American Journal of Advanced Technology and Engineering Solutions
The American Journal of Advanced Technology and Engineering Solutions (AJATES) Online ISSN (3067-0470) is a leading international, peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in technology and engineering. AJATES serves as a platform for researchers, academicians, industry professionals, and innovators to present cutting-edge solutions, novel methodologies, and interdisciplinary studies that address contemporary challenges in engineering and technology. Published monthly, AJATES focuses on fostering innovation and collaboration across diverse disciplines, including mechanical engineering, electrical engineering, computer science, materials science, robotics, and sustainable technologies. By bridging the gap between theory and practice, the journal contributes to the global discourse on technological advancements and their practical applications.
Development Of an ARC-Flash Hazard Mitigation Framework for Brownfield Industrial Power Distribution Retrofits in U.S. Manufacturing
This study examined the development of an arc-flash hazard mitigation framework for brownfield industrial power distribution retrofits in U.S. manufacturing facilities. The problem addressed in the study is that many brownfield manufacturing environments operate with aging switchgear, outdated circuit protection, undocumented modifications, incomplete electrical drawings, limited shutdown windows, and inconsistent safety practices, which increase uncertainty in arc-flash assessment and expose workers to high incident energy, delayed fault clearing, poor hazard labeling, and unsafe energized work conditions. The purpose of the study was to assess how technical, organizational, planning, and maintenance-related factors influence arc-flash hazard mitigation effectiveness and electrical safety performance. A quantitative, cross-sectional, case-based research design was adopted, using structured questionnaire data collected from 220 valid respondents out of 260 distributed questionnaires, representing an 84.6% response rate. The sample included electrical engineers, maintenance engineers, safety managers, facility managers, plant or project engineers, and technical supervisors involved in enterprise-scale brownfield manufacturing retrofit and safety decisions. The key variables were electrical infrastructure condition, protective device coordination, safety compliance practices, retrofit planning quality, maintenance and inspection practices, arc-flash hazard mitigation effectiveness, and electrical safety performance. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression modeling. The findings showed high agreement across all major variables, with safety compliance practices recording the highest mean score of 4.12, followed by arc-flash mitigation effectiveness at 4.10 and protective device coordination at 4.08. The regression model was significant, F(5,214) = 46.82, p < 0.001, and explained 52.2% of the variance in mitigation effectiveness. Safety compliance practices were the strongest predictor, β = 0.29, followed by protective device coordination, β = 0.24, maintenance and inspection practices, β = 0.21, retrofit planning quality, β = 0.18, and electrical infrastructure condition, β = 0.15. Arc-flash mitigation effectiveness also significantly improved electrical safety performance, β = 0.34, p < 0.001. The study implies that safer brownfield retrofits require integrated engineering controls, compliance discipline, maintenance verification, accurate documentation, and structured retrofit planning.
Copyright (c) 2025 Mohammad Shoriful Hossan Shohel (Author)
Machine Learning–Based Assessment of Environmental and Public Health Risks in Sewerage Sludge Management Systems
This quantitative study examined machine learning–based assessment of environmental and public health risks in sewerage sludge management systems using sludge quality measurements, contaminant concentration data, treatment performance indicators, and public health risk variables. The study adopted a quantitative predictive design and analyzed a final dataset of 250 observations collected from selected sludge management facilities after 18 incomplete records and 7 duplicate observations were removed during data screening. Descriptive analysis showed substantial variation across the measured variables, with mean heavy metal concentration recorded at 178.42 mg/kg, mean organic pollutant concentration at 91.35 mg/kg, mean moisture content at 68.52%, and mean treatment efficiency at 83.67%. The average environmental risk score was 61.24, while the mean public health risk index was 58.13, indicating moderate overall risk across the sampled facilities. Comparative analysis across sludge treatment categories showed that primary sludge produced the highest mean environmental risk score at 72.45, whereas treated biosolids produced the lowest mean score at 47.63. Regression analysis demonstrated that treatment efficiency, heavy metal concentration, pathogen indicator score, and organic pollutant concentration were significant predictors of environmental and public health risk outcomes. The model explained 74.2% of the variance in risk scores, confirming strong predictive power. Machine learning analysis indicated that the Random Forest model achieved the highest predictive performance, with 93.8% testing accuracy, 92.6% precision, 94.2% recall, an F1-score of 93.4%, and an AUC of 0.961. Artificial Neural Network and Support Vector Machine models also demonstrated strong performance, with testing accuracies of 91.7% and 89.9%, respectively. Feature importance analysis identified treatment efficiency as the strongest predictor, contributing 28.4% to model performance, followed by heavy metal concentration at 24.7% and pathogen indicator score at 18.3%. Overall, the findings confirmed that machine learning models provided reliable quantitative tools for classifying environmental and public health risks in sewerage sludge management systems.
Copyright (c) 2025 Mohammad Badrul Alam, Md. Mominul Haque (Author)
A Machine Learning Approach to Intelligent Streetlight Control for Sustainable Energy Management in Smart Cities
This study examines how machine learning based intelligent streetlight control can improve sustainable energy management in smart cities by addressing the persistent problem of traditional fixed time streetlighting, which often consumes excessive electricity, increases municipal operating costs, and lacks real time responsiveness to traffic, pedestrian movement, ambient light, weather, and fault conditions. The purpose of the study is to evaluate the role of machine learning, IoT sensors, adaptive dimming, predictive brightness control, and remote monitoring in transforming conventional streetlights into intelligent urban energy assets. A quantitative, cross sectional, case-based research design was adopted using secondary evidence from cloud enabled, edge supported, and enterprise level smart city streetlighting cases reported in the literature. The sample consisted of selected smart lighting cases involving IoT connected streetlights, cloud management platforms, sensor-based control systems, and machine learning enabled prediction models. The key variables included machine learning prediction capability, IoT and sensor integration, adaptive dimming, predictive control, energy saving percentage, maintenance improvement, operational reliability, and sustainability outcomes. The analysis plan involved thematic coding, cross case comparison, Likert based evidence scoring, and numeric synthesis using the Energy Saving Percentage formula to compare traditional streetlighting with intelligent ML based systems. The findings indicate strong quantitative support for ML based intelligent lighting, with an overall support score of 4.42 out of 5.00, 86% of reviewed studies supporting ML or AI based streetlight control, IoT and sensor integration scoring 4.55, machine learning prediction and optimization scoring 4.48, and sustainability outcomes scoring 4.31. Energy saving patterns ranged from 25% to 60%, with an estimated average saving of 38.7%. Comparative results showed that traditional fixed time systems scored only 2.10, sensor based adaptive systems scored 3.95, and ML based predictive systems scored 4.62. The study implies that smart cities can reduce energy waste, improve lighting reliability, lower operational costs, support predictive maintenance, and strengthen sustainability goals by integrating machine learning with IoT based streetlight infrastructure.
Copyright (c) 2026 Syed Nurul Islam (Author)
Adaptive Cybersecurity Threat Intelligence Using Explainable Artificial Intelligence for Resilient Protection of U.S. Critical Information Systems
This study examined the relationship among adaptive cybersecurity threat intelligence, explainable artificial intelligence, analyst trust, detection accuracy, response efficiency, and cyber resilience within U.S. critical information systems. The increasing sophistication of cyber threats targeting healthcare, finance, telecommunications, transportation, energy, and governmental infrastructures created significant demand for intelligent cybersecurity systems capable of improving operational resilience, transparency, and response coordination. The study used a quantitative, cross-sectional, correlational research design grounded in adaptive cybersecurity theory, cyber resilience theory, and explainable artificial intelligence theory. Data were collected from 128 cybersecurity professionals working across critical infrastructure sectors 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 adaptive cybersecurity threat intelligence, explainable AI transparency, analyst trust, detection accuracy, response efficiency, and cyber resilience. Adaptive cybersecurity threat intelligence demonstrated a high mean score of 4.18, while cyber resilience reported the highest overall mean score of 4.22, indicating strong organizational perceptions regarding AI-supported cybersecurity effectiveness. Pearson correlation analysis revealed a strong positive relationship between adaptive threat intelligence and cyber resilience (r = 0.79, p < 0.01), while response efficiency demonstrated the strongest correlation with cyber resilience (r = 0.84, p < 0.01). Multiple linear regression analysis indicated that the independent variables collectively explained 76% of the variance in cyber resilience outcomes (R² = 0.76, p < 0.001). Adaptive threat intelligence emerged as the strongest predictor of cyber resilience (β = 0.432, p < 0.001), followed by response efficiency (β = 0.401, p < 0.001) and explainable AI transparency (β = 0.314, p < 0.001). The findings further demonstrated that organizations with greater levels of explainable AI integration and adaptive cybersecurity implementation experienced stronger operational continuity, improved analyst trust, enhanced threat visibility, and reduced operational disruption during cybersecurity incidents affecting critical information systems.
Copyright (c) 2025 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
Residual Stress Mitigation and Distortion Control in Laser Powder Bed Fusion Components for High-Reliability Engineering Applications
This study examined residual stress mitigation and distortion control in Laser Powder Bed Fusion (LPBF) components for high-reliability engineering applications. The problem addressed was the limited quantitative evidence on how process instability, residual stress, distortion, post-processing, and inspection jointly influence LPBF component reliability. The purpose was to determine whether these control practices significantly predict reliability outcomes in demanding engineering cases such as aerospace, biomedical, automotive, energy, defense, and precision tooling applications. A quantitative, cross-sectional, case-based design was used with 150 valid responses from additive manufacturing engineers, materials engineers, quality assurance professionals, design engineers, reliability/process engineers, and academic specialists. The key variables were process parameter stability, residual stress mitigation, distortion control, post-processing treatment, quality assurance and inspection, and LPBF component reliability. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple regression, and hypothesis testing. The findings showed high agreement across all major constructs, with post-processing treatment recording the highest mean score (M = 4.18), followed by residual stress mitigation (M = 4.11), LPBF component reliability (M = 4.07), distortion control (M = 4.03), quality assurance and inspection (M = 3.96), and process parameter stability (M = 3.89). The research instrument was reliable, with an overall Cronbach’s alpha of 0.892. Correlation results showed significant positive relationships between reliability and post-processing treatment (r = 0.704), residual stress mitigation (r = 0.681), distortion control (r = 0.653), quality assurance (r = 0.625), and process stability (r = 0.603), all at p < 0.01. Regression analysis confirmed that the model explained 59.7% of the variance in LPBF component reliability, R² = 0.597, F (5,144) = 42.67, p < 0.001. Post-processing treatment was the strongest predictor (β = 0.312), followed by residual stress mitigation (β = 0.286) and distortion control (β = 0.241). The study implies that reliable LPBF manufacturing requires integrated process control, stress reduction, distortion management, post-processing, and inspection systems.
Copyright (c) 2025 Md Arman Hossain (Author)
Digital Payment Adoption as a Driver of Revenue Growth in Small Businesses: Evidence from Global Markets
This study addresses the problem that small businesses increasingly adopt digital payment tools, yet decision makers lack evidence on whether deeper adoption using cloud-based and enterprise payment solutions translates into revenue growth. The purpose was to test the impact of Digital Payment Adoption Intensity (DPAI) on revenue growth and to explain when and how this effect occurs. Using a quantitative cross-sectional, case-based design, data were collected from N = 312 SMEs across retail (41.7%), services (38.1%), and mixed trade/logistics (20.2%) cases. Key variables included DPAI (M = 3.68, SD = 0.71), trust/security perception (α = .86), fee burden (α = .81), customer reach (α = .84), operational efficiency (α = .85), and perceived revenue growth (α = .83), with overall scale reliability acceptable across constructs (α = .81 to .88). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, multiple regression with controls (firm size, firm age, sector, location, digital readiness), bootstrapped mediation, and moderation via interaction terms, supported by a placebo specificity test. Headline findings show that DPAI was associated with revenue growth (r = .52, p < .001) and remained significant in the controlled model (β = .38, t = 7.92, p < .001; Adjusted R² = .32). Mechanism tests indicated partial mediation through customer reach (indirect effect = 0.11, 95% CI [0.06, 0.17]) and operational efficiency (indirect effect = 0.08, 95% CI [0.03, 0.14]). Boundary conditions mattered: trust/security strengthened returns to adoption (DPAI×Trust β = .12, p = .004), while fee burden weakened them (DPAI×Fees β = −.10, p = .011). A typology check showed a clear dose–response pattern where high adopters reported higher growth (M = 3.89) than low adopters (M = 3.12). Implications suggest SMEs should prioritize operational integration and trust-building while actively managing transaction fees, and providers and policymakers should support secure, reliable, and affordable payment infrastructures to unlock measurable growth benefits.
Digital Payment Adoption Intensity (DPAI)
Copyright (c) 2022 Taru Binte Amin, Iftekhar Ahmed (Author)
Utilizing Non-Contact GMR Sensors for Real-Time State Estimation of Aging Bulk Electric System Assets: A Strategy for Mitigating Failure Risks in Deteriorating Infrastructure
This study investigates the persistent problem of limited real-time visibility into the condition of aging bulk electric system assets, where conventional inspection-based monitoring often fails to detect deterioration early enough to prevent costly outages and reliability losses. The purpose of the research was to examine whether utilizing non-contact giant magnetoresistance (GMR) sensors can improve real-time state estimation and mitigate failure risks in deteriorating infrastructure through a quantitative, cross-sectional, case-based design. Data were collected from 214 technically qualified respondents representing clouded enterprise-style utility and infrastructure management cases, including transmission utilities, substation operations units, asset management departments, maintenance contractors, and reliability/planning divisions. The study assessed key variables comprising non-contact GMR sensor utilization, real-time state estimation capability, failure signal detectability, failure-risk mitigation, predictive maintenance support, asset reliability and operational continuity, and infrastructure risk prioritization, using a 5-point Likert instrument with high overall reliability (Cronbach’s alpha = 0.87). The analysis plan combined descriptive statistics, reliability testing, Pearson correlation, simple regression, multiple regression, and ANOVA. Findings showed strong agreement across the major constructs, with mean scores of 4.18 for GMR sensor utilization, 4.24 for state estimation capability, 4.20 for failure-risk mitigation, and 4.22 for asset reliability and operational continuity. GMR sensor utilization had a strong positive relationship with real-time state estimation (r = 0.740, p < .001) and significantly predicted it (β = 0.63, t = 11.42, R² = 0.548, p < .001). Real-time state estimation significantly predicted failure-risk mitigation (β = 0.58, t = 9.87, R² = 0.472, p < .001), while the extended regression model explained 61.4% of the variance in failure-risk mitigation (Adjusted R² = 0.614, F = 68.31, p < .001). Critically deteriorating assets recorded the highest overall mean benefit (4.39), confirming that sensor-enabled monitoring becomes more valuable as asset aging severity increases. The study implies that non-contact GMR sensing can serve as a practical reliability-centered maintenance tool for prioritizing monitoring resources, strengthening predictive maintenance, and improving resilience in aging power infrastructure.
Copyright (c) 2023 Md Siam Taluckder, Md. Sultan Mahamud (Author)
Quantitative Simulation-Based Model for Short-Circuit Analysis, Arc-Flash Risk Evaluation, and Protection Coordination in Industrial Electrical Systems
This study addresses the persistent safety and reliability problem in industrial electrical systems where short-circuit faults, arc-flash hazards, and poorly coordinated protective devices can jointly cause equipment damage, worker injury, unnecessary outages, and reduced operational continuity. The purpose of the research was to develop and test an integrated quantitative simulation-based model showing how short-circuit analysis, arc-flash risk evaluation, and protection coordination influence industrial electrical system safety and performance. Using a quantitative, cross-sectional, case-based design, the study combined survey data from 210 valid respondents drawn from industrial enterprise case environments, including electrical engineers, maintenance engineers, safety officers, technicians, and supervisors, with simulation outputs from key electrical locations such as the main LV switchboard, MCCs, and feeder buses. The main independent variables were short-circuiting analysis, arc-flash risk evaluation, and protection coordination, while the dependent variable was industrial electrical system safety and operational performance. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. The findings showed high mean scores for all major constructs, including short-circuit analysis (M = 4.18, SD = 0.61), arc-flash risk evaluation (M = 4.24, SD = 0.57), protection coordination (M = 4.31, SD = 0.54), and industrial electrical safety and performance (M = 4.27, SD = 0.59). Simulation results identified the Main LV Switchboard Bus as the highest fault-current location at 31.6 kA, while arc-flash incident energy at MCC-1 decreased from 9.8 cal/cm² to 5.9 cal/cm² after coordination refinement, a reduction of about 39.8%, and clearing time improved from 0.42 s to 0.24 s. Correlation analysis revealed strong positive relationships with system safety and performance for short-circuit analysis (r = 0.71), arc-flash risk evaluation (r = 0.76), and protection coordination (r = 0.83), all at p < .001. Regression results further showed that the model explained 72.4% of the variance in safety and performance (R² = 0.724, F(3,206) = 180.24, p < .001), with protection coordination emerging as the strongest predictor (β = 0.401), followed by arc-flash risk evaluation (β = 0.287) and short-circuit analysis (β = 0.249). The study implies that industrial organizations can significantly improve electrical safety and system dependability by integrating fault studies, arc-flash assessment, and coordination review into a single protection management framework.
Copyright (c) 2023 Shamsul Arifeen, Md. Shahinur Islam (Author)
Machine Learning and Deep Learning for Alzheimer’s Disease Diagnosis: A Survey of MRI, PET, Multimodal Fusion, Transformers, Graph Neural Networks, and Explainable AI
Alzheimer’s disease is the leading cause of dementia and remains a major challenge in neurological diagnosis because early symptoms often overlap with normal aging and other neurodegenerative disorders. In recent years, machine learning and deep learning methods have been widely applied to neuroimaging and multimodal clinical data to improve automated diagnosis, risk prediction, and disease-stage classification. This survey reviews representative studies on Alzheimer’s disease diagnosis using traditional machine learning, convolutional neural networks, multimodal fusion methods, transformer-based architectures, graph neural networks, and explainable artificial intelligence. The literature shows a clear transition from handcrafted-feature pipelines to end-to-end deep models trained on magnetic resonance imaging, positron emission tomography, and multimodal data. It also suggests that multimodal systems often outperform single-modality systems because they integrate complementary structural and metabolic information, while graph and transformer methods aim to capture more global and relational disease patterns. At the same time, the field still faces major challenges, including dataset dependence, limited external validation, inconsistent evaluation settings, class imbalance, insufficient interpretability assessment, and weak evidence for clinical deployment. This survey organizes the literature by modeling paradigm and data modality, compares representative methods, and outlines future directions for robust, explainable, and clinically useful Alzheimer’s disease diagnosis systems.
Copyright (c) 2026 Imran Ahmad, Tasfia Tarannum, Mohammed Majbah Uddin (Author)
Digital Twin Simulation for Optimizing Emergency Response and Evacuation Protocols in Large-Scale Manufacturing Environments
This study examined how digital twin simulation optimizes emergency response and evacuation protocols in large-scale manufacturing environments where static safety plans often fail to reflect dynamic hazards, blocked routes, worker-density variation, machinery risks, and real-time coordination needs. The problem addressed was the limited adaptability of traditional emergency planning in complex industrial facilities, where emergencies such as fire, chemical leakage, machinery explosion, blocked exits, and power disruption can rapidly affect evacuation safety and response efficiency. The purpose was to quantify the influence of digital twin simulation on five key emergency-management outcomes: emergency response efficiency, evacuation route optimization, bottleneck detection, coordination and decision-making, and preparedness confidence. A quantitative, cross-sectional, case-based design was adopted, using structured 5-point Likert-scale survey data from cloud-enabled and enterprise-oriented manufacturing safety cases involving plant engineers, safety officers, supervisors, emergency response personnel, and operations managers. From 230 distributed questionnaires, 214 valid responses were analyzed, producing a usable response rate of 93.0%. The analysis plan included descriptive statistics, reliability testing, correlation analysis, and regression modeling using SPSS, with Excel used for data preparation. Findings showed high perceived digital twin capability with a mean of 4.18 and strong ratings for emergency response efficiency (M = 4.09), evacuation route optimization (M = 4.14), bottleneck detection (M = 4.11), coordination and decision-making (M = 4.07), and preparedness confidence (M = 4.16). Reliability was strong, with Cronbach’s alpha values ranging from 0.84 to 0.89 and an overall scale alpha of 0.91. Correlation results showed significant positive relationships between digital twin simulation and all outcome variables, ranging from r = .67 to r = .76. Regression results confirmed that digital twin simulation significantly predicted emergency response efficiency (R² = .504), route optimization (R² = .548), bottleneck detection (R² = .476), coordination and decision-making (R² = .449), and preparedness confidence (R² = .578). The headline finding is that digital twin simulation most strongly improved preparedness confidence and evacuation route optimization, implying that manufacturing firms can strengthen worker safety, hazard anticipation, route intelligence, and organizational resilience by integrating digital twins into emergency planning systems.
Copyright (c) 2023 Md Abubakar Siddique Akash, Aditya Dhanekula (Author)
A Systematic Review of AI-Driven Business Intelligence Architectures for Data-Informed Strategic Decision-Making Methods (2019–2026)
This study conducted a quantitative systematic review of AI-driven business intelligence (BI) architectures to evaluate their effectiveness in enabling data-informed strategic decision-making between 2019 and 2026. A total of 142 peer-reviewed studies were systematically identified and analyzed, revealing a 130% increase in publications from 2019 (n = 12) to 2024 (n = 28), indicating rapid growth in this research domain. The dataset showed that 58.4% of studies employed quantitative methods, 29.6% used mixed approaches, and 41.5% relied on large-scale datasets exceeding 10,000 records. Primary findings demonstrated that AI integration improved system efficiency by an average of 31.5%, reduced processing time, and enhanced predictive accuracy by 18.7%, with 74.2% of studies reporting significant accuracy gains. Decision-making outcomes improved substantially, as 71.1% of studies reported enhanced decision quality and a 27.3% reduction in decision latency due to real-time processing capabilities. Regression analysis indicated that AI integration explained 42.5% of the variance in organizational performance, with strong beta coefficients exceeding 0.60 for key variables. Sub-group analysis showed that finance (26.1%) and healthcare (22.5%) sectors achieved the highest predictive accuracy improvements above 22%, while supply chain (30.1%) and retail (28.4%) sectors demonstrated greater operational efficiency gains. Effect sizes ranged from 0.65 to 0.75, confirming moderate to strong impacts across performance indicators. Overall, the findings provided robust quantitative evidence that AI-driven BI architectures significantly enhanced efficiency, analytical accuracy, and strategic decision-making effectiveness across industries and regions.
Copyright (c) 2026 Md Aminul Islam (Author)
A Comparative Study of Machine Learning Models for Predictive Business Analytics in Digital Commerce (2018–2026)
This study examined the comparative performance of machine learning models for predictive business analytics in digital commerce using a quantitative experimental design across datasets spanning from 2018 to 2026. The analysis was based on a large-scale aggregated dataset comprising approximately 5.8 million observations, including structured transactional data and unstructured behavioral and textual data. The study evaluated traditional models such as logistic regression and decision trees, ensemble models including random forest, gradient boosting, and XGBoost, and deep learning models such as convolutional neural networks and long short-term memory networks. Performance was assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the curve. The findings indicated that advanced models significantly outperformed traditional approaches. XGBoost achieved the highest overall accuracy of 92.8% and AUC of 95.1%, while LSTM demonstrated the best performance in unstructured data environments with an accuracy of 93.2% and F1-score of 92.1%. Random forest exhibited strong stability with an accuracy of 89.6% across datasets. In contrast, logistic regression showed lower performance, with accuracy averaging 78.4%. Ensemble models reduced prediction error in demand forecasting to as low as 8.6%, compared to 18.5% for traditional models. In fraud detection, advanced models improved recall from 72.4% to 88.9% after addressing class imbalance. Statistical analysis confirmed significant differences among models, with p-values below 0.05 and large effect sizes exceeding 1.0 in several comparisons. The study also revealed that dataset size and feature engineering significantly influenced model performance, with large datasets achieving accuracy levels above 93% and reduced variance. Overall, the results demonstrated that ensemble and deep learning models provide substantial improvements in predictive accuracy, robustness, and scalability, highlighting their effectiveness for complex and high-dimensional digital commerce applications.
A Comparative Study of Machine Learning Applications in Enterprise Network Fault Detection and Self-Healing Infrastructure (2018–2026)
This study conducted a comprehensive quantitative comparative analysis of machine learning applications in enterprise network fault detection and self-healing infrastructure over the period 2018 to 2026. The research adopted a retrospective cross-sectional design, analyzing 124 empirical studies sourced from major academic databases to evaluate the performance and applicability of different machine learning approaches. The analysis focused on key model categories, including traditional machine learning, deep learning, hybrid and ensemble models, unsupervised techniques, and reinforcement learning, across multiple enterprise network domains such as telecommunications, cloud computing, software-defined networks, and IoT-based systems. The findings revealed that deep learning models achieved the highest overall detection performance, with an average accuracy of 94.2%, followed by hybrid models at 92.6%, while traditional machine learning models recorded comparatively lower accuracy at 88.4%. Unsupervised methods demonstrated moderate effectiveness, particularly in anomaly detection scenarios, whereas reinforcement learning showed distinct advantages in recovery-related metrics, achieving the lowest mean time to repair of 19.6 seconds and the highest system uptime of 98.3%. Domain-level analysis indicated that cloud computing and software-defined networks outperformed other environments, achieving accuracy levels of 94.8% and 93.6%, respectively, while IoT and edge systems lagged behind with an average accuracy of 88.5%. Subgroup analysis further demonstrated that studies using benchmark datasets reported higher accuracy levels of 93.8% compared to 89.6% for real-world datasets, highlighting the influence of data realism on model performance. Additionally, k-fold cross-validation yielded more robust results, with an average accuracy of 92.9%, compared to 89.7% for holdout validation. Inferential statistical analysis confirmed that these differences were statistically significant, with effect sizes ranging from moderate to large across key performance indicators. Overall, the study concluded that advanced machine learning architectures, particularly deep learning and hybrid models, combined with reinforcement learning for recovery optimization, offer the most effective solutions for enterprise network fault detection and self-healing systems. The findings provide both theoretical insights and practical guidance for designing scalable, data-driven, and resilient network management frameworks.
An Empirical Analysis of AI-Enabled Network Observability Platforms for Financial Infrastructure Security
This study conducted a comprehensive quantitative analysis of AI-enabled network observability platforms to evaluate their impact on financial infrastructure security and operational performance. A quasi-experimental research design was employed, incorporating a sample of 120 financial network systems, of which 62 utilized AI-enabled observability platforms and 58 relied on traditional monitoring approaches. The study analyzed key performance indicators including anomaly detection accuracy, mean time to detect, mean time to resolve, false alert rate, throughput stability, and system recovery performance. Descriptive and inferential statistical techniques were applied using SPSS, R, and Python to assess differences between the two system categories and to identify the predictive influence of observability maturity and automation intensity. The findings revealed that AI-enabled observability systems significantly outperformed traditional monitoring environments across all major performance metrics. The mean time to detect incidents was reduced from 12.6 minutes in traditional systems to 4.8 minutes in AI-enabled systems, while mean time to resolve decreased from 34.7 minutes to 18.3 minutes. Detection accuracy improved from 81.2% to 94.5%, and false alert rates declined from 18.9% to 6.8%. Statistical testing confirmed that these differences were significant at p < 0.05, with large effect sizes observed across all variables, indicating substantial operational impact. Regression analysis further demonstrated that observability maturity and automation intensity were strong predictors of detection efficiency and response performance. Sub-group analysis showed that cloud-based and hybrid infrastructures experienced the greatest performance improvements, with detection accuracy exceeding 96% and response time reductions reaching over 39%. High-volume systems also demonstrated enhanced throughput stability of 93.2%, compared to 82.1% in lower-volume environments. Temporal analysis indicated sustained improvements over a 12-month observation period, reflecting system learning and performance stabilization. Overall, the study provided strong empirical evidence that AI-enabled network observability platforms significantly enhanced financial infrastructure security, operational efficiency, and system resilience.
Copyright (c) 2026 Taru Binte Amin (Author)
Securing SCADA Communications Over OPGW And ADSS Fiber In U.S. Bulk Electric Systems: A NERC CIP-Aligned Engineering Framework
This study addresses the growing problem of securing SCADA communications transmitted over Optical Ground Wire and All-Dielectric Self-Supporting fiber in U.S. Bulk Electric Systems, where technically robust fiber links still remain exposed to cyber, physical, and operational vulnerabilities that can affect visibility, command integrity, and system resilience. The purpose of the research was to develop and empirically test a NERC CIP-aligned engineering framework for improving SCADA communication security and resilience by examining the influence of Engineering Security Controls, Communication Path Protection, Access Control Measures, Monitoring and Detection Capability, and NERC CIP Alignment on SCADA Communication Security and Resilience. The study adopted a quantitative, cross-sectional, case-based design and collected data from 220 valid professional responses drawn from cloud-connected and enterprise utility operational cases after screening 250 distributed questionnaires, yielding an 88.0% usable response rate. Respondents represented SCADA and OT engineers, utility communication engineers, cybersecurity professionals, protection and control engineers, substation automation specialists, and compliance personnel. Analysis was conducted using descriptive statistics, Cronbach’s alpha, correlation, and multiple regression in SPSS. The findings showed a strong overall security posture, with SCADA Communication Security and Resilience recording a mean of 4.08, Engineering Security Controls 4.21, Monitoring and Detection Capability 4.18, Communication Path Protection 4.11, Access Control Measures 4.05, and NERC CIP Alignment 3.97. Reliability was strong across all constructs, with Cronbach’s alpha values ranging from 0.81 to 0.89 and an overall instrument reliability of 0.87. Correlation results indicated significant positive relationships between SCADA Communication Security and Resilience and Monitoring and Detection Capability (r = 0.74), Engineering Security Controls (r = 0.71), NERC CIP Alignment (r = 0.68), Communication Path Protection (r = 0.66), and Access Control Measures (r = 0.62), all at p < .001. Regression analysis confirmed that the model was statistically significant, F(5,214) = 52.84, p < .001, explaining 55.2% of the variance (R² = 0.552), with Monitoring and Detection Capability emerging as the strongest predictor (β = 0.29). The study also found that ADSS environments were perceived as more vulnerable than OPGW environments, with mean vulnerability scores of 3.88 and 3.54 respectively, implying the need for medium-specific protective strategies and stronger integration of engineering, monitoring, and compliance functions in critical utility communication security.
Copyright (c) 2026 Abu Naser Md Golam Mosharraf (Author)
The Role of Pharmaceutical Quality Control in Preventing Public Health Risks in United States
This study examined the role of pharmaceutical quality control systems in preventing public health risks in the United States through a comprehensive quantitative analysis of manufacturing and regulatory data. The dataset comprised 150 FDA-registered pharmaceutical manufacturing facilities, 12,500 batch production records, and 2,300 inspection reports collected over a five-year period. The analysis evaluated the relationship between quality control compliance and key public health risk indicators, including product recalls, adverse drug events, and regulatory warning letters. The findings revealed a statistically significant inverse relationship between quality control rigor and risk outcomes, with facilities implementing advanced quality systems demonstrating a 42% reduction in product recall rates and a 37% decrease in FDA warning letters compared to those using conventional methods. Additionally, adverse drug event rates were reduced from 6.2 to 3.8 per 1,000 batches in high-compliance facilities, indicating substantial improvements in patient safety outcomes. Regression analysis further confirmed that each unit increase in compliance score was associated with a 0.28 decrease in recall probability, while high-compliance facilities were 2.8 times less likely to experience critical quality failures (OR = 0.36, 95% CI: 0.28–0.46, p < 0.001). Sub-group analysis indicated that sterile injectable manufacturers achieved the highest reduction in contamination-related recalls (55%), whereas oral solid dosage manufacturers showed a moderate reduction of 29%. Large-scale facilities exhibited lower defect rates (2.3%) compared to smaller facilities (4.9%), and facilities adopting AI-driven quality control systems demonstrated near-zero defect rates (0.8%). Temporal analysis further showed that increased adoption of advanced quality systems from 52% to 82% over five years corresponded with a decline in recall rates from 6.8% to 3.6%. Overall, the study demonstrated that robust pharmaceutical quality control systems significantly reduced public health risks and enhanced manufacturing reliability. These findings underscored the importance of regulatory enforcement, technological integration, and continuous quality monitoring in ensuring patient safety and strengthening pharmaceutical governance.
Copyright (c) 2025 Beatrice Onyinyechi Mkpa (Author)
A Systematic Review of Cloud-Based Machine Learning Deployment Frameworks and Architectural Practices
This study addresses the persistent problem of fragmented and insufficiently consolidated knowledge on cloud-based machine learning deployment frameworks and the architectural practices required for successful production implementation, a gap that continues to widen as organizations move from model experimentation to real-world operationalization. The purpose of the study was to develop a structured understanding of the major deployment frameworks, dominant architectural practices, operational benefits, and recurring risks associated with cloud-based machine learning deployment across real-world cases. To achieve this, the study adopted a cross-sectional, case-based systematic review design with quantitative descriptive support, synthesizing evidence from 60 reviewed studies, including 46 empirical or case-based studies (76.7%), with cases drawn from healthcare (25.0%), manufacturing and industry (23.3%), enterprise and business analytics (20.0%), smart systems and IoT (18.3%), and multi-sector contexts (13.4%). The key variables examined were deployment framework capability, architectural maturity, lifecycle governance, operational trust, scalability, maintainability, interoperability, monitoring readiness, and deployment risks. Data were analyzed through structured screening, eligibility assessment, thematic coding, frequency analysis, percentage distribution, and five-point Likert evidence scoring. The findings show that 48 studies (80.0%) identified cloud deployment frameworks as central to operational success, 51 studies (85.0%) emphasized architecture-related practices as critical to production readiness, and 44 studies (73.3%) linked deployment quality to lifecycle governance, monitoring, and maintainability. Container-orchestration ecosystems emerged as the most prominent framework category, appearing in 42 studies (70.0%) with a mean capability score of 4.45/5.00, while managed cloud ML platforms appeared in 39 studies (65.0%) with a score of 4.33/5.00. Among architectural practices, containerization was reported in 46 studies (76.7%) with a mean of 4.52/5.00, monitoring and observability in 45 studies (75.0%) with 4.49/5.00, and orchestration in 43 studies (71.7%) with 4.46/5.00. Overall deployment effectiveness scored 4.24/5.00, while major challenges remained monitoring and model drift (65.0%), security and privacy concerns (60.0%), and interoperability complexity (58.3%). The study implies that machine learning deployment success in cloud environments depends not only on framework adoption, but also on mature architecture, disciplined lifecycle governance, and context-sensitive operational design that supports repeatability, portability, trust, and long-term sustainability.
Copyright (c) 2023 Mohammad Robel Miah, Md Aminul Islam (Author)
Advanced Financial Data Analytics for Anomaly Detection and Pattern Discovery in Large-Scale Financial Data Pipelines
This quantitative study examined advanced financial data analytics for anomaly detection and pattern discovery within large-scale, distributed financial data pipelines. Using an observational, benchmark-oriented design, the study analyzed 52,480 event-level scored transactions and 9,600 entity-window observations retained after rigorous screening for missing identifiers, reconciliation conflicts, duplicate events, and incomplete pipeline-stage logs. Anomaly detection outcomes were measured using continuous anomaly scores, calibrated alert flags, ranking concentration metrics, and detection latency, while pattern discovery outcomes were evaluated using cluster stability indices, recurrence counts of sequential patterns, and network-structure descriptors. Descriptive results showed that anomaly scores were strongly right-skewed, with the top 5% of events accounting for approximately 47% of total anomaly intensity, and a mean calibrated alert rate of 2.9% across evaluation windows. Mean detection latency was 2.84 seconds (SD = 1.12), reflecting variability in window completion and late-arrival handling under streaming conditions. Pattern discovery analysis revealed uneven behavioral segmentation, with a mean cluster size of 184 entities, a median of 97, and an average cluster stability index of 0.71, indicating moderate-to-high reproducibility across resampled windows. Reliability testing supported aggregation of telemetry- and behavior-derived indicators, as all retained multi-item composite constructs achieved acceptable internal consistency, with Cronbach’s alpha values ranging from 0.77 to 0.88. Robust multivariable regression explained a substantial portion of anomaly score variance (R² = 0.54), demonstrating that transaction deviation intensity, novelty and switching behavior, geographic irregularity, peer-group deviation, and temporal drift indicators were positively associated with anomaly intensity (p < .01), while behavioral baseline coherence was negatively associated (p = .001). Pipeline moderators, including processing latency, throughput, and late-arrival proportion, showed statistically significant associations with anomaly scores. Mixed-effects modeling identified meaningful within-entity clustering (ICC = 0.19). Moderation analysis indicated that drift-related effects were significantly stronger in cross-border contexts and high-risk channels. At the entity-window level, behavioral baseline coherence increased cluster stability, pipeline instability reduced stability, and temporal drift increased pattern recurrence. Collectively, the findings demonstrated that anomaly detection and pattern discovery performance was jointly shaped by data behavior, temporal regimes, and pipeline execution context within large-scale financial analytics systems.
Copyright (c) 2022 Tanjina Binte Sohrab, Sazzadul Islam (Author)
AI-Driven Vulnerability Prioritization for Enterprise Networks: A Quantitative Study Using Attack-Graph Models
This study examined the effectiveness of AI-driven vulnerability prioritization in enterprise networks through the integration of attack-graph models and machine learning techniques. Traditional vulnerability assessment approaches, which rely primarily on static severity scoring systems, often fail to capture the contextual and structural complexity of modern cyber threats. To address this limitation, a quantitative, cross-sectional research design was employed using a dataset of 2,450 vulnerability instances mapped within enterprise network attack graphs. The study incorporated graph-derived features such as node centrality, path frequency, and asset criticality alongside conventional vulnerability attributes to develop predictive prioritization models. The findings demonstrated that AI-driven models significantly outperformed traditional methods across all evaluation metrics. The average classification accuracy of AI-based models reached 0.87 compared to 0.71 for baseline approaches, while precision improved from 0.68 to 0.85 and recall increased from 0.64 to 0.83. The area under the receiver operating characteristic curve (AUC) also showed a substantial improvement, rising from 0.74 in traditional models to 0.91 in AI-enhanced models, indicating superior discrimination capability. Subgroup analysis further revealed that vulnerabilities associated with high-centrality nodes achieved the highest predictive performance, with accuracy values reaching 0.91, while those in low-centrality nodes showed reduced performance at 0.78. Statistical analysis confirmed that these improvements were significant, with p-values below 0.05 and large effect sizes across all key metrics. The results highlighted the importance of incorporating network topology and contextual risk factors into vulnerability prioritization frameworks. By leveraging attack-graph structures and machine learning, the proposed approach provided a more accurate, scalable, and context-aware method for identifying high-risk vulnerabilities. These findings demonstrated that AI-driven prioritization can significantly enhance enterprise cybersecurity decision-making by improving risk prediction and optimizing resource allocation in complex network environments.
Copyright (c) 2023 Istiaq Ahmed, Tanjina Binte Sohrab (Author)
Enhancing Renewable Interconnection Performance through Bio-Inspired Adaptive Control Strategies
This study addresses the growing problem that conventional grid-following solar assets remain dependent on external voltage and frequency references, which limits their ability to support stability, resilience, and reliable renewable interconnection in weak-grid and high-renewable environments. The purpose of the research was to examine whether bio-inspired control algorithms can enable the transformation of solar assets from grid followers to grid formers and thereby improve renewable interconnection performance. Using a quantitative, cross-sectional, case-based design, the study analyzed 150 valid responses drawn from cloud and enterprise-style renewable interconnection cases and technically informed solar integration contexts, from 180 distributed questionnaires, yielding an 83.3% valid response rate. The key variables were Bio-Inspired Control Algorithms as the independent variable, the Grid-Forming Readiness Index as the mediating variable, and Renewable Interconnection Performance as the dependent variable, with additional case comparisons between grid-following and grid-forming operational profiles. Data were analyzed through descriptive statistics, correlation analysis, and regression modeling. The findings showed strong positive evaluations for bio-inspired control algorithms (M = 4.18, SD = 0.61), grid-forming readiness (M = 4.09, SD = 0.66), and renewable interconnection performance (M = 4.14, SD = 0.58). Bio-inspired control significantly predicted grid-forming readiness (β = 0.720, t = 11.84, p < .001), explaining 51.8% of its variance, while the combined model of bio-inspired control and grid-forming readiness explained 61.0% of the variance in renewable interconnection performance (R² = 0.610, F = 114.83, p < .001). Correlation results further confirmed strong positive associations among the variables, including r = 0.720 between bio-inspired control and readiness, and r = 0.760 between readiness and interconnection performance. Grid-forming solar assets also outperformed grid-following assets across voltage support, frequency contribution, fault recovery, and weak-grid resilience, with an overall mean advantage of 0.83 points. The study implies that intelligent, adaptive inverter control is a practical pathway for strengthening solar asset autonomy, modern grid support capability, and renewable interconnection reliability in next-generation power systems.
Copyright (c) 2026 Riadul Islam (Author)
Artificial Intelligence–Driven Enterprise Decision Support Systems: A Framework for Intelligent Business Process Automation in Digital Economies
This study investigates the growing problem that many organizations deploy artificial intelligence, analytics, and workflow automation in fragmented ways, limiting their ability to convert decision intelligence into coordinated business process automation in digital economies. The purpose of the research was to examine how AI-driven enterprise decision support systems improve intelligent business process automation and related operational outcomes. Using a quantitative, cross-sectional, case-based design, the study collected primary data through structured questionnaires from 312 valid respondents drawn from enterprise cases operating in digitally enabled organizational environments with direct exposure to AI-supported decision and automation systems. The major variables included AI-driven decision support capability, AI-enabled analytics, automation intelligence, enterprise automation readiness, and intelligent business process automation, with outcome dimensions covering business process efficiency, decision quality, organizational responsiveness, and productivity. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression in SPSS. The findings showed strong positive perceptions across the core constructs, with mean scores of 4.08 for AI-driven decision support capability, 4.02 for AI-enabled analytics, 3.96 for automation intelligence, 3.89 for enterprise automation readiness, and 4.11 for intelligent business process automation. Decision quality recorded the highest outcome mean at 4.19, followed by business process efficiency at 4.14, productivity at 4.06, and organizational responsiveness at 4.05. Reliability was strong, with Cronbach’s alpha ranging from 0.81 to 0.90. Correlation results indicated significant positive relationships with intelligent business process automation, including AI-driven decision support capability (r = 0.71), AI-enabled analytics (r = 0.67), automation intelligence (r = 0.69), and enterprise automation readiness (r = 0.63). The regression model explained 64% of the variance in intelligent business process automation (R² = 0.64, F = 108.42, p < .001), while AI-driven decision support capability (β = 0.29), automation intelligence (β = 0.26), AI-enabled analytics (β = 0.21), and readiness (β = 0.18) significantly predicted the outcome; the moderation effect was also significant (β = 0.12, p = .004). The study implies that enterprises achieve stronger automation performance when AI capability is aligned with readiness, data quality, and system integration.
Copyright (c) 2026 MD Hasibur Rahman Murad (Author)
ERP-Integrated Financial Analytics Systems for Corporate Financial Reporting, Cash Flow Analysis, and Strategic Investment Planning
This study examined the role of ERP-Integrated Financial Analytics Systems in enhancing corporate financial reporting, cash flow analysis, and strategic investment planning within enterprise environments, addressing the persistent problem that many organizations adopt ERP platforms without fully realizing their analytical value for finance-related decision-making. The purpose of the research was to determine whether ERP-enabled financial analytics significantly improves reporting quality, liquidity analysis, investment planning, and broader financial decision readiness. The study adopted a quantitative, cross-sectional, case-based design focused on cloud and enterprise ERP user cases, using purposive sampling to collect data from 178 valid respondents drawn from finance professionals, accountants, analysts, treasury personnel, controllers, ERP users, and managers working in organizations that had implemented ERP systems with financial analytics capabilities. The key independent variable was ERP-Integrated Financial Analytics Systems, while the dependent variables were Corporate Financial Reporting, Cash Flow Analysis, Strategic Investment Planning, and Financial Decision Readiness. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and linear regression. Reliability results were strong, with Cronbach’s alpha ranging from 0.856 to 0.912 and an overall instrument reliability of 0.903. Descriptive findings showed high respondent agreement across all major constructs, including ERP-Integrated Financial Analytics Systems (M = 4.18, SD = 0.61), Corporate Financial Reporting (M = 4.11, SD = 0.58), Cash Flow Analysis (M = 4.06, SD = 0.64), Strategic Investment Planning (M = 3.98, SD = 0.67), and Financial Decision Readiness (M = 4.14, SD = 0.60). Correlation analysis revealed significant positive relationships between ERP analytics capability and Corporate Financial Reporting (r = 0.720), Cash Flow Analysis (r = 0.690), Strategic Investment Planning (r = 0.640), and Financial Decision Readiness (r = 0.750), all at p < .01. Regression results further confirmed significant predictive effects for Corporate Financial Reporting (β = 0.680, R² = 0.460), Cash Flow Analysis (β = 0.630, R² = 0.410), Strategic Investment Planning (β = 0.580, R² = 0.360), and Financial Decision Readiness (β = 0.700, R² = 0.490), indicating that ERP-integrated analytics most strongly improved decision readiness and reporting quality. The study implies that organizations should treat ERP-integrated financial analytics as a strategic capability for improving reporting accuracy, liquidity visibility, investment evaluation, and evidence-based financial decision-making.
Copyright (c) 2023 Md Tohidul Islam (Author)
Machine Learning–Driven Forecasting Pipelines for Financial Volatility Detection in Integrated Enterprise ERP Environments
This study examined how machine learning driven forecasting pipelines improve financial volatility detection in integrated enterprise ERP environments, addressing the problem that many organizations possess large volumes of financial and operational data but still struggle to identify early signs of instability in a timely and reliable manner. The purpose of the research was to assess whether ERP data integration, machine learning forecasting pipeline capability, real time analytics capability, forecasting pipeline automation, and machine learning model performance significantly strengthen financial volatility detection and related managerial outcomes in enterprise settings. A quantitative, cross sectional, case-based design was adopted using survey data from 214 valid respondents drawn from cloud enabled and enterprise ERP cases across manufacturing, banking and financial services, retail and trade, logistics and supply chain, and technology and service organizations. Data were collected through a five-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression in SPSS. The findings showed high levels of ERP data integration (M = 4.18, SD = 0.61), machine learning forecasting pipeline capability (M = 4.11, SD = 0.66), real time analytics capability (M = 4.07, SD = 0.70), and financial volatility detection (M = 4.15, SD = 0.63). Correlation results indicated that financial volatility detection was positively associated with ERP data integration (r = 0.62, p < .001), machine learning forecasting pipeline capability (r = 0.71, p < .001), real time analytics capability (r = 0.68, p < .001), forecasting pipeline automation (r = 0.57, p < .001), and machine learning model performance (r = 0.73, p < .001). Regression analysis confirmed that all predictors significantly influenced financial volatility detection, with machine learning model performance emerging as the strongest predictor (β = 0.29, p < .001), and the overall model explaining 52.9% of the variance (R² = 0.529, F = 46.82, p < .001). The study implies that enterprises can strengthen financial monitoring, risk awareness, and decision support by embedding high performing machine learning forecasting systems within integrated ERP based information environments.
Copyright (c) 2022 Md. Morshedul Islam, Rukaiya Khatun Moury, Khairum Nahar Pinky (Author)
A Trust-Centered AI and Security Modeling Approach for Early Cancer Diagnosis, Population-Level Health Analysis, and Secure Deployment in U.S. Healthcare Infrastructure
This study addresses the persistent gap between high-performing cancer AI prototypes and real-world adoption by proposing and testing a trust-centered AI plus security-modeling blueprint for early cancer diagnosis and population-level health analysis within U.S. healthcare infrastructure. The purpose was to quantify how often published “enterprise-ready” capabilities actually co-occur with deploy ability pillars such as interpretability, robustness, security, and equity. Using a quantitative, cross-sectional, case-based review design, each included paper was treated as a case reflecting cloud and enterprise healthcare deployment contexts (for example, multi-site systems, integrated EHR and imaging stacks, or networked inference services). The sample comprised 45 cases (N = 45). Key variables were five Likert-scored readiness dimensions (1–5): clinical validation rigor, interpretability and communication support, robustness and generalization evidence, security and privacy modeling, and fairness and equity evidence, plus composite indicators such as trust-mechanism presence and a rubric-scaled Trust-Centered Deployment Readiness (TDR). The analysis plan applied descriptive statistics (counts, percentages, means), cross-tabs between trust-mechanism grouping and validation readiness, and a composite readiness summary. Headline findings show that radiology and pathology cases dominated (31/45, 68.9%), interpretability appeared in 28/45 (62.2%) but comprehensive interpretability was limited (12/45, 26.7%; mean M = 3.1/5), external validation or multi-site evaluation occurred in only 16/45 (35.6%), and explicit security or privacy-by-design elements were present in 14/45 (31.1%) with the lowest readiness mean (M = 2.4/5). Trust-mechanism studies (19/45, 42.2%) showed higher validation readiness (M = 3.6/5) and more external validation (11/19, 57.9%) than performance-only studies (5/26, 19.2%). Overall, only 8/45 (17.8%) met high composite readiness (≥0.75), while 21/45 (46.7%) were moderate and 16/45 (35.6%) low. Implications indicate that healthcare AI procurement and governance should prioritize a complete evidence package that couples external validation, calibrated trust cues, security controls across the lifecycle, and subgroup equity reporting, rather than selecting models based on accuracy alone.
Copyright (c) 2020 Md Fokhrul Alam, Md Fardaus Alam, Md Ashraful Alam (Author)
Advanced Computing–Based Modeling of Steel Connection Behavior and Stability Performance using ETABS And STAAD Pro
Advanced computational modeling has become an essential tool for evaluating the structural performance of steel frame systems and their connection behavior under complex loading conditions. This study examined the structural behavior and stability performance of steel connections using advanced computing-based modeling through ETABS and STAAD Pro. A quantitative comparative simulation design was adopted to analyze the response of multistory steel frame structures under standardized modeling conditions. A total of 24 three-dimensional steel structural models were developed and analyzed using identical geometric properties, material characteristics, and boundary conditions in both software platforms. The analytical dataset consisted of 960 structural observations derived from multiple load combinations including gravity, wind, and seismic loading scenarios. Key structural performance indicators evaluated in the study included maximum displacement, interstory drift, column axial force, beam shear force, bending moment distribution, and structural stability indices. The findings demonstrated that both ETABS and STAAD Pro produced generally consistent structural response patterns across the simulated models. The average maximum displacement predicted by ETABS was 40.56 mm, compared with 37.12 mm generated by STAAD Pro, indicating slightly greater lateral deformation predicted by ETABS. Similarly, the average interstory drift ratio was 0.0023 in ETABS and 0.0021 in STAAD Pro, confirming similar deformation trends between the two platforms. In contrast, STAAD Pro simulations produced higher internal force values in structural members, with average column axial forces of 1287.66 kN compared with 1218.43 kN in ETABS, and average beam bending moments of 366.88 kN·m compared with 349.72 kN·m in ETABS. Subgroup analysis further showed that structural models with semi-rigid connections produced greater deformation responses, with an average displacement of 42.63 mm compared with 35.18 mm in rigid connection models. Statistical testing confirmed that several structural indicators exhibited significant differences between the two software platforms, although effect size analysis indicated that these differences were generally small to moderate in magnitude. Overall, the results demonstrated that both ETABS and STAAD Pro provide reliable analytical capabilities for modeling steel structural behavior while exhibiting modest numerical variation in certain structural performance indicators.
Copyright (c) 2023 Md. Mainuddin Faisal, Palash Chandra Das (Author)
Advanced Computing Frameworks for Real-Time SAP S/4HANA Retail Business Intelligence: Optimizing Data Processing, Latency, and System Reliability
This study addresses the persistent problem that SAP S/4HANA retail business intelligence environments often struggle to achieve simultaneously low latency, high data freshness, and dependable reliability at enterprise scale, particularly when cloud and hybrid deployments introduce additional integration and operational complexity. The purpose was to quantify which advanced computing framework combinations are most consistently associated with improved real-time BI outcomes in retail decision workflows. Using a quantitative cross-sectional, case-based design, the study coded a sample of 60 eligible enterprise and cloud-oriented cases from the reviewed corpus (N = 60). Key variables included framework adoption categories (for example CDC or incremental refresh, in-memory or HTAP, streaming, distributed processing, cloud-native orchestration), outcome constructs measured via a 5-point Likert evidence scale (Latency Improvement Evidence LIE, Processing Efficiency Evidence PEE, Reliability and Continuity Evidence RCE), and workflow clusters (inventory, promotions, fulfillment, anomaly detection). The analysis plan applied frequency distributions and descriptive statistics, then conducted evidence-based hypothesis aggregation using a predefined support rule (at least 60% directional support and mean evidence score at or above 3.50). Findings show strong concentration of CDC or incremental refresh integration at 73.3% (44/60), in-memory or HTAP execution at 68.3% (41/60), streaming at 60.0% (36/60), cloud-native orchestration at 51.7% (31/60), and distributed processing at 48.3% (29/60). Overall evidence strength was high for latency and processing (LIE M = 3.84, SD = 0.71; PEE M = 3.76, SD = 0.69) with moderate-strong reliability (RCE M = 3.62, SD = 0.74). Headline results supported all three hypotheses: hybrid multi-layer stacks outperformed single-layer designs for latency (77.8% supportive, 28/36; hybrid LIE M = 4.08 vs 3.41), CDC and event-driven strategies outperformed batch refresh for freshness and reporting delay (CDC subgroup LIE M = 3.97 vs 3.22), and observability plus automated failover improved reliability versus monitoring-only patterns (high RCE 74.1% vs 42.1%; RCE M = 3.98 vs 3.28). Implications suggest practitioners should prioritize CDC-driven ingestion and incremental maintenance, adopt hybrid stacks that combine in-memory, streaming, and distributed compute, and operationalize reliability through observability and automated recovery, while tracking end-to-end latency as a decomposed pipeline metric aligned to core retail workflows where inventory (30.0%) and promotion monitoring (26.7%) dominate use cases.
Copyright (c) 2022 Zakia Afroz, Khairum Nahar Pinky (Author)
Advanced Computing–Enabled Secure Financial Information Systems for Real-Time Fraud Detection in U.S. Digital Payments: A Quantitative Analysis
This study addressed the persistent problem that U.S. digital payment ecosystems require fraud detection decisions in real time, yet many organizations struggle to align scalable advanced computing with secure financial information system controls in ways that measurably improve fraud detection effectiveness while remaining auditable and operationally usable. The purpose was to quantify how advanced computing enablement (ACE) and secure control strength (SCS) predict real-time fraud detection effectiveness (RTFDE), and to examine whether alert trust and actionability (ATA) strengthens practical effectiveness within enterprise and cloud-based operational cases. Using a quantitative, cross-sectional, case-based design, data were collected from a multi-role sample of enterprise digital-payment stakeholders (N = 210), including fraud and risk operations (38.1%), IT and data engineering (34.3%), and security and compliance (27.6%), with mean experience of 6.8 years (SD = 3.9). Key variables were ACE (8 items), SCS (9 items), RTFDE (8 items), ATA (6 items), feature adoption and maturity (FAM), and operational error hotspots (OEH), all measured on 5-point Likert scales with strong internal consistency (ACE α = .88; SCS α = .91; RTFDE α = .89; ATA α = .86). The analysis plan applied descriptive statistics, Pearson correlations, and hierarchical multiple regression with diagnostic checks (VIF 1.31–2.08). Headline findings showed moderately high capability ratings (ACE M = 3.74, SD = 0.61; SCS M = 3.88, SD = 0.55; RTFDE M = 3.69, SD = 0.63) and strong positive associations with effectiveness (ACE–RTFDE r = .62, p < .001; SCS–RTFDE r = .58, p < .001). Regression results indicated ACE alone explained 32% of variance in RTFDE (R² = .32; β = .57, p < .001); adding SCS increased explanatory power to 40% (ΔR² = .08; ACE β = .41, p < .001; SCS β = .29, p < .001); adding ATA raised total explained variance to 44% (ΔR² = .04; ACE β = .32, p < .001; SCS β = .21, p = .002; ATA β = .24, p < .001). Operationally, the largest constraints clustered in data quality and feature completeness (HS = 3.92), alert routing and prioritization (HS = 3.71), and manual review capacity (HS = 3.63), implying that improving feature integrity and workflow throughput can yield gains even where baseline security controls are mature. Implications suggest that payment organizations should jointly invest in real-time analytics maturity and security traceability, while optimizing alert usability to reduce false-positive burden and strengthen defensible decision trails.
Copyright (c) 2022 Md Mehedi Hasan, Md. Fardous (Author)
Fraud-Detection Algorithms for Identifying Anomalous Transactions in Retail Banking Networks
This study addresses a practical problem in retail banking where fraud-detection algorithms can appear effective yet still produce low-trust alerts, heavy false-positive workload, and weak readiness for shifting fraud patterns, which reduces real operational value. Using a quantitative, cross-sectional, case-study–based design grounded in an enterprise retail banking network, the purpose was to test how six fraud-detection capability dimensions (Data Quality and Feature Readiness, Real-Time Processing, Model Robustness, Explainability, Integration and Scalability, and Monitoring and Updating) predict the primary outcome Anomalous Transaction Identification Performance and three trust-centered outcomes: Alert Quality Index, False-Positive Burden Score, and Drift Readiness Score . The sample comprised N = 200 professionals embedded in fraud operations, risk/compliance, and analytics/IT roles within the case environment. The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression for hypothesis testing. Headline results show capability maturity was highest for Data Quality (M = 3.98, SD = 0.63) and Integration (M = 3.85, SD = 0.69), while the primary outcome ATIP was moderately high (M = 3.81, SD = 0.66); trust outcomes indicated moderate alert quality (AQI M = 3.76, SD = 0.68) but noticeable false-positive burden (FPBS M = 3.12, SD = 0.81) and comparatively weaker drift readiness (DRS M = 3.33, SD = 0.79). Reliability was strong across constructs (α = .81–.88). Correlations with ATIP were strongest for Data Quality (r = .62), Explainability (r = .58), and Monitoring and Updating (r = .55), all p < .001. The regression model explained substantial variance in ATIP (R² = 0.54; F(6,193) = 37.6; p < 0.001), with significant predictors including Data Quality (β = 0.28, p < 0.001), Explainability (β = 0.22, p = 0.002), Monitoring and Updating (β = 0.19, p = 0.006), and Real-Time Processing (β = 0.12, p = 0.041), while Robustness and Integration were not significant after controls. These findings imply that banks seeking measurable improvement should prioritize data readiness, explanation-centered alert design, and monitoring and update governance to raise operational trust, reduce unnecessary investigative load, and sustain performance under drift within enterprise fraud-detection deployments.
Copyright (c) 2021 Mahfuj Ahmed Ruzel, Mostafa Kamal (Author)
A Smart Contract Framework for Automated Settlement and Compliance in Renewable Energy and Distributed Energy Resources
This study addresses the persistent problem that renewable-energy and distributed energy resource (DER) transactions generate high-volume, interval-based micro-transactions that strain conventional centralized billing, reconciliation, and compliance reporting, creating delays, disputes, and weak audit trails. The purpose was to evaluate a smart contract framework that integrates automated settlement and compliance evidence generation within cloud-hosted, enterprise-grade DER transaction workflows using a quantitative, cross-sectional, case-based design. A purposive case sample of n = 214 informed stakeholders was drawn from enterprise settlement and compliance settings, including settlement and billing personnel (31.3%), compliance and audit professionals (24.8%), DER operations and aggregation roles (22.4%), and platform/ICT specialists (21.5%). Key variables were modeled as composite 5-point Likert constructs: Smart Contract Quality (SCQ), Data Integrity and Oracle Reliability (DIO), Interoperability (INT), Security and Privacy Controls (SP), Regulatory Alignment (RA), Automated Settlement Effectiveness (ASE), Compliance Automation Performance (CAP), and Operational or Market Performance (OMP). The analysis plan applied descriptive statistics, internal consistency reliability (Cronbach’s alpha), Pearson correlations, and multiple regression hypothesis testing. Findings showed high capability and outcome perceptions, including SCQ M = 4.12 (SD = 0.61), DIO M = 4.05 (SD = 0.67), SP M = 4.18 (SD = 0.58), ASE M = 4.15 (SD = 0.60), and CAP M = 4.22 (SD = 0.55), with strong reliabilities (α = 0.82–0.91 across constructs). Correlations supported the framework pathways, notably SCQ–ASE r = 0.63, DIO–ASE r = 0.58, SP–CAP r = 0.66, RA–CAP r = 0.61, and CAP–OMP r = 0.68 (all p < 0.001). Regression results confirmed that settlement effectiveness was predicted by SCQ (β = 0.34, p < 0.001), DIO (β = 0.29, p < 0.001), and INT (β = 0.21, p = 0.002) with R² = 0.496, while compliance performance was driven by SP (β = 0.38, p < 0.001) and RA (β = 0.31, p < 0.001) plus organizational readiness (β = 0.19, p = 0.002) with R² = 0.538; together ASE (β = 0.29) and CAP (β = 0.41) explained OMP (R² = 0.572). Audit evidence readiness was also strong (ARS M = 4.25) and artifact coverage exceeded 82.7%–88.3% for trace logs, access logs, dispute logs, and provenance links, indicating compliance-grade outputs at scale. Implications suggest that enterprise implementations should prioritize contract correctness assurance, oracle and meter-data governance, secure per missioning with privacy-aware transparency, and explicit regulatory alignment to convert automation into measurable operational and audit benefits.
Copyright (c) 2023 Tasnim Kabir, Zaheda Khatun (Author)
An Empirical Evaluation of Anomaly Detection Techniques in Smart Grid Systems Using Real-Time Operational Data
This study addresses the problem that smart grid operators increasingly rely on enterprise and cloud enabled monitoring platforms, yet anomaly detection outputs are often difficult to trust due to data quality variability, limited alarm explainability, weak workflow integration, and false alarm overload. The purpose was to quantify which technical and organizational factors most strongly predict perceived detection effectiveness and adoption readiness for anomaly detection techniques in a real smart grid case. A quantitative cross sectional, case-based design was applied using a structured 5-point Likert survey administered to N = 182 stakeholders drawn from enterprise smart grid environments spanning AMI and smart metering, SCADA and EMS or DMS, PMU or WAMS, and supporting IT and cybersecurity functions (operations 28.6%, metering 20.3%, data and IT 20.9%, protection 15.9%, cybersecurity 14.3%). Key variables included Data Quality Adequacy (DQ), Robustness and Adaptability (RB), Explainability of Alarms (EX), System Integration Capability (SI), Perceived Detection Effectiveness (PDE), Adoption Readiness and Trust (ART), and False Alarm Burden and Operational Impact Index (FABOI). The analysis plan used descriptive statistics, scale reliability (Cronbach alpha .81 to .88), Pearson correlations, and multiple regression models. Headline findings showed moderately high perceptions of DQ (M = 3.92, SD = 0.61) and PDE (M = 3.81, SD = 0.62), while false alarm burden was nontrivial (FABOI M = 3.41, SD = 0.73). PDE correlated with DQ (r = .52) and RB (r = .48), while ART correlated most strongly with EX (r = .54) and PDE (r = .58), and decreased with FABOI (r = −.49), all p < .01. Regression confirmed PDE was predicted by DQ (β = .31, p < .001) and RB (β = .27, p < .001), R² = .39, whereas ART was driven by EX (β = .33, p < .001), PDE (β = .29, p < .001), SI (β = .21, p = .002) and reduced by FABOI (β = −.24, p < .001), R² = .47. Implications suggest utilities should prioritize data governance and robustness for effectiveness, but invest in explainable alarms, workflow integration, and alarm burden management to increase operational trust and sustained adoption.
Technology Organization Environment (TOE)
Copyright (c) 2025 S M Shakil, Alamgir Hossain, Md Mijanur Rahman (Author)