American Journal of Scholarly Research and Innovation
The American Journal of Scholarly Research and Innovation (AJSRI) ONLINE ISSN (3067-2163) is a leading Peer-Reviewed, Open Access International Journal dedicated to fostering innovation and excellence across a wide array of academic disciplines. The journal provides a platform for scholars, researchers, and professionals to disseminate groundbreaking research that addresses real-world challenges through interdisciplinary collaboration. As a monthly publication, AJSRI releases 12 issues annually, maintaining a consistent schedule that ensures the timely distribution of high-quality, impactful research. The journal encompasses diverse fields, including Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering, making it a truly interdisciplinary platform. AJSRI collaborates with international and national conferences to publish extended versions of selected papers, furthering the visibility and impact of researchers’ work. The journal is committed to publishing original, high-quality research that strengthens academic knowledge and drives innovation.
Contingency-Based Resilience Assessment of Critical Utility Substations: An ETAP Framework for Accelerating Safe Interconnection of High-Density AI Data Center Loads
This study examined the resilience of a critical utility substation under the proposed interconnection of a high-density AI data center load using a contingency-based ETAP framework. The study was motivated by the growing pressure placed on utility substations by concentrated digital infrastructure demand, particularly where safe interconnection depends not only on base-case capacity but also on the ability of the substation to remain within acceptable thermal, voltage, and protection limits during abnormal operating conditions. A quantitative nonexperimental case study design was adopted, and the analytical framework focused on measurable resilience indicators including transformer loading margin, bus voltage deviation, feeder loading utilization, short-circuit current magnitude, breaker duty margin, and contingency violation counts. The modeled system consisted of 2 power transformers, 8 buses, 6 outgoing feeders, 12 evaluated circuit breakers, and 10 credible contingency cases, with a final dataset of 12 retained operating states and 480 complete observations. The findings showed that the proposed AI data center interconnection materially reduced the resilience margin of the hosting substation. Mean transformer loading increased from 61.30% in the base-case condition to 79.80% in the post-interconnection normal condition and 86.70% under post-contingency operation, while the average reserve margin declined from 38.70% to 13.30%. Mean bus voltage deviation rose from 1.90% in the base case to 3.20% after interconnection and 4.80% under contingency, with the lowest recorded bus voltage falling from 0.981 p.u. to 0.941 p.u. Short-circuit current also increased from a mean of 21.70 kA to 24.10 kA and then to 28.10 kA across the same conditions. In parallel, breaker duty margin declined significantly, and contingency-related noncompliance rose from 0.00 violations in the base case to 1.00 in the post-interconnection normal state and 3.50 under post-contingency conditions. The results demonstrated that the substation remained broadly serviceable in selected normal states but experienced substantial reduction in disturbance tolerance after the large-load addition. The study concluded that contingency-based resilience assessment provided a rigorous basis for evaluating the safe interconnection readiness of critical utility substations exposed to high-density AI data center demand.
Copyright (c) 2024 Md. Sultan Mahamud (Author)
MPLS-TP and SONET Security Hardening for Utility SCADA Networks: Threat Modeling and Mitigation Strategies for Energy Fiber Infrastructure
This study addresses the problem that utility SCADA communication backbones using MPLS-TP, SONET, and hybrid transport environments remain highly critical to grid operations, yet transport-layer security is often assessed less rigorously than broader SCADA cybersecurity despite exposure to unauthorized access, misconfiguration, weak IT/OT segmentation, denial-of-service, insider misuse, and physical fiber threats. The purpose of the study was to examine how threat modeling, security hardening controls, and mitigation strategies influence security performance and resilience in utility energy fiber infrastructures through a quantitative, cross-sectional, case-based design grounded in cloud and enterprise style utility communication cases. Data were collected through a structured five-point Likert questionnaire from professionals involved in SCADA operations, utility telecom, OT security, and infrastructure management. Of 150 distributed questionnaires, 132 were returned and 126 usable responses were analyzed, yielding an 84.0% usable response rate. The sample included respondents from MPLS-TP-only cases (22.2%), SONET-only cases (19.0%), and hybrid MPLS-TP/SONET cases (58.7%). Key variables were threat modeling practices, security hardening controls, mitigation strategies, and security and resilience outcomes. Analysis was conducted using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression. Reliability was strong across constructs, with alpha values ranging from 0.86 to 0.90. Descriptive findings showed high mean scores for threat modeling (M = 4.08, SD = 0.62), security hardening (M = 4.21, SD = 0.57), mitigation strategies (M = 4.16, SD = 0.60), and security outcomes (M = 4.19, SD = 0.55). Correlation results indicated significant positive relationships with security outcomes for threat modeling (r = 0.61), security hardening (r = 0.74), and mitigation strategies (r = 0.68), all at p < .01. Regression results showed that the model was significant, F = 41.87, p < .001, explaining 50.7% of the variance in security and resilience outcomes (R² = 0.507), with security hardening as the strongest predictor (β = 0.41), followed by mitigation strategies (β = 0.29) and threat modeling (β = 0.24). The study implies that utility operators should prioritize protocol-aware hardening, especially in hybrid environments, to strengthen resilience, continuity, and infrastructure protection.
Copyright (c) 2025 Abu Naser Md Golam Mosharraf (Author)
Data-Driven Detection of Out-of-Specification Trends in Pharmaceutical Production: A Public Health Imperative
Ensuring the consistent quality of pharmaceutical products is central to public health protection, as undetected deviations in production can translate into substandard medicines reaching patients. This study examined the effectiveness of data analytics in detecting out-of-specification (OOS) trends in pharmaceutical production, with the objective of strengthening quality assurance systems and mitigating downstream public health risks. A quantitative longitudinal design was adopted, drawing on 1,248 pharmaceutical production batches and 9,732 critical quality observations extracted from manufacturing execution and laboratory information management systems. Descriptive analysis indicated that 93.8% of observations remained within specification limits, while 6.2% were classified as out-of-trend (OOT) and 0.9% were confirmed as OOS events. Statistical process control (SPC) flagged 214 instances of process instability, and predictive analytics successfully anticipated 79 of 87 OOS cases, yielding a detection accuracy of 92.1%, compared with 79.4% achieved through traditional monitoring methods. Multivariate modelling further enhanced analytical performance, raising sensitivity to 85.3% and increasing variance explained to 68.5%, relative to 51.2% under univariate approaches. Predictive models also demonstrated a lead detection advantage of 3.4 batches, with sensitivity and specificity values of 91.5% and 92.8%, respectively. Secondary analyses revealed meaningful variability across production lines, with OOT frequencies ranging from 4.7% to 8.9% and 62.5% of deviations clustering within specific operational periods, suggesting systemic rather than random origins. Regression findings identified temperature variation (effect size = 0.61) and mixing time (effect size = 0.48) as the most influential predictors of deviation occurrence, while visualization of impurity profiles revealed variability increases of up to 14.6%. Collectively, these findings confirm that integrated data analytics substantially enhances early detection, reduces false negatives, and improves overall process control in pharmaceutical manufacturing. The study supports the adoption of advanced analytical systems, encompassing SPC, predictive modelling, and multivariate diagnostics, as a strategic mechanism for strengthening quality assurance and, by extension, safeguarding public health.
Copyright (c) 2026 Beatrice Onyinyechi Mkpa, Ferdous Ara Ahmed (Author)
AI-Augmented Business Intelligence for Campaign Performance Optimization in U.S. Retail and e-Commerce: A Mixed-Methods Study of Marketing ROI
This study investigates the growing problem that many U.S. retail and e-commerce firms invest in digital marketing technologies and data systems but still struggle to convert campaign data into consistently optimized performance and stronger marketing return on investment. The purpose of the research was to examine whether AI-augmented business intelligence improves campaign performance optimization and marketing ROI, and to identify which analytical dimensions matter most in practice. The study adopted a quantitative, cross-sectional, case-based design focused on cloud-enabled and enterprise retail and e-commerce campaign environments, using survey evidence from 214 valid professional respondents drawn from campaign managers, BI analysts, e-commerce managers, and digital marketing specialists. The key independent variables were predictive analytics capability, real-time insight capability, customer segmentation intelligence, and decision automation support, while the main dependent variables were campaign performance optimization and marketing ROI. Data were analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed strong measurement quality, with Cronbach’s alpha values ranging from 0.77 to 0.88, and high construct means, including AI-augmented BI capability (M = 4.08, SD = 0.61), campaign performance optimization (M = 4.14, SD = 0.57), and marketing ROI (M = 4.02, SD = 0.64). Correlation results indicated that AI-augmented BI was strongly associated with campaign performance optimization (r = .710, p < .001) and marketing ROI (r = .640, p < .001), while campaign optimization was also strongly related to ROI (r = .680, p < .001). Regression analysis revealed that AI-BI dimensions explained 58.4% of the variance in campaign optimization (R² = .584, F = 73.48, p < .001), with predictive analytics capability emerging as the strongest predictor (β = .310, p < .001), followed by customer segmentation intelligence (β = .270, p < .001), real-time insight capability (β = .220, p = .002), and decision automation support (β = .140, p = .018). AI-BI and campaign optimization jointly explained 52.1% of the variance in marketing ROI (R² = .521, F = 114.06, p < .001). The study implies that firms can improve campaign precision, responsiveness, budget efficiency, and financial returns by strategically embedding AI-enhanced BI into campaign decision processes rather than using BI only for descriptive reporting.
Copyright (c) 2025 Md Aminul Islam, Zakia Afroz (Author)
Sustainable Infrastructure Through Intelligent Maintenance and Energy Optimization Frameworks
This study investigated how intelligent maintenance systems and energy optimization frameworks improve sustainable infrastructure performance in operational infrastructure environments, addressing the persistent problem that many infrastructure systems continue to rely on fragmented maintenance routines and disconnected energy management practices, which weaken asset reliability, increase inefficiency, and reduce long-term sustainability outcomes. The purpose of the study was to examine the individual and joint effects of intelligent maintenance systems and energy optimization frameworks on sustainable infrastructure performance using a quantitative, cross-sectional, case-based research design. Primary data were collected through a structured five-point Likert scale questionnaire from 200 valid respondents selected from infrastructure-related case environments, including public infrastructure agencies, institutional and commercial facilities, utility and service organizations, and transport-related facilities. The sample included engineers, maintenance officers, facility managers, energy management staff, and technical supervisors. The key variables were intelligent maintenance systems and energy optimization frameworks as independent variables, and sustainable infrastructure performance as the dependent variable. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression analysis in SPSS. The findings showed high respondent agreement across all constructs, with composite means of 4.08 for intelligent maintenance systems, 4.15 for energy optimization frameworks, and 4.21 for sustainable infrastructure performance. Reliability values were strong, with Cronbach’s alpha coefficients of 0.861, 0.884, and 0.892 respectively, and 0.901 for the overall instrument. Correlation analysis revealed significant positive relationships between intelligent maintenance systems and sustainable infrastructure performance (r = 0.680, p < .01), energy optimization frameworks and sustainable infrastructure performance (r = 0.730, p < .01), and intelligent maintenance systems and energy optimization frameworks (r = 0.610, p < .01). Regression results confirmed that both intelligent maintenance systems (β = 0.340, t = 5.92, p < .001) and energy optimization frameworks (β = 0.490, t = 8.11, p < .001) significantly predicted sustainable infrastructure performance, with the model explaining 62.4% of the variance (R² = 0.624; F = 163.410, p < .001). The study concludes that sustainable infrastructure is significantly strengthened when maintenance intelligence and energy optimization operate as integrated management capabilities, with important implications for infrastructure managers, policymakers, and facility operators seeking greater efficiency, reliability, asset longevity, and environmental sustainability.
Copyright (c) 2026 K M Tanvir Anjum Anick (Author)
Integrated Modeling of Condition Monitoring Data for Predictive Maintenance of Electrical Power Plant Systems
This study examined the effectiveness of integrated modeling of condition monitoring data for predictive maintenance in electrical power plant systems using a quantitative, quasi-experimental research design. The analysis was conducted on a dataset comprising 12,480 observations collected over an 18-month monitoring period across turbines, generators, transformers, and auxiliary systems. The study integrated multiple condition monitoring variables, including vibration, temperature, and electrical load, to develop predictive models using machine learning techniques. The results demonstrated that integrated models significantly outperformed single-source models, achieving a classification accuracy of 92.6% compared to 81.4%. Precision and recall values also improved, reaching 91.8% and 93.4%, respectively, indicating enhanced fault detection capability. Regression analysis revealed a substantial reduction in prediction error, with root mean square error decreasing from 0.58 to 0.42 and mean absolute error from 0.45 to 0.31. Additionally, integrated models identified faults approximately 36 hours in advance compared to 22 hours for single-source models, representing a 63.6% improvement in early detection capability. Component-level analysis indicated that turbines and generators achieved the highest predictive accuracy at 94.2% and 92.8%, respectively, while auxiliary systems showed comparatively lower accuracy at 87.3% due to higher operational variability. Statistical testing confirmed that these improvements were significant at a probability level below 0.05, with large effect sizes observed across key performance metrics. The findings also highlighted the importance of data quality, feature engineering, and model optimization in achieving reliable predictive outcomes. Overall, the study demonstrated that integrated condition monitoring significantly enhances predictive maintenance performance by improving accuracy, reducing uncertainty, and enabling proactive maintenance planning. These results provide strong empirical support for the adoption of integrated data-driven maintenance strategies in electrical power plant systems and contribute to the advancement of intelligent industrial maintenance practices.
Copyright (c) 2025 Md. Shahinur Islam (Author)
IoT-Driven Digital Transformation in Global Supply Chains: Implications for Financial Risk Monitoring and Investment Efficiency
The increasing complexity of global supply chains has intensified the need for advanced digital monitoring systems capable of improving financial risk monitoring and investment efficiency. This study examined the influence of Internet of Things (IoT) technologies and digital monitoring infrastructures on financial risk monitoring effectiveness and investment performance within globally integrated supply chain environments. A quantitative research design was employed to investigate the relationships among IoT technology adoption, digital monitoring capability, operational visibility, financial risk monitoring effectiveness, and investment efficiency. Data were collected from 214 professionals working in supply chain management, logistics coordination, procurement operations, and financial risk management roles across multiple industries, including manufacturing, logistics, retail distribution, and international trading organizations. Descriptive and inferential statistical analyses were conducted using correlation analysis, multiple regression, and structural equation modeling to evaluate the proposed conceptual relationships. The descriptive findings indicated relatively high levels of digital technology adoption across participating organizations, with 46.3% of firms reporting high levels of IoT adoption, while 36.9% demonstrated moderate adoption levels. Correlation analysis revealed strong positive relationships among the main constructs, with the highest correlation observed between digital monitoring capability and operational visibility (r = 0.71). Regression results further demonstrated that IoT technology adoption significantly predicted digital monitoring capability (β = 0.61, p < 0.001), explaining 37% of the variance in monitoring capability across organizations. Digital monitoring capability also exerted a significant influence on financial risk monitoring effectiveness (β = 0.63, p < 0.001) and investment efficiency (β = 0.58, p < 0.001). Structural equation modeling confirmed that monitoring capability functioned as a mediating factor linking IoT adoption with improved financial outcomes. The structural model demonstrated satisfactory fit indicators, including CFI = 0.94, TLI = 0.92, and RMSEA = 0.056, confirming the adequacy of the proposed model. The findings indicated that organizations with stronger IoT-enabled monitoring infrastructures demonstrated greater operational transparency, improved financial risk detection, and more efficient allocation of supply chain investments. Industries characterized by complex logistics operations, particularly manufacturing and transportation sectors, exhibited the strongest benefits from digital monitoring systems. Overall, the study provided empirical evidence that integrated IoT-based monitoring technologies significantly enhance financial oversight and investment decision-making within global supply chain networks.
Copyright (c) 2024 Mahfuj Ahmed Ruzel (Author)
Deploying Low-Latency Edge AI in Medical IOT Networks: A Case Study of Secure Real-Time Patient Monitoring Systems
This study examined the deployment of low-latency Edge Artificial Intelligence (Edge AI) within Medical Internet of Things (MIoT) networks to enhance secure real-time patient monitoring systems through a quantitative experimental case study design. The research aimed to evaluate system performance across key indicators, including latency, throughput, predictive accuracy, energy consumption, and security overhead, by comparing edge-based and cloud-based architectures under controlled operational conditions. A structured dataset comprising 480 experimental iterations was analyzed, incorporating physiological data streams such as heart rate, oxygen saturation, respiratory rate, and electrocardiographic signals. The findings demonstrated that the edge-based system significantly reduced end-to-end latency, achieving a mean delay of 37.8 milliseconds compared to 109.6 milliseconds in the cloud configuration, representing a reduction of approximately 65.5%. Throughput performance was also improved, with the edge system processing 6.2 MB per minute versus 5.5 MB per minute in the cloud system. Predictive accuracy remained high, reaching 96.4% in the edge environment compared to 94.1% in the cloud setup. Energy efficiency analysis indicated that overall system energy consumption was reduced, with edge devices averaging 2.9 watts compared to 3.7 watts for cloud-based processing. Statistical analysis confirmed that these differences were significant at p < 0.05, with large effect sizes observed for latency and processing time improvements. Additionally, the edge system maintained stable performance under high workload and low bandwidth conditions, demonstrating enhanced scalability and network resilience. Security evaluation revealed that encryption overhead introduced a smaller latency increase in the edge system, further supporting its suitability for real-time applications. These results provided strong quantitative evidence that Edge AI significantly improved responsiveness, efficiency, and reliability in MIoT-based patient monitoring systems, offering a robust framework for optimizing healthcare technology deployment in latency-sensitive environments.
Copyright (c) 2024 Istiaq Ahmed (Author)
Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation
This study addresses the persistent problem that many national disaster management systems remain fragmented, reactive, and weakly integrated despite rising disaster complexity, which limits early warning, coordinated response, and the reduction of economic losses. The purpose of the research was to examine how advanced computing and AI-driven national information systems influence predictive disaster risk management and how this, in turn, supports economic loss mitigation. Using a quantitative, cross-sectional, case-based design, the study drew on 210 valid responses from disaster management officials, ICT and system professionals, emergency response coordinators, policy and planning officers, and analysts involved in national disaster information environments, following 240 distributed questionnaires, 218 returns, and an effective response rate of 87.5%. The key variables were Advanced Computing Capability, AI-Driven Forecasting Capability, National Information System Integration, Real-Time Decision Support, Predictive Disaster Risk Management, and Economic Loss Mitigation. Data were analyzed through descriptive statistics, reliability testing, Pearson correlation, and regression analysis using SPSS. The results showed high mean scores across the main constructs, including Advanced Computing Capability (M = 4.18, SD = 0.64), AI-Driven Forecasting Capability (M = 4.11, SD = 0.69), Predictive Disaster Risk Management (M = 4.14, SD = 0.62), and Economic Loss Mitigation (M = 4.02, SD = 0.71). Reliability was strong, with an overall Cronbach’s alpha of 0.903. Correlation results indicated that Predictive Disaster Risk Management was strongly associated with AI-Driven Forecasting Capability (r = 0.724, p < .01), Real-Time Decision Support (r = 0.701, p < .01), and Economic Loss Mitigation (r = 0.756, p < .01). Multiple regression showed that the four predictors explained 62.8% of the variance in Predictive Disaster Risk Management (R² = 0.628), with AI-Driven Forecasting Capability emerging as the strongest predictor (β = 0.312, p = 0.001). Predictive Disaster Risk Management also explained 57.1% of the variance in Economic Loss Mitigation (R² = 0.571; β = 0.756, p < .001). The study implies that governments should prioritize AI forecasting, integrated national systems, and real-time decision support to strengthen predictive readiness and reduce disaster-related economic disruption.
Copyright (c) 2024 Md. Mehedi Hasan, Khairum Nahar Pinky (Author)
AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency
This study examined why supply chains still face preventable execution errors and inconsistent KPI performance despite dashboards, because reporting visibility does not consistently translate exceptions into coordinated corrective action. The purpose was to test whether AI-enabled enterprise scorecard capability improves operational error reduction and supply chain consistency, and whether AI alert trust and actionability act as mechanisms in an enterprise case setting. A quantitative, cross-sectional, case-based design used a five-point Likert survey of N = 210 scorecard users from planning, procurement, warehouse, logistics, and BI or reporting functions. Key variables were Scorecard Capability (SC_CAP), Alert Trust (TRUST), Alert Actionability (ACT), Operational Error Reduction (OER), and Supply Chain Consistency (SCC). Analysis combined descriptive statistics, reliability testing, Pearson correlations, and multiple regression for hypotheses and mechanisms. Reliability was acceptable to excellent (α = .86–.91; SC_CAP α = .89; OER α = .91; SCC α = .90), supporting composite-index modeling. Respondents reported moderately high capability (SC_CAP M = 3.86, SD = 0.62) and broad availability of KPI dashboards (82%), drill-down traceability (74%), and automated exception alerts (69%). Correlations supported the expected direction: SC_CAP related to OER (r = .62, p < .001) and SCC (r = .58, p < .001), and OER related to SCC (r = .55, p < .001). In regression, SC_CAP predicted OER (β = .47, p < .001, R² = .43) and SCC (β = .34, p < .001, R² = .38), and OER predicted SCC (β = .29, p < .001, R² = .31). Adding TRUST and ACT increased explained variance (OER R² = .51; SCC R² = .49) and showed partial mediation: SC_CAP’s OER effect reduced to β = .36 while TRUST (β = .18, p = .004) and ACT (β = .21, p = .001) remained significant, and ACT was especially influential for SCC (β = .24, p < .001). Implications are that organizations should manage AI scorecards as operational control infrastructure by strengthening KPI governance and drill-down traceability and by improving alert credibility and usability through clear ownership and response playbooks, so exceptions lead to repeatable actions that reduce errors and stabilize execution.
Copyright (c) 2024 Md Khaled Hossain, Md. Morshedul Islam (Author)
Quantitative Evaluation of Snort IDS Rule Effectiveness against DDOS, Brute-Force, and MITM Attacks in a Controlled Lab Network
Intrusion detection systems play a critical role in protecting network infrastructures from increasingly sophisticated cyber threats. This study quantitatively evaluated the effectiveness of Snort intrusion detection system rules in detecting three common categories of cyberattacks, namely distributed denial-of-service attacks, brute-force authentication attempts, and man-in-the-middle interception attacks within a controlled laboratory network environment. A quantitative experimental design was implemented in which simulated attack scenarios were executed across multiple experimental runs while network traffic was monitored using Snort IDS. The dataset consisted of packet capture logs, IDS alert records, and rule activation events generated during 30 experimental attack sessions and 10 baseline monitoring sessions. Across all sessions, a total of approximately 1,245,600 network packets were captured and analyzed, producing 360 confirmed attack events used to evaluate IDS detection performance. Detection effectiveness was measured using several quantitative indicators including detection rate, alert frequency, rule trigger counts, false positive events, and false negative occurrences. The results indicated that detection performance differed significantly across the three attack categories. Distributed denial-of-service attacks achieved the highest detection rate at 94.2%, with 113 out of 120 attack events successfully identified by the IDS. Brute-force authentication attacks produced a detection rate of 88.3%, with 106 detected events out of 120 attempts. In contrast, man-in-the-middle attack simulations produced a lower detection rate of 76.7%, with 92 detected events and 28 undetected attack instances. The IDS generated a total of 1,128 alerts and 1,542 rule activation events during monitoring, with DDoS scenarios producing the highest alert frequency due to the high-volume traffic patterns associated with flooding attacks. Statistical analysis confirmed that the differences in detection performance across attack categories were statistically significant. Effect size analysis further demonstrated a large practical difference between DDoS and MITM detection performance. The findings indicated that rule-based intrusion detection systems were highly effective in identifying volumetric and repetitive attack patterns but showed comparatively lower performance in detecting subtle packet interception behaviors. These results contribute empirical evidence regarding the operational strengths and limitations of rule-based intrusion detection systems in controlled network security environments.
Copyright (c) 2024 Md. Morshedul Islam, MD Zahedul Islam (Author)
Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting
This study addresses the persistent problem that finance teams in cloud-enabled and ERP-integrated enterprises often lack decision-grade visibility into short-horizon cash flow and liquidity, because working-capital timing is driven by heterogeneous receivables and payables behaviors that are poorly captured by simple rules or baseline statistical forecasts. The purpose was to quantify, using a quantitative cross-sectional, case-based evidence map, how predictive analytics and machine learning improve cash flow and liquidity forecasting for working capital management across enterprise cases documented in the literature. The study design treated each documented application instance as a “case” and synthesized N = 35 cases spanning cloud and enterprise data ecosystems (ERP, treasury workflows, and banking-adjacent settings), with sector-identifiable enterprise contexts reported in 18 of 35 cases (51.4%). Key variables included forecasting target (AR cash-in, cash position, AP cash-out, liquidity shortfall), model family (boosting/ensembles, regression, SVM, deep sequence, hybrid), feature strategy (aging, calendar, behavioral history, bank-reconciliation signals), and implementation conditions (governance, explainability, workflow integration, monitoring). The analysis plan used frequency distributions, cross-tabulation-style comparisons, and hypothesis-aligned evidence-strength scoring, plus improvement-rate coding versus baselines. Headline findings show ML outperformed statistical or rule baselines in 24/35 studies (68.6%) with strongest support for H1 (4.2/5), while tree-based ensembles were most prevalent (57.1%) and had the highest within-family improvement rate (75.0%). Feature enrichment mattered: among cases using aging structures, 82.6% reported better performance when behavioral or process-state features were added (evidence strength 4.0/5). For liquidity stress handling, hybrids were tested in 40.0% of cases and outperformed single models under volatility proxies in 71.4% of that subset (evidence strength 3.6/5). Practical implications are that enterprises gain the most when ML forecasting is embedded into governance and workflows: governance detail appeared in 42.9% of cases with 80.0% uptake, and workflow integration appeared in 40.0% with 78.6% uptake, indicating that trust and integration convert accuracy gains into operational impact.
Quantitative Impact Assessment of Digital Payment Solutions on Small Business Revenue Panel Data Analysis From 1,200 U.S. SMES
This study assessed the revenue impact of Digital Payment Solutions (DPS) using a longitudinal panel dataset of 1,200 U.S. small and medium-sized enterprises (SMEs) observed over 14,400 firm-period observations. The sample included SMEs from retail (30.0%), food service (20.0%), personal services (17.5%), professional services (16.7%), and repair-based industries (15.8%), with 70.0% employing fewer than 20 employees. Descriptive results showed mean logged revenue of 10.42 (SD = 0.88) and an average digital sales share of 49.6% (SD = 24.3%). DPS adoption increased from 68.0% in the early period to 85.0% by the final period. Reliability analysis confirmed strong internal consistency for the main constructs, including DPS intensity (α = 0.86), breadth (α = 0.79), and integration (α = 0.82). Two-way fixed effects regression models controlling for firm size, age, sector, location, channel mix, and competitive density indicated that DPS adoption was positively associated with revenue (β = 0.082, p < 0.001), corresponding to an average revenue increase of approximately 8.2% relative to pre-adoption levels. Usage intensity demonstrated the strongest effect (β = 0.214, p < 0.001). Payment breadth was also significant (β = 0.036, p = 0.010), while integration showed a moderate positive effect (β = 0.051, p = 0.020). Dynamic models confirmed revenue persistence (lagged revenue β = 0.641, p < 0.001) and maintained coefficient stability. The baseline model explained 42% of within-firm revenue variation (within R² = 0.42). Overall, findings provided robust quantitative evidence that DPS adoption, particularly when used intensively and integrated operationally, was positively associated with SME revenue performance in the U.S. context.
Copyright (c) 2024 Iftekhar Ahmed, Md Tohidul Islam (Author)
Cyber-Resilient Marketing Intelligence Systems for Fraud Detection and Data Integrity in Digital Campaigns
This quantitative study examined the role of cyber-resilient marketing intelligence system capability in shaping data integrity reliability and fraud detection performance within digital campaign analytics environments. Drawing on data collected from 300 organizations operating multi-platform digital marketing campaigns, the study employed multivariate regression and integrated modeling techniques to evaluate direct, indirect, and conditional relationships among cyber resilience capability, data integrity reliability, and fraud detection performance while accounting for marketing intelligence capability, platform complexity, and contextual controls. Descriptive findings indicated moderate-to-high levels across core constructs, with mean values of 3.78 for cyber-resilient marketing intelligence capability, 3.81 for data integrity reliability, and 3.59 for fraud detection performance on a five-point scale, suggesting relatively advanced analytics and resilience maturity within the sample. Correlation analysis showed significant positive associations between cyber resilience capability and data integrity reliability (r = 0.62), cyber resilience capability and fraud detection performance (r = 0.54), and data integrity reliability and fraud detection performance (r = 0.58), providing preliminary support for the proposed relationships without indicating problematic construct overlap. Regression results demonstrated that cyber resilience capability was the strongest predictor of data integrity reliability (β = 0.48, p < .001) and fraud detection performance (β = 0.29, p < .001), even after controlling for marketing intelligence capability and platform complexity. Mediation analysis revealed that data integrity reliability partially mediated the relationship between cyber resilience capability and fraud detection performance, with a statistically significant indirect effect (β = 0.17, p < .001) alongside a remaining direct effect. Moderation analysis further showed that platform complexity strengthened the positive effect of cyber resilience capability on fraud detection performance (interaction β = 0.11, p = .018), indicating greater resilience benefits in more complex, multi-platform environments. Overall, the findings demonstrated that cyber-resilient marketing intelligence systems were associated with more reliable data handling, reduced integrity degradation, and more stable fraud detection outcomes, supporting an integrated quantitative perspective on resilience, integrity, and analytical performance in digital marketing campaigns.
Copyright (c) 2023 Molla Al Rakib Hasan, Md. Towhidul Islam (Author)
AI-Driven Diagnostic Modelling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems
This study examined AI-Driven Diagnostic Modeling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems using a framework-level quantitative approach that integrated predictive performance, calibration reliability, and privacy-risk evaluation. Structured evidence mapping process reviewed 72 peer-reviewed papers to define constructs, metrics, and privacy threat considerations, after which a retrospective multi-site design was implemented to compare centralized non-private modeling, differentially private training, federated learning, and hybrid privacy configurations under standardized cohort rules and leakage-resistant validation. The analytic dataset included 48,620 adult patients contributing 162,904 encounters across three health systems, with a mean age of 57.8 years (SD = 16.4) and 52.6% female representation. Median encounter density was 3.0 encounters per patient (IQR = 2.0–5.0), and 18.9% of patients were classified as low-contact (≤1 encounter during the lookback window). Data completeness varied by domain, with missingness of 6.8% for vital signs, 18.4% for core laboratories, and 9.6% for medication indicators. Overall diagnostic outcome prevalence was 8.6%, ranging from 7.5% to 9.6% across sites. Correlation analysis indicated a strong relationship between encounter density and measurement frequency (r = 0.58) and a moderate association between comorbidity burden and outcome occurrence (r = 0.34). Collinearity diagnostics showed elevated redundancy among utilization predictors, including a variance inflation factor of 6.8 for total encounters and 5.9 for inpatient admissions, supporting composite consolidation before regression. Multivariable regression showed that differentially private training was associated with a modest reduction in discrimination (ΔAUC = −0.012) and increased calibration error (+0.021), while federated learning showed minimal average discrimination change (ΔAUC = −0.004) but greater cross-site dispersion. Privacy-risk evaluation indicated reduced membership inference leakage under privacy-preserving configurations, with leakage reductions of −0.083 for differentially private training and −0.071 for hybrid training relative to baseline. Overall, accuracy and privacy outcomes co-varied as system-level properties shaped by data quality, institutional heterogeneity, and framework design choices.
Copyright (c) 2026 Jinnat Ara, Samiha Binte Abdullah (Author)
Digital Ledger Optimization Techniques for Enhancing Transaction Speed and Reporting Accuracy in Accounting Systems
This quantitative study examined how digital ledger optimization techniques influenced transaction speed and reporting accuracy in accounting systems using archival operational data. A panel dataset comprising 360 unit–period observations from 30 accounting environments observed over 12 reporting periods was analyzed. Optimization intensity was measured across three layers—data, process, and control—while transaction speed was captured through posting latency, throughput, and queue waiting time, and reporting accuracy was measured using reconciliation mismatch rates, journal error rates, and correction entry frequency and value. Descriptive results showed that median posting latency was 9.6 seconds, while tail latency reached 44.2 seconds at the 95th percentile, confirming pronounced peak-load effects. Regression findings indicated that data-layer optimization was strongly associated with reduced posting latency (β = –0.41, p < .001), while process-layer optimization was most strongly associated with increased throughput (β = 0.39, p < .001). Control-layer optimization demonstrated the strongest relationships with reporting accuracy outcomes, including lower reconciliation mismatch rates (β = –0.51, p < .001) and reduced correction entry frequency (β = –0.53, p < .001). Moderation analysis revealed that validation strictness reduced the magnitude of batching-related speed gains (interaction β = 0.13, p = .020), while automation maturity strengthened the relationship between optimization intensity and reporting accuracy (interaction β = –0.15, p = .009). Robustness checks confirmed that results remained stable when alternative dependent-variable definitions were applied, including tail latency and mismatch value. Overall, the findings demonstrated that transaction speed improvements were most strongly associated with data- and process-layer optimization, while reporting accuracy improvements were primarily associated with control-layer practices. The study provided empirical evidence that layered digital ledger optimization contributed to faster processing and more reliable financial reporting under real operational conditions.
Copyright (c) 2022 Faysal Khan, Shamsunnahar Chadni (Author)
AI Based Quantitative Optimization Models for FMCG Supply Chain Efficiency in High-Demand Markets: A Linear Programming and Mixed-Integer Programming Approach
This study addressed a persistent supply chain performance gap in fast moving consumer goods environments where AI forecasting and visibility tools exist, yet efficiency declines under demand surges because replenishment and distribution decisions are not optimized under real capacity and lead time constraints. The purpose was to quantify how AI enabled analytics capability and related planning practices influence supply chain efficiency and to validate the statistical results with prescriptive optimization outcomes. A quantitative, cross sectional, case-based design was used, combining survey evidence from 168 supply chain professionals with cloud and enterprise planning cases modeled under normal and high demand conditions. Key variables included AI analytics capability, forecasting effectiveness, inventory optimization practice, logistics and distribution planning quality, supplier coordination and lead time reliability. The analysis plan applied reliability testing, descriptive statistics, Pearson correlation, and multiple regression, followed by linear programming and mixed integer programming scenario evaluation with feasibility checks and forecast noise sensitivity testing. Reliability was acceptable across constructs (Cronbach alpha 0.79 to 0.88). Descriptive results showed moderately high AI analytics capability (M 3.84, SD 0.62) and forecasting effectiveness (M 3.71, SD 0.66), while overall efficiency remained moderate (M 3.52, SD 0.67). Supply chain efficiency correlated strongly with AI analytics capability (r 0.62, p < 0.001). The regression model explained 54 percent of variance in efficiency (R2 0.54, p < 0.001), with AI analytics capability as the strongest predictor (beta 0.34, p < 0.001), followed by forecasting effectiveness (beta 0.21, p = 0.001), inventory optimization practice (beta 0.17, p = 0.005), and supplier coordination (beta 0.14, p = 0.017); logistics planning was positive but not statistically significant (p = 0.075). Optimization results reinforced these findings: under normal demand, total cost decreased 11.8 percent (1.72M to 1.52M), service level improved from 92.1 to 96.0 percent, and stockouts reduced 18.4 percent; under high demand, cost decreased 9.3 percent (2.05M to 1.86M), service improved from 88.4 to 93.2 percent, and stockouts reduced 15.1 percent, with a 0.8 percent optimality gap and solutions remaining feasible across scenarios. With plus or minus 10 percent forecast noise, cost rose only 2.1 percent and service declined 0.9 points, indicating robustness. The findings imply that enterprises should prioritize analytics and forecasting governance, enforce disciplined inventory policy execution, and embed constraint-based optimization into routine planning to sustain cost and service performance during demand peaks.
Copyright (c) 2026 Md Shahab Uddin (Author)
GPU-Accelerated Physics-Informed Digital Twins for Real-Time State Estimation and Fault Localization in Distribution Grids
This study addresses a persistent operational problem in distribution-grid management: utilities need fast, trustworthy state estimation and fault localization to support restoration and switching decisions, yet conventional analytics are often too slow for real-time use and can be difficult to trust under changing grid conditions. The purpose of the study was to evaluate whether GPU-accelerated, physics-informed digital twins can measurably improve real-time state estimation, fault localization effectiveness, and operational decision support in cloud and enterprise deployment contexts. Using a quantitative, cross-sectional, case-based design, data were collected from a sample of N = 210 respondents drawn from cloud and enterprise distribution-grid digital-twin cases. The key independent variables were GPU Acceleration Capability, Physics-Informed Modeling Strength, Digital Twin Fidelity, and Data Integration Quality; the dependent variables were Real-Time State Estimation Performance, Fault Localization Effectiveness, and Operational Decision Support Value, with an additional trust indicator for physics-consistency confidence. The analysis plan employed reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling to estimate direct effects and the enabling pathway from acceleration and physics constraints to operational value. Reliability results indicated strong internal consistency across constructs (α = .83 to .91). Correlation results showed that GPU capability was positively associated with state estimation (r = .56, p < .001) and that physics-informed strength had an even stronger association with state estimation (r = .61, p < .001); state estimation was strongly related to fault localization (r = .63, p < .001). Regression findings confirmed that state estimation performance was significantly predicted by GPU capability (β = .29, p < .001) and physics-informed strength (β = .37, p < .001), explaining 54% of variance (R² = .54). Fault localization effectiveness was significantly predicted by state estimation (β = .41, p < .001), digital twin fidelity (β = .28, p < .001), and data integration quality (β = .19, p = .002), explaining 62% of variance (R² = .62). Operational decision support value was driven primarily by fault localization (β = .46, p < .001) and state estimation (β = .21, p = .004), with GPU providing incremental contribution (β = .14, p = .030), explaining 58% of variance (R² = .58). Trust results further indicated high physics-consistency confidence (M = 4.12, SD = 0.48). Overall, the study implies that utilities should prioritize physics-consistent modeling and GPU-ready architectures, while investing in data integration quality and twin fidelity to convert computational speed into reliable, actionable grid decisions.
Copyright (c) 2024 Zaheda Khatun, Md Hamidur Rahman (Author)
Machine Learning–Based Financial Forecasting and Valuation Models for Investment Decision-Making and Capital Allocation Efficiency
This study addresses a recurring enterprise problem: capital allocation decisions often rely on forecasts and valuation estimates that are inconsistent across tools and teams, leading to delayed approvals, mispriced projects, and inefficient resource deployment. The purpose of the study was to quantify how machine learning based forecasting and machine learning based valuation contribute to higher investment decision-making quality and improved capital allocation efficiency within cloud and enterprise decision contexts. A quantitative cross-sectional, case-based design was used, drawing on cloud and enterprise cases where ML dashboards and analytics were actively used in screening, budgeting, and portfolio governance. Data were collected using a structured 5-point Likert survey from N = 162 participants involved in the forecasting–valuation–allocation pipeline (analysts/associates 46.3%, managers/senior managers 32.1%, committee or strategic roles 21.6%). The key variables were ML Forecasting Effectiveness (FCAST), ML Valuation Effectiveness (VAL), Investment Decision-Making Quality (DMQ), and Capital Allocation Efficiency (CAE), with role and experience treated as controls; two additional indices were examined to clarify adoption conditions, Model Trust and Adoption Readiness (MTAI) and Forecasting–Valuation Alignment (FVAD). The analysis plan included reliability testing, descriptive statistics, Pearson correlations, multiple regression, and bootstrap mediation to test whether DMQ transmits the effects of FCAST and VAL to CAE. Reliability was strong (Cronbach’s alpha: FCAST = .88; VAL = .86; DMQ = .84; CAE = .87). Mean scores were above neutral (FCAST M = 3.94, SD = 0.61; VAL M = 3.88, SD = 0.64; DMQ M = 3.76, SD = 0.58; CAE M = 3.71, SD = 0.62), indicating generally favorable perceptions of ML support. Correlations were positive and significant (FCAST–DMQ r = .56; VAL–DMQ r = .52; DMQ–CAE r = .62; all p < .001). In regression, FCAST and VAL jointly predicted DMQ (R² = .41; FCAST β = .39, p < .001; VAL β = .31, p < .001), while CAE was explained by DMQ with additional direct effects (R² = .53; DMQ β = .45, p < .001; FCAST β = .18, p = .007; VAL β = .12, p = .049). Mediation results showed meaningful indirect effects via DMQ for forecasting (indirect = .18, 95% CI [ .10, .28]) and valuation (indirect = .14, 95% CI [ .07, .23]). Implementation implications are clear: enterprises should govern ML forecasting and valuation as an integrated decision system, strengthen model trust and adoption readiness (MTAI M = 3.82, SD = 0.55; 68.5% high readiness), and actively manage forecast–valuation alignment because misalignment is associated with lower allocation efficiency (FVAD–CAE r = −.34, p < .001).
Copyright (c) 2026 Shamsunnahar Chadni (Author)
AI-Driven Credit Scoring and Default Probability Modeling for Basel III Risk-Weighted Asset Optimization in Banking
This study addresses a problem: AI-driven credit scoring can strengthen probability of default (PD) estimation, but uneven data quality, governance controls, and explainability reduce model trust and constrain Basel III risk-weighted asset (RWA) optimization. The purpose was to quantify the pathway from AI capability to PD modeling effectiveness and from PD effectiveness to Basel III RWA optimization in a cloud-enabled enterprise case bank. A quantitative cross-sectional, case-based survey was administered; 300 questionnaires were distributed and 212 valid responses were analyzed (70.7% effective response rate). Key predictors were AI-driven credit scoring capability, data quality readiness, model governance maturity, and explainability readiness; PD modeling effectiveness served as the mechanism, and Basel III RWA optimization effectiveness was the outcome. Reliability was strong across constructs (Cronbach’s α = 0.84–0.90). The analysis plan combined statistics, Pearson correlations, and multiple regression with moderation testing. Mean ratings were positive (AI capability M = 3.82; PD effectiveness M = 3.91; RWA optimization M = 3.76 on a 1–5 scale). Correlations supported the framework, including an association between PD effectiveness and RWA optimization (r = 0.66, p < .001). In regression Model 1, PD effectiveness was predicted by AI capability (β = 0.34, p < .001), data quality (β = 0.21, p = .002), governance maturity (β = 0.25, p < .001), and explainability readiness (β = 0.14, p = .018), with R² = 0.57. In Model 2, PD effectiveness predicted RWA optimization (β = 0.51, p < .001) and governance retained a direct effect (β = 0.19, p = .004; R² = 0.49). Moderation results showed a significant AI × governance interaction (β = 0.11, p = .031; ΔR² = 0.02), indicating that stronger governance amplifies the benefits of AI capability for PD outcomes. Implications are that banks seeking Basel III capital efficiency via AI should invest not only in scoring capability, but also in data readiness, governance discipline, and explainability practices so that PD gains translate into defensible RWA optimization.
Copyright (c) 2026 Sazzadul Islam (Author)
A Hybrid Lean-Six Sigma Model with Automated Kaizen for Real-Time Quality Improvement
This study addresses the problem that many enterprise quality improvement programs generate dashboards and alerts yet still fail to convert real-time deviations into owned corrective actions and verified closure, which sustains defect recurrence and process instability. The purpose was to test whether a Hybrid Lean Six Sigma (LSS) capability base, strengthened by Automated Kaizen execution workflows, predicts Real-Time Quality Improvement (RQI) in a quantitative, cross-sectional, case-based model. The sample comprised n = 210 valid responses collected from operational roles across cloud-enabled enterprise quality cases, including operators/technicians (35.2%), QA/QC staff (21.0%), supervisors (19.5%), engineers (15.2%), and CI or Lean Six Sigma team members (9.0%). Key variables were LSS capability, Automated Kaizen effectiveness, signal-to-action actionability, Kaizen closure confidence, DMAIC-stage functioning under automation, and RQI, measured on 5-point Likert scales and aggregated as composite indices. The analysis plan applied reliability testing (Cronbach’s alpha), descriptive statistics, Pearson correlations, and multiple regression models to estimate both baseline effects and incremental explanatory power. Measurement reliability was strong (α = .85–.90), including LSS α = .88, Automated Kaizen α = .90, and RQI α = .87. Descriptively, respondents reported high LSS capability (M = 3.94) and moderate-to-high Automated Kaizen (M = 3.78) and RQI (M = 3.83). Correlations supported the hypothesized relationships: LSS correlated with RQI (r = .62, p < .001) and with Automated Kaizen (r = .58, p < .001), while Automated Kaizen correlated more strongly with RQI (r = .67, p < .001). Regression results showed LSS predicting RQI (β = .62; R² = .38); when Automated Kaizen was added, it became the stronger predictor (β = .49) while LSS remained significant but reduced (β = .33), increasing explained variance to R² = .55, consistent with Automated Kaizen serving as a practical execution pathway. Mechanism evidence highlighted that signal-to-action actionability strongly related to RQI (r = .69; β = .41, p < .001) and automation’s highest perceived leverage appeared in the DMAIC Measure (M = 3.96) and Control (M = 3.91) stages. Implications indicate that enterprises seeking real-time quality gains should prioritize alert relevance, workflow routing integrity, clear ownership, and control-plan updates so that digital signals reliably trigger, track, verify, and standardize Kaizen actions at scale.
Copyright (c) 2023 Shofiul Azam Tarapder, Md. Al Amin Khan (Author)
A Quantitative Data-Driven Evaluation of Cost Efficiency in Cloud and Distributed Computing for Machine Learning Pipelines
This study presents a quantitative evaluation of cost efficiency, performance behavior, and resource utilization in machine learning pipelines executed under cloud-based and distributed computing environments. Using a consolidated dataset that integrated pipeline execution logs, fine-grained telemetry, and infrastructure billing records, the analysis examined 120 replicated pipeline runs spanning training-dominant and inference-dominant workloads. Each run was decomposed into five pipeline stages—ETL, preprocessing, training, evaluation, and serving—enabling both run-level and stage-level assessment of cost and runtime behavior. Descriptive results indicated that cloud executions exhibited higher median total cost per run (USD 42.80, IQR 31.20–66.50) compared to distributed executions (USD 38.10, IQR 29.40–52.30), alongside greater cost dispersion driven by autoscaling, orchestration overhead, and network egress. Distributed runs demonstrated higher average compute utilization (median 74.8% vs. 61.2%) and lower idle time shares (11.6% vs. 18.9%), contributing to more stable cost behavior. Multivariate regression models explained a substantial proportion of cost variability (R² = 0.68 for total cost; R² = 0.54 for normalized cost efficiency). Job completion time (β = 0.018, p < 0.001) and network egress (β = 0.031, p = 0.001) emerged as the strongest positive predictors of total cost, while higher average compute utilization was associated with lower cost after controlling for runtime (β = −0.007, p < 0.001). In the cost-efficiency model, higher training throughput (β = 0.00042, p < 0.001) and higher utilization intensity improved efficiency, whereas orchestration overhead (β = −0.091, p = 0.015), higher inference concurrency (β = −0.033, p = 0.026), and increased tail latency reduced efficiency. Hypothesis testing confirmed statistically significant differences favoring distributed environments for normalized cost efficiency (Cohen’s d = 0.44) and training completion time, while cloud environments achieved lower inference tail latency (p95 difference ≈ −65 ms). Overall, the findings demonstrate that cost efficiency in machine learning pipelines is driven less by median throughput differences than by utilization stability, orchestration behavior, and network-driven variability. By empirically linking telemetry-derived predictors to monetary outcomes, this study provides robust quantitative evidence for infrastructure-aware cost optimization in modern machine learning systems.
Copyright (c) 2023 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
COMPUTATIONAL MODELING AND SIMULATION TECHNIQUES FOR MANAGING RAIL–URBAN INTERFACE CONSTRAINTS IN METROPOLITAN TRANSPORTATION SYSTEMS
Rail–urban interface constraints in metropolitan transportation systems reduce reliability and safety because dense stations, corridor conflict points, and community exposure can turn small disruptions into network-wide delays and crowding. This study examined whether computational modeling and simulation capability (CMSC) improves constraint-management effectiveness (CME) in an enterprise-scale metro rail case were cloud and enterprise analytics support scenario testing and decisions. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered to N = 312 professionals from operations/control (26.3%), planning/timetabling (20.5%), station management (17.9%), engineering/maintenance (19.9%), and safety/risk (15.4%); 41.0% were direct model users and 37.8% indirect users. Key variables were rail–urban interface constraint severity (RICS), CMSC, decision integration (DI), and CME. Data screening showed mean missingness of 1.8% and Harman single-factor variance of 32.6%. Reliability was strong (α: RICS .88, CMSC .91, DI .87, CME .90). The most severe constraints were station crowding/circulation (M = 4.21, SD = 0.62) and peak dwell-time variability (M = 4.08, SD = 0.67). CMSC was moderate-high (M = 3.78, SD = 0.64), with scenario analysis strongest (M = 3.92) and validation weakest (M = 3.49). CMSC and DI correlated positively with CME (r = .62 and .58; p < .001), while RICS correlated negatively (r = −.41; p < .001). Regression was significant (R² = .51): CMSC (β = .38) and DI (β = .29) increased CME, and RICS reduced it (β = −.17); CMSC benefits were stronger at higher DI (ΔR² = .03). Implications emphasize station-area actions, stronger validation governance, and institutionalized use of simulation outputs in routine decisions.
Copyright (c) 2024 Masud Rana, Hammad Sadiq (Author)
THE IMPACT OF DEEP LEARNING AND SPEAKER DIARIZATION ON ACCURACY OF DATA-DRIVEN VOICE-TO-TEXT TRANSCRIPTION IN NOISY ENVIRONMENTS
This quantitative, cross-sectional, case-based study investigated why cloud and enterprise voice-to-text deployments still produce variable transcription accuracy in noisy, multi-speaker settings and whether Deep Learning ASR Capability (DL) and Speaker Diarization Quality (SD) function as complementary drivers of perceived Transcription Accuracy in Noise (TA). Using a one-time Likert-scale survey (1 = strongly disagree to 5 = strongly agree), the study retained N = 156 usable responses from users and reviewers embedded in operational enterprise and cloud transcription cases. The research problem centers on the persistent gap between real-world acoustic difficulty and dependable transcript quality for analytics, compliance, and decision support. In the model, DL and SD were the core independent variables, TA was the dependent variable, and Noise Severity and Overlap Frequency were included as contextual controls to isolate technical effects from environmental difficulty. Descriptively, the case context was genuinely adverse, with Noise Severity M = 3.94 (SD = 0.71) and Overlap Frequency M = 3.52 (SD = 0.82), while outcomes remained only moderate (TA M = 3.46, SD = 0.64); DL was rated moderately high (M = 3.62, SD = 0.59) and SD moderate (M = 3.38, SD = 0.67). Measurement quality supported inferential testing, with strong internal consistency (DL α = 0.88, SD α = 0.85, TA α = 0.90). The analysis plan applied composite scoring, reliability testing, Pearson correlations, and multiple regression. Correlation results showed strong positive relationships between DL and TA (r = 0.61, p < .001) and SD and TA (r = 0.55, p < .001), alongside negative associations between TA and noise (r = −0.31, p < .001) and TA and overlap (r = −0.28, p < .01). Regression findings confirmed joint predictive power: the model was significant (F(4,151) = 39.18, p < .001) and explained 51% of TA variance (R² = 0.51; Adjusted R² = 0.49); DL was the strongest positive predictor (B = 0.47, β = 0.43, p < .001) and SD added an independent positive contribution (B = 0.34, β = 0.31, p < .001), while noise (B = −0.11, p = .046) and overlap (B = −0.10, p = .021) reduced accuracy. A robustness check using TA groups further reinforced the pattern: 29.5% low TA (≤ 3.0), 52.6% moderate TA, and 17.9% high TA (> 3.75), with monotonic increases in DL and SD means across groups (DL 3.18 → 3.63 → 4.12; SD 2.97 → 3.36 → 3.98). Overall, the findings imply that enterprise teams should optimize ASR and diarization together, prioritize overlap-aware diarization improvements, and treat noise and overlap profiling as first-class deployment controls to improve transcript trust and downstream usability.
Copyright (c) 2023 Abdulla Mamun, Alifa Majumder Nijhum (Author)
AI-DRIVEN PREDICTIVE ANALYTICS FRAMEWORK FOR ELECTRONIC FUNDS TRANSFER, LOAN ORIGINATION, AND AML COMPLIANCE IN DIGITAL BANKING
This study addresses the problem that electronic funds transfer (EFT) monitoring, loan origination decisioning, and anti-money laundering (AML) compliance are often governed as separate control silos in digital banking, which limits risk visibility and reduces audit ready decision defensibility. The purpose was to validate an AI driven predictive analytics framework and quantify how Predictive Analytics Capability (PAC) influences EFT monitoring effectiveness, loan origination decision quality, AML monitoring effectiveness, and overall digital banking risk control performance (DBRCP). A quantitative cross sectional, case-based survey was administered across a cloud enabled digital banking environment, yielding 268 responses from EFT operations (31.7%), lending or underwriting (27.6%), AML or compliance (24.3%), and risk, analytics, or IT (16.4%). PAC (20 items) operationalized capability maturity across data integration, data quality, model development and validation, model governance and documentation, and user competence; outcome constructs were measured as Likert 1 to 5 composites. The analysis plan combined descriptive profiling, internal consistency testing, Pearson correlations, and hypothesis driven regression models. Reliability was adequate (Cronbach’s alpha: PAC 0.91, EFT_EFF 0.88, LOAN_QUAL 0.90, AML_EFF 0.89, DBRCP 0.92). Descriptively, respondents rated PAC at M = 3.84 (SD = 0.56), with governance and documentation the lowest dimension (M = 3.68), while EFT_EFF (M = 3.79), LOAN_QUAL (M = 3.73), AML_EFF (M = 3.76), and DBRCP (M = 3.76) were all above the scale midpoint. PAC correlated positively and significantly with EFT_EFF (r = 0.56), LOAN_QUAL (r = 0.52), AML_EFF (r = 0.59), and DBRCP (r = 0.63) at p < .001. Regression results showed that PAC predicted EFT_EFF (beta = 0.48, R2 = 0.31), LOAN_QUAL (beta = 0.44, R2 = 0.27), and AML_EFF (beta = 0.51, R2 = 0.35), all p < .001, indicating the strongest capability to outcome contribution in AML. In the integrated model, EFT_EFF (beta = 0.26), LOAN_QUAL (beta = 0.21), and AML_EFF (beta = 0.37) jointly explained DBRCP (R2 = 0.58), underscoring that coordinated improvements across payments, credit, and compliance drive risk control. Implications are that banks should invest in PAC foundations, particularly governance and documentation, to translate predictive models into consistent operational decisions and demonstrable compliance outcomes.
Copyright (c) 2025 Md Nahid Hossain (Author)
PRIVACY-PRESERVING SECURITY MODEL FOR EARLY CANCER DIAGNOSIS, POPULATION-LEVEL EPIDEMIOLOGY, AND SECURE INTEGRATION INTO U.S. HEALTHCARE SYSTEMS
The increasing use of data-intensive analytics for early cancer diagnosis and population-level epidemiology has heightened concerns regarding patient privacy, data security, and analytical reliability within healthcare systems. Early detection and cancer surveillance depend on large-scale integration of clinical, imaging, and epidemiological data across institutional boundaries, creating complex environments with elevated risks of disclosure, re-identification, and system vulnerability. This study investigates the effectiveness of a privacy-preserving security model for supporting early cancer diagnosis and population-level epidemiological analysis within regulated healthcare data environments in the United States. Using a quantitative, cross-sectional design, the study analyzed 120 oncology-related analytic units, including healthcare systems, cancer registries, and public health platforms, to assess the implementation of privacy controls, security architectures, and analytical performance outcomes. Results indicate a mean Privacy Control Index score of 68.4 (SD = 11.7), reflecting moderate to high adoption of privacy-preserving mechanisms with notable variability across institutions. Security architectures were more consistently implemented, with a mean Security Control Score of 72.9 (SD = 9.6), and were found to be a significant positive predictor of analytical performance. Analytical outputs remained stable under privacy constraints, with an overall performance stability score of 0.84 (SD = 0.06). The relationship between privacy strength and analytical performance was weak and non-linear (r = −0.21, p < .05), indicating that stronger privacy controls did not substantially degrade analytic utility. At the population level, secure aggregation mechanisms achieved high consistency, with a mean Aggregation Consistency Index of 88.1 (SD = 6.4), supporting reliable epidemiological analysis while limiting disclosure risk. Overall, the findings provide empirical evidence that privacy-preserving security models can be effectively integrated into cancer analytics systems, demonstrating that privacy, security, and analytical performance can function as complementary components within modern healthcare data infrastructures.
Healthcare Data Security And Interoperability
Copyright (c) 2022 Md Fokhrul Alam, Md Ashraful Alam, Md Fardaus Alam (Author)
ASSESSING ORGANIZATIONAL AI READINESS IN CRITICAL INFRASTRUCTURE: AN INTEGRATED MATURITY FRAMEWORK FOR HEALTHCARE SYSTEMS AND SUPPLY CHAIN MANAGEMENT
Critical infrastructure sectors face unprecedented challenges requiring artificial intelligence (AI) integration to enhance operational resilience and national security. However, AI adoption remains limited due to inadequate organizational readiness assessment frameworks. This research develops and validates a comprehensive AI readiness maturity model adapted from the Capability Maturity Model Integration (CMMI) framework for healthcare systems and supply chain management. Drawing on Technology-Organization-Environment (TOE) theory, Resource-Based View (RBV), and Dynamic Capabilities Theory, we propose an integrated assessment framework encompassing six dimensions: Technical Infrastructure, Data Capabilities, Organizational Capabilities, Strategic Readiness, Governance and Ethics, and Ecosystem Integration. Through multi-method validation involving survey data from 312 organizations, 45 semi-structured interviews, and four longitudinal case studies, we demonstrate strong psychometric properties and significant positive relationships between AI readiness maturity and infrastructure resilience outcomes. Results reveal that only 23% of healthcare organizations and 18% of supply chain organizations achieve optimized maturity levels, with significant gaps in data capabilities and governance structures. Our findings provide actionable frameworks for practitioners, inform policy development for critical infrastructure protection, and establish theoretical foundations for future research on AI-enabled infrastructure resilience.
Copyright (c) 2025 Tonay Pal, Md Maruf Islam (Author)
REINFORCEMENT LEARNING APPROACHES TO OPTIMIZE IT SERVICE MANAGEMENT UNDER DATA SECURITY CONSTRAINTS
This quantitative study evaluated reinforcement learning (RL) approaches to optimize IT Service Management (ITSM) decision-making under explicit data security constraints using a retrospective observational design and historical operational logs. The analytic dataset was extracted from a 12-month observation window and contained 128,450 raw incident and service-request tickets. After deterministic preprocessing, including timestamp normalization, deduplication, linkage resolution, and censoring under pre-specified rules, 112,680 tickets were retained for analysis, representing 46.3% incidents and 53.7% service requests across 10 service lines and 5 regions. Mandatory ITSM fields were complete for 96.8% of retained records, and 78.4% of tickets were linked to usable telemetry context for state construction. Descriptive statistics indicated distributional asymmetry typical of service systems: the median time-to-acknowledge was 0.42 hours and the median time-to-resolve was 14.6 hours, with substantial tail behavior (P90 resolution time 63.4 hours; P95 resolution time 97.6 hours). Governance indicators were consistently recorded, with audit-log completeness averaging 98.9%. Correlational analysis showed that time-to-resolve was positively associated with congestion and workflow friction, including relationships with backlog intensity (0.46), ticket aging (0.52), reassignment count (0.41), and escalation occurrence (0.35), motivating multivariable modeling with collinearity screening. Adjusted regression models controlling for ticket severity, category, region, service line, workload context, and major-incident regime indicated that the RL policy was associated with improved performance relative to baseline handling, including reduced time-to-acknowledge (β = −0.08 hours; p < .001) and improved resolution performance (log-linear β = −0.12; p < .001), alongside improved percentile-oriented SLA attainment (P90 threshold OR = 1.28, p < .001; P95 threshold OR = 1.19, p = .001). Process-quality outcomes also improved, including reduced reopen occurrence (OR = 0.86; p < .001) and reduced reassignment occurrence (OR = 0.89; p < .001). Constraint-related models showed no statistically significant increases in privileged-action events (IRR = 0.99; p = .61), exception approvals (IRR = 0.98; p = .44), or restricted-field access (IRR = 1.01; p = .69), and audit completeness remained statistically unchanged, supporting admissibility of security constraints while service outcomes improved.
Copyright (c) 2023 Md Mohaiminul Hasan, Md Muzahidul Islam (Author)
NEUROBIOTECHNOLOGY-DRIVEN REGENERATIVE THERAPY FRAMEWORKS FOR POST-TRAUMATIC NEURAL RECOVERY
This study addresses the problem that neurobiotechnology-driven regenerative therapies for post-traumatic neural injury often yield inconsistent recovery across organizations because framework implementation strength and its operational drivers are rarely measured and compared systematically. The purpose was to quantify Neurobiotechnology-Driven Regenerative Therapy Framework Strength (NDRTF) and test its association with Post-Traumatic Neural Recovery Outcomes (PNRO), and to identify which framework dimensions most strongly predict outcomes. Using a quantitative, cross-sectional, case-study-based design, a five-point Likert questionnaire was administered in three enterprise-scale case settings with technology-enabled monitoring and protocol tracking (Case A, B, C). The sample comprised 180 professionals (60 per case) spanning clinicians/therapists (52.8%), biomedical or neurotech staff (30.0%), and program coordinators (17.2%). Measurement quality was verified via reliability testing (α = 0.88 for the 25-item NDRTF scale; α = 0.86 for the 10-item PNRO scale). The analysis plan combined descriptive profiling, Pearson correlations, multiple regression, and case-specific regressions. Descriptively, overall NDRTF was moderate to high (M = 3.62, SD = 0.54) and PNRO was similar (M = 3.58, SD = 0.57), with Case A highest (NDRTF M = 3.74; PNRO M = 3.70) and Case C lowest (NDRTF M = 3.52; PNRO M = 3.49). Overall framework strength correlated strongly with recovery (r = 0.71, p < .001). In the multivariate model, the five NDRTF dimensions explained 59% of PNRO variance (R² = 0.59; F(5,174) = 49.30, p < .001), with Monitoring and Feedback Quality (β = 0.29, p < .001), Multidisciplinary Coordination (β = 0.23, p = .001), and Bio-Neurotech Integration (β = 0.19, p = .004) as the strongest predictors. Case models showed R² = 0.64 (A), 0.57 (B), and 0.49 (C). Implications include prioritizing monitoring feedback loops, cross-team coordination, and integrated biological plus neurotechnology delivery, supported by secure cloud data governance to sustain fidelity and scale outcomes.
Copyright (c) 2022 Md. Akbar Hossain, Sharmin Ara (Author)
DATA-DRIVEN FRAMEWORKS FOR IMPROVING DECISION-MAKING IN U.S. PUBLIC AND PRIVATE ORGANIZATIONS
This study examined the statistical relationships between data-driven framework maturity and decision-making effectiveness across U.S. public and private organizations using a quantitative, explanatory, cross-sectional comparative design. Analysis was conducted at the organizational level for 140 organizations, including 68 public-sector and 72 private-sector organizations. Data-driven framework maturity was operationalized through five dimensions—governance maturity, data quality management, architecture and integration depth, analytics capability, and decision integration—while decision-making effectiveness was assessed using five outcome families: decision timeliness, decision consistency, accuracy/error sensitivity, compliance-adjusted outcomes, and performance attainment. Descriptive results showed higher mean digital maturity in private organizations (M = 3.62, SD = 0.68) than public organizations (M = 3.28, SD = 0.74), while regulatory intensity was higher in public organizations (M = 4.01, SD = 0.63) than private organizations (M = 3.42, SD = 0.77). Correlation analysis indicated positive associations among maturity dimensions, with the strongest inter-correlation between analytics capability and decision integration (r = 0.71). Reliability results were acceptable to strong across constructs, with Cronbach’s alpha ranging from 0.80 to 0.90 and composite reliability from 0.83 to 0.91; confirmatory factor analysis supported the measurement model (CFI = 0.94, TLI = 0.93, RMSEA = 0.061, SRMR = 0.049). Collinearity diagnostics were within acceptable limits (VIF range: 1.20–2.56). Regression results showed that maturity dimensions improved explanatory power beyond controls, with adjusted R² reaching 0.41 for performance attainment and 0.36 for decision timeliness. Decision integration was positively associated with decision timeliness (β = 0.31, 95% CI [0.14, 0.46]), accuracy/error sensitivity (β = 0.28, 95% CI [0.11, 0.43]), and performance attainment (β = 0.30, 95% CI [0.13, 0.45]). Analytics capability was also positively related to performance attainment (β = 0.26, 95% CI [0.10, 0.40]), while governance maturity aligned most strongly with compliance-adjusted outcomes (β = 0.29, 95% CI [0.12, 0.44]).
Copyright (c) 2022 Md. Wahid Zaman Raj, Sai Praveen Kudapa (Author)