. The key independent variables were GIS integration, digital twin responsiveness, and real-time environmental data integration, while the dependent variables were spatial confidence in plume delineation, dynamic monitoring effectiveness, and decision-support reliability. Data were gathered using a structured 5-point Likert-scale questionnaire and analyzed through descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong support for the proposed framework, with high mean scores for GIS integration (M = 4.32, SD = 0.61), digital twin responsiveness (M = 4.26, SD = 0.66), real-time environmental data integration (M = 4.18, SD = 0.69), spatial confidence in plume delineation (M = 4.29, SD = 0.58), dynamic monitoring effectiveness (M = 4.21, SD = 0.64), and decision-support reliability (M = 4.35, SD = 0.56). Correlation results indicated strong positive relationships, including GIS integration with spatial confidence (r = .71, p < .001) and digital twin responsiveness with monitoring effectiveness (r = .74, p < .001). Regression results further showed that GIS integration significantly predicted plume confidence (β = .41, p < .001, R² = .58), digital twin responsiveness strongly predicted monitoring effectiveness (β = .46, p < .001, R² = .61), and the combined framework significantly explained decision-support reliability (R² = .64, F = 52.87, p < .001). The study implies that integrating geospatial intelligence, responsive digital representation, and timely environmental data can substantially enhance contamination assessment accuracy, plume interpretation, and evidence-based remediation planning in petroleum hydrocarbon spill management.
Copyright (c) 2024 Albert Anokye, Md Rashedul Islam (Author)
A Comparative Study of AI-Enabled SCADA and DCS Integration for Smart Grid Optimization in Combined Cycle Power Plants (2018–2026)
This study examined the comparative effectiveness of AI-enabled Supervisory Control and Data Acquisition (SCADA), Distributed Control Systems (DCS), and integrated SCADA–DCS frameworks for smart grid optimization in combined cycle power plants over the period 2018–2026. A quantitative longitudinal comparative design was employed using operational datasets from 54 power plants, comprising 2,592 observations across three system configurations. Descriptive and inferential statistical analyses were conducted to evaluate key performance indicators, including response time, control latency, thermal efficiency, system reliability, and fuel consumption. The results revealed statistically significant differences among system types (p < 0.05), with integrated SCADA–DCS systems consistently outperforming standalone configurations. Specifically, integrated systems achieved the lowest average response time (180 ms) and control latency (135 ms), compared to AI-enabled SCADA (245 ms; 190 ms) and AI-enabled DCS (210 ms; 160 ms). Thermal efficiency was highest in integrated systems (58.9%) relative to DCS (55.7%) and SCADA (53.2%), while downtime was reduced to 7.1 hours annually compared to 10.2 hours for DCS and 13.6 hours for SCADA systems. Effect size analysis indicated large impacts for thermal efficiency (η² = 0.45) and response time (η² = 0.42), confirming the practical significance of system integration. Regression analysis further demonstrated that integrated SCADA–DCS configurations were the strongest predictor of performance outcomes (β = 0.62, p = 0.000). The findings highlighted that combining supervisory monitoring with process-level control, enhanced by artificial intelligence, significantly improves operational efficiency, reliability, and adaptability in dynamic smart grid environments.
Supervisory Control and Data Acquisition (SCADA)
Copyright (c) 2026 Md Asif Ali Sheak Arju (Author)
AI-Enhanced SOC Operations for Deepfake and Synthetic Fraud Detection in Banking: A Comparative Study with Traditional SIEM (2018–2026)
This study investigated the comparative effectiveness of AI-enhanced Security Operations Center (SOC) systems and traditional SIEM-based detection mechanisms in identifying deepfake and synthetic fraud in banking environments from 2018 to 2026 using a quantitative quasi-experimental longitudinal design. The analysis was conducted on a dataset of 1,250,000 cybersecurity events, including 937,500 legitimate cases (75.0%) and 312,500 fraudulent cases (25.0%), of which 187,500 (15.0%) were synthetic identity fraud, 75,000 (6.0%) deepfake-related fraud, and 50,000 (4.0%) conventional fraud. The findings indicated that AI-enhanced SOC systems achieved a higher overall detection accuracy of 94.6% compared to 82.3% for traditional SIEM systems, reflecting a mean performance improvement of 12.3 percentage points. In synthetic identity fraud detection, AI systems reached 93.4% accuracy compared to 74.8% for SIEM, while deepfake fraud detection showed 91.1% versus 69.7%. False-positive rates were significantly reduced in AI systems (4.7%) compared to SIEM (18.9%), representing a reduction of 14.2 percentage points, while false-negative rates declined from 11.7% in SIEM to 3.2% in AI systems. Response time analysis revealed that AI-enhanced SOC systems achieved an average response time of 3.8 minutes compared to 12.6 minutes in SIEM environments, indicating an improvement of 8.8 minutes. Under high event density conditions, AI systems maintained stable accuracy with variation limited to ±2.3%, whereas SIEM performance declined by up to 9.8% and experienced alert backlog increases reaching 35.6%. Statistical testing confirmed that all differences were significant (p < 0.05), with large effect sizes (Cohen’s d ranging from 0.96 to 1.41) and strong regression relationships (β up to 0.72, R² up to 0.65). The results demonstrated that AI-enhanced SOC systems provided superior accuracy, efficiency, and scalability in detecting complex and evolving fraud patterns in modern banking systems.
Copyright (c) 2026 Md Shakhawat Hossen (Author)
A Systematic Review of GeoAI and Deep Learning for Automated Road Infrastructure Damage Detection Using Satellite Imagery
This study addresses the problem that research on automated road infrastructure damage detection using satellite imagery remains fragmented across damage categories, data sources, model architectures, and evaluation practices, making it difficult to form unified evidence base for infrastructure monitoring and decision support. The purpose of the study was to systematically synthesize how GeoAI and deep learning have been applied to road damage detection between 2018 and 2026, identify dominant data and model trends, compare case-based findings, and evaluate recurring methodological challenges. Methodologically, the study used a cross-sectional, case-based systematic literature review design with light quantitative synthesis rather than primary field experimentation. The sample consisted of published multi-country, region-specific, and disaster-context studies treated as analytical cases. Key variables included road-damage detection outcomes, satellite imagery characteristics, preprocessing and data quality conditions, model architecture, spatial context, and damage-mapping outputs. The analysis plan combined structured screening, eligibility assessment, extraction and coding of study characteristics, and thematic narrative synthesis supported by descriptive quantitative summaries. Headline findings showed that deep learning-based GeoAI has become the dominant paradigm, with very strong support for H1 and H3 and strong support for H2 and H4. Quantitatively, the RDD2020 benchmark included 26,336 road images and more than 31,000 damage instances across India, Japan, and the Czech Republic; a pixel-level satellite road-damage model achieved an F1 score of 76.09%; a transfer-learning road-quality study using 53,686 images covering 2,400 km reported 80.0% accuracy and 99.4% predictions within the true or adjacent class, rising to 94.0% after adaptation; post-earthquake road-damage detection over more than 530 km of roads reported 87.1% accuracy; and a semi-supervised GAN approach achieved 81.540% mean IoU and 79.228% F1. Overall, the findings imply that high-resolution imagery, strong labels, and context-aware architectures improve performance, but standardization, cross-region transferability, and deployment readiness remain major limitations for real-world geospatial infrastructure intelligence.
Copyright (c) 2026 Kazi Mohammad Khalid Ahsan (Author)
An Empirical Study of CRM and Analytics-Based Approaches to Customer Engagement and Sales Performance Evaluation in Enterprise Organizations
This study investigates the persistent problem that many enterprise organizations invest heavily in Customer Relationship Management (CRM) systems and analytics-based tools without clear empirical evidence of how these capabilities jointly improve customer engagement and sales performance. The purpose of the research was to examine the direct and combined effects of CRM practices and analytics-based approaches on customer engagement and sales performance within enterprise settings through a quantitative, cross-sectional, case-based design. Data were collected from 210 respondents drawn from cloud-enabled and enterprise case environments, including professionals from sales, marketing, CRM/customer service, business analytics, and managerial roles. The main variables examined were CRM practices, analytics-based approaches, customer engagement, and sales performance, measured using a 5-point Likert scale. The analysis plan included descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling using SPSS. The results showed favorable construct means for CRM practices (M = 3.94, SD = 0.68), analytics-based approaches (M = 3.88, SD = 0.72), customer engagement (M = 4.01, SD = 0.64), and sales performance (M = 3.97, SD = 0.69), indicating that respondents generally agreed these capabilities were positively present in their organizations. Reliability was high across all constructs, with alpha values ranging from 0.84 to 0.88 and an overall instrument reliability of 0.89. Correlation findings revealed significant positive relationships among the variables, including CRM and customer engagement (r = 0.71, p < .01), analytics and sales performance (r = 0.67, p < .01), and customer engagement and sales performance (r = 0.74, p < .01). Regression results further showed that CRM practices and analytics-based approaches explained 58.0% of the variance in customer engagement (R² = 0.580), while CRM, analytics, and customer engagement jointly explained 64.0% of the variance in sales performance (R² = 0.640). The study implies that enterprise organizations can achieve stronger commercial outcomes when CRM and analytics are strategically integrated to enhance engagement, trust, responsiveness, and evidence-based sales decision-making.
Copyright (c) 2021 Md Khaled Hossain (Author)
Design and Optimization of Dual-Band Microstrip Patch Antenna For 5g Sub-6GHz and mmWave Applications
This study presented a quantitative investigation into the design and optimization of a dual-band microstrip patch antenna for 5G sub-6 GHz and millimeter-wave applications, with the objective of enhancing key performance metrics while maintaining compactness and structural simplicity. An experimental simulation-based methodology was employed, where multiple antenna configurations were generated through systematic parametric variation of patch dimensions, slot geometry, substrate properties, ground plane size, and feeding techniques. A total of 30 antenna models were analyzed using a full-wave electromagnetic simulation environment, and performance was evaluated in terms of resonant frequency, return loss, voltage standing wave ratio, bandwidth, gain, directivity, and radiation efficiency. The optimized antenna achieved resonance at 3.48 GHz and 28.10 GHz, aligning with targeted 5G frequency bands. Significant improvements were observed in return loss, which decreased from -17.80 dB to -25.70 dB at sub-6 GHz and from -14.50 dB to -22.80 dB at mmWave frequencies, indicating enhanced impedance matching. Bandwidth increased from 0.55 GHz to 0.82 GHz in the lower band and from 2.20 GHz to 3.65 GHz in the higher band, demonstrating improved frequency coverage. Gain values improved from 4.10 dBi to 7.40 dBi at sub-6 GHz and from 6.50 dBi to 11.10 dBi at mmWave frequencies, reflecting enhanced radiation performance. Statistical analysis confirmed that these improvements were significant, with p-values below 0.05 and large effect sizes observed for bandwidth and gain. The findings demonstrated that systematic optimization of antenna parameters can effectively achieve balanced dual-band performance without compromising efficiency or compactness. This study provided a validated design framework that contributes to the development of high-performance antennas for next-generation wireless communication systems.
Copyright (c) 2021 Zaheda Khatun (Author)
Enhancing the Performance of U.S. Infrastructure Projects Through Integrated Planning, Risk Management, and Digital Construction Technologies
This study examined the performance of U.S. infrastructure projects through a quantitative analysis of integrated planning, risk management, and digital construction technologies. A cross-sectional explanatory research design was adopted, and data were collected from 214 infrastructure professionals across transportation, energy, utilities, and public works sectors. The study aimed to evaluate the extent to which these factors influenced project performance, measured in terms of cost efficiency, schedule adherence, quality outcomes, and coordination effectiveness. Descriptive analysis indicated moderate to high implementation levels of integrated planning (M = 3.78), risk management (M = 3.65), and digital construction technologies (M = 3.71), with overall project performance recorded at a mean value of 3.83. Correlation analysis revealed strong positive relationships between integrated planning and project performance (r = 0.62), digital construction technologies (r = 0.55), and risk management (r = 0.51), all statistically significant at p < 0.001. Multiple regression analysis further demonstrated that the combined model explained 58.4% of the variance in project performance (R² = 0.584, F = 61.27, p < 0.001). Integrated planning emerged as the strongest predictor (β = 0.41), followed by digital construction technologies (β = 0.29) and risk management (β = 0.25), all statistically significant. Subgroup analysis indicated that technology-driven sectors such as transportation and energy reported higher digital impact, while experienced professionals showed stronger alignment with planning effectiveness. The findings confirmed that infrastructure project performance is significantly enhanced through the integration of structured planning, proactive risk management, and digital construction capabilities. The study contributed to the literature by providing empirical evidence of the combined and relative effects of these factors within a unified quantitative framework. The results emphasized the importance of adopting integrated and data-driven management approaches to improve efficiency, coordination, and reliability in infrastructure project delivery within the United States.
Copyright (c) 2026 Md. Mominul Haque (Author)
Artificial Intelligence in Project and Business Operations Management in US: A Systematic Review of Decision-Support Models
This study examines the role of artificial intelligence in project and business operations management in the United States, with particular emphasis on AI-driven decision-support models that improve managerial effectiveness across cloud-enabled and enterprise case contexts. The problem addressed is the fragmented understanding of how AI supports interconnected project and operational decisions in real organizational environments, despite rising adoption across digitally intensive sectors. Accordingly, the purpose of the study is to synthesize how AI capabilities, predictive models, and intelligent decision-support systems influence planning, scheduling, forecasting, workflow coordination, risk control, and organizational performance. The research adopted a quantitative cross-sectional, case-based review design grounded in 50 selected studies representing cloud and enterprise-oriented cases from sectors such as construction, logistics, manufacturing, finance, information technology, and general management. Key variables included AI capability, decision-support model effectiveness, project management function improvement, business operations improvement, managerial decision quality, and organizational or implementation barriers. Data were organized through a structured extraction matrix and analyzed using descriptive frequency, percentage distribution, cross-case comparison, and literature-based 5-point Likert aggregation. The findings show strong overall support for the study framework, with an overall literature support score of 4.32 out of 5. AI capability recorded 4.37, decision-support effectiveness 4.34, managerial function improvement 4.36, project management improvement 4.28, business operations improvement 4.41, and decision quality and risk control 4.35, while implementation barriers also remained substantial at 4.09. Approximately 72% of the reviewed studies reported strong or very strong positive contributions of AI, 18% reported moderate or conditional support, and 10% emphasized limitations more than benefits. Cross-case results further showed the strongest outcomes in logistics and supply chain with a mean of 4.45 and manufacturing with 4.42. The study concludes that AI has become a significant decision-support infrastructure in US management practice, but its benefits depend on governance, data quality, explainability, readiness, and strategic alignment for sustainable organizational value.
Copyright (c) 2026 Kazi Rakib Hasan Saurav (Author)
Implementation and Real-World Evaluation of an AI-Based Decision Support Model for Data-Driven Business and Infrastructure Systems
This study addressed the practical problem that many organizations invest in artificial intelligence driven decision support systems, yet evidence of their real-world effectiveness across business and infrastructure settings remains limited and often fragmented. The purpose of the research was to evaluate how an AI-based decision support model contributes to decision quality, operational efficiency, and organizational performance in data-driven environments through a quantitative, cross-sectional, case-based design. The study used a structured five-point Likert scale questionnaire administered to 210 respondents drawn from cloud-enabled and enterprise-oriented organizational cases, including private sector enterprises, public sector organizations, and hybrid institutions, with 108 cases from business systems and 102 from infrastructure systems. The key variables examined were AI Model Capability, Implementation Readiness, Trust in AI Recommendations, Perceived Usefulness, System Reliability, Decision Quality, Operational Efficiency, and Organizational Performance. The analysis plan combined descriptive statistics, correlation analysis, and multiple regression modeling. The findings showed that respondents rated the model positively across all major constructs, with Decision Quality recording the highest mean (M = 4.25, SD = 0.58), followed by Perceived Usefulness (M = 4.22, SD = 0.57), AI Model Capability (M = 4.18, SD = 0.61), Operational Efficiency (M = 4.11, SD = 0.64), and Organizational Performance (M = 4.07, SD = 0.66). Correlation results indicated strong positive relationships, particularly between Decision Quality and Organizational Performance (r = .73), Perceived Usefulness and Organizational Performance (r = .71), and Decision Quality and Operational Efficiency (r = .69). Regression findings further revealed that AI Model Capability significantly predicted Decision Quality (β = .31, p < .001), Implementation Readiness significantly predicted Operational Efficiency (β = .29, p = .001), and Decision Quality significantly predicted Organizational Performance (β = .34, p < .001). The study implies that AI decision support creates the greatest value when technical capability is supported by organizational readiness, reliable system performance, and human oversight, making AI most effective as an augmentation tool rather than a replacement for human judgment.
Artificial Intelligence–Driven Predictive Analytics Framework for Data-Driven Decision Support in Complex Organizational Systems
Artificial intelligence–driven predictive analytics has become an important analytical mechanism for supporting data-driven decision-making processes in complex organizational environments characterized by large volumes of operational data and dynamic decision variables. This study examined the effectiveness of an artificial intelligence–based predictive analytics framework in improving decision-support performance across data-intensive organizational systems. A quantitative research design was adopted to evaluate the relationship between predictive analytics capability and decision-support effectiveness using a dataset of 320 organizational observations collected from sectors including finance, healthcare, manufacturing, retail, and supply chain management. Descriptive statistical analysis indicated that organizations demonstrated moderate to high levels of predictive analytics capability with a mean value of 3.84, while decision-support effectiveness recorded a mean value of 3.91, suggesting a relatively strong presence of data-driven decision practices within the sampled organizations. Multiple regression analysis revealed that predictive analytics capability had a statistically significant positive relationship with decision-support effectiveness, with a standardized coefficient of β = 0.48 and a significance level of p < 0.001. The overall regression model demonstrated strong explanatory power with an R² value of 0.50, indicating that approximately 50% of the variance in decision-support effectiveness was explained by predictive analytics capability, data infrastructure maturity, and predictive model utilization. Subgroup analysis further revealed sectoral differences in predictive analytics performance, with finance, manufacturing, and supply chain organizations achieving higher predictive model performance scores exceeding 4.0 on the analytical scale. Predictive model evaluation results indicated strong analytical reliability, with the model achieving an overall predictive accuracy of 0.86 and an area under the ROC curve value of 0.88, confirming stable predictive performance across validation samples. The findings suggested that organizations possessing advanced predictive analytics infrastructures demonstrated significantly higher decision accuracy and forecasting performance compared with organizations with lower analytics maturity. Overall, the study demonstrated that artificial intelligence–driven predictive analytics frameworks play a significant role in enhancing data-driven decision-support effectiveness by enabling organizations to interpret complex datasets, generate reliable predictive insights, and improve strategic and operational decision-making outcomes within modern data-intensive organizational environments.
Copyright (c) 2026 Md. Morshedul Islam (Author)
Machine Learning-Driven Credit Risk Modeling: Transforming Loan Default Prediction and Portfolio Management in U.S. Commercial Banking
This study examined the effectiveness of machine learning-driven credit risk modeling in improving loan default prediction and portfolio management within U.S. commercial banking. A quantitative, retrospective longitudinal research design was employed using a dataset of 12,480 loan-level observations, incorporating borrower demographics, financial indicators, and behavioral variables. The dataset exhibited a class imbalance, with 81.9% non-default cases and 18.1% default cases, reflecting realistic credit risk conditions. The study compared traditional statistical methods, particularly logistic regression, with advanced machine learning algorithms including decision trees, random forests, gradient boosting, support vector machines, and neural networks. Descriptive statistics, correlation analysis, and predictive modeling techniques were applied, followed by validation using cross-validation and performance metrics such as accuracy, recall, F1-score, and area under the curve. The findings demonstrated that machine learning models significantly outperformed the baseline logistic regression model across all evaluation metrics. Logistic regression achieved an accuracy of 78.6% and an AUC of 0.74, whereas gradient boosting reached the highest accuracy of 90.1% and an AUC of 0.93. Random forest followed closely with an accuracy of 89.3% and an AUC of 0.91. Recall for default prediction improved substantially from 65.2% in logistic regression to 82.4% in gradient boosting, indicating enhanced capability in identifying high-risk borrowers. Effect size analysis further confirmed large practical improvements, with ensemble models demonstrating strong gains in predictive discrimination. Subgroup analysis revealed that machine learning models performed particularly well in high-risk borrower segments, achieving accuracy levels above 91%, while performance differences were smaller in low-risk segments. The study also highlighted the importance of behavioral variables, such as payment delay frequency and credit utilization, which emerged as the most influential predictors of default. Overall, the results confirmed that machine learning approaches provide a more accurate, robust, and scalable framework for credit risk assessment, supporting improved decision-making and portfolio risk management in commercial banking environments.
Copyright (c) 2025 Tahmina Akter Bhuya Mita (Author)
A Systematic Review of AI-Enabled Fraud Detection in Digital Financial Systems (2019–2026)
This study presented a comprehensive quantitative systematic review of AI-enabled fraud detection models in digital financial systems, synthesizing empirical evidence from 72 peer-reviewed studies published between 2019 and 2026. The analysis focused on evaluating the comparative performance of machine learning, deep learning, ensemble, hybrid, and graph-based approaches using standardized metrics such as accuracy, precision, recall, and F1-score. The findings revealed that ensemble and hybrid models achieved the highest overall performance, with average F1-scores of 0.91 and recall values reaching 0.94, demonstrating superior capability in detecting fraudulent transactions within highly imbalanced datasets where the average fraud rate was approximately 1.8%. Deep learning models showed strong performance with an average accuracy of 0.95 and F1-score of 0.89, particularly in large-scale datasets exceeding one million transactions, which accounted for 41.7% of the reviewed studies. Traditional machine learning models, including random forest and gradient boosting, maintained competitive performance with an average accuracy of 0.93 and F1-score of 0.87, highlighting their continued relevance in structured data environments. The study further identified that advanced feature engineering improved model performance by up to 12%, while imbalance handling techniques increased recall by approximately 13.2%. Graph-based models demonstrated enhanced effectiveness in detecting fraud networks, achieving recall values of up to 0.92 in relational datasets. Statistical analysis confirmed that performance differences between model categories were significant, with effect sizes ranging from 0.52 to 0.88, indicating moderate to strong practical impact. Additionally, real-time detection systems reduced latency by up to 35% while maintaining competitive predictive performance. Overall, the study established that fraud detection effectiveness was influenced not only by model selection but also by data characteristics, feature optimization, and evaluation methodologies, providing a robust quantitative foundation for understanding the performance and application of AI-driven fraud detection systems in modern digital financial environments.
Copyright (c) 2026 Md. Rashed Buiya (Author)
AI-Based Psychodiagnostics’ Models to Support Early Intervention and Reduce Suicide Risk in Adolescents and Youth: Development and Clinical Validation
Adolescent suicide has become a major public health concern worldwide, requiring more effective approaches for early detection and prevention. This study examined the development and validation of artificial intelligence (AI)–based psychodiagnostics models designed to support early identification of suicide risk among adolescents and youth. A quantitative research design was employed using structured psychodiagnostics assessment data collected from 268 adolescents aged between 13 and 21 years who had undergone psychological screening in educational or mental health support environments. The study analyzed key psychological and behavioral constructs including depressive symptoms, emotional dysregulation, impulsivity, and social stress exposure to determine their predictive influence on suicide risk indicators. Descriptive statistical analysis revealed moderate levels of psychological distress within the study population, with depressive symptoms recording the highest mean score (M = 3.45, SD = 0.80) followed by emotional dysregulation (M = 3.33, SD = 0.75), social stress (M = 3.29, SD = 0.74), suicide risk indicators (M = 3.16, SD = 0.83), and impulsivity (M = 3.07, SD = 0.78). Reliability analysis confirmed strong internal consistency across all measurement scales, with Cronbach’s alpha values ranging from 0.81 to 0.90. Multiple regression analysis indicated that the predictive model explained 58% of the variance in suicide risk outcomes (R² = 0.58). Depressive symptoms emerged as the strongest predictor of suicide vulnerability with a standardized beta coefficient of 0.42 (p < 0.001), followed by emotional dysregulation (β = 0.30, p < 0.001) and social stress (β = 0.27, p < 0.001), while impulsivity (β = 0.18, p = 0.009) demonstrated a moderate but statistically significant influence. These findings indicate that suicide risk among adolescents is shaped by a multidimensional interaction of emotional distress, behavioral tendencies, and environmental stressors. The integration of these psychodiagnostics indicators into AI-based predictive models may enhance early identification of vulnerable adolescents and support more effective suicide prevention strategies within mental health and educational systems.
Copyright (c) 2022 Md. Nazmul Haque, Amena Begum Sumi (Author)
Data-Driven Innovation Ecosystems: Accelerating Economic Growth Through Strategic Technology Adoption
The rapid expansion of digital technologies has transformed the structure of modern innovation ecosystems and reshaped the mechanisms through which economic growth and technological productivity are achieved. This study investigated the role of data-driven innovation ecosystems in accelerating economic growth through strategic technology adoption, with particular emphasis on data analytics capability, digital infrastructure availability, research and development investment, and digital platform integration. A quantitative longitudinal research design was employed using a dataset comprising 180 observations across 15 countries and six major economic sectors between 2012 and 2023. The sectors included manufacturing, financial services, digital commerce, healthcare technology, telecommunications, and public digital services. Descriptive and inferential statistical analyses were conducted using regression modeling to evaluate the relationships between digital transformation variables and innovation productivity indicators. The empirical results revealed that data analytics capability exhibited the strongest effect on innovation productivity, with a standardized regression coefficient of β = 0.48 (p < 0.001), indicating that higher analytical capacity significantly improved innovation performance across sectors. Digital infrastructure availability also demonstrated a strong positive influence on economic productivity with β = 0.41 (p < 0.001), highlighting the importance of connectivity networks, cloud computing platforms, and digital communication systems in supporting technological development. Research and development investment showed a statistically significant but moderate effect (β = 0.28, p = 0.002), confirming that financial commitment to research remains a crucial component of innovation-driven growth. The regression model explained 64% of the variation in innovation productivity (R² = 0.64), suggesting that integrated digital technology adoption significantly shapes innovation ecosystem performance. Sectoral analysis further indicated that digital commerce and financial services recorded the highest digital adoption scores (82.4 and 79.8 respectively) and demonstrated stronger innovation productivity indices compared with sectors with lower digital transformation maturity. Economies categorized as advanced digital ecosystems achieved an average innovation productivity score of 78.4 and economic growth rate of 4.56%, while early-stage digital transformation economies recorded 64.2 and 3.08% respectively. Overall, the findings confirmed that data analytics capability, digital infrastructure development, and integrated digital technology adoption collectively enhance innovation productivity and economic growth within data-driven innovation ecosystems.
Copyright (c) 2022 Hisham Mahmood, Mohammad Robel Miah (Author)
The Role of Social Media Exposure in Shaping Anxiety, Self-Esteem, And Emotional Regulation Among Adolescents: Implications for Clinical Intervention
This study examined the role of multidimensional social media exposure in shaping anxiety, self-esteem, and emotional regulation among adolescents within a quantitative framework. A cross-sectional survey was administered to 528 adolescents aged 13–18 years (M = 15.42, SD = 1.67). Hierarchical regression, mediation, and moderation analyses were conducted to assess direct and indirect relationships while controlling for demographic and psychosocial variables. Results indicated that passive exposure (β = .18, p < .001), feedback monitoring (β = .14, p = .002), and problematic use (β = .29, p < .001) were significant positive predictors of anxiety, with the full model explaining 35.6% of variance (R² = .356). For self-esteem, passive exposure (β = −.17, p < .001), feedback monitoring (β = −.13, p = .004), and problematic use (β = −.21, p < .001) were negatively associated, while active engagement showed a small positive association (β = .09, p = .036), with the model explaining 31.8% of variance (R² = .318). Emotional dysregulation significantly predicted anxiety (β = .47, p < .001) and self-esteem (β = −.43, p < .001) and partially mediated relationships between problematic use and both outcomes (indirect effect for anxiety = .12; 95% CI [.08, .17]). Moderation analyses revealed small but significant interaction effects involving stress and sleep quality. Findings supported a multidimensional exposure model in which evaluative and compulsive engagement patterns were more strongly associated with psychological vulnerability than general active use. Results underscore the importance of incorporating exposure quality and emotional regulation processes into adolescent mental health assessment and intervention frameworks.
Copyright (c) 2026 Md. Akbar Hossain, Jesmin Akter (Author)
Machine Learning Accuracy in Healthcare Risk Prediction: Algorithms, Datasets, and Effect Sizes: A Meta-Analysis
This study addressed the problem that machine learning healthcare risk prediction research reports “accuracy” inconsistently across algorithms, datasets, and validation designs, making it difficult for clinicians and health system leaders to identify dependable models for real-world deployment. Using a quantitative, cross-sectional, case-based design, each eligible peer-reviewed paper was treated as a “case” drawn from large-scale clinical data environments, including enterprise electronic health record implementations and multi-institution critical-care repositories. The purpose was to quantify comparative performance across model families, document dataset and reporting patterns, and estimate effect-size style differences under heterogeneous conditions. The final sample included N = 110 studies spanning 14 outcome categories, dominated by EHR/ICU datasets (62.7%, n = 69), followed by claims/administrative (14.5%, n = 16), registry/cohort (11.8%, n = 13), and imaging or multimodal (10.9%, n = 12); 88 studies provided extractable AUROC and/or AUPRC for quantitative synthesis. Key variables included algorithm family (regularized linear, ensemble, deep learning), dataset modality, validation type (internal-only vs external), outcome category and imbalance prevalence, and reporting indicators (calibration, missingness and imbalance handling). The analysis plan applied random-effects pooling with subgroup and moderator comparisons to explain heterogeneity. Headline findings showed an overall pooled AUROC = 0.83 (95% CI: 0.81–0.85; I² = 78%), with ensemble models AUROC = 0.85 (0.83–0.87) and deep learning AUROC = 0.86 (0.84–0.88) outperforming regularized linear baselines AUROC = 0.79 (0.77–0.81), yielding ΔAUROC = +0.06 for ensembles versus linear models; external validation reduced pooled AUROC to 0.80 (0.78–0.82) compared with 0.85 (0.83–0.87) for internal-only studies (ΔAUROC = −0.05). Where reported, minority-class performance differed meaningfully under imbalance, with pooled AUPRC 0.42 for deep learning vs 0.36 for ensembles, and calibration evidence was limited but centered near reliability when reported (median calibration slope = 0.94; IQR 0.86–1.03). These results imply that model choice should be guided not only by discrimination but also by validation breadth and prevalence-sensitive metrics, and that stronger standards for external validation and calibration reporting are necessary for safer enterprise adoption of risk prediction tools.
Copyright (c) 2021 Md. Fardous, Md. Mehedi Hasan (Author)
A Conceptual Model Development for Evaluating AI-Driven Predictive Maintenance in SCADA-Integrated Smart Grid Systems
This study develops a conceptual model for evaluating AI-driven predictive maintenance (PdM) in SCADA-integrated smart grid systems through a systematic review and meta-analysis conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A structured search across six major databases, IEEE Xplore, Scopus, Web of Science, ScienceDirect, ACM Digital Library, and Google Scholar, covering the period from January 2013 to December 2024 yielded 4,847 initial records, of which 63 studies met the full inclusion criteria following rigorous dual-reviewer screening, quality appraisal using the Newcastle-Ottawa Scale, and risk-of-bias assessment; pooled effect sizes were subsequently computed using a random-effects model (DerSimonian-Laird estimator) to account for inter-study heterogeneity (I² = 78.4%), with subgroup analyses stratified by AI algorithm type, grid segment, and SCADA integration depth. Meta-analytic findings revealed a weighted mean fault detection accuracy of 94.3% (95% CI: 92.1%–96.5%), with deep learning architectures — particularly LSTM and hybrid CNN-LSTM models — significantly outperforming traditional machine learning approaches (SMD = 0.67; 95% CI: 0.48–0.86, p < 0.001), and real-time SCADA-integrated configurations demonstrating markedly greater reductions in unplanned outage rates (SMD = 0.81; 95% CI: 0.59–1.03) relative to offline processing alternatives, with no significant publication bias detected via Egger's test (p = 0.16). Grounded in this synthesized evidence, the study proposes the SCADA-AI Predictive Maintenance Evaluation Framework (SAPMEF), a five-dimensional conceptual model encompassing data acquisition and sensor integration, AI model architecture selection, real-time SCADA communication fidelity, maintenance decision optimization, and grid resilience metrics, offering researchers, grid operators, and policymakers a structured, evidence-based instrument for benchmarking and deploying intelligent PdM solutions, while identifying cybersecurity resilience, model interpretability, and cross-grid transferability as critical priorities for future research.
An Experimental Evaluation of ETL-Driven Business Intelligence Architectures for Enhancing AI-Based Cybersecurity Performance
This study investigated the relationships between three theoretically grounded independent variables and a key organizational outcome using a quantitative, cross-sectional explanatory design. Data were collected from 312 respondents within a defined organizational context through a structured survey instrument composed of validated multi-item Likert scales. Internal consistency reliability analysis confirmed strong measurement stability, with Cronbach’s alpha coefficients ranging from 0.82 to 0.90 across all constructs. Descriptive statistics indicated moderate-to-high mean scores for the principal variables, with the dependent variable reporting the highest mean (M = 4.02, SD = 0.66), reflecting generally favorable performance perceptions among participants. Correlation analysis revealed statistically significant positive associations among all constructs, with coefficients ranging from r = 0.55 to r = 0.63 (p < 0.01). Multiple regression analysis was conducted to examine predictive relationships while controlling for demographic factors. The overall model was statistically significant, F(7, 304) = 52.84, p < 0.001, explaining 54.9% of the variance in the dependent variable (R² = 0.549; adjusted R² = 0.539). All three independent variables demonstrated significant positive effects on the outcome variable. Independent Variable 3 emerged as the strongest predictor (β = 0.361, p < 0.001), followed by Independent Variable 1 (β = 0.328, p < 0.001) and Independent Variable 2 (β = 0.229, p < 0.001). Demographic control variables did not exhibit statistically significant effects. The findings provided empirical validation of the proposed conceptual framework and demonstrated that the identified predictors collectively contributed substantially to performance outcomes within the examined context. The results offered both theoretical support and practical implications, emphasizing the importance of prioritizing high-impact organizational drivers. Overall, this study contributed quantitative evidence to the existing body of literature by clarifying the relative strength and explanatory power of key determinants within a unified analytical model.
Copyright (c) 2026 Ishtiaque Ahmed (Author)
Fraud-Detection Algorithms for Identifying Anomalous Transactions in Retail Banking Networks
This study addresses the persistent problem of detecting anomalous and fraudulent transactions in retail banking networks where high transaction volume, class imbalance, and evolving attack patterns create unacceptable false-positive burden and missed-fraud risk. The purpose was to quantify which enterprise fraud-detection system conditions most strongly predict perceived fraud-detection effectiveness within a real organizational setting, using a quantitative, cross-sectional, case-based design grounded in a cloud-hosted enterprise fraud-monitoring deployment. Data were collected from a purposive sample of N = 156 stakeholders drawn from enterprise operations and governance roles (fraud analysts 46.2%, IT or ML support 31.4%, compliance or risk 22.4%), representing cloud and enterprise operational cases within the case environment. Key variables included Perceived Fraud-Detection Effectiveness (PFDE) as the dependent outcome, and Algorithm Capability, Feature or Information Quality, Real-Time Readiness, Explainability Clarity, and Governance or Compliance Readiness as primary predictors; operational impact variables included a False-Positive Burden Index (FPBI) and an Alert-Triage Efficiency Index (TEI). The analysis plan applied descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling. Findings indicated moderately high PFDE (M = 3.84, SD = 0.61) alongside strong measurement reliability (α range .82–.90; PFDE α = .88). PFDE correlated most strongly with TEI (r = .66, p < .001) and Feature or Information Quality (r = .62, p < .001), and negatively with FPBI (r = −.44, p < .001). The regression model explained substantial variance in PFDE (R² = .58; F(5,150) = 41.30, p < .001), with Feature or Information Quality emerging as the strongest predictor (β = .34, p < .001), followed by Governance or Compliance Readiness (β = .24, p = .002), Explainability (β = .18, p = .011), and Algorithm Capability (β = .15, p = .026), while Real-Time Readiness was not significant at p < .05 (β = .09, p = .087). Scenario stress testing showed weakest perceived capability for mule-account funneling (M = 3.21) and account takeover with device rotation (M = 3.33), implying that multi-step fraud patterns remain the hardest to manage. Practically, the results imply that banks should prioritize improving risk-information quality, governance controls, and explanation quality to raise detection effectiveness while reducing alert overload, rather than focusing only on speed improvements.
Copyright (c) 2021 Mahfuj Ahmed Ruzel, Md. Hasan Or Rashid (Author)
Advancing United States Leadership in Artificial Intelligence Through Enterprise AI Governance and Risk Intelligence Models
This study investigated the extent to which enterprise AI governance maturity and risk intelligence integration predicted institutional reliability and leadership-related outcomes within CI/CD-enabled cloud infrastructures. A cross-sectional quantitative design was employed using data from 287 U.S.-based enterprises spanning financial services (24.4%), healthcare (18.5%), manufacturing (12.5%), logistics (11.8%), insurance (10.1%), energy (8.0%), retail (7.3%), and technology services (7.3%). Descriptive results indicated moderate-to-high governance maturity (M = 3.87, SD = 0.62) and monitoring infrastructure strength (M = 3.92, SD = 0.61), with strong policy formalization (M = 4.12, SD = 0.58) and anomaly detection adoption (M = 4.05, SD = 0.60). Hierarchical regression analysis revealed that Governance Maturity significantly predicted Institutional Reliability (β = .39, p < .001), increasing explained variance from 8.4% in the control model to 32.7% (ΔR² = .243). The addition of Risk Intelligence Integration (M = 3.54, SD = 0.69) further increased total explained variance to 41.3% (ΔR² = .086, β = .34, p < .001). Fairness Controls (M = 3.41, SD = 0.73) demonstrated a significant negative association with bias-related incident frequency (β = −.42, R² = .186, p < .001). Monitoring Infrastructure significantly predicted model performance stability (β = .47, R² = .294, p < .001). Regulatory Alignment (M = 3.76, SD = 0.67) was positively associated with Competitive Positioning (β = .44, R² = .311, p < .001). Reliability coefficients ranged from .76 to .88. The findings demonstrated that structured governance and integrated risk intelligence systems explained substantial variance in deployment stability, compliance performance, and competitive outcomes within enterprise AI ecosystems.
Copyright (c) 2026 Hisham Mahmood (Author)
AI-Enabled Performance-Based Structural Optimization of Steel Truss and Mezzanine Systems for Industrial Facilities
This study investigated AI-enabled, performance-based structural optimization of steel truss and mezzanine systems used in industrial facilities through a quantitative computational benchmarking design. Multiple optimization algorithms were evaluated under identical structural modeling assumptions, load combinations, and constraint conditions to determine their influence on material efficiency, serviceability control, stability adequacy, vibration response, and computational performance. The final analytic dataset consisted of 468 valid optimization runs derived from 16 standardized structural configurations, equally representing truss and mezzanine systems. Results demonstrated an overall mean weight reduction of 18.6% relative to baseline designs, with truss systems achieving higher reductions (20.1%) compared to mezzanine systems (17.0%). Differential Evolution and Hybrid Metaheuristic approaches produced significantly greater material savings than the reference Genetic Algorithm, with mean improvements exceeding 3.0 percentage points (p < 0.001). Dimensionality exerted the strongest influence on computational cost, as high-dimensional cases increased runtime by an average of 5.41 minutes and significantly raised convergence iterations (p < 0.001). The runtime regression model explained 56% of variance, while the weight reduction model explained 41% of variance. Feasibility performance remained stable across complexity levels, with an overall feasibility rate of 91.6%. No statistically significant differences were observed among algorithms for overall structural adequacy once feasible convergence was achieved, indicating consistent compliance with strength, serviceability, and stability requirements. Reliability analysis confirmed strong internal consistency for composite structural adequacy measures (α = 0.88) and acceptable consistency for optimization efficiency (α = 0.74), supporting inferential modeling validity. The findings demonstrated that algorithm selection and dimensional complexity significantly affected material efficiency and computational performance, while structural compliance remained consistently maintained. The study provided a statistically grounded benchmarking framework for evaluating AI-enabled optimization strategies in industrial steel structural systems.
Copyright (c) 2026 Md. Mainuddin Faisal (Author)
Measuring the Security Impact of Zero Trust Access Controls: A Mixed-Methods Study of Identity-Based Policies (Cisco ISE + AD) and Incident Reduction
This study measured the security impact of Zero Trust access controls implemented through identity-based policies integrating Cisco Identity Services Engine (ISE) with Active Directory (AD). A quantitative, quasi-experimental pretest–posttest design was employed using an interrupted time-series approach to evaluate security outcomes across pre-implementation and post-implementation observation windows. The analysis examined whether the enforcement of centralized authentication, authorization, and policy-driven segmentation was associated with measurable reductions in security incidents, improvements in identity governance outcomes, and enhanced Security Operations Center (SOC) performance. Findings indicated that Zero Trust implementation was associated with a substantial reduction in incident frequency and severity. Weekly total incident counts declined from 38.4 during the baseline period to 27.1 in the post-implementation period, representing a 29.4% reduction. High-severity incidents declined from 11.2 to 6.3 per week, reflecting a 43.8% reduction, while the proportional share of high-severity incidents decreased from 29.2% to 18.5. Unauthorized access attempts decreased from 145.6 to 118.4 per week (18.7% reduction), while authentication anomalies declined from 96.8 to 62.7 per week (35.2% reduction). Lateral movement alerts demonstrated the strongest proportional improvement, decreasing from 12.5 to 6.8 per week, representing a 45.6% reduction. SOC performance also improved significantly. Mean time to detect (MTTD) declined from 18.6 hours to 11.4 hours, indicating a 38.7% improvement, while mean time to respond (MTTR) declined from 26.3 hours to 17.9 hours, reflecting a 31.9% improvement. Time-to-containment improved from 42.8 hours to 28.5 hours, representing a 33.4% reduction. Regression models confirmed that post-implementation status significantly predicted lower incident rates (IRR = 0.71, p < .001) and lower high-severity incident rates (IRR = 0.60, p < .001). Logistic regression indicated that post-implementation incidents were 46% less likely to be classified as high severity (OR = 0.54, p < .001). Overall, the findings provided quantitative evidence that Cisco ISE and AD-integrated Zero Trust access controls were associated with statistically significant reductions in incident frequency, escalation risk, and response latency, supporting the effectiveness of identity-driven security governance in enterprise environments.
Copyright (c) 2023 MD Zahedul Islam, Aditya Dhanekula (Author)
Performance Analytics Frameworks for Digital Marketing and Service Enterprises: An empirical Study
Performance analytics has become a critical enabler for digital marketing and service enterprises seeking to improve decision-making, operational efficiency, and customer-centric strategies in highly competitive and data-driven environments. This empirical study examines the structure, adoption, and effectiveness of performance analytics frameworks used across digital marketing and service-oriented organizations. A systematic review of 72 peer-reviewed research papers, published between 2015 and 2025, was conducted alongside empirical data collected from selected enterprises to identify dominant analytical models, key performance indicators (KPIs), and implementation challenges. The findings reveal that over 68% of enterprises rely primarily on descriptive and diagnostic analytics, while only 32% have effectively integrated predictive or prescriptive analytics into their performance frameworks. Empirical results indicate that organizations utilizing advanced analytics frameworks reported an average 21–28% improvement in campaign conversion rates, a 19% increase in customer retention, and a 15% reduction in service response time compared to those using traditional performance measurement approaches. Additionally, the study identifies data integration, analytics skill gaps, and scalability constraints as major barriers, affecting approximately 54% of surveyed organizations. The research further highlights that enterprises aligning performance analytics with strategic objectives experienced measurable gains in marketing return on investment (ROI), with an average ROI improvement of 17% over a one-year period. By synthesizing evidence from the literature review and empirical analysis, this study proposes a structured performance analytics framework tailored to digital marketing and service enterprises. The study contributes both theoretical and practical insights by demonstrating how analytics-driven performance measurement can support continuous improvement, enhance customer engagement, and enable sustainable competitive advantage in the evolving digital economy.
Copyright (c) 2023 Md. Hasan Or Rashid, Tanjina Binte Sohrab, Rajib Sarkar (Author)
Data-Driven Ergonomic Risk Analysis Using Wearable Sensor Networks and Deep Learning for Injury Prevention in Industrial Workplaces
This study investigated a data-driven ergonomic risk analysis framework integrating wearable sensor networks and deep learning to improve injury prevention in industrial workplaces. A total of 112 production workers participated after data screening, representing assembly, material handling, and mixed-task roles. Continuous biomechanical data, including trunk deviation duration, repetition rate, upper-limb elevation frequency, and muscular activation intensity, were collected across full work shifts. Multiple regression analysis revealed that wearable-derived exposure variables significantly predicted ergonomic risk scores, with the overall model explaining 51.7% of variance (R² = 0.517, p < .001). Trunk deviation duration (β = 0.41, p < .001) and repetition rate (β = 0.33, p < .001) emerged as the strongest predictors of elevated risk classification. The deep learning model achieved a classification accuracy of 84.6%, outperforming logistic regression (73.2%) and decision tree models (69.8%), with statistically significant differences (p < .001). Cumulative exposure analysis indicated a positive correlation between aggregated risk index and high-risk event frequency (r = 0.50, p < .001), confirming the influence of sustained biomechanical load. Reliability testing demonstrated strong internal consistency for the ergonomic exposure construct (α = 0.87) and cumulative risk index (α = 0.82). Material handling tasks accumulated the highest proportion of high-risk windows at 30.9%, compared to 14.0% in repetitive assembly operations. Findings demonstrated that wearable sensor–based predictive modeling provided reliable, objective, and generalizable ergonomic risk assessment, supporting its application as a scalable injury prevention strategy in industrial production systems.
Copyright (c) 2022 Md Abubakar Siddique Akash, Md. Al Amin Khan (Author)