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.
Deploying the Model as a Surveillance and Decision-Support Tool for U.S. Food Safety
This study has examined the deployment of a model as a surveillance and decision-support tool for U.S. food safety, addressing the problem that food safety systems generate large volumes of surveillance, laboratory, inspection, recall, complaint, and supply-chain data, but these data are not always integrated into timely and actionable decision-support outputs. The purpose of the study has been to determine whether model deployment capability, data integration quality, predictive surveillance readiness, decision-support usability, and organizational preparedness improve food safety surveillance effectiveness and food safety decision-making performance. The study has followed a quantitative, cross-sectional, case-based design within the U.S. food safety surveillance and enterprise decision-support context. Data have been collected from 246 valid respondents, representing an 82.0% valid response rate from 300 distributed questionnaires, including food safety and quality assurance officers, public health monitoring personnel, regulatory compliance and inspection personnel, laboratory staff, supply-chain safety personnel, and data or risk-analysis personnel. The analysis plan has included descriptive statistics, reliability analysis, correlation analysis, regression modeling, mediation analysis, a Model-Readiness Index, and a Surveillance-to-Decision Conversion Assessment. The findings have shown high agreement across the major variables, with data integration quality recording the highest mean score of 4.18, followed by food safety surveillance effectiveness at 4.14, predictive surveillance readiness at 4.11, food safety decision-making performance at 4.07, model deployment capability at 4.05, decision-support usability at 3.98, and organizational preparedness at 3.92. Reliability has been strong, with Cronbach’s alpha values ranging from .82 to .91 and overall instrument reliability at .94. Regression results have shown that readiness variables explained 63.0% of the variance in surveillance effectiveness, while surveillance effectiveness and readiness variables explained 67.0% of the variance in decision-making performance. Surveillance effectiveness has also partially mediated the relationship between model deployment capability and decision-making performance, with an indirect effect of .19. The Model-Readiness Index has been 4.05, and the Surveillance-to-Decision Conversion Rate has been 98.31%. These findings have implied that U.S. food safety organizations should strengthen data integration, predictive readiness, usability, and organizational preparedness to convert surveillance signals into faster, evidence-based food safety decisions.
Copyright (c) 2025 Bushra Mumtaz (Author)
A Decade of Architectural Evolution: A Comprehensive Survey of Deep Learning Model Implementations
This comprehensive survey paper presents a rigorous, systematic evaluation of deep learning model implementations, engineering milestones, and cross-layer architectural transformations over the highly transformative decade spanning from 2016 to 2026. During this chronological period, the global artificial intelligence paradigm shifted decisively from highly heterogeneous, task-specific convolutional configurations and recurrent layer hierarchies to standardized, uniform, global Transformer-based execution pipelines, ultimately consolidating into contemporary sparse Mixture of Experts (MoE) topologies and multi-stage test-time compute reinforcement layouts. We map out the granular mathematical and systemic developments that enabled this massive scaling behavior, focusing heavily on core attention mechanism modifications (including Multi-Head, Multi-Query, Grouped-Query, and Multi-head Latent Attention structures), performance-driven layer normalization changes (such as LayerNorm and RMSNorm frameworks), and rotary positional transformations. Additionally, we analyze system-level hardware-aware engineering breakthroughs that successfully mitigated physical compute boundaries, highlighting critical memory tiling strategies like FlashAttention, sharded optimizer parameters via ZeRO optimization pipelines, and mixed-precision low-bit computation layouts (FP8/FP4 mixed tensor configurations). Finally, we characterize the latest 2025–2026 operational shift toward dynamic test-time scaling systems, where inference-phase processing budgets scale fluidly with automated verification chains, tree search methods, and self-correction loops. By structuring this comprehensive multi-layer technological taxonomy, this survey traces the critical structural, co-designed software-hardware execution pipelines that form the foundational baseline of modern generative artificial intelligence networks across global deployment landscapes.
Copyright (c) 2025 Md Ahnaf Tajwar Kamal, Nawfat Kamal Munifa, Fahmida Yasmin (Author)
AI-Driven Predictive Modeling for Customer Acquisition in Digital Payment Platforms
This study examines the impact of predictive modeling on customer acquisition rates in digital payment platforms, addressing the practical problem that many fintech firms generate large volumes of transaction and behavioral data but still struggle to convert those data into efficient customer acquisition strategies. The purpose of the research is to determine whether predictive modeling, supported by machine learning based transaction analysis, improves customer targeting efficiency and acquisition outcomes in cloud-based and enterprise digital payment cases. Using a quantitative, cross-sectional, case-based design, the study collected data from 218 respondents drawn from cloud and enterprise payment platform contexts, including professionals in data analytics, marketing, operations, product, and management roles. The core variables were predictive modeling, machine learning based transaction analysis, customer targeting efficiency, and customer acquisition rate. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression analysis. The findings show strong reliability across constructs, with Cronbach’s alpha values ranging from 0.838 to 0.881, and an overall scale reliability of 0.889. Descriptive results indicated high agreement across all variables, including predictive modeling (M = 4.18, SD = 0.61), machine learning based transaction analysis (M = 4.11, SD = 0.66), customer targeting efficiency (M = 4.07, SD = 0.64), and customer acquisition rate (M = 4.14, SD = 0.59). Correlation analysis revealed significant positive relationships between predictive modeling and customer acquisition rate (r = 0.681, p < .001), transaction analysis and customer acquisition rate (r = 0.643, p < .001), and targeting efficiency and customer acquisition rate (r = 0.701, p < .001). Regression results further showed that predictive modeling alone explained 46.4% of the variance in acquisition outcomes, while the combined model of predictive modeling, transaction analysis, and targeting efficiency explained 59.3% of the variance, with targeting efficiency emerging as the strongest predictor (β = 0.374, p < .001). The study implies that digital payment firms can improve acquisition performance by embedding predictive analytics into evidence-based targeting and campaign decision systems.
Machine Learning Based Transaction Analysis
Copyright (c) 2026 Noor Alam Siddiqui (Author)
Enhancing Stability and Reliability of Smart Grids with Solar Energy Integration
The integration of solar energy into smart grids is essential for achieving sustainable and environmentally friendly power systems. However, the intermittent and variable nature of solar power presents challenges related to grid stability, reliability, voltage regulation, and frequency control. Smart grid technologies offer advanced solutions through real-time monitoring, automated control, and intelligent energy management systems. This paper examines the key challenges associated with solar energy integration and explores strategies to enhance grid stability and reliability. Critical technologies such as energy storage systems, smart inverters, demand response programs, distributed energy resource management systems, and artificial intelligence-based forecasting are discussed. The study highlights the role of these technologies in mitigating power fluctuations, improving power quality, and increasing grid resilience. Furthermore, emerging innovations such as vehicle-to-grid systems, grid-forming inverters, and autonomous energy management are evaluated for their potential to support future renewable energy integration. The findings indicate that a combination of advanced control technologies, energy storage, and intelligent grid management is essential for ensuring stable, reliable, and efficient operation of solar-integrated smart grids, thereby facilitating the transition toward a cleaner and more sustainable energy future.
Copyright (c) 2026 Md Jakaria Talukder, Md. Shahinur Islam (Author)
Machine Learning–Driven Supply Chain Workflow Optimization and Process Analytics for Operational Efficiency and Resilience in U.S. Organizations
This study investigates how machine learning-driven supply chain workflow optimization and process analytics improve operational efficiency and resilience in U.S. organizations, addressing the persistent problem of supply chain workflow bottlenecks such as manual approval delays, repetitive data entry, communication gaps, uneven task allocation, and limited real-time visibility that continue to reduce speed, coordination, resource efficiency, and the capacity to absorb disruption even in digitally enabled enterprises. The purpose of the study was to examine the direct and combined effects of machine learning-driven supply chain workflow optimization and process analytics on operational efficiency and resilience using a quantitative, cross-sectional, case-based design. Data were collected through a structured five-point Likert scale questionnaire distributed across selected cloud-enabled and enterprise supply chain operational cases in U.S. organizations, with 300 questionnaires issued, 272 returned, and 260 valid responses retained for analysis. The key independent variables were machine learning-driven supply chain workflow optimization and process analytics, while operational efficiency and resilience served as the dependent outcomes. Analysis was conducted using descriptive statistics, Pearson correlation, and multiple regression in SPSS. The findings showed high levels of agreement across all core variables, with mean scores of 3.94 for machine learning-driven supply chain workflow optimization, 4.08 for process analytics, and 4.12 for operational efficiency and resilience. Correlation analysis revealed that machine learning-driven supply chain workflow optimization had a significant positive association with operational efficiency and resilience (r = .648, p < .001), while process analytics showed an even stronger positive relationship (r = .721, p < .001). Regression results further demonstrated that machine learning-driven supply chain workflow optimization independently explained 42.0% of the variance in operational efficiency and resilience (R² = .420, β = .648, p < .001), whereas process analytics explained 52.0% (R² = .520, β = .721, p < .001). In the combined model, both predictors jointly explained 61.3% of the variance in operational efficiency and resilience (R² = .613, Adjusted R² = .610, F = 203.41, p < .001), with process analytics remaining the stronger predictor (β = .451) over machine learning-driven supply chain workflow optimization (β = .349). The study implies that organizations can achieve stronger operational performance and disruption resilience by integrating process visibility with intelligent workflow decision support across their supply chains, thereby improving coordination, reducing delays, and strengthening data-driven operational management.
Machine learning-driven supply chain workflow optimization
Copyright (c) 2026 Sumyta Haque (Author)
AI-Driven Predictive Maintenance for Stationary Hydrogen Storage and Distribution Infrastructure: A Systematic Review of SCADA/PLC Integration and Implications for U.S. Pipeline Safety Policy
The increasing deployment of hydrogen as a clean energy carrier has intensified the need for reliable, safe, and cost-effective maintenance strategies for stationary hydrogen storage and distribution infrastructure. This study systematically reviewed the application of artificial intelligence (AI)-driven predictive maintenance technologies in hydrogen pipelines, storage vessels, compressors, valves, sensors, and integrated SCADA/PLC environments, while also examining implications for infrastructure reliability and operational safety. A quantitative systematic review methodology was employed to synthesize evidence from 126 eligible studies published between 2014 and 2025. Data were extracted and analyzed using descriptive statistics, comparative performance analysis, correlation analysis, regression modeling, independent-sample t-tests, and one-way ANOVA. The review evaluated machine learning, deep learning, anomaly detection, and hybrid predictive maintenance frameworks using performance indicators including failure detection accuracy, remaining useful life estimation, downtime reduction, maintenance cost optimization, asset availability improvement, and anomaly detection reliability. The findings demonstrated that AI-driven predictive maintenance significantly improved infrastructure performance across all hydrogen asset categories. Compressor systems achieved the highest failure detection accuracy (94.6%), followed by pipelines (93.2%), while predictive maintenance reduced operational downtime by up to 34.7% and maintenance costs by as much as 29.8%. Hybrid analytical frameworks produced the strongest predictive performance, achieving a mean accuracy of 96.2%, followed by transformer-based models (95.4%) and long short-term memory networks (94.8%). Regression analysis indicated that SCADA- and PLC-derived variables explained approximately 73% of the variance in predictive maintenance outcomes (R² = 0.73, p < 0.001), with vibration monitoring quality (β = 0.43), pressure monitoring consistency (β = 0.38), and compressor current reliability (β = 0.35) emerging as the most influential predictors. Studies utilizing high-quality operational datasets achieved anomaly detection accuracies of 94.8%, compared with 86.9% for lower-quality datasets.
Copyright (c) 2026 Adar Chowdhury (Author)
Quantitative Risk Assessment and Safety Performance Benchmarking for Structural Integrity Management in Retail Supply Chain Infrastructure
Structural integrity management plays a critical role in ensuring the safety, reliability, and operational continuity of retail supply chain infrastructure. This study quantitatively assessed structural integrity risks and benchmarked safety performance across retail logistics facilities by examining the relationships among structural condition, maintenance effectiveness, operational loading intensity, safety incident frequency, infrastructure downtime, and overall safety performance. A quantitative cross-sectional correlational research design was employed using data collected from 120 retail supply chain facilities, including warehouses, distribution centers, loading docks, storage rack systems, roofing structures, foundations, and support frames. Data were obtained through structured inspection checklists, maintenance records, safety incident logs, downtime reports, and operational loading assessments. Descriptive statistics, Pearson correlation analysis, multiple linear regression, independent-samples t-tests, analysis of variance, and benchmarking techniques were conducted using quantitative statistical procedures. The findings revealed that the mean structural condition score was 76.9, while the mean maintenance effectiveness score was 73.4. Safety incident frequency averaged 4.3 incidents annually, and average infrastructure downtime was 36.8 hours per year. Correlation analysis showed a strong positive relationship between structural condition and safety performance (r = 0.814, p < 0.01) and between maintenance effectiveness and safety performance (r = 0.758, p < 0.01). Multiple regression analysis demonstrated that structural condition was the strongest predictor of safety performance (β = 0.548, p < 0.001), followed by maintenance effectiveness (β = 0.361, p < 0.001), while operational loading intensity exhibited a significant negative effect (β = -0.218, p < 0.001). The regression model explained 72.1% of the variance in safety performance (R² = 0.721). Benchmarking analysis classified 31.7% of facilities as high-performing, 45.0% as moderate-performing, and 23.3% as low-performing. Storage rack systems and loading docks demonstrated the highest incident frequencies and lowest safety performance ratings among infrastructure categories. The study concluded that structural condition, maintenance quality, and operational exposure significantly influenced safety outcomes and that quantitative risk assessment provides an effective framework for evaluating infrastructure reliability and benchmarking safety performance within retail supply chain environments.
Copyright (c) 2022 Mukut Kanti Barua, Rony Saha (Author)
Multi-Agency Coordination in Sustained Healthcare Supply Shortages: Lessons from Covid-19, Drug Shortages, and Critical Medical Supply Disruptions
This study examined the problem of sustained healthcare supply shortages and the extent to which multi-agency coordination improves shortage management across COVID-19-related disruptions, drug shortages, and critical medical supply disruptions. The purpose was to determine how coordinated communication, shared data, policy alignment, resource sharing, and emergency preparedness contribute to effective healthcare supply shortage management in healthcare enterprise and agency-based cases. A quantitative, cross-sectional, case-based design was used, with data collected from 250 respondents representing hospitals and healthcare facilities, public health agencies, pharmaceutical and logistics organizations, emergency response bodies, regulatory institutions, and policy-related offices. The key independent variables were inter-agency communication, shared data and inventory visibility, policy alignment, resource-sharing mechanisms, and emergency preparedness planning, while the dependent variable was effective healthcare supply shortage management. Data were gathered through a five-point Likert-scale questionnaire and analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed high perceived coordination readiness, with a Multi-Agency Coordination Readiness Index of 3.95. Inter-agency communication recorded the highest mean score of 4.12, followed by emergency preparedness planning at 4.05, effective shortage management at 4.01, shared data and inventory visibility at 3.98, policy alignment at 3.84, and resource-sharing mechanisms at 3.76. Reliability results were strong, with Cronbach’s alpha values ranging from 0.78 to 0.89 and an overall instrument reliability of 0.91. Correlation results confirmed significant positive relationships between all coordination variables and shortage management, with shared data and inventory visibility showing the strongest association, r = 0.71, p < 0.01. Regression analysis revealed that the five predictors jointly explained 64% of the variance in effective shortage management, R² = 0.64, F (5,244) = 86.42, p < 0.001. Shared data and inventory visibility was the strongest predictor, β = 0.29, followed by inter-agency communication, β = 0.24, emergency preparedness, β = 0.21, policy alignment, β = 0.18, and resource sharing, β = 0.15. The study implies that healthcare systems should strengthen real-time inventory visibility, emergency communication protocols, coordinated procurement policies, and resource-sharing agreements to improve resilience during prolonged supply disruptions.
Copyright (c) 2026 Rony Saha, Md Ashrafuzzaman (Author)
DSCSA Implementation and Unit-Level Serialization Challenges at the U.S. Dispenser Layer: Evidence from Hospital and Community Pharmacy Operations
The Drug Supply Chain Security Act (DSCSA) established a national framework for enhancing pharmaceutical supply chain security through electronic traceability and unit-level serialization. As dispenser organizations represent the final point of pharmaceutical distribution before medications reach patients, understanding the challenges associated with DSCSA implementation at hospital and community pharmacies is essential for evaluating compliance readiness and operational effectiveness. This study investigated the determinants of DSCSA implementation and unit-level serialization challenges at the U.S. dispenser layer by examining technological, organizational, financial, and workforce-related factors influencing compliance performance. A quantitative cross-sectional survey design was employed, and data were collected from 312 pharmacy professionals representing hospital pharmacies, health-system pharmacies, independent community pharmacies, chain community pharmacies, and specialty pharmacies across the United States. Descriptive statistics, independent-samples t-tests, one-way ANOVA, Pearson correlation analysis, and multiple linear regression were conducted using statistical software to evaluate relationships among study variables and identify significant predictors of serialization implementation success. The findings revealed that information systems readiness (M = 4.01, SD = 0.62), interoperability capability (M = 3.92, SD = 0.65), leadership support (M = 4.08, SD = 0.61), workforce preparedness (M = 3.78, SD = 0.67), and financial resource availability significantly influenced DSCSA compliance performance. Correlation analysis indicated strong positive relationships between interoperability capability and compliance performance (r = .721, p < .001) and between information systems readiness and compliance performance (r = .684, p < .001). Multiple regression analysis demonstrated that interoperability capability was the strongest predictor of compliance performance (β = .341, p < .001), followed by information systems readiness (β = .286, p < .001), leadership support (β = .221, p < .001), workforce preparedness (β = .183, p = .002), and financial resources (β = .149, p = .011). The regression model explained 58.8% of the variance in compliance performance (R² = .588). Comparative analysis further showed that hospital pharmacies achieved significantly higher compliance performance scores (M = 4.12, SD = 0.48) than community pharmacies (M = 3.74, SD = 0.57). The study concluded that successful DSCSA implementation at the dispenser level depended on the integration of robust information systems, effective interoperability capabilities, organizational support, workforce readiness, and adequate resource allocation. These findings provide quantitative evidence regarding the critical factors influencing serialization adoption and pharmaceutical traceability compliance within U.S. pharmacy operations.
Impact of Phishing Simulation Campaigns and Security Awareness Training on Human Risk Mitigation in Financial Institutions: A Quantitative Study
This study examined the impact of phishing simulation campaigns and security awareness training on human risk mitigation within financial institutions using a quantitative quasi-experimental design. A total of 286 employees from multiple departments, including banking operations, customer service, finance, compliance, and administrative units, participated in the study. Baseline findings indicated moderate cybersecurity awareness (M = 62.4, SD = 8.7) and notable phishing susceptibility, with a click rate of 27.6%, credential submission rate of 11.3%, and reporting rate of 18.9%. The intervention consisted of structured awareness training combined with repeated phishing simulation campaigns conducted over multiple cycles. Post-intervention results demonstrated significant behavioral improvement across all key indicators. The click rate decreased from 27.6% to 14.2%, while credential submission declined from 11.3% to 4.9%, indicating enhanced resistance to phishing attempts. Reporting behavior improved substantially, increasing from 18.9% to 36.5%, reflecting greater employee vigilance and proactive engagement with cybersecurity protocols. Statistical analysis confirmed the significance of these changes, with p-values below 0.05 and effect sizes ranging from moderate to large (Cohen’s d = 0.69–0.91). Regression analysis identified training participation (β = 0.42) and simulation frequency (β = 0.37) as strong predictors of improved cybersecurity behavior. Sub-group analysis revealed variations across departments and experience levels, with customer-facing roles showing higher baseline susceptibility but greater improvement over time. Longitudinal trends across five simulation cycles indicated a progressive decline in risky behaviors and consistent improvement in reporting rates. The findings demonstrate that integrated training and simulation interventions significantly reduce human-related cybersecurity risks. The study contributes to quantitative cybersecurity research by providing empirical evidence that structured, continuous, and data-driven interventions can effectively enhance employee behavior and strengthen organizational resilience against phishing threats in financial institutions.
Copyright (c) 2024 Md. Shakhawat Hossen, Md. Fardous (Author)
Integrated Infectious Disease Preparedness: Multilingual Health Communication Strategies for Public Health Risk Mitigation, Disease Intervention and Prevention for Culturally Diverse Population in United States
This study examined the role of multilingual health communication strategies in strengthening integrated infectious disease preparedness, public health risk mitigation, disease intervention, and disease prevention among culturally diverse populations in the United States. The increasing linguistic and cultural diversity of the population has created challenges for public health agencies seeking to ensure equitable access to health information and preparedness resources during infectious disease emergencies. A quantitative cross-sectional research design was employed to investigate the relationships between multilingual communication accessibility and preparedness-related outcomes. Data were collected from 412 participants representing diverse ethnic, linguistic, immigrant, and minority communities across the United States through a structured multilingual survey questionnaire. Descriptive statistics, Pearson correlation analysis, multiple regression analysis, independent-samples t-tests, and one-way analysis of variance were conducted using SPSS Version 29. The findings revealed that multilingual communication accessibility was significantly associated with infectious disease preparedness outcomes. Strong positive correlations were identified between communication accessibility and preventive health behavior adoption (r = .701, p < .001), risk awareness (r = .648, p < .001), trust in public health information (r = .621, p < .001), and disease intervention participation (r = .592, p < .001). Multiple regression analysis demonstrated that multilingual communication accessibility was the strongest predictor of preparedness outcomes (β = .452, p < .001), while communication satisfaction (β = .284, p < .001) and trust in public health information (β = .221, p < .001) also contributed significantly. The regression model explained 61.0% of the variance in preparedness outcomes (R² = .610). Participants receiving health information in their preferred language reported significantly higher preparedness awareness (M = 4.26), intervention participation (M = 4.12), and preventive behavior adoption (M = 4.33) compared with those relying on secondary-language resources. The findings demonstrated that multilingual communication accessibility substantially enhanced preparedness effectiveness, strengthened intervention engagement, improved preventive health behaviors, and reduced preparedness disparities across culturally diverse populations. The study highlights the importance of integrating multilingual communication within public health preparedness frameworks to promote equitable health outcomes and strengthen infectious disease response capacities.
Copyright (c) 2024 Shima Ali Sadia, Musomi Khandaker, Sonya Ghosh (Author)
Multi-Agent Reinforcement Learning for Dynamic Resource Scheduling in Engineering Project Networks
This study examines the problem of ineffective dynamic resource scheduling in engineering project networks, where limited labor, machinery, materials, technical expertise, and budget must be continuously allocated across interdependent project activities under uncertainty. The purpose of the study was to determine how Multi-Agent Reinforcement Learning (MARL) capabilities influence dynamic resource scheduling performance by improving decision-making, coordination, adaptability, resource optimization, and intelligent scheduling support. A quantitative, cross-sectional, case-based research design was used, focusing on enterprise engineering project cases involving project managers, project engineers, resource and scheduling planners, operations managers, and digital or AI project officers. From 220 distributed questionnaires, 190 were returned, and 180 valid responses were used for final analysis, producing an 81.8% usable response rate. The key independent variables were MARL-based decision-making, agent coordination, dynamic scheduling adaptability, MARL-enabled resource optimization, and intelligent scheduling system capability, while the dependent variable was dynamic resource scheduling performance. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation analysis, and multiple regression modeling. The findings showed strong positive perceptions of all constructs, with mean scores of 4.18 for MARL-based decision-making, 4.11 for agent coordination, 4.23 for dynamic scheduling adaptability, 4.16 for resource optimization, 4.09 for intelligent scheduling capability, and 4.21 for scheduling performance. Reliability was strong, with Cronbach’s Alpha values ranging from 0.82 to 0.89. Correlation results showed significant positive relationships with scheduling performance, including MARL-based decision-making (r = 0.68), agent coordination (r = 0.64), dynamic scheduling adaptability (r = 0.72), resource optimization (r = 0.69), and intelligent scheduling capability (r = 0.61), all at p < 0.001. Regression analysis revealed that the model explained 66.6% of the variance in scheduling performance, R² = 0.666, F (5,174) = 69.42, p < 0.001. Dynamic scheduling adaptability was the strongest predictor (β = 0.29), followed by resource optimization (β = 0.24), MARL-based decision-making (β = 0.21), agent coordination (β = 0.18), and intelligent scheduling capability (β = 0.14). The study implies that engineering organizations should adopt adaptive MARL-enabled scheduling systems to reduce conflicts, improve resource utilization, strengthen coordination, and enhance project cost-time performance.
Copyright (c) 2024 Chapal Barua (Author)
Machine Learning Models for Bank Reconciliation Anomaly Detection in High-Volume Financial Transactions
This study examined the role of machine learning models in improving bank reconciliation anomaly detection in high-volume financial transaction environments, where manual review, spreadsheet-based matching, and fixed rule-based systems often fail to identify duplicate entries, unmatched records, amount mismatches, missing references, timing delays, failed payments, reversals, and suspicious transaction patterns quickly enough. The purpose of the study was to determine whether machine learning model capability, transaction data quality, automated anomaly detection capability, reconciliation system automation, and organizational readiness significantly influence bank reconciliation anomaly detection performance. A quantitative, cross-sectional, case-based research design was adopted, using cloud-supported and enterprise financial operation cases involving banks, fintech firms, payment processors, accounting units, audit departments, risk teams, compliance units, and financial control professionals. Data were collected through a structured five-point Likert-scale questionnaire from 204 valid respondents out of 250 distributed questionnaires, producing an 81.6% valid response rate. The key variables included machine learning model capability, transaction data quality, automated anomaly detection capability, reconciliation efficiency, operational risk reduction, financial control reliability, organizational readiness, environmental pressure, and bank reconciliation anomaly detection performance. The analysis plan involved descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, multiple linear regression, anomaly typology ranking, maturity index assessment, and hypothesis testing using SPSS. The findings showed strong quantitative support for the study model: automated anomaly detection capability recorded the highest mean score of 4.22, followed by bank reconciliation anomaly detection performance at 4.20 and machine learning capability at 4.18. Reliability was strong, with an overall Cronbach’s Alpha of 0.91. Correlation results showed significant positive relationships between anomaly detection performance and automated anomaly detection capability, r = 0.72, machine learning capability, r = 0.68, operational risk reduction, r = 0.66, transaction data quality, r = 0.63, and financial control reliability, r = 0.61. Regression results confirmed that the model explained 62.4% of the variance in anomaly detection performance, R² = 0.624, F = 54.27, p < 0.001, with automated anomaly detection as the strongest predictor, β = 0.31, followed by machine learning capability, β = 0.26, and transaction data quality, β = 0.22. The study implies that banks and enterprise financial systems should integrate machine learning-based anomaly detection with high-quality transaction data, automated workflows, staff training, governance controls, and audit-ready reporting to improve reconciliation efficiency, reduce operational risk, and strengthen financial control reliability.
Copyright (c) 2024 Mst Shurovi Akter (Author)
Perceived Privacy Risks of Passive WiFi-Based Human Sensing Technologies: A Quantitative Study
This study examined the relationship among passive WiFi sensing awareness, perceived sensing capability, perceived privacy risk, perceived surveillance exposure, trust in data governance, and privacy-protective behavioral intention within environments served by passive WiFi-based human sensing technologies in the United States. The rapid maturation of channel state information analytics, machine learning inference pipelines, and the emerging IEEE 802.11bf WLAN sensing standard has transformed ordinary wireless infrastructure into a pervasive human sensing medium capable of detecting presence, movement, activity, gesture, identity, and vital signs without any device carried by the sensed individual. The study used a quantitative, cross-sectional, correlational research design grounded in privacy calculus theory, contextual integrity theory, and protection motivation theory. Data were collected from 134 technology, networking, privacy, and facilities professionals working across residential, corporate, healthcare, retail, education, and public-sector environments in the United States using a structured Likert-scale survey instrument. Descriptive statistics, Pearson correlation analysis, and multiple linear regression analysis were conducted using SPSS, R, and Python statistical software to examine the relationships among the study variables. The findings revealed strong positive relationships among perceived sensing capability, perceived privacy risk, perceived surveillance exposure, and privacy-protective behavioral intention, alongside significant negative relationships involving trust in data governance. Perceived privacy risk demonstrated a high mean score of 4.12, while trust in data governance reported the lowest overall mean score of 2.87, indicating strong professional concern regarding the governance of passive sensing data. Pearson correlation analysis revealed a strong positive relationship between perceived privacy risk and privacy-protective behavioral intention (r = 0.74, p < 0.01), while perceived surveillance exposure also correlated strongly with protective intention (r = 0.69, p < 0.01). Multiple linear regression analysis indicated that the independent variables collectively explained 68% of the variance in privacy-protective behavioral intention (R² = 0.68, p < 0.001). Perceived privacy risk emerged as the strongest predictor of protective intention (β = 0.331, p < 0.001), followed by perceived surveillance exposure (β = 0.251, p < 0.001) and perceived sensing capability (β = 0.221, p = 0.002), while trust in data governance demonstrated a significant negative predictive relationship (β = −0.176, p = 0.004).
Copyright (c) 2026 Md Tarek Hasan, Md Zahin Hossain George (Author)
Commissioning Failures in Integrated Life Safety Systems (FA + HVAC Shutdown + Elevators): A Multi-Site Case Study Based on Field Testing and Authority Inspections
This study investigated commissioning failures in integrated life safety systems, specifically focusing on fire alarm (FA) systems, HVAC shutdown mechanisms, and elevator recall operations across multiple building sites. A quantitative multi-site case study design was employed using commissioning records, field testing logs, and authority inspection reports collected from 42 building projects. The dataset included key variables such as system complexity, number of integrated subsystems, device counts, failure frequencies, response times, and inspection outcomes. Descriptive and inferential statistical analyses were conducted to evaluate patterns of commissioning performance and identify key determinants of system reliability. The findings revealed a total of 786 recorded deficiencies, with communication failures accounting for 28.6% and integration failures representing 26.1% of total issues, indicating that cross-system interaction was the primary source of commissioning challenges. Logic-related and equipment failures contributed 22.4% and 22.9%, respectively. The mean number of failures per site was 18.72, with high-complexity systems exhibiting significantly higher averages of 26.38 failures compared to 12.94 in low-complexity systems. Response time analysis showed a mean system response of 7.84 seconds, with delayed performance observed in more complex systems. Initial inspection pass rates were recorded at 57.1%, while 42.9% of sites required corrective actions, with an average resolution duration of 16.4 days. Statistical analysis demonstrated strong correlations between system complexity and failure frequency (r = 0.71), as well as significant predictive relationships identified through regression modeling (R² = 0.62). Comparative analysis further indicated that network-based systems and high-rise or healthcare buildings experienced higher variability and longer corrective durations. The results confirmed that commissioning performance was strongly influenced by system integration complexity and coordination quality. This study provided a quantitative framework for understanding commissioning failures and highlighted the critical role of integrated system validation in ensuring reliable life safety performance.
A Quantitative Assessment of Disaster Recovery and Business Continuity Frameworks in Capital Market IT Infrastructure
This study addresses the critical problem of insufficiently tested and unevenly governed disaster recovery and business continuity frameworks in capital market IT infrastructure, where system failures can disrupt high-value, time-sensitive financial operations. The purpose of the research is to quantitatively assess how disaster recovery planning, business continuity practices, backup and recovery systems, testing and simulation, risk assessment and governance, and disaster recovery maturity influence IT infrastructure resilience. A quantitative, cross-sectional, case-based design was adopted using cloud and enterprise capital market IT environments as the empirical context. Data were collected from 206 valid respondents out of 250 distributed questionnaires, representing professionals in IT operations, cybersecurity, risk, and continuity management, using a five-point Likert scale instrument. The key variables included disaster recovery planning, business continuity practices, backup and recovery systems, disaster recovery testing and simulation, risk assessment and governance, disaster recovery maturity, and IT infrastructure resilience. The analysis plan incorporated descriptive statistics, reliability testing with Cronbach’s Alpha exceeding 0.80 for all constructs, correlation analysis, and multiple regression modeling. The findings revealed strong positive relationships among all variables, with backup and recovery systems showing the highest correlation with resilience (r = 0.72, p < 0.01) and the strongest predictive effect (β = 0.29, p < 0.001). The regression model explained 62.4% of the variance in IT infrastructure resilience (R² = 0.624, F = 55.08, p < 0.001). Disaster recovery planning (β = 0.22), governance (β = 0.20), and business continuity practices (β = 0.18) also showed significant contributions; while testing and simulation had a comparatively weaker effect (β = 0.14). The Disaster Recovery Maturity Index averaged 4.05, with 69.9% of institutions classified as highly mature, and critical system ranking identified trading platforms as the highest recovery priority (mean = 4.63). The study implies that integrated, well-governed, and technically validated recovery frameworks significantly enhance resilience in financial IT systems and should be continuously improved through testing and maturity-based assessment.
Engineering Smart Manufacturing Systems to Address Labor Shortages in U.S. Industry: A Scalable Framework for Workforce Resilience and Productivity
Labor shortages have emerged as a significant challenge for the U.S. manufacturing sector, affecting production efficiency, workforce stability, and organizational competitiveness. In response, smart manufacturing technologies have gained increasing attention as strategic tools for improving productivity while reducing operational dependence on scarce labor resources. This study investigated the influence of Smart Manufacturing Systems on Industrial Productivity, with Workforce Resilience serving as a mediating variable and Labor Shortage Severity acting as a moderating variable. A quantitative, explanatory, cross-sectional research design was employed using survey data collected from 412 manufacturing professionals representing automotive, aerospace, electronics, food processing, pharmaceutical, machinery, and industrial equipment industries across the United States. Data were analyzed using SPSS and Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine direct, indirect, and moderating relationships among the study variables. The findings revealed that Smart Manufacturing Systems had a significant positive effect on Industrial Productivity (β = 0.451, p < 0.001) and Workforce Resilience (β = 0.638, p < 0.001). Workforce Resilience also demonstrated a significant positive influence on Industrial Productivity (β = 0.426, p < 0.001), confirming its role as an important organizational capability within digitally enabled manufacturing environments. Mediation analysis indicated a significant indirect effect of Smart Manufacturing Systems on Industrial Productivity through Workforce Resilience (β = 0.272, p < 0.001), demonstrating partial mediation. The moderation analysis further showed that Labor Shortage Severity significantly strengthened the relationship between Smart Manufacturing Systems and Workforce Resilience (β = 0.183, p < 0.001). The structural model explained 45.1% of the variance in Workforce Resilience (R² = 0.451) and 63.5% of the variance in Industrial Productivity (R² = 0.635), indicating strong explanatory power. Effect size analysis revealed that Smart Manufacturing Systems exerted large effects on both Workforce Resilience (f² = 0.491) and Industrial Productivity (f² = 0.336). The study concluded that smart manufacturing technologies significantly enhanced industrial productivity through both direct technological benefits and indirect workforce-related mechanisms. The findings provide empirical evidence that integrating advanced manufacturing technologies with workforce resilience capabilities offers an effective framework for addressing labor shortages and sustaining productivity performance in U.S. manufacturing organizations.
Copyright (c) 2025 Md Tahmidul Islam (Author)
A Meta-Analysis of Agile and Lean Project Management Methodologies in Large-Scale Engineering and IT Integration Projects
This study examined the performance problem faced by large-scale engineering and IT integration projects, where schedule delays, cost overruns, rework, weak stakeholder alignment, resource inefficiency, and changing requirements often reduce project success. The purpose was to assess how Agile practices, Lean practices, Agile Lean integration maturity, stakeholder collaboration, project adaptability, and project complexity methodology fit influence project performance. A quantitative, cross-sectional, case-based design was applied using survey data from 180 professionals involved in engineering integration, IT integration, digital transformation, infrastructure, automation, cloud, and enterprise system cases. The key variables included Agile project management practices, Lean project management practices, stakeholder collaboration, project adaptability, Agile Lean Integration Maturity, Project Complexity Methodology Fit, and project performance. Data were analyzed using descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, and multiple regression modeling. The findings showed strong reliability across constructs, with the overall instrument achieving Cronbach’s Alpha = 0.93. Descriptive results indicated high mean scores for Agile practices (M = 4.12), Lean practices (M = 4.05), stakeholder collaboration (M = 4.18), project adaptability (M = 4.09), Agile Lean integration maturity (M = 3.92), methodology fit (M = 3.88), and project performance (M = 4.11). Correlation results confirmed significant positive relationships between project performance and Agile practices (r = 0.62), Lean practices (r = 0.59), stakeholder collaboration (r = 0.65), adaptability (r = 0.61), Agile Lean maturity (r = 0.68), and methodology fit (r = 0.57), all at p < 0.01. Regression analysis showed that the model explained 59.8% of project performance variance, R² = 0.598, F(6,173) = 42.86, p < 0.001, with Agile Lean integration maturity as the strongest predictor (β = 0.31, p < 0.001). The study implies that organizations can improve cost control, schedule performance, quality outcomes, stakeholder satisfaction, adaptability, and integration success by using a mature, hybrid, contingency-based Agile Lean approach.
Copyright (c) 2024 Md Asif Ali Sheak Arju (Author)
A Quantitative Assessment of Automated Dashboard Ecosystems Using ETL Pipelines and Real-Time Data Streaming for Business Performance Monitoring in SMEs
This study addresses the critical problem faced by small and medium-sized enterprises (SMEs) in transforming fragmented, delayed, and inconsistent operational data into timely and reliable performance insights. The purpose of this research is to quantitatively evaluate how automated dashboard ecosystems supported by ETL pipelines and real-time data streaming enhance business performance monitoring. The study adopts a quantitative, cross-sectional, case-study–based design, using a structured five-point Likert scale survey distributed across cloud-enabled and enterprise SME cases in sectors such as retail, finance, logistics, and e-commerce, with 210 valid responses (84.0% response rate). Key variables include ETL pipeline efficiency, real-time data streaming capability, dashboard automation, dashboard usability, data accuracy, and business performance monitoring. The analysis plan incorporates descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression modeling. The findings reveal strong statistical significance across all variables, with the regression model explaining 61% of the variance in business performance monitoring (R² = 0.61, F = 63.82, p < 0.001). Data accuracy emerged as the strongest predictor (β = 0.31, p < 0.001), followed by dashboard automation (β = 0.26), real-time streaming (β = 0.22), ETL efficiency (β = 0.19), and dashboard usability (β = 0.15). Correlation results further confirm strong relationships, particularly between data accuracy and performance monitoring (r = 0.72) and dashboard automation and monitoring (r = 0.70). Additionally, SMEs with advanced dashboard maturity achieved significantly higher performance monitoring scores (mean = 4.61) compared to low-maturity firms (mean = 3.12). These findings indicate that integrated dashboard ecosystems significantly improve KPI visibility, decision response speed, and operational awareness. The study implies that SMEs should prioritize data accuracy, automation, and real-time integration to achieve effective data-driven decision-making and competitive advantage.
Copyright (c) 2024 Md Aminul Islam (Author)
Artificial Intelligence and Digital Twins in Smart Real Estate Asset Management: A Framework for Sustainable Urban Development
The rapid urbanization of global economies, increasing pressure on infrastructure systems, and growing sustainability concerns have accelerated the adoption of advanced digital technologies within the real estate sector. Among these technologies, Artificial Intelligence (AI) and Digital Twins have emerged as transformative tools capable of reshaping real estate asset management practices through enhanced operational efficiency, predictive decision-making, and sustainability optimization. AI enables intelligent analysis of large-scale datasets generated by buildings, occupants, and infrastructure systems, while Digital Twins create dynamic virtual representations of physical assets that facilitate real-time monitoring, simulation, and performance optimization. This study employed a systematic literature review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, synthesizing approximately 180 peer-reviewed studies published between 2015 and 2026 across eight major academic databases. The review examined the technological ecosystem supporting intelligent asset management, including the Internet of Things (IoT), Building Information Modeling (BIM), cloud and edge computing, and big-data analytics, and investigated how AI-driven Digital Twin systems facilitate predictive maintenance, occupancy analytics, energy optimization, risk assessment, and lifecycle asset management. The findings indicate that AI-enabled Digital Twins substantially improve decision-making capability, operational resilience, and sustainability performance across the real estate lifecycle, with reported reductions in maintenance cost and improvements in energy efficiency and decision-making efficiency consistently documented in the empirical literature. The study proposes an integrated conceptual framework combining AI, Digital Twins, IoT, and sustainability principles to support next-generation smart real estate ecosystems, and identifies implementation challenges, research gaps, and future opportunities associated with autonomous building management, generative and explainable AI, and sustainable smart-city development. The framework provides guidance for researchers, practitioners, and policymakers seeking to accelerate digital transformation within the built environment.
Copyright (c) 2026 MKA Shahinoor Rahman Jahid (Author)
AI-Driven Building Automation Systems for Energy Optimization and Predictive Maintenance: A Quantitative Case Study of Smart Building Performance Enhancement
This study examined the effectiveness of artificial intelligence (AI)-enabled building automation systems in improving energy performance and maintenance reliability through a quantitative case-study design integrating Internet of Things (IoT) sensing, machine learning inference, intelligent heating, ventilation, and air-conditioning (HVAC) optimization, and predictive maintenance analytics. The research analyzed twelve months of pre-implementation baseline data and twelve months of post-implementation operational data drawn from a 185,000-square-foot commercial office building accommodating approximately 950 occupants and supported by multiple HVAC assets. The findings revealed that AI-enabled controls substantially reduced building energy consumption, with monthly reductions ranging from 36.2% in the heating-dominant winter to 51.7% during the peak cooling month, and an overall annual energy reduction of 45.3%. Energy savings were strongly associated with cooling-degree intensity, indicating that machine-learning-based setpoint optimization, occupancy-aware scheduling, and weather-responsive control delivered their largest gains under high thermal load. Predictive maintenance analytics reduced maintenance expenditures by 34.8%, lowered emergency work orders from 62 to 24, decreased equipment failures from 31 to 11, and reduced unplanned downtime from 196 to 78 hours, demonstrating a shift from reactive to condition-based intervention. Regression analysis indicated that cooling-degree days, occupancy variability, and predictive-model lead time jointly explained a substantial proportion of the variance in monthly energy reduction, while correlation analysis confirmed strong relationships between anomaly-detection accuracy and downtime reduction. Overall, the results demonstrated that integrating AI with IoT-enabled building infrastructure produced measurable improvements in both energy efficiency and operational reliability, offering empirical support for the broader adoption of intelligent automation across commercial, institutional, and public building portfolios.
AI-Native SDN, Zero Trust, and NGFW Architectures for Autonomous Threat Intelligence in Regulated U.S. Digital Systems
This study quantitatively examined the operational effectiveness of AI-native software-defined networking, Zero Trust Architecture, and next-generation firewall systems in strengthening autonomous threat intelligence across regulated U.S. digital infrastructures. The increasing complexity of cyber threats targeting healthcare systems, financial institutions, telecommunications networks, government infrastructures, and critical operational environments has intensified the need for adaptive cybersecurity ecosystems capable of real-time threat detection, predictive analytics, automated response coordination, and intelligent governance management. The study adopted a quantitative experimental research design grounded in cybersecurity risk management theory, defense-in-depth theory, and artificial intelligence decision-making frameworks to evaluate the relationships between integrated intelligent cybersecurity architectures and operational cybersecurity performance outcomes. The final dataset consisted of 150 enterprise cybersecurity infrastructures operating across regulated sectors within the United States. Statistical analysis was conducted using SPSS, R, and Python-based cybersecurity analytics tools to evaluate intrusion detection accuracy, anomaly detection precision, malware containment efficiency, compliance governance performance, operational continuity indicators, and adaptive mitigation responsiveness. The findings demonstrated that integrated AI-driven cybersecurity ecosystems significantly improved operational cybersecurity performance across participating enterprise environments. Intrusion detection accuracy increased from 74.5% within traditional security infrastructures to 93.8% within integrated AI-native environments, while malware containment efficiency improved from 71.3% to 94.5%. Incident response speed demonstrated a 58.4% improvement, with average response times reduced from 8.9 minutes to 3.7 minutes following implementation of integrated AI-native SDN, Zero Trust, and NGFW architectures. Autonomous threat intelligence systems produced the strongest predictive influence on operational cyber resilience with a regression coefficient of β = 0.527 and statistical significance at p = 0.000. Adaptive threat mitigation efficiency reached 91.2%, while operational continuity performance increased by 41.0% across organizations implementing multilayered intelligent cybersecurity ecosystems. The findings further revealed that healthcare, financial, and federal government infrastructures achieved substantial reductions in unauthorized access incidents and improvements in compliance governance performance through centralized orchestration, continuous authentication, and AI-driven behavioral analytics systems.
Copyright (c) 2026 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
Formalizing Adaptive Sampling Strategies in Database Management System Performance Modeling Using Transfer Learning
Database Management Systems (DBMSs) sit at the heart of modern data-driven applications, exposing hundreds of tunable parameters whose interactions are notoriously difficult to model. Machine-learning–based performance prediction has emerged as a scalable alternative to manual tuning, but most published pipelines silently assume that the environment in which a model is trained matches the environment in which it is deployed. In production, that assumption almost never holds: developers train on virtualised testbeds, then deploy onto hardware with different core counts, memory bandwidth, storage characteristics and concurrency profiles. The resulting domain shift quietly degrades predictive accuracy and motivates transfer learning as a remedy. This paper proposes ChimeraTL, a formalised, feedback-driven adaptive sampling pipeline for DBMS performance modelling. Rather than treating all configuration parameters uniformly, ChimeraTL partitions them into two behaviourally distinct sets using the Bhattacharyya distance between their per-parameter source and target performance distributions. Parameters with low cross-domain divergence are designated linear and reused through an affine projection of source data; parameters with high divergence are designated non-linear and are explored fresh in the target domain under an active-sampling regime that consumes online error feedback. A confidence-driven gate triggers the transition from the linear phase to the non-linear phase, so computation is allocated proportionally to the difficulty of each parameter subset. An evaluation on PostgreSQL and MySQL using TPC-C and TPC-H workloads, across a four-core virtual machine (source) and a twelve-core SSD-backed server (target), shows that ChimeraTL reduces prediction mean-squared error by up to 54% and the number of target queries to reach 90% of final accuracy by approximately 63% relative to a no-transfer baseline. ChimeraTL also outperforms linear projection, hybrid sampling and Bayesian optimisation across all measured dimensions. The architecture is modular, containerisable and integrates with standard DBMS telemetry, making it a practical foundation for next-generation autonomous tuning frameworks.
Copyright (c) 2026 Mst Shamima Akter, Md Atiqur Rahman Khan (Author)
AI-Driven Anomaly Detection On SCADA-Bearing Fiber Networks: A Machine Learning Framework for Real-Time Intrusion Detection in Electric Utility Environments
This study developed and evaluated an AI-driven anomaly detection framework for SCADA-bearing fiber networks in electric utility environments, emphasizing real-time intrusion detection performance. An experimental quantitative design was implemented using a dataset of 58,420 observations, where 42,115 records (72.08%) represented normal operations and 16,305 records (27.92%) represented anomalous events, including 6,120 denial-of-service cases, 3,845 command manipulation instances, 2,960 replay attacks, and 3,380 communication disruptions. The proposed framework was assessed using multiple machine learning and deep learning models across performance metrics such as accuracy, precision, recall, F1-score, false positive rate, false negative rate, and detection latency. The CNN-LSTM model achieved the highest performance with 96.84% accuracy, 95.72% precision, 97.91% recall, and 96.80% F1-score, while maintaining a low false positive rate of 3.12% and a detection latency of 0.028 seconds. In comparison, Random Forest achieved 94.26% accuracy and 94.88% recall, while Support Vector Machine achieved 92.73% accuracy and 93.02% recall. Feature optimization improved the CNN-LSTM model accuracy to 97.42% and reduced latency from 0.028 to 0.024 seconds. Sub-group analysis revealed that denial-of-service attacks were detected with 98.63% recall, while replay attacks showed lower recall at 94.88%. Statistical analysis confirmed significant differences among models with F-values exceeding 18.72 and large effect sizes up to 1.42. The findings demonstrated that integrating SCADA control signals, network traffic, and fiber-optic features improved detection performance by over 1.5% compared to network-only models. Overall, the framework provided a highly accurate and efficient solution for real-time intrusion detection in SCADA-bearing fiber networks.
Copyright (c) 2024 Abu Naser Md Golam Mosharraf (Author)
A Quantitative Assessment of Generative AI Applications in Public-Sector Financial Reporting and Audit Preparation
This study quantitatively assessed the influence of generative artificial intelligence applications on public-sector financial reporting and audit preparation systems within governmental institutions. The research examined the extent to which AI adoption, technological readiness, AI utilization frequency, and organizational support affected financial reporting accuracy, audit preparation efficiency, fraud detection capability, compliance verification effectiveness, administrative productivity, and transparency outcomes across public-sector accounting environments. A quantitative cross-sectional research design was employed using structured survey questionnaires distributed electronically to accountants, auditors, finance officers, procurement personnel, compliance specialists, and financial administrators working within ministries, municipalities, taxation agencies, healthcare institutions, and public audit authorities. A total of 420 questionnaires were distributed, and 389 valid responses were retained after data screening, producing a response rate of 92.6%. Statistical analyses were conducted using SPSS and SmartPLS software packages through descriptive statistics, correlation analysis, regression analysis, and structural equation modeling procedures. The findings demonstrated strong positive relationships between generative artificial intelligence adoption and operational performance indicators within governmental accounting systems. Financial reporting accuracy recorded the strongest correlation with AI adoption (r = 0.768), followed by administrative productivity (r = 0.754), audit preparation efficiency (r = 0.741), and compliance verification effectiveness (r = 0.719). Regression analysis further revealed that AI adoption significantly predicted financial reporting accuracy (β = 0.482, p = 0.000), while AI utilization frequency significantly influenced audit preparation efficiency (β = 0.451, p = 0.000) and administrative productivity (β = 0.463, p = 0.000). The coefficient of determination values showed that the statistical models explained 62.1% of the variance in financial reporting accuracy, 60.4% in administrative productivity, 58.7% in audit preparation efficiency, and 55.3% in fraud detection capability. Subgroup analysis additionally revealed that large institutions achieved a mean reporting accuracy score of 4.51 compared to 3.26 among smaller organizations. Institutions possessing advanced digital infrastructure demonstrated significantly higher transparency performance (mean = 4.51) than institutions operating with basic infrastructure systems (mean = 3.17). The study concluded that generative artificial intelligence substantially improved public-sector financial reporting quality, audit preparation effectiveness, fraud detection capability, compliance oversight, and administrative efficiency within governmental accounting environments.
Copyright (c) 2026 S M Arif Al Sany (Author)
A Systematic Review of AI-Driven Financial Information Systems for Budget Optimization in U.S. Public Sector Institutions (2018–2026)
This study examines the problem that many U.S. public sector institutions collect large volumes of financial and operational data but still face difficulties converting those data into accurate budget forecasts, efficient allocation decisions, real-time expenditure monitoring, fraud detection, and accountable financial governance. The purpose of the study is to assess how AI-driven financial information systems support budget optimization in public institutions through predictive analytics, machine learning, robotic process automation, anomaly detection, intelligent dashboards, and decision-support algorithms. The research follows a quantitative, cross-sectional, case-based design supported by systematic evidence synthesis from 35 selected studies and documented U.S. public sector cases, including federal agencies, state departments, local governments, public universities, public healthcare institutions, public transportation agencies, and public procurement offices. The key variables include AI-driven financial information systems, forecasting accuracy, budget allocation efficiency, expenditure monitoring, anomaly detection, transparency, accountability, data quality, institutional readiness, cybersecurity maturity, and AI governance. The analysis plan involved structured screening, coding, thematic synthesis, descriptive frequency analysis, percentage distribution, and five-point Likert-style evidence scoring to evaluate the strength of literature-based findings. The headline findings show that 29 of 35 studies, or 82.9%, reported that AI improved at least one major budget function, with an overall evidence score of 4.25 out of 5.00. Forecasting accuracy was supported by 27 studies, or 77.1%, with a score of 4.32; allocation efficiency was supported by 24 studies, or 68.6%, with a score of 4.08; expenditure monitoring and fiscal accountability were supported by 26 studies, or 74.3%, with a score of 4.21; and institutional readiness and governance factors were identified in 28 studies, or 80.0%, with the highest score of 4.40. The findings imply that public agencies should strengthen data quality, system integration, staff capability, cybersecurity safeguards, explainable AI governance, and human oversight to ensure that AI-driven financial information systems improve budget credibility, reduce waste, enhance fraud control, and support transparent public value creation.
Copyright (c) 2026 Samia Hossain Swarnali (Author)
AI-Driven Risk Analytics Models for Early Detection of Financial Noncompliance in Multi-Branch Banking Systems
This study addresses the persistent problem of financial noncompliance in multi-branch banking systems, where rule-based monitoring often produces high false positives, uneven alert burdens across branches, and delayed identification of emerging suspicious patterns; accordingly, the purpose is to evaluate whether AI-driven risk analytics can improve early detection performance and governance readiness when deployed across distributed branch environments as enterprise-grade (including cloud-enabled) compliance surveillance cases. Using a quantitative, cross-sectional, case-based synthesis design, the study analyzed 78 peer-reviewed banking compliance “cases” published from 2005–2023, selected after screening 214 records and removing 46 duplicates, with cases spanning AML monitoring (41%, n=32), KYC/onboarding compliance (23%, n=18), sanctions screening risk (15%, n=12), and internal policy or conduct noncompliance (21%, n=16). Key variables included model family (supervised, unsupervised, graph-based, hybrid rule–ML), branch-awareness (peer normalization, branch identifiers, aggregation transparency), and governance readiness (explainability, audit trails, validation and monitoring), while outcomes focused on early-detection lift, precision and recall behavior under class imbalance, false-positive burden, and investigator actionability. The analysis plan combined descriptive statistics, cross-tabulation by compliance domain and model family, and standardized 5-point Likert indices (Evidence Strength Rating, Alert Sustainability Likert Score) to enable comparable quantitative synthesis across heterogeneous studies. Headline findings show that 74% (n=58) of studies reported comparisons against rule-based baselines, and 84% (n=49) of these reported measurable gains, including precision improvements of 11%–27%, recall gains of 8%–19%, and mean false-positive reductions of 18.6%; in AML contexts, average AUC increased from 0.76 (rule-based) to 0.88 (AI-driven). Branch-aware approaches (46%, n=36) reduced branch-level alert variance by 14.2% and improved precision stability across branches by 12.5%. Explainability and governance integration (67%, n=52) was associated with 21% average reductions in investigator triage time and lower audit rejection rates (6% vs. 19%). Implications indicate that enterprise deployments should prioritize hybrid rule–ML architectures, branch-normalized baselines, and audit-defensible explanation and logging to achieve sustainable early warning without overwhelming investigative capacity.
Copyright (c) 2026 Md Mostafizur Rahman, Md Jamil Ahmmed (Author)
A Systematic Review of Artificial Intelligence-Based Customer Analytics in Personalized Digital Marketing (2019–2026)
Artificial intelligence (AI)-driven customer analytics has emerged as a transformative force in personalized marketing within modern digital commerce ecosystems, driven by the rapid growth of big data, real-time consumer interaction platforms, and intelligent recommendation technologies. This systematic review investigates the impact of AI-driven customer analytics on personalized marketing strategies across digital commerce platforms during the period from 2019 to 2026. The study explores how AI technologies, including machine learning, predictive analytics, recommendation systems, natural language processing, sentiment analysis, marketing automation, and AI-enabled customer relationship management systems, affect customer engagement, purchasing behavior, personalization performance, and organizational outcomes in digital environments. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological rigor, transparency, and systematic synthesis of existing literature. Relevant academic publications were collected from major scholarly databases, including Scopus, Web of Science, ScienceDirect, IEEE Xplore, SpringerLink, Emerald Insight, Wiley Online Library, and Google Scholar. After applying screening, eligibility, and quality assessment criteria, 126 peer-reviewed studies were selected for qualitative and thematic analysis. The findings indicate that AI-driven customer analytics substantially enhances personalization accuracy, customer segmentation, recommendation quality, and real-time engagement across e-commerce websites, mobile commerce applications, social media platforms, and omnichannel retail environments. The review further reveals that predictive analytics, recommendation engines, conversational AI systems, and automated marketing technologies contribute significantly to customer retention, loyalty, conversion optimization, and overall customer satisfaction. In addition, big data infrastructures and cloud computing technologies were identified as essential enablers of adaptive and real-time personalized marketing strategies. The study also highlights the importance of consumer trust, transparency, privacy protection, and ethical governance in shaping user acceptance of AI-powered personalization technologies. Several studies reported concerns regarding algorithmic bias, behavioral surveillance, cybersecurity vulnerabilities, and regulatory compliance in digital commerce ecosystems. Furthermore, the review emphasizes the increasing influence of social media analytics, AI-powered advertising systems, and influencer engagement technologies on consumer behavior and purchasing decisions. Overall, the study concludes that AI-driven customer analytics has become a strategic foundation for personalized marketing, customer relationship management, and competitive advantage within global digital commerce ecosystems.
Copyright (c) 2026 H M Mahir Uddin (Author)
System Dynamics Modeling of Critical Chain Project Management in Multi-Project Engineering Environments
This study examined the problem of persistent schedule instability, resource bottlenecks, rework cycles, buffer overconsumption, and weak dynamic control in multi-project engineering environments where several projects compete for shared engineers, technical teams, equipment, budgets, and decision-making resources. The purpose of the study was to assess how system dynamics modeling strengthens Critical Chain Project Management and improves project performance by supporting better understanding of feedback loops, delay propagation, resource constraints, buffer behavior, and project interdependence. A quantitative, cross-sectional, case-based research design was adopted, focusing on engineering enterprise cases where multiple projects were managed simultaneously under shared-resource conditions. The sample consisted of 220 respondents, including project managers, project engineers, planning engineers, operations and resource managers, technical supervisors, consultants, and project control professionals with direct experience in scheduling, resource allocation, CCPM practices, and engineering project execution. The key variables included system dynamics modeling, resource constraint management, buffer management, feedback-loop analysis, CCPM implementation, critical chain buffer consumption control, and project performance. The analysis plan used descriptive statistics, reliability testing, Pearson correlation, multiple regression, and hypothesis testing through SPSS, with Likert-scale data supported by conceptual system dynamics visualization. The headline findings showed high agreement across all major variables: CCPM implementation recorded the highest mean score of 4.20, followed by system dynamics modeling at 4.18, feedback-loop analysis at 4.15, project performance at 4.13, resource constraint management at 4.11, and buffer management at 4.07. Reliability was strong, with Cronbach’s alpha values ranging from 0.83 to 0.88 and an overall alpha of 0.91. Correlation results showed that CCPM implementation had the strongest association with project performance, r = 0.71, p < 0.001. Regression analysis explained 64% of the variance in project performance, R² = 0.64, adjusted R² = 0.63, with CCPM implementation as the strongest predictor, β = 0.31, p < 0.001, followed by system dynamics modeling, β = 0.27, p < 0.001. The findings imply that engineering organizations should integrate dynamic feedback analysis, constraint-based planning, resource prioritization, and buffer monitoring to improve schedule reliability, project control, resource utilization, and delivery performance in complex multi-project environments.
Copyright (c) 2025 Chapal Barua (Author)
Secure Distributed Data Processing Using Privacy-Preserving Artificial Intelligence and Zero Trust Architecture for Enterprise Risk Identification and Performance Evaluation
This study examined the problem of increasing privacy, security, and governance risks in enterprise distributed data processing environments where sensitive operational, customer, compliance, and security data are processed across cloud platforms, enterprise applications, remote systems, and third-party infrastructures. The purpose of the study was to assess how privacy-preserving artificial intelligence and Zero Trust Architecture strengthen secure distributed data processing for enterprise risk identification and performance evaluation. A quantitative, cross-sectional, case-based research design was adopted, focusing on cloud and enterprise cases involving distributed data systems, cybersecurity tools, AI-supported analytics, compliance systems, and digital governance practices. The sample consisted of 214 valid respondents selected from IT and system professionals, cybersecurity professionals, data and analytics professionals, compliance and risk officers, managers, and digital transformation officers, with a usable response rate of 85.6%. The key variables included privacy-preserving artificial intelligence, Zero Trust Architecture, secure distributed data processing, enterprise risk identification, and enterprise performance evaluation. The analysis plan used descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, regression analysis, readiness index assessment, and a secure distributed data processing trustworthiness matrix. The findings showed high mean scores for all major constructs, including Zero Trust Architecture, M = 4.14, secure distributed data processing, M = 4.11, privacy-preserving AI, M = 4.08, enterprise risk identification, M = 4.06, and enterprise performance evaluation, M = 4.02. Reliability was strong, with Cronbach’s alpha values ranging from 0.854 to 0.891 and an overall scale reliability of 0.914. Correlation results showed that Zero Trust Architecture had a strong relationship with secure distributed data processing, r = 0.721, p < 0.001, while secure distributed data processing was strongly related to enterprise risk identification, r = 0.697, p < 0.001. Regression results confirmed that Zero Trust Architecture significantly predicted secure distributed data processing, β = 0.442, p < 0.001, and secure distributed data processing significantly predicted enterprise risk identification, β = 0.514, p < 0.001. The combined model explained 58.4% of enterprise security performance variance. The study implies that enterprises should integrate privacy-aware AI, Zero Trust controls, access governance, anomaly detection, and auditability into distributed data systems to improve risk visibility, compliance, resilience, and performance evaluation.
Privacy-preserving artificial intelligence
Copyright (c) 2024 Md. Arifur Rahman, B. M. Taslimul Haque (Author)