American Journal of Data Science and Analytics
type: channel
description: 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.
publisher: American Scholarly Publishing Group Inc
language: en
publication name: American Journal of Data Science and Analytics
items: Deploying the Model as a Surveillance and Decision-Support Tool for U.S. Food Safety
items: A Decade of Architectural Evolution: A Comprehensive Survey of Deep Learning Model Implementations
items: AI-Driven Predictive Modeling for Customer Acquisition in Digital Payment Platforms
items: Enhancing Stability and Reliability of Smart Grids with Solar Energy Integration
items: Machine Learning–Driven Supply Chain Workflow Optimization and Process Analytics for Operational Efficiency and Resilience in U.S. Organizations
items: 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
items: Quantitative Risk Assessment and Safety Performance Benchmarking for Structural Integrity Management in Retail Supply Chain Infrastructure
items: Multi-Agency Coordination in Sustained Healthcare Supply Shortages: Lessons from Covid-19, Drug Shortages, and Critical Medical Supply Disruptions
items: DSCSA Implementation and Unit-Level Serialization Challenges at the U.S. Dispenser Layer: Evidence from Hospital and Community Pharmacy Operations
items: Impact of Phishing Simulation Campaigns and Security Awareness Training on Human Risk Mitigation in Financial Institutions: A Quantitative Study
items: https://ajdsa-journal.org/index.php/ajdsa/article/view/49
items: Multi-Agent Reinforcement Learning for Dynamic Resource Scheduling in Engineering Project Networks
items: Machine Learning Models for Bank Reconciliation Anomaly Detection in High-Volume Financial Transactions
items: Perceived Privacy Risks of Passive WiFi-Based Human Sensing Technologies: A Quantitative Study
items: Commissioning Failures in Integrated Life Safety Systems (FA + HVAC Shutdown + Elevators): A Multi-Site Case Study Based on Field Testing and Authority Inspections
items: A Quantitative Assessment of Disaster Recovery and Business Continuity Frameworks in Capital Market IT Infrastructure
items: Engineering Smart Manufacturing Systems to Address Labor Shortages in U.S. Industry: A Scalable Framework for Workforce Resilience and Productivity
items: A Meta-Analysis of Agile and Lean Project Management Methodologies in Large-Scale Engineering and IT Integration Projects
items: A Quantitative Assessment of Automated Dashboard Ecosystems Using ETL Pipelines and Real-Time Data Streaming for Business Performance Monitoring in SMEs
items: Artificial Intelligence and Digital Twins in Smart Real Estate Asset Management: A Framework for Sustainable Urban Development
items: AI-Driven Building Automation Systems for Energy Optimization and Predictive Maintenance: A Quantitative Case Study of Smart Building Performance Enhancement
items: AI-Native SDN, Zero Trust, and NGFW Architectures for Autonomous Threat Intelligence in Regulated U.S. Digital Systems
items: Formalizing Adaptive Sampling Strategies in Database Management System Performance Modeling Using Transfer Learning
items: AI-Driven Anomaly Detection On SCADA-Bearing Fiber Networks: A Machine Learning Framework for Real-Time Intrusion Detection in Electric Utility Environments
items: A Quantitative Assessment of Generative AI Applications in Public-Sector Financial Reporting and Audit Preparation
items: A Systematic Review of AI-Driven Financial Information Systems for Budget Optimization in U.S. Public Sector Institutions (2018–2026)
items: AI-Driven Risk Analytics Models for Early Detection of Financial Noncompliance in Multi-Branch Banking Systems
items: A Systematic Review of Artificial Intelligence-Based Customer Analytics in Personalized Digital Marketing (2019–2026)
items: System Dynamics Modeling of Critical Chain Project Management in Multi-Project Engineering Environments
items: Secure Distributed Data Processing Using Privacy-Preserving Artificial Intelligence and Zero Trust Architecture for Enterprise Risk Identification and Performance Evaluation

Deploying the Model as a Surveillance and Decision-Support Tool for U.S. Food Safety
type: item
description: 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 …
subject: Articles
  topic: section
subject: Model Deployment
  topic: keywords
subject: Food Safety Surveillance
  topic: keywords
subject: Decision-Support System
  topic: keywords
subject: Data Integration Quality
  topic: keywords
subject: Predictive Surveillance Readiness
  topic: keywords
creator: Bushra Mumtaz
rights: Copyright (c) 2025 Bushra Mumtaz (Author)
date: 2025-03-08
publication date: 2025-03-08
starting page: 01
ending page: 41
doi: 10.63125/gp709767

A Decade of Architectural Evolution: A Comprehensive Survey of Deep Learning Model Implementations
type: item
description: 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.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Transformer Architecture
  topic: keywords
subject: Mixture of Experts
  topic: keywords
subject: Test-Time Compute
  topic: keywords
subject: Systems Co-design
  topic: keywords
subject: Attention Taxonomy
  topic: keywords
creator: Md Ahnaf Tajwar Kamal
creator: Nawfat Kamal Munifa
creator: Fahmida Yasmin
rights: Copyright (c) 2025 Md Ahnaf Tajwar Kamal, Nawfat Kamal Munifa, Fahmida Yasmin (Author)
date: 2025-10-05
publication date: 2025-10-05
starting page: 32
ending page: 42
doi: 10.63125/y7e5vr53

AI-Driven Predictive Modeling for Customer Acquisition in Digital Payment Platforms
type: item
description: 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 …
subject: Articles
  topic: section
subject: Predictive Modeling
  topic: keywords
subject: Digital Payment Platforms
  topic: keywords
subject: Customer Acquisition Rate
  topic: keywords
subject: Machine Learning Based Transaction Analysis
  topic: keywords
subject: Customer Targeting Efficiency
  topic: keywords
creator: Noor Alam Siddiqui
rights: Copyright (c) 2026 Noor Alam Siddiqui (Author)
date: 2026-06-06
publication date: 2026-06-06
starting page: 113
ending page: 155
doi: 10.63125/fvtbwz35

Enhancing Stability and Reliability of Smart Grids with Solar Energy Integration
type: item
description: 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.
subject: Articles
  topic: section
subject: Smart Grid
  topic: keywords
subject: Solar Photovoltaic (PV)
  topic: keywords
subject: Renewable Energy Integration
  topic: keywords
subject: Power System Stability
  topic: keywords
subject: Power System Reliability
  topic: keywords
subject: Energy Storage Systems
  topic: keywords
creator: Md Jakaria Talukder
creator: Md. Shahinur Islam
rights: Copyright (c) 2026 Md Jakaria Talukder, Md. Shahinur Islam (Author)
date: 2026-06-05
publication date: 2026-06-05
starting page: 92
ending page: 112
doi: 10.63125/xfqxbp80

Machine Learning–Driven Supply Chain Workflow Optimization and Process Analytics for Operational Efficiency and Resilience in U.S. Organizations
type: item
description: 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 i…
subject: Articles
  topic: section
subject: Machine learning-driven supply chain workflow optimization
  topic: keywords
subject: Process analytics
  topic: keywords
subject: Operational efficiency
  topic: keywords
subject: Operational resilience
  topic: keywords
subject: Workflow bottlenecks
  topic: keywords
subject: U.S. organizations
  topic: keywords
creator: Sumyta Haque
rights: Copyright (c) 2026 Sumyta Haque (Author)
date: 2026-06-04
publication date: 2026-06-04
starting page: 49
ending page: 91
doi: 10.63125/kcvqee28

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
type: item
description: 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), pr…
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Hydrogen Infrastructure
  topic: keywords
subject: SCADA Integration
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
creator: Adar Chowdhury
rights: Copyright (c) 2026 Adar Chowdhury (Author)
date: 2026-06-02
publication date: 2026-06-02
starting page: 01
ending page: 48
doi: 10.63125/hy31x271

Quantitative Risk Assessment and Safety Performance Benchmarking for Structural Integrity Management in Retail Supply Chain Infrastructure
type: item
description: 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). B…
subject: Articles
  topic: section
subject: Structural Integrity Management
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Safety Performance
  topic: keywords
subject: Infrastructure Reliability
  topic: keywords
subject: Supply Chain
  topic: keywords
creator: Mukut Kanti Barua
creator: Rony Saha
rights: Copyright (c) 2022 Mukut Kanti Barua, Rony Saha (Author)
date: 2022-12-04
publication date: 2022-12-04
starting page: 42
ending page: 88
doi: 10.63125/n88vth90

Multi-Agency Coordination in Sustained Healthcare Supply Shortages: Lessons from Covid-19, Drug Shortages, and Critical Medical Supply Disruptions
type: item
description: 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 analy…
subject: Articles
  topic: section
subject: Multi-Agency Coordination
  topic: keywords
subject: Healthcare Supply Shortages
  topic: keywords
subject: Inventory Visibility
  topic: keywords
subject: Emergency Preparedness
  topic: keywords
subject: Supply Chain Resilience
  topic: keywords
creator: Rony Saha
creator: Md Ashrafuzzaman
rights: Copyright (c) 2026 Rony Saha, Md Ashrafuzzaman (Author)
date: 2026-01-08
publication date: 2026-01-08
starting page: 181
ending page: 221
doi: 10.63125/5v6t8515

DSCSA Implementation and Unit-Level Serialization Challenges at the U.S. Dispenser Layer: Evidence from Hospital and Community Pharmacy Operations
type: item
description: 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 performa…
subject: Articles
  topic: section
subject: DSCSA
  topic: keywords
subject: Pharmaceutical Serialization
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Pharmacy Compliance
  topic: keywords
subject: Traceability
  topic: keywords
creator: Rony Saha
rights: Copyright (c) 2026 Rony Saha (Author)
date: 2026-06-08
publication date: 2026-06-08
starting page: 163
ending page: 209
doi: 10.63125/392zt207

Impact of Phishing Simulation Campaigns and Security Awareness Training on Human Risk Mitigation in Financial Institutions: A Quantitative Study
type: item
description: 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 resea…
subject: Articles
  topic: section
subject: Phishing
  topic: keywords
subject: Security Training
  topic: keywords
subject: Human Risk
  topic: keywords
subject: Cybersecurity Behavior
  topic: keywords
subject: Financial Institutions
  topic: keywords
creator: Md. Shakhawat Hossen
creator: Md. Fardous
rights: Copyright (c) 2024 Md. Shakhawat Hossen, Md. Fardous (Author)
date: 2024-10-03
publication date: 2024-10-03
starting page: 48
ending page: 85
doi: 10.63125/4j8h1484

https://ajdsa-journal.org/index.php/ajdsa/article/view/49
type: item
title: Integrated Infectious Disease Preparedness: Multilingual Health Communication Strategies for Public Health Risk Mitigation, Disease Intervention and Prevention for Culturally Diverse Population in United States
description: 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 signi…
subject: Articles
  topic: section
subject: Multilingual Communication
  topic: keywords
subject: Infectious Disease Preparedness
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: Disease Prevention
  topic: keywords
subject: Health Equity
  topic: keywords
creator: Shima Ali Sadia
creator: Musomi Khandaker
creator: Sonya Ghosh
rights: Copyright (c) 2024 Shima Ali Sadia, Musomi Khandaker, Sonya Ghosh (Author)
date: 2024-10-02
publication date: 2024-10-02
starting page: 01
ending page: 47
doi: 10.63125/ehk4mt02

Multi-Agent Reinforcement Learning for Dynamic Resource Scheduling in Engineering Project Networks
type: item
description: 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.…
subject: Articles
  topic: section
subject: Multi-Agent Reinforcement Learning
  topic: keywords
subject: Dynamic Resource Scheduling
  topic: keywords
subject: Engineering Project Networks
  topic: keywords
subject: Agent Coordination
  topic: keywords
subject: Resource Optimization
  topic: keywords
creator: Chapal Barua
rights: Copyright (c) 2024 Chapal Barua (Author)
date: 2024-12-08
publication date: 2024-12-08
starting page: 355
ending page: 389
doi: 10.63125/qrs97v10

Machine Learning Models for Bank Reconciliation Anomaly Detection in High-Volume Financial Transactions
type: item
description: 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…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Bank Reconciliation
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Transaction Data Quality
  topic: keywords
subject: Financial Control
  topic: keywords
creator: Mst Shurovi Akter
rights: Copyright (c) 2024 Mst Shurovi Akter (Author)
date: 2024-12-08
publication date: 2024-12-08
starting page: 314
ending page: 354
doi: 10.63125/mmwb6467

Perceived Privacy Risks of Passive WiFi-Based Human Sensing Technologies: A Quantitative Study
type: item
description: 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 in…
subject: Articles
  topic: section
subject: Passive WiFi Sensing
  topic: keywords
subject: Channel State Information
  topic: keywords
subject: Privacy Risk
  topic: keywords
subject: Surveillance
  topic: keywords
subject: Device-Free Sensing
  topic: keywords
subject: Data Governance
  topic: keywords
creator: Md Tarek Hasan
creator: Md Zahin Hossain George
rights: Copyright (c) 2026 Md Tarek Hasan, Md Zahin Hossain George (Author)
date: 2026-01-06
publication date: 2026-01-06
starting page: 155
ending page: 180
doi: 10.63125/dbx5br91

Commissioning Failures in Integrated Life Safety Systems (FA + HVAC Shutdown + Elevators): A Multi-Site Case Study Based on Field Testing and Authority Inspections
type: item
description: 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 influence…
subject: Articles
  topic: section
subject: Integrated Systems
  topic: keywords
subject: Commissioning Failures
  topic: keywords
subject: Life Safety
  topic: keywords
subject: System Complexity
  topic: keywords
subject: Fire Alarm
  topic: keywords
creator: Amir Razaq
rights: Copyright (c) 2024 Amir Razaq (Author)
date: 2024-12-08
publication date: 2024-12-08
starting page: 280
ending page: 313
doi: 10.63125/nst5ee49

A Quantitative Assessment of Disaster Recovery and Business Continuity Frameworks in Capital Market IT Infrastructure
type: item
description: 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…
subject: Articles
  topic: section
subject: Disaster Recovery
  topic: keywords
subject: Business Continuity
  topic: keywords
subject: IT Infrastructure Resilience
  topic: keywords
subject: Capital Market Systems
  topic: keywords
subject: Recovery Maturity Index
  topic: keywords
creator: Binayan Dey
rights: Copyright (c) 2024 Binayan Dey (Author)
date: 2024-12-08
publication date: 2024-12-08
starting page: 241
ending page: 279
doi: 10.63125/vw44nd82

Engineering Smart Manufacturing Systems to Address Labor Shortages in U.S. Industry: A Scalable Framework for Workforce Resilience and Productivity
type: item
description: 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 varian…
subject: Articles
  topic: section
subject: Smart Manufacturing Systems
  topic: keywords
subject: Workforce Resilience
  topic: keywords
subject: Industrial Productivity
  topic: keywords
subject: Labor Shortage Mitigation
  topic: keywords
subject: Industry 4.0
  topic: keywords
creator: Md Tahmidul Islam
rights: Copyright (c) 2025 Md Tahmidul Islam (Author)
date: 2025-12-05
publication date: 2025-12-05
starting page: 86
ending page: 124
doi: 10.63125/vspkkb40

A Meta-Analysis of Agile and Lean Project Management Methodologies in Large-Scale Engineering and IT Integration Projects
type: item
description: 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 …
subject: Articles
  topic: section
subject: Agile Lean Integration
  topic: keywords
subject: Project Performance
  topic: keywords
subject: Large-Scale Engineering Projects
  topic: keywords
subject: IT Integration
  topic: keywords
subject: Project Complexity Methodology Fit
  topic: keywords
creator: Md Asif Ali Sheak Arju
rights: Copyright (c) 2024 Md Asif Ali Sheak Arju (Author)
date: 2024-12-07
publication date: 2024-12-07
starting page: 202
ending page: 240
doi: 10.63125/6281tn40

A Quantitative Assessment of Automated Dashboard Ecosystems Using ETL Pipelines and Real-Time Data Streaming for Business Performance Monitoring in SMEs
type: item
description: 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 accurac…
subject: Articles
  topic: section
subject: Automated Dashboard Ecosystems
  topic: keywords
subject: ETL Pipelines
  topic: keywords
subject: Real-Time Data Streaming
  topic: keywords
subject: SME Performance Monitoring
  topic: keywords
subject: Data Accuracy
  topic: keywords
creator: Md Aminul Islam
rights: Copyright (c) 2024 Md Aminul Islam (Author)
date: 2024-12-07
publication date: 2024-12-07
starting page: 163
ending page: 201
doi: 10.63125/6q1exw70

Artificial Intelligence and Digital Twins in Smart Real Estate Asset Management: A Framework for Sustainable Urban Development
type: item
description: 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 iden…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Twins
  topic: keywords
subject: Smart Real Estate
  topic: keywords
subject: Asset Management
  topic: keywords
subject: Sustainable Urban Development
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Building Information Modeling
  topic: keywords
subject: Sustainability
  topic: keywords
creator: MKA Shahinoor Rahman Jahid
rights: Copyright (c) 2026 MKA Shahinoor Rahman Jahid (Author)
date: 2026-05-06
publication date: 2026-05-06
starting page: 125
ending page: 162
doi: 10.63125/3gq6b691

AI-Driven Building Automation Systems for Energy Optimization and Predictive Maintenance: A Quantitative Case Study of Smart Building Performance Enhancement
type: item
description: 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 broade…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Building Automation Systems
  topic: keywords
subject: Smart Buildings
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: HVAC Optimization
  topic: keywords
creator: Ammar Bajwa
rights: Copyright (c) 2026 Ammar Bajwa (Author)
date: 2026-01-06
publication date: 2026-01-06
starting page: 130
ending page: 154
doi: 10.63125/y92z0052

AI-Native SDN, Zero Trust, and NGFW Architectures for Autonomous Threat Intelligence in Regulated U.S. Digital Systems
type: item
description: 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-nati…
subject: Articles
  topic: section
subject: AI-native SDN
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Next-generation Firewall
  topic: keywords
subject: Autonomous Threat Intelligence
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
creator: Md. Arifur Rahman
creator: B. M. Taslimul Haque
rights: Copyright (c) 2026 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
date: 2026-05-05
publication date: 2026-05-05
starting page: 115
ending page: 159
doi: 10.63125/7xgbqm21

Formalizing Adaptive Sampling Strategies in Database Management System Performance Modeling Using Transfer Learning
type: item
description: 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 B…
subject: Articles
  topic: section
subject: Transfer Learning
  topic: keywords
subject: DBMS Tuning
  topic: keywords
subject: Adaptive Sampling
  topic: keywords
subject: Bhattacharyya Distance
  topic: keywords
subject: Parameter Classification
  topic: keywords
subject: Performance Modelling
  topic: keywords
subject: Active Learning
  topic: keywords
subject: Configuration Optimisation
  topic: keywords
creator: Mst Shamima Akter
creator: Md Atiqur Rahman Khan
rights: Copyright (c) 2026 Mst Shamima Akter, Md Atiqur Rahman Khan (Author)
date: 2026-05-04
publication date: 2026-05-04
starting page: 93
ending page: 114
doi: 10.63125/kv173d65

AI-Driven Anomaly Detection On SCADA-Bearing Fiber Networks: A Machine Learning Framework for Real-Time Intrusion Detection in Electric Utility Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: SCADA
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: Fiber Networks
  topic: keywords
subject: Anomaly Detection
  topic: keywords
creator: Abu Naser Md Golam Mosharraf
rights: Copyright (c) 2024 Abu Naser Md Golam Mosharraf (Author)
date: 2024-12-06
publication date: 2024-12-06
starting page: 125
ending page: 162
doi: 10.63125/z5wx8t42

A Quantitative Assessment of Generative AI Applications in Public-Sector Financial Reporting and Audit Preparation
type: item
description: 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)…
subject: Articles
  topic: section
subject: Generative Artificial Intelligence
  topic: keywords
subject: Public-Sector Accounting
  topic: keywords
subject: Financial Reporting
  topic: keywords
subject: Audit Preparation
  topic: keywords
subject: Digital Governance
  topic: keywords
creator: S M Arif Al Sany
rights: Copyright (c) 2026 S M Arif Al Sany (Author)
date: 2026-05-03
publication date: 2026-05-03
starting page: 42
ending page: 92
doi: 10.63125/k7epd298

A Systematic Review of AI-Driven Financial Information Systems for Budget Optimization in U.S. Public Sector Institutions (2018–2026)
type: item
description: 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 iden…
subject: Articles
  topic: section
subject: AI-driven Financial Information Systems
  topic: keywords
subject: Budget Optimization
  topic: keywords
subject: Public Sector Budgeting
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Fiscal Accountability
  topic: keywords
creator: Samia Hossain Swarnali
rights: Copyright (c) 2026 Samia Hossain Swarnali (Author)
date: 2026-04-04
publication date: 2026-04-04
starting page: 124
ending page: 166
doi: 10.63125/jgx19749

AI-Driven Risk Analytics Models for Early Detection of Financial Noncompliance in Multi-Branch Banking Systems
type: item
description: 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-awa…
subject: Articles
  topic: section
subject: AI-driven risk analytics
  topic: keywords
subject: Financial noncompliance
  topic: keywords
subject: Multi-branch banking
  topic: keywords
subject: Explainable compliance monitoring
  topic: keywords
subject: Branch-aware normalization
  topic: keywords
creator: Md Mostafizur Rahman
creator: Md Jamil Ahmmed
rights: Copyright (c) 2026 Md Mostafizur Rahman, Md Jamil Ahmmed (Author)
date: 2026-04-04
publication date: 2026-04-04
starting page: 81
ending page: 123
doi: 10.63125/9ra9fp12

A Systematic Review of Artificial Intelligence-Based Customer Analytics in Personalized Digital Marketing (2019–2026)
type: item
description: 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,…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Customer Analytics
  topic: keywords
subject: Personalized Marketing
  topic: keywords
subject: Digital Commerce
  topic: keywords
subject: Recommendation Systems
  topic: keywords
creator: H M Mahir Uddin
rights: Copyright (c) 2026 H M Mahir Uddin (Author)
date: 2026-03-14
publication date: 2026-03-14
starting page: 295
ending page: 339
doi: 10.63125/x3e0dx27

System Dynamics Modeling of Critical Chain Project Management in Multi-Project Engineering Environments
type: item
description: 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…
subject: Articles
  topic: section
subject: System Dynamics Modeling
  topic: keywords
subject: Critical Chain Project Management
  topic: keywords
subject: Multi-Project Engineering
  topic: keywords
subject: Resource Constraint Management
  topic: keywords
subject: Project Performance
  topic: keywords
creator: Chapal Barua
rights: Copyright (c) 2025 Chapal Barua (Author)
date: 2025-12-04
publication date: 2025-12-04
starting page: 43
ending page: 85
doi: 10.63125/bwn1zn18

Secure Distributed Data Processing Using Privacy-Preserving Artificial Intelligence and Zero Trust Architecture for Enterprise Risk Identification and Performance Evaluation
type: item
description: 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 proce…
subject: Articles
  topic: section
subject: Privacy-preserving artificial intelligence
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Secure distributed data processing
  topic: keywords
subject: Enterprise risk identification
  topic: keywords
subject: Performance evaluation
  topic: keywords
creator: Md. Arifur Rahman
creator: B. M. Taslimul Haque
rights: Copyright (c) 2024 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
date: 2024-12-05
publication date: 2024-12-05
starting page: 86
ending page: 124
doi: 10.63125/4vnhya53