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.
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)