American Journal of Interdisciplinary Studies
The American Journal of Interdisciplinary Studies (AJIS) (ISSN 3067-5146) is a distinguished, peer-reviewed, open-access international journal dedicated to fostering a global platform for scholars, academicians, researchers, and professionals from diverse disciplines. The journal serves as a bridge for interdisciplinary research, encouraging the exchange of knowledge and innovative ideas that transcend traditional academic boundaries. By publishing high-quality, original research articles and critical reviews, AJIS contributes significantly to various domains, including but not limited to Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering. Recognizing the ever-evolving landscape of research and scholarship, AJIS maintains rigorous editorial and peer-review standards to ensure that every publication upholds the highest levels of academic integrity, originality, and scholarly contribution. The journal is committed to promoting intellectual discourse, evidence-based research, and theoretical advancements that have practical implications across multiple sectors.
AI-ENABLED FINANCIAL ACCURACY MODELS FOR IMPROVING ERROR DETECTION AND REPORTING INTEGRITY IN CORPORATE ACCOUNTING SYSTEMS
This quantitative study examined the effectiveness of AI-enabled financial accuracy models in improving error detection and reporting integrity within corporate accounting systems. Using a retrospective observational design, the analysis was based on 48,620 accounting transactions extracted from integrated accounting information systems, including general ledger entries, subledger postings, and period-end adjustments. Confirmed error outcomes were identified through documented reversals, reclassifications, reconciliation resolutions, and audit adjustment linkages, yielding an observed error rate of 2.5% (1,215 transactions). Transaction-level and account-period indicators capturing transaction magnitude, posting lateness, exception frequency, reconciliation variance, account-linkage intensity, and workflow complexity were operationalized and analyzed using statistical and machine learning techniques. Descriptive findings showed that error-labeled transactions exhibited substantially higher reconciliation variance (mean USD 1,420 vs 180), longer posting delays (mean 5.6 days vs 1.8 days), and extended correction cycle times (mean 6.9 days vs 1.1 days) compared with non-error transactions. Correlation analysis demonstrated coherent positive associations among timing disruption, exception frequency, and reconciliation variance, while reporting integrity indicators showed strong alignment with audit adjustment volume (r = 0.63). Regression results indicated that posting lateness (odds ratio 1.48), exception frequency (1.36), reconciliation variance (1.29), and account-linkage intensity (1.21) were significant predictors of confirmed error occurrence after controlling for organizational and system factors. Comparative model evaluation showed that AI-based classifiers outperformed baseline rule-based exception detection, with a tree-based ensemble model achieving an AUC of 0.87, precision of 0.49, and F1-score of 0.60, compared with an AUC of 0.68 and precision of 0.22 for the rule-based approach. Overall, the findings demonstrated that AI-enabled financial accuracy models provided superior discriminatory power and more balanced detection performance by capturing multivariate process, control, and linkage-driven risk patterns that traditional rule-based mechanisms failed to identify consistently.
AI-ENABLED CUSTOMER-INTERACTION MODELS FOR IMPROVING SERVICE EFFICIENCY IN U.S. HOSPITALITY AND RETAIL OPERATIONS
This study examined the relationship between AI-enabled customer-interaction models and service efficiency outcomes in U.S. hospitality and retail operations using a quantitative, observational research design. The analysis was informed by an extensive review of 82 peer-reviewed academic studies spanning service operations, information systems, retailing, hospitality management, and technology-mediated service research, which provided the conceptual and methodological foundation for variable selection, model specification, and interpretation of results. Empirical analysis was conducted using a multi-source dataset comprising 68,420 customer-service interaction episodes collected from 132 hospitality and retail sites, integrating operational interaction logs, site-level performance records, and linked customer feedback data where available. AI exposure was operationalized through the share of interactions handled within AI-mediated channels and the extent of automated containment, while capability quality was captured using response latency, knowledge coverage proxies, escalation behavior, and containment outcomes. Service efficiency was measured using time-based indicators, including average handling time, time-to-resolution, wait time, and service cycle time, alongside productivity and cost metrics. Descriptive results indicated that AI-mediated interactions accounted for approximately 51% of total recorded service volume, with containment achieved in 64% of AI-handled interactions compared to 19% in human-mediated channels. Regression findings demonstrated that higher AI exposure was associated with statistically significant reductions in average handling time (β = −1.12 minutes, p < .001), time-to-resolution (β = −4.85 minutes, p < .001), and overall service cycle time (β = −5.60 minutes, p < .001), after controlling for channel type, inquiry category, severity, peak demand, and site scale. Response latency and escalation were positively associated with longer resolution cycles, while containment was associated with lower recontact risk and higher first-contact resolution. Multilevel modeling showed that AI-related effects were primarily driven by within-site variation, although meaningful site-level differences persisted. Overall, the findings provided quantitative evidence that AI-enabled customer-interaction models functioned as operational mechanisms that improved service efficiency through faster resolution, higher containment, and reduced repeat contact when deployed within coherent omnichannel service architectures.
Copyright (c) 2026 Mohammad Towhidul Islam (Author)
QUANTITATIVE EVALUATION OF RISK MANAGEMENT AND STRUCTURAL INTEGRITY IN LARGE-SCALE GLOBAL CIVIL CONSTRUCTION PROJECTS
This study addresses a persistent problem in enterprise, case-based civil construction delivery: risk tools are common, yet structural integrity outcomes still vary substantially across interfaces, increasing exposure to nonconformities, rework, and reliability risks. The purpose was to quantify how Risk Management Effectiveness (RME) predicts Structural Integrity Performance (SIP) and to identify which risk-cycle dimensions drive integrity outcomes. A quantitative cross-sectional, case-study–based design used a five-point Likert questionnaire in a large-scale global civil construction project case (N = 210 valid responses after screening). Key variables were overall RME and four dimensions (risk identification, risk assessment and prioritization, mitigation or planning, monitoring and communication) and SIP as the dependent construct (compliance discipline, inspection and testing rigor, materials control, and defect closure). The analysis plan applied data screening, composite indexing, descriptive statistics, reliability testing, Pearson correlations, and hierarchical multiple regression controlling for role, experience, and phase. Reliability was strong (RME α = 0.91; SIP α = 0.88). Descriptively, RME was high (M = 3.87, SD = 0.54), with monitoring and communication the lowest RME dimension (M = 3.71, SD = 0.66); SIP was high (M = 3.76, SD = 0.57) but showed weaker areas in defect closure (M = 3.62, SD = 0.69) and materials traceability and QC consistency (M = 3.67, SD = 0.65). RME correlated strongly with SIP (r = 0.68, p < .001), and each dimension was positively related to SIP (r = 0.56 to 0.63, p < .001). In regression, controls explained 12% of SIP variance (R² = 0.12), while adding overall RME increased explanatory power to 49% (ΔR² = 0.37; β = 0.66, p < .001). The dimensional model reached R² = 0.53, with risk assessment (β = 0.22, p = .004), mitigation or planning (β = 0.31, p < .001), and monitoring or communication (β = 0.19, p = .011) as significant unique predictors, whereas identification was marginal (β = 0.12, p = .071). The findings imply that integrity improvements depend most on execution-focused risk governance that strengthens prioritization discipline, mitigation ownership, and monitoring and escalation, especially to speed defect closure and improve traceability across subcontract tiers.
Copyright (c) 2026 Syed Zaki Uddin (Author)
AI-ENABLED STRUCTURAL HEALTH MONITORING AND SAFETY OPTIMIZATION MODELS FOR HIGH-SPEED RAIL INFRASTRUCTURE IN SEISMIC REGIONS
This study had evaluated AI-enabled structural health monitoring (SHM) inference and safety optimization linkages for high-speed rail infrastructure in seismic regions using a corridor-scale quantitative dataset and decision-output records. The analyzed dataset had included 48 assets across 12 corridor segments observed for 365 days, producing 312,480 train-pass windows and 96 seismic event windows. Data integrity had remained high overall, with mean system uptime of 93.6%, while missingness had varied by modality, with accelerometers at 4.2% and displacement/tilt channels at 11.7%. Outcome distributions had been highly imbalanced, with damage-present windows representing 1.8% of all windows and severe outcomes representing 0.2%, confirming rare-event conditions for model evaluation. Correlation analysis had shown strong redundancy among amplitude features, where peak and RMS response had correlated at 0.84, and confounding patterns where train speed had correlated with peak acceleration at 0.63 and temperature had correlated with dominant-frequency proxies at –0.46. Reliability assessment had shown acceptable within-regime stability for key indicators, including antiregime consistency of 0.82 for peak acceleration and 0.76 for dominant-frequency proxies. Collinearity diagnostics had identified inflated overlap in the full feature set (peak acceleration VIF 6.8), which had been reduced after screening (retained amplitude indicator VIF 2.6). Regression results had indicated statistically meaningful contributions from probabilistic risk and selected engineered features to damage detection and condition scoring, with the detection model achieving PR-AUC 0.56 compared with 0.41 for a baseline feature-only specification and the condition-score model achieving adjusted R² 0.48. Operational decision alignment had been supported by correlations between risk score and speed restriction tier (0.71) and inspection priority rank (0.67), indicating coherent translation from inferred risk to safety actions under corridor constraints.
Copyright (c) 2026 Hammad Sadiq (Author)
EFFECTIVENESS OF A TASK-BASED LEARNING (TBL) MODULE IN ENHANCING GOOD MANUFACTURING PRACTICE (GMP) MASTERY IN PHARMACEUTICAL ENGINEERING PROGRAMS AT CHINESE VOCATIONAL COLLEGES
The issuance of China’s Guidelines for the Modernisation of Vocational Education by 2025, together with the Opinions on Promoting the High-Quality Development of Modern Vocational Education, underscores the urgent need to transform traditional teaching models and enhance instructional methodologies within vocational education. As a core and compulsory course for pharmaceutical engineering students in higher education institutions, Good Manufacturing Practice (GMP) plays a critical role in developing industry-ready competencies. However, existing GMP instruction remains largely reliant on conventional teaching approaches, which are often misaligned with contemporary educational reforms and evolving industrial demands. In response to this gap, the present study investigates the effectiveness of Task-Based Learning (TBL) in enhancing students’ academic performance, learning interest, and practical proficiency in GMP courses. The study considers individual student competencies and learning abilities, while also examining the challenges encountered by instructors in implementing TBL to strengthen GMP mastery in pharmaceutical engineering programs at Chinese vocational colleges. To achieve an in-depth analysis, a survey was administered to 91 undergraduate students enrolled in pharmaceutical engineering programs in China. In addition, semi-structured interviews were conducted with four instructors from two classes, each possessing a minimum of five years of teaching experience in vocational GMP courses. The findings indicate that TBL has a significant positive impact on students’ academic performance and skill development. Moreover, the results suggest that the TBL approach enhances instructional effectiveness and supports more engaging learning environments in vocational education settings. Nevertheless, the study also reveals that instructors’ pedagogical competence—particularly their ability to stimulate students’ critical thinking—plays a pivotal role in the successful implementation of TBL. Overall, this study contributes to the integration of theoretical and practical perspectives by highlighting the pedagogical value of TBL in improving GMP education within pharmaceutical engineering programs in the Chinese vocational education context.
Copyright (c) 2024 Tan Lingzi, Khairul Azhar Bin Jamaludin (Author)
AN ARTIFICIAL INTELLIGENCE-DRIVEN FRAMEWORK FOR AUTOMATION IN INDUSTRIAL ROBOTICS: REINFORCEMENT LEARNING-BASED ADAPTATION IN DYNAMIC MANUFACTURING ENVIRONMENTS
Industrial robots often struggle to sustain throughput, quality, and operational flexibility during disturbances because reinforcement learning (RL) adaptation is implemented without adequate sensing, data pipelines, governance controls, and workforce readiness across enterprise automation systems. This study tested an AI-driven enterprise framework linking organizational and technical enablement factors to RL-based adaptation effectiveness and, in turn, to automation performance outcomes. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered across cloud and enterprise manufacturing cases; after screening, N = 210 valid responses were retained (96.2% completeness; item-level missingness < 3.0%) from engineering, production, maintenance, quality, and operations roles. Independent variables were real-time sensing and integration (X1), data quality and accessibility (X2), digital twin or simulation support (X3), governance and safety readiness (X4), and human-robot collaboration readiness (X5); RL-based adaptation effectiveness (M1) acted as the mediator and automation performance (Y) as the outcome. Analyses included descriptive statistics, reliability testing, Pearson correlations, and multiple regression with multicollinearity diagnostics. Mean scores indicated generally favorable conditions (X1 M = 3.92, SD = 0.63; M1 M = 3.77, SD = 0.64; Y M = 3.85, SD = 0.60) and internal consistency (Cronbach’s α = 0.81 to 0.89). M1 correlated with enablement factors (r = 0.39 to 0.56, all p < .001) and Y correlated with M1 (r = 0.62, p < .001). The regression model predicting Y was significant (R² = 0.54; Adjusted R² = 0.52; F(6,203) = 39.80, p < .001) with collinearity within limits (VIF 1.28 to 2.11). M1 was the strongest predictor (β = 0.41, p < .001), followed by X2 (β = 0.19, p = .002), X4 (β = 0.15, p = .007), X1 (β = 0.12, p = .039), and X5 (β = 0.10, p = .046), while X3 was not significant (p = .133). Implications are that enterprises should prioritize data quality, safety governance, and human readiness; digital twins add value when integrated with data and control processes.
Copyright (c) 2025 Shoflul Azam Tarapder (Author)
POLICY OPTIMIZATION FOR SUSTAINABLE ENERGY SECURITY: DATA-DRIVEN COMPARATIVE ANALYSIS BETWEEN THE U.S. AND SOUTH ASIA
This study investigates how policy optimization shapes sustainable energy security in a comparative context between the United States and South Asia, responding to the problem that many energy security reforms are selected through narrative judgment rather than evidence-backed prioritization. The purpose was to test a quantitative model linking Policy Optimization (PO) to Sustainable Energy Security (SES) and to identify which policy levers most strongly predict energy security outcomes across two real-world cloud and enterprise informed cases (one U.S. case and one South Asia case). Using a quantitative, cross sectional, case-based design, data were collected through structured five-point Likert instruments from 420 stakeholders drawn from energy enterprises, regulators, and institutional policy units (n = 210 per case). Key independent variables were six PO dimensions: regulatory quality and enforcement, renewable integration policy strength, grid modernization policy, energy efficiency and demand side policy, affordability and equity policy, and climate resilience policy. The dependent construct was SES, measured through reliability and resilience, affordability and access, sustainability and acceptability, and supply risk management. The analysis plan applied reliability testing, independent samples t tests for case comparison, Pearson correlation to assess association, and multiple regression to estimate predictive effects with controls. Internal consistency was strong (Cronbach’s alpha = .91 for PO and .89 for SES). Case results showed higher PO in the United States than South Asia (M = 3.74, SD = 0.61 vs M = 3.21, SD = 0.67) and higher SES (M = 3.86, SD = 0.58 vs M = 3.12, SD = 0.63). The largest SES gaps were observed in reliability and resilience (4.01 vs 3.05) and supply risk management (4.03 vs 3.13). Overall, PO was positively associated with SES (r = .62, p < .001). In the pooled regression, policy levers significantly explained SES variance (Adjusted R² = .54; F(10, 409) = 50.12; p < .001), with grid modernization emerging as the strongest predictor (β = .31), followed by regulatory quality (β = .24) and affordability and equity (β = .21). Region specific models indicated different leverage points: the United States was most driven by grid modernization (β = .35) and regulatory quality (β = .22), while South Asia was most driven by affordability and equity (β = .29), regulatory quality (β = .26), and climate resilience (β = .20). The findings imply that energy security gains can be accelerated by prioritizing enforceable governance and grid modernization in mature systems, while emphasizing affordability, governance capacity, and resilience delivery in constrained systems.
Copyright (c) 2022 FNU Zulqarnain (Author)
PREDICTIVE DATA-DRIVEN MODELS LEVERAGING HEALTHCARE BIG DATA FOR EARLY INTERVENTION AND LONG-TERM CHRONIC DISEASE MANAGEMENT TO STRENGTHEN U.S. NATIONAL HEALTH INFRASTRUCTURE
The rapid expansion of healthcare big data—derived from electronic health records (EHRs), medical imaging, genomics, wearable devices, and population-level public health systems—has created unprecedented opportunities to transform chronic disease management and early clinical intervention in the United States. Predictive data-driven models leverage advanced analytics, machine learning, and artificial intelligence to extract actionable insights from these heterogeneous and high-volume datasets, enabling proactive rather than reactive healthcare delivery. This study examines the role of predictive healthcare analytics in strengthening the U.S. national health infrastructure by supporting early disease detection, personalized treatment planning, and long-term management of chronic conditions such as diabetes, cardiovascular diseases, cancer, and respiratory disorders. The abstract emphasizes how data-driven predictive models enhance clinical decision-making, optimize resource allocation, reduce avoidable hospitalizations, and improve population health outcomes. By integrating longitudinal patient data with real-time monitoring systems, these models facilitate risk stratification, disease progression forecasting, and timely interventions across care continuums. Furthermore, the study highlights the strategic significance of scalable, interoperable, and secure health data ecosystems in supporting public health resilience, cost containment, and equitable access to care. The findings underscore that predictive healthcare models are not merely technological innovations but foundational components of a robust, sustainable, and prevention-oriented national health infrastructure. Their effective implementation can substantially advance early intervention strategies, improve chronic disease outcomes, and reinforce the overall efficiency and responsiveness of the U.S. healthcare system.
Copyright (c) 2020 Md Ashraful Alam, Md Fokhrul Alam, Md Fardaus Alam (Author)
MACHINE LEARNING–ENHANCED MOLECULAR DOCKING AND VIRTUAL SCREENING FOR DRUG REPURPOSING IN INFLAMMATORY DISEASES
Inflammatory diseases represent a broad class of acute and chronic conditions characterized by dysregulated immune responses and persistent tissue damage, posing substantial clinical and socioeconomic burdens worldwide. Drug repurposing has emerged as a time- and cost-efficient strategy to identify new therapeutic indications for existing compounds with established safety profiles. In this context, machine learning–enhanced molecular docking and virtual screening have gained increasing prominence as integrative computational approaches capable of accelerating the identification of candidate drugs targeting inflammation-associated biomolecules. This study presents a comprehensive framework that combines classical structure-based molecular docking with supervised and deep learning models to improve binding affinity prediction, ranking accuracy, and hit enrichment during virtual screening campaigns. Molecular descriptors, protein–ligand interaction fingerprints, and docking-derived scores are leveraged as input features to train predictive models that discriminate high-affinity binders from inactive compounds across multiple inflammatory targets. The proposed workflow is applied to curated drug libraries and key protein targets implicated in inflammatory signaling pathways, enabling the prioritization of repurposable compounds with favorable interaction profiles. Results demonstrate that machine learning integration significantly enhances screening performance compared to conventional docking-only strategies by reducing false positives and improving early recognition of promising candidates. This approach underscores the potential of hybrid computational methodologies to support rational drug repurposing in inflammatory diseases and provides a scalable platform for translational pharmacological research.
Copyright (c) 2025 Md Abdul Ahad Maraj (Author)
IMPACT OF DIGITAL CONSTRUCTION MANAGEMENT PLATFORMS ON PROJECT PERFORMANCE POST-COVID-19
The COVID-19 pandemic accelerated the adoption of digital construction management platforms as organizations sought to mitigate disruptions, enhance coordination, and maintain project continuity under unprecedented constraints. This study examines the impact of digital construction management platforms on project performance in the post-COVID-19 construction environment, with particular emphasis on cost efficiency, schedule adherence, quality outcomes, and stakeholder collaboration. Drawing on recent empirical studies and industry reports, the abstract highlights how platforms integrating cloud-based collaboration, Building Information Modeling (BIM), real-time data analytics, and remote monitoring tools have transformed traditional project management practices. The findings suggest that post-COVID-19 construction projects leveraging digital platforms demonstrate improved decision-making speed, enhanced transparency, reduced rework, and greater resilience to supply chain and workforce disruptions. Moreover, the increased reliance on digital workflows has contributed to improved risk management and more effective communication among geographically dispersed project teams. However, the analysis also acknowledges persistent challenges related to data integration, cybersecurity, workforce digital skills, and organizational resistance to change. Overall, the study underscores the strategic role of digital construction management platforms in strengthening project performance and organizational adaptability in a post-pandemic construction landscape, positioning digitalization as a critical enabler of sustainable and resilient project delivery models.
Copyright (c) 2020 Muhammad Mohiul Islam (Author)
LSTM AND GRU-BASED FORECASTING MODELS FOR PREDICTING HEALTH FLUCTUATIONS USING WEARABLE SENSOR STREAMS
This quantitative, cross-sectional, case-study–based research addresses the problem that short-horizon health fluctuations in wearable streams are difficult to operationalize and forecast, limiting actionable monitoring. The purpose was to define fluctuation outcomes, identify wearable indicators linked to perceived variability, and compare LSTM versus GRU forecasting pipelines under matched preprocessing and evaluation. The bounded case sample comprised 120 monitored participant cases retained after quality screening (mean wear-time 12.8 hours/day, SD 2.1; overall missingness 9.6%, SD 6.4; 84.2% high adherence at or above 10 hours/day). The primary subjective variable, Health Fluctuation Severity, was a 6-item 5-point Likert composite with acceptable reliability (Cronbach’s alpha .86; mean 3.18, SD 0.74), and the primary objective variable was an Objective Fluctuation Index (mean 0.61, SD 0.19). The analysis plan combined descriptive statistics, reliability checks, Pearson correlations, and regression with data-quality controls, alongside forecasting evaluation using MAE and RMSE and a paired t-test on participant-level errors. Headline findings showed convergence between subjective and objective measures (r .52, p < .001) and significant associations with resting heart-rate shift (r .41), sleep disruption (r .46), activity instability (r .29), and HRV proxy (r −.38) (all p ≤ .001). GRU outperformed LSTM on the test set (MAE 0.072 vs 0.081; RMSE 0.094 vs 0.106; t(119) 4.62, p < .001) and increased explanatory power when combined with wearable features (R squared 0.41; delta R squared 0.07, p = .004). The results imply that baseline-centered indices plus GRU forecasting can support validated fluctuation monitoring in real-world programs when adherence and missingness are managed.
Copyright (c) 2021 Jinnat Ara, Md. Kamrul Khan (Author)
IS THE METAVERSE THE NEXT FRONTIER FOR CORPORATE GROWTH AND INNOVATION? EXPLORING THE POTENTIAL OF THE ENTERPRISE METAVERSE
This study addresses the problem that evidence exists on whether enterprise metaverse adoption improves corporate innovation and growth and which enablers create value. The purpose was to test a readiness to adoption to outcomes framework in a cloud enterprise case using a quantitative, cross-sectional, case-based survey. Purposive and convenience sampling produced N = 210 responses from staff. Predictors were Technology Readiness, Governance and Security Preparedness, Facilitating Conditions, Strategic Alignment, Adoption Intensity, and Collaboration Quality; outcomes were Corporate Innovation and Corporate Growth. Analysis used statistics, Cronbach alpha, Pearson correlations, and regression. Readiness and adoption were above the midpoint (Technology Readiness M = 3.74, SD = 0.68), and reliability was strong (alpha = 0.81 to 0.90). Collaboration Quality showed the strongest association with Innovation (r = 0.66, p < .001), while Strategic Alignment related to Growth (r = 0.60, p < .001). The innovation regression explained 58% of variance (F(6,203) = 46.70, p < .001; R2 = 0.58) with Collaboration Quality as the dominant predictor (beta = 0.38, p < .001) and Adoption Intensity also significant (beta = 0.24, p = .002). The growth regression explained 52% (F(6,203) = 36.90, p < .001; R2 = 0.52) and was led by Strategic Alignment (beta = 0.31, p < .001) with additional effects from Adoption Intensity (beta = 0.22, p = .004). Implications are that metaverse value depends less on technology presence and more on aligned strategy, governed scaling, sustained use, and collaboration design for performance gains across functions.
Copyright (c) 2025 Md Harun-Or-Rashid Mollah (Author)
ASSESSING THE ROLE OF STATISTICAL MODELING TECHNIQUES IN FRAUD DETECTION ACROSS PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS
This study addresses the problem that fraud detection in procurement and international trade workflows often depends on rule-based checks and fragmented records, weakening detection and investigation prioritization. The purpose was to quantify how statistical modeling techniques relate to fraud detection effectiveness in enterprise case environments. Using a quantitative cross sectional, case-based design, 220 usable survey responses were retained after screening (12 removed from 232; item missingness 1.6%), covering procurement (n=108, 49.1%), international trade (n=92, 41.8%), and hybrid exposure (n=20, 9.1%). Key variables were descriptive analytics use (DAU), correlation-based screening (CBS), regression modeling practice (RMP), data readiness (DR), process control context (PCC), and fraud detection effectiveness (FDE), measured as 1–5 Likert composites with strong internal consistency (α=.81–.88). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, and multiple regression predicting FDE from DAU, CBS, RMP, DR, and PCC with controls. Respondents reported moderate to high adoption (DAU M=3.92, CBS M=3.71, RMP M=3.54) and relatively high effectiveness (FDE M=3.84). FDE correlated with DAU (r=.52), CBS (r=.45), and RMP (r=.49), all p<.001. In regression, the model was significant (R²=.48; F (7,212) =27.61, p<.001) and showed that DAU (β=.24, p<.001), CBS (β=.15, p=.017), RMP (β=.21, p=.001), DR (β=.18, p=.003), and PCC (β=.17, p=.004) each contributed uniquely to FDE. Subgroup models indicated procurement emphasized descriptive monitoring and controls (R²=.51) while trade emphasized regression scoring and data readiness (R²=.44). Implications are that fraud programs should implement layered, interpretable analytics and invest in data readiness and process controls to translate modeling capability into detection gains.
Copyright (c) 2022 Rifat Chowdhury, Khairul Alam Talukder (Author)
A SYSTEMATIC REVIEW OF HUMAN-AI COLLABORATION IN IT SUPPORT SERVICES: ENHANCING USER EXPERIENCE AND WORKFLOW AUTOMATION
This study addresses the problem that enterprise IT service desks increasingly embed AI assistants in support portals and ticket workflows, yet many organizations lack quantitative evidence on whether human oversight and automation quality jointly improve user experience and service performance. The purpose was to test a quantitative, cross-sectional, case study-based model linking Human-AI Collaboration (HAC) and Workflow Automation Effectiveness (WAE) to User Experience (UX) and perceived IT Support Service Performance (SP). Survey data were collected from enterprise support cases; 320 questionnaires were distributed, 259 were returned, and 247 valid responses were analyzed (77.2% usable response rate; 71.7% end users and 28.3% IT support personnel; 54.3% used AI support weekly or more). Constructs were measured with multi-item five-point Likert scales and showed favorable perceptions: HAC M = 3.91 (SD = 0.64), WAE M = 3.84 (SD = 0.69), UX M = 3.88 (SD = 0.62), and SP M = 3.79 (SD = 0.66), with good to excellent reliability (Cronbach alpha 0.86 to 0.91). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, multiple regression, and bootstrapped mediation (5,000 samples). Associations were positive and significant (HAC with UX r = 0.62 and UX with SP r = 0.63, both p < .001). Regression indicated that HAC (beta = 0.41) and WAE (beta = 0.33) explained 49% of UX variance (R2 = 0.49, p < .001); WAE (beta = 0.38), HAC (beta = 0.21), and UX (beta = 0.29) explained 56% of SP variance (R2 = 0.56, p < .001). UX partially mediated the HAC to SP relationship (indirect beta = 0.29, 95% CI [0.19, 0.40]). Implications suggest that AI enabled IT support should be governed as a hybrid workflow with clear escalation rules and reliable automation, and continuously evaluated using joint metrics that track experience alongside efficiency outcomes.
Copyright (c) 2025 Rakibul Hasan (Author)
THE INFLUENCE OF IOT AND DIGITAL TECHNOLOGIES ON FINANCIAL RISK MONITORING AND INVESTMENT EFFICIENCY IN GLOBAL SUPPLY CHAINS
This study addresses the problem that many global supplies chain enterprises still experience delayed, fragmented financial risk monitoring and suboptimal investment efficiency because operational data are not consistently captured and integrated across cloud and enterprise platforms. The purpose was to test whether Internet of Things capability (IoT) and digital technology integration (DTI) improve financial risk monitoring effectiveness (FRM) and, in turn, investment efficiency (IE). Using a quantitative, cross-sectional, case-based design, survey data were collected from enterprise functions (supply chain, logistics, procurement, operations, and finance/risk) in a single organizational case (N = 210), with 62.4% operations-facing and 37.6% finance/risk-facing respondents. Constructs were measured on 5-point Likert scales and showed strong reliability (αIoT = .86, αDTI = .88, αFRM = .90, αIE = .85), with above-neutral mean scores (IoT M = 3.78, DTI M = 3.69, FRM M = 3.73, IE M = 3.61). The analysis plan included descriptive statistics, Pearson correlations, multiple regression with controls (size, supply chain complexity, tenure, digital maturity), and mediation testing using bootstrap resampling (5,000). Headline findings show that IoT and DTI significantly predicted FRM (β = .28, p < .001; β = .41, p < .001; R² = .52), FRM predicted IE (β = .43, p < .001; IE model R² = .53), and both IoT and DTI retained significant direct effects on IE (β = .12, p = .031; β = .17, p = .004). Mediation results indicate partial mediation via FRM, with significant indirect effects for IoT (0.12, 95% CI [0.07, 0.19]) and DTI (0.18, 95% CI [0.11, 0.27]). The findings imply that enterprises should prioritize end-to-end data integration and finance-ready monitoring routines, because improved FRM is a primary mechanism through which digital capability translates into more disciplined, efficient investment decisions.
Copyright (c) 2023 Md Arman Hossain, Md Nahid Hossain (Author)
POST-QUANTUM CRYPTOGRAPHY FRAMEWORKS FOR SECURING GLOBAL CLOUD SYSTEMS
This study addresses the emerging problem that quantum attacks can undermine classical public key cryptography that secures global cloud platforms, leaving long lived, cross border data at risk, and evaluates how far organizations have progressed toward post quantum cryptography (PQC) frameworks. The purpose is to quantify PQC framework maturity and its security, compliance, and performance implications in cloud and enterprise cases. Using a quantitative, cross sectional, case-based design, survey data were collected from 220 organizations that provide or consume multi region cloud services, with key informants in security, cloud architecture, and compliance rating Likert five-point items. Core variables included PQC awareness, adoption intention, regulatory and contractual pressure, security governance capability, perceived performance impact, PQC framework maturity, perceived quantum resilient security posture, perceived regulatory compliance, and perceived operational performance. Descriptive statistics, reliability and validity tests, Pearson correlations, and multiple regression models with sector, size, region, and deployment model as controls were applied. Results show moderate PQC maturity (mean 3.21) but higher awareness (3.68) and adoption intention (3.55). PQC maturity correlated strongly with quantum resilient security posture (r = 0.68) and regulatory compliance (r = 0.64), and significantly predicted both outcomes (β = 0.59 and β = 0.55, R² = 0.47 and 0.42). Regulatory pressure (β = 0.34) and governance capability (β = 0.18) were also significant drivers of maturity. These findings imply that building systematic, governance anchored PQC frameworks can measurably strengthen cloud security and compliance while maintaining acceptable performance, guiding prioritized, phased PQC migration for global cloud providers and enterprise users.
Copyright (c) 2021 Sai Srinivas Matta, Manish Bolli (Author)
COMPARATIVE STUDY OF U.S. AND SOUTH ASIAN AGRIBUSINESS MARKETS: LEVERAGING ARTIFICIAL INTELLIGENCE FOR GLOBAL MARKET INTEGRATION
This study investigates how artificial intelligence enabled, data driven decision making supports global market integration among agribusiness firms in structurally different systems in the United States and South Asia. The problem addressed is the lack of quantitative, comparative evidence on whether and how AI capabilities translate into higher export intensity, market diversification and participation in global value chains for agribusiness enterprises. The purpose is to test a TOE based model linking AI adoption, organizational and environmental conditions and data driven decision practices to firm level global market integration. A quantitative, cross sectional, case-based design was applied using a structured five-point Likert survey of 320 agribusiness enterprise cases, comprising 162 U.S. firms and 158 South Asian firms. Key variables included AI Adoption, Data Driven Decision Making, Supply Chain Connectivity, Organizational Readiness, Environmental Support and Global Market Integration indices. Reliability was high (α = 0.89 for AI adoption, 0.87 for data driven decisions, 0.88 for market integration), and analysis combined descriptive statistics, correlation and multiple regression. AI adoption (β = 0.34, p < 0.001) and data driven decision making (β = 0.28, p < 0.001) together raised explained variance in global market integration to 41.5 percent, with a strong bivariate correlation between AI adoption and integration (r = 0.56, p < 0.001). U.S. firms showed higher mean AI adoption (M = 3.84 vs 3.21) and a stronger AI integration linkage than South Asian firms. The findings imply that AI should be treated as a strategic, enterprise level capability for agribusiness internationalization, while investments in organizational readiness, secure data infrastructure and supply chain connectivity are especially critical in South Asia for converting AI use into durable global market gains.
Copyright (c) 2023 Abdul Hye, SM. Toufiqur Rahman (Author)
MACHINE LEARNING AND SECURE DATA PIPELINE FRAMEWORKS FOR IMPROVING PATIENT SAFETY WITHIN U.S. ELECTRONIC HEALTH RECORD SYSTEMS
This study examined how secure data pipelines operated as quantitative determinants of machine learning (ML) reliability and patient safety outcomes in Electronic Health Record (EHR) environments among U.S. healthcare providers. A retrospective, multi-site quantitative design was applied to de-identified EHR encounter streams, linking provider-level pipeline maturity (confidentiality, integrity, availability, and data quality) with ML safety-prediction performance and EHR-derived safety endpoints. The methodology and findings were grounded in an overall review of spanning secure healthcare data pipelines, EHR-driven ML safety prediction, cybersecurity incident impacts, fairness in clinical ML, and EHR safety endpoint operationalization; this number should match the total reported across the methods and findings sections of the article. The analytic cohort comprised 12 providers contributing 184,732 adult encounters, with a median of 14,980 encounters per provider. Pipeline maturity demonstrated cross-site differentiation (mean = 72.8, SD = 9.4), accompanied by measurable variability in quality and security indicators: missingness averaged 6.4% (SD = 3.1), timestamp misalignment averaged 3.8 per 1,000 events (SD = 1.9), unit harmonization errors averaged 5.6 per 10,000 labs (SD = 2.4), audit-log completeness averaged 91.5% (SD = 4.7), encryption coverage averaged 94.2% (SD = 3.9), and downtime averaged 2.7 hours per quarter (SD = 1.4). ML models for safety prediction showed stable reliability across endpoints, with mean discrimination AUROC = 0.84 (SD = 0.03), calibration slope = 0.97 (SD = 0.06), false-alarm burden = 14.9 alerts per 100 (SD = 3.5), lead-time advantage for deterioration alerts = 3.6 hours (SD = 1.1), and cross-provider transportability loss ΔAUROC = 0.04 (SD = 0.02). Safety outcomes occurred at clinically meaningful rates: preventable adverse drug events = 1.9%, abnormal-result follow-up delays = 7.6%, deterioration/failure-to-rescue events = 2.4%, and hospital-acquired harms = 3.1%. Multilevel regression indicated that higher pipeline maturity predicted lower composite harm incidence (β = −0.21, p < .001), while ML reliability independently reduced harms (β = −0.18, p < .001). Mediation analysis showed a significant indirect pathway through ML reliability (indirect effect = −0.09, p = .002) alongside a remaining direct maturity effect (β = −0.11, p = .007). Moderation tests indicated stronger maturity-to-reliability effects under higher interoperability (interaction β = 0.14, p = .019). Overall, the results demonstrated a statistically linked infrastructure–analytics pathway through which secure pipelines enhanced ML reliability and corresponded to lower patient-harm burdens in EHR-driven care.
Copyright (c) 2024 Md. Jobayer Ibne Saidur, Aditya Dhanekula (Author)
EXPLAINABLE AI (XAI) MODELS FOR CLOUD-BASED BUSINESS INTELLIGENCE: ENSURING COMPLIANCE AND SECURE DECISION-MAKING
This study had investigated Explainable AI (XAI) models within cloud-based business intelligence (BI) systems to determine how explain ability quality supported regulatory compliance assurance and secure decision-making. A quantitative comparative design had been implemented in a simulated cloud BI pipeline, and 210 cross-functional participants had completed four BI tasks (demand forecasting, fraud/risk scoring, anomaly detection, and resource optimization) under intrinsic, post-hoc, and hybrid XAI conditions. Descriptive results had shown that hybrid XAI produced the strongest explain ability Quality (EQ) profile, with higher fidelity (M=4.31, SD=0.49), stability (M=4.18, SD=0.52), and human agreement (M=4.12, SD=0.50) than intrinsic and post-hoc conditions, while post-hoc methods displayed the widest stability dispersion (SD=0.70). Compliance Assurance (CA) was strongest under hybrid XAI for audit traceability (M=4.24, SD=0.50) and decision reproducibility (M=4.16, SD=0.54), whereas post-hoc models showed weaker reproducibility (M=3.61, SD=0.69) and higher fairness deviation (M=2.98, SD=0.73). Secure Decision-Making (SDM) outcomes followed the same direction, with hybrid XAI yielding higher robust decision integrity (M=4.20, SD=0.51) and adversarial detection (M=4.05, SD=0.58) and post-hoc explanations showing higher leak risk (M=3.42, SD=0.71). Regression analysis had indicated that EQ significantly predicted CA (R²=.534, ΔR²=.318), with stability (β=.31, p<.001) and fidelity (β=.24, p<.001) as dominant contributors. EQ also predicted SDM (R²=.460), led by stability (β=.28, p<.001) and fidelity (β=.21, p<.001), while sparsity/complexity showed a small negative SDM effect (β=−.09, p=.041). Mediation testing had confirmed a partial indirect pathway through CA (β_indirect=.17, p=.003). Overall, high-quality XAI had functioned as a measurable governance mechanism that improved compliance readiness and secure BI decision reliability in cloud environments.
Copyright (c) 2023 Md Muzahidul Islam, Md Mohaiminul Hasan (Author)
EPIDEMIOLOGICAL TRENDS IN ZOONOTIC DISEASES COMPARATIVE INSIGHTS FROM SOUTH ASIA AND THE U.S.
This study investigated how data analytics capability influenced workflow optimization outcomes in U.S. digital enterprises, while accounting for the complementary roles of data-driven decision making and data-driven culture. A structured quantitative synthesis of prior evidence was first conducted, reviewing a total of 45 peer-reviewed studies to establish the conceptual relationships among analytics capability, decision routines, cultural reinforcement, and operational performance. Guided by this literature base, a cross-sectional survey design was applied to collect standardized responses from managerial and professional participants across multiple digital enterprise cases. All constructs were measured using validated multi-item Likert scales and were aggregated into composite indices after reliability and validity confirmation. Descriptive results indicated generally high levels of analytics capability (M = 3.98, SD = 0.58) and data-driven decision making (M = 3.82, SD = 0.62), while data-driven culture showed slightly lower but positive central tendency (M = 3.69, SD = 0.65); workflow optimization outcomes were favorable overall (M = 3.76, SD = 0.63). Pearson correlations revealed significant positive associations across constructs, with the strongest linkage between analytics capability and workflow optimization (r = 0.71). Reliability diagnostics demonstrated strong internal consistency (α = 0.84–0.89) and composite reliability (CR = 0.85–0.90), while convergent and discriminant validity indicators met accepted thresholds. Multicollinearity remained within safe limits (VIF = 1.66–1.92). Regression analysis showed that analytics capability was a dominant predictor of workflow optimization (β = 0.71, p < .001), explaining 50% of outcome variance (R² = 0.50). Hierarchical modeling demonstrated incremental contributions from data-driven decision making (ΔR² = 0.11; β = 0.28, p < .001) and data-driven culture (ΔR² = 0.04; β = 0.22, p < .01), raising explanatory power to 65% in the final model (R² = 0.65). The coefficient reductions across steps supported partial mediation, indicating that analytics capability aligned with workflow gains both directly and through decision routines and cultural reinforcement. Overall, the findings provided a robust quantitative account of how technical capability, evidence-based decision use, and cultural support jointly co-varied with workflow optimization in digitally intensive U.S. enterprises.
Copyright (c) 2023 Pankaz Roy Sarkar (Author)
AI-POWERED SMART HOME AUTOMATION: ENHANCING SECURITY, ENERGY EFFICIENCY, AND USER EXPERIENCE IN MODERN HOUSING
This quantitative study examined how AI-powered smart home automation capability influenced three outcome domains in modern housing—security performance, energy efficiency, and user experience—within one integrated empirical framework. The study’s theoretical grounding was developed through a structured review of 112 peer-reviewed studies covering AI-enabled home security analytics, residential energy management, and smart-home UX and trust measurement. Empirically, a non-experimental, explanatory cross-sectional time-series (panel) design was applied to a balanced household dataset. The final panel retained 420 smart-home households tracked over 12 monthly windows, yielding 5,040 household–time observations across apartments and detached houses, multiple climate zones, and varying interoperability conditions. Descriptive results indicated meaningful heterogeneity in AI capability (M = 0.61, SD = 0.17), high average security performance (M = 82.4/100, SD = 7.9), moderate energy-efficiency gains (M = 14.8%, SD = 6.6), favorable user-experience scores (M = 3.89/5, SD = 0.54), and low-to-moderate manual override behavior (M = 3.6 per month). Zero-order correlations showed coherent bivariate alignment: AI capability correlated positively with security (r = 0.41), energy efficiency (r = 0.49), and UX (r = 0.44), and negatively with overrides (r = −0.38). Fixed-effects panel regressions confirmed statistically significant direct effects of AI capability on security performance (β = 0.287, p < .001), energy-efficiency gain (β = 0.352, p < .001), and UX (β = 0.318, p < .001), controlling for household heterogeneity, seasonality, and weather. Moderation analyses indicated that interoperability amplified AI benefits across domains, while baseline energy intensity and hot–humid climates strengthened the efficiency pathway, and digital literacy strengthened the UX pathway. Mediation tests showed partial transmission through occupancy inference for energy outcomes and personalization accuracy for UX. Overall, the findings provided robust multi-domain evidence that higher AI automation capability was associated with safer, more energy-efficient, and more satisfying residential performance in heterogeneous modern housing contexts.
Copyright (c) 2025 MKA Shahinoor Rahman Jahid (Author)
PREDICTIVE ANALYTICS FOR IMPROVING FINANCIAL FORECASTING AND RISK MANAGEMENT IN U.S. CAPITAL MARKETS
This study addresses the problem that, despite widespread investment in advanced analytics, there is limited empirical evidence on how predictive analytics actually improves financial forecasting and risk management in U.S. capital markets. The purpose is to quantify the contribution of predictive analytics capability to forecasting accuracy and risk management effectiveness at the institutional level. A quantitative cross-sectional, case-based survey design was applied to 214 professionals from 65 U.S. capital market enterprise cases using predictive analytics on cloud enabled trading and risk platforms. Key variables included predictive analytics capability, perceived forecasting accuracy, risk management effectiveness, data quality, analytical capability, governance strength, and risk management culture, each measured with reliable Likert scales. The analysis plan combined descriptive statistics, Pearson correlations, multiple regression, and moderation tests. Results show that predictive analytics capability is strongly associated with forecasting accuracy (r = 0.62, β = 0.51, p < 0.001, R² = 0.49) and risk management effectiveness (r = 0.58, β = 0.47, p < 0.001, R² = 0.45). Overall, 68.2 percent of respondents agreed that predictive analytics improved forecasts and 64.0 percent agreed that it enhanced risk management. Data quality and analytical capability significantly strengthen forecasting gains, while governance strength and risk culture significantly reinforce risk management benefits. These findings imply that U.S. capital market institutions should treat predictive analytics as an integrated strategic capability and prioritize investments in data governance, skilled analytics teams, and robust risk governance to convert model outputs into consistently better forecasts and more resilient risk control.
Copyright (c) 2021 Arfan Uzzaman, Sai Praveen Kudapa, Alifa Majumder Nijhum (Author)
PREDICTIVE ARTIFICIAL INTELLIGENCE MODELS FOR EARLY DETECTION AND MANAGEMENT OF CHRONIC DISEASES TO STRENGTHEN NATIONAL HEALTHCARE RESILIENCE IN THE UNITED STATES
Chronic diseases remain the leading drivers of morbidity, mortality, and healthcare expenditure in the United States, underscoring a national need for earlier detection and more proactive management strategies. Advances in predictive artificial intelligence (AI) have created new opportunities for identifying risk trajectories, monitoring disease progression, and supporting clinical decision-making across large and heterogeneous patient populations. This study investigates the performance, methodological structure, and clinical relevance of predictive AI models applied to longitudinal datasets for early detection and chronic disease management within U.S. healthcare systems. Using electronic health records, imaging outputs, laboratory sequences, wearable data, and temporal clinical indicators, the analysis evaluates model discrimination, calibration, feature representation, and subgroup performance across major chronic conditions including cardiovascular disease, diabetes, chronic kidney disease, respiratory disorders, and oncological pathways. Quantitative case study methods, supported by multivariate modeling, temporal analyses, and diagnostic performance metrics, reveal that predictive AI systems can identify high-risk individuals with strong accuracy (AUC 0.80–0.88), consistent calibration, and meaningful alignment with biomarker trajectories. Findings also show that predictive performance varies across demographic segments and comorbidity burdens, reflecting structural differences in physiological patterns and healthcare utilization. Data reliability assessments confirm that model behavior remains stable under conditions of missingness, irregular sampling, and multimodal integration. Overall, the study demonstrates that predictive AI models provide a statistically coherent and clinically relevant approach to supporting early detection and management of chronic diseases. These results highlight the capacity of predictive analytics to strengthen national healthcare resilience by improving risk stratification, enhancing longitudinal monitoring, and enabling earlier intervention across chronic disease populations.
Copyright (c) 2022 Md Fokhrul Alam, Md Fardaus Alam (Author)
QUANTITATIVE STUDY ON WORKFLOW OPTIMIZATION THROUGH DATA ANALYTICS IN U.S. DIGITAL ENTERPRISES
This study addresses the empirical gap in understanding how data analytics concretely improves workflow performance inside U.S. digital enterprises, where digitally mediated processes generate rich but underutilized operational data. The purpose is to quantify the relationships between data analytics capability, data driven decision making, data driven culture, and workflow optimization outcomes such as cycle time reduction, error minimization, and coordination quality. A quantitative cross sectional, case-based survey design was employed, using a structured five-point Likert questionnaire administered to 210 professionals involved in workflow design and monitoring across 35 U.S. digital enterprises operating on cloud and enterprise platforms. Key variables included data analytics capability, data driven decision making, data driven culture, and a composite workflow optimization index. Descriptive statistics, reliability analysis, Pearson correlations, and multiple regression with interaction terms were used for analysis. Results show moderate to high adoption of analytics, with mean scores of 3.82 for data analytics capability, 3.76 for data driven decision making, 3.54 for data driven culture, and 3.69 for workflow optimization. Data analytics capability is strongly associated with workflow optimization (r = 0.62, p < .001) and remains a significant predictor in regression models (β = 0.41, p < .001), alongside data driven decision making (β = 0.28, p < .001), explaining 53 percent of the variance in workflow optimization. Data driven culture significantly strengthens this relationship (interaction β = 0.17, p = .001, R² = 0.59), while analytics use in workflow dashboards is linked to shorter cycle times (β = -0.33, p < .001). The findings imply that digital enterprises should treat analytics as a workflow facing capability and build a supportive data driven culture to realize sustained gains in efficiency and quality.
Copyright (c) 2023 Abdulla Mamun, Md. Wahid Zaman Raj (Author)
EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) APPROACHES FOR CYBER RISK ASSESSMENT IN FINANCIAL SERVICES
This study investigated the quantitative influence of explainable artificial intelligence on cyber risk assessment within financial systems by evaluating how interpretability metrics contributed to model performance, alert clarity, and operational decision outcomes. Using three large-scale financial cybersecurity datasets consisting of 1.2 million fraud records, 4.8 million network intrusion events, and 930,000 authentication logs, the study analyzed both interpretable and non-interpretable machine learning models. Detection performance metrics, including precision, recall, F1-score, and AUC, were examined alongside interpretability measures such as fidelity, stability, and explanation complexity. Results showed that fidelity demonstrated strong positive correlations with performance metrics, ranging from r = .69 to r = .76 across datasets, while stability showed moderate to strong correlations (r = .64 to r = .72). Explanation complexity exhibited negative correlations with detection performance (r = –.49 to –.57), indicating that more complex explanations corresponded with weaker classification behavior. Multiple regression models revealed that fidelity significantly predicted improvements in F1-scores (β = .41, p < .001) and AUC (β = .47, p < .001), while stability also contributed positively but with smaller effects (β = .33 and β = .29). Complexity negatively predicted both outcomes (β = –.26 to –.31). Alert-quality analysis showed that higher interpretability reduced ambiguous alerts by 18–27% and increased explanation-assisted analyst accuracy by 22–31%. Cross-validation and bootstrapped reliability tests demonstrated low performance variability (SD range: .016–.028) and high stability for explanation metrics among interpretable models. Validity assessments confirmed strong construct, convergent, and criterion validity across all interpretability measures. Overall, the study provided robust numerical evidence that explainability was a significant operational factor in financial cyber risk modeling. Interpretability metrics were shown to enhance detection effectiveness, increase alert clarity, and improve the practical usability of machine learning outputs, particularly for complex black-box models used in high-stakes cybersecurity environments.
Copyright (c) 2023 Saba Ashfaq, Tonoy Kanti Chowdhury (Author)
HIGH-PERFORMANCE COMPUTING ARCHITECTURES TO STRENGTHEN CLOUD INFRASTRUCTURE SECURITY
This study examined how high-performance computing architectures enhanced cloud infrastructure security by analyzing the computational, networking, storage, and isolation factors that shaped cryptographic efficiency and security-analytics responsiveness. Experimental evaluation was conducted using CPU-only, GPU-accelerated, and FPGA-enabled configurations, supported by multiple interconnects and storage hierarchies. Quantitative findings showed that GPU-based nodes achieved the highest encryption throughput, averaging 1,860 MB/s, compared to 1,240 MB/s on FPGA-enabled systems and 420 MB/s on CPU-only nodes. Encryption latency demonstrated similar patterns, with GPU nodes averaging 7.9 MS, FPGA nodes 6.2 MS, and CPU-based systems 18.4 Ms. Interconnect performance emerged as a critical determinant of secure data movement; InfiniBand links recorded packet-loss rates as low as 0.43%, compared to 2.81% on Ethernet under matched workloads. Storage subsystems also demonstrated substantial performance variation, with NV Me-based configurations supporting log-ingestion rates of 184,000 events per second, nearly doubling the rate observed on SSD-based systems. Virtualization and containerization strategies influenced isolation stability, with cross-tenant interference measuring 18.6% under KVM and 24.9% in Docker-based environments. Regression results confirmed accelerator type (β = +0.61), interconnect type (β = –0.73), and storage configuration (β = +0.81) as significant predictors of key security-performance metrics, indicating that architectural decisions directly shaped encryption stability, packet-flow reliability, and detection timeliness. By integrating experimental results with insights from 312 reviewed studies, the research demonstrated that coordinated improvements across compute, network, storage, and virtualization layers substantially strengthened cloud-security performance in high-demand environments.
Copyright (c) 2024 Tonoy Kanti Chowdhury, Saba Ashfaq (Author)
DIGITAL TWIN-DRIVEN PROCESS MODELING FOR ENERGY EFFICIENCY AND LIFECYCLE OPTIMIZATION IN INDUSTRIAL FACILITIES
This quantitative cross-sectional, case-based study investigates how digital twin driven process modeling supports energy efficiency and lifecycle optimization in industrial facilities. The problem addressed is the limited empirical evidence on whether digital twin capabilities translate into measurable improvements in plant level performance. Using a structured survey, data were collected from 180 cloud or enterprise facility cases, targeting operations, maintenance, energy and digitalization professionals. Key independent variables were digital twin implementation level, data integration and quality, real time analytics capability and process model fidelity, measured on Likert 5-point scales, while dependent variables captured perceived energy efficiency performance and lifecycle optimization performance. The analysis plan combined dehmmscriptive statistics, reliability testing, Pearson correlations and multiple regression with controls for facility size, industry type and equipment age. Results show moderate to high maturity of digital twin practices (means 3.55 to 3.92) and positive outcomes for energy and lifecycle performance (means 3.58 to 3.65). Digital twin dimensions were moderately to strongly correlated with energy and lifecycle indices (r = 0.42 to 0.57, p < 0.001). Regression models explained 49 percent of the variance in energy efficiency and 46 percent in lifecycle optimization; data integration and implementation were the strongest predictors of energy efficiency (β = 0.29 and 0.24), while real time analytics, implementation, data integration and model fidelity significantly predicted lifecycle performance (β = 0.27, 0.23, 0.21, 0.15). The findings imply that industrial facilities should prioritize robust data integration and embedded analytics when scaling digital twin initiatives to secure durable gains in energy efficiency and asset lifecycle performance.
Copyright (c) 2023 Shaikh Shofiullah, Md. Tahmid Farabe Shehun (Author)
REAL-TIME FAULT DETECTION IN INDUSTRIAL ASSETS USING ADVANCED VIBRATION DYNAMICS AND STRESS ANALYSIS MODELING
Real-time fault detection plays a pivotal role in condition-based maintenance (CBM) by enabling early identification of abnormal equipment behavior before failures escalate into safety, reliability, or productivity losses. This study evaluates the technical performance and organizational acceptance of an integrated real-time monitoring framework that combines vibration dynamics and stress analysis modeling for rotating and load-bearing industrial assets. Using a quantitative, cross-sectional, case-study–based design, the research draws on two complementary data sources: high-frequency vibration and stress measurements linked to documented fault events, and perceptual data from maintenance engineers, reliability specialists, supervisors, and operators collected through a structured five-point Likert survey (N = 120). Composite vibration and stress health indices were constructed from standardized signal features and used to model variations in fault severity, detection accuracy, detection time, and false-alarm rates. Findings show that both vibration and stress indicators were strong and significant predictors of fault behavior (r = 0.61 and r = 0.55 with fault severity, respectively). Multiple regression analysis demonstrated that vibration (β = 0.43, p < .001) and stress (β = 0.35, p < .001) indicators jointly explained 58% of the variance in fault detection performance, while their interaction (β = 0.18, p = .012) provided additional diagnostic value, indicating that multi-modal sensing detects faults more accurately and more quickly than either modality alone. The integrated system achieved an average detection accuracy of 89.5% and an average detection time of 4.2 hours. User perceptions were similarly positive: perceived reliability (M = 3.98), usability (M = 3.86), and usefulness (M = 4.05) exceeded the neutral midpoint, with strong internal consistency (α = 0.86–0.91). In perceptual modeling, perceived reliability (β = 0.38, p < .001) and usability (β = 0.29, p < .001) emerged as the strongest predictors of user acceptance (R² = 0.69), alongside contributions from perceived usefulness and the system’s objective detection performance. The study provides robust empirical evidence that integrating vibration and stress analysis enhances real-time diagnostic capability and strengthens user trust and acceptance. These findings contribute to prognostics and health management (PHM) theory by demonstrating how multi-sensor health indicators and user-centered perceptions jointly shape the effectiveness and adoption of real-time monitoring systems in industrial environments.
Copyright (c) 2021 Saikat Sarkar (Author)
MACHINE LEARNING-BASED PAVEMENT CONDITION PREDICTION MODELS FOR SUSTAINABLE TRANSPORTATION SYSTEMS
This study conducts a quantitative investigation into machine learning–based pavement condition prediction models, emphasizing their role in advancing sustainable transportation infrastructure management. Using a cross-sectional research design, the analysis integrates statistical modeling, computational learning, and environmental assessment to quantify how traffic, climatic, and material variables jointly influence pavement deterioration. Data were sourced from national pavement databases, automated condition surveys, and sensor-based monitoring systems, covering multiple climatic zones and roadway classifications. The methodological framework comprised two phases—model development and validation—featuring data preprocessing, feature optimization, and predictive modeling using algorithms such as random forest, gradient boosting, and artificial neural networks. Model evaluation employed statistical indicators including the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), supported by cross-validation and residual diagnostics to ensure robustness and reproducibility. The findings revealed that temperature anomalies and traffic load intensity were the most influential predictors, explaining nearly 87% of the variance in pavement condition indices. AI-enhanced regression models demonstrated up to an 8% improvement in predictive accuracy compared to traditional statistical methods, confirming their superior capacity to capture nonlinear deterioration patterns. Correlation analyses identified strong positive associations between thermal stress, axle repetitions, and distress frequency, while material thickness and binder resilience exhibited protective effects against deterioration. Reliability and validity testing produced high Cronbach’s alpha and composite reliability values, affirming measurement stability and structural coherence. Overall, the results validate that integrating machine learning with quantitative modeling enhances predictive precision, supports data-driven maintenance prioritization, and contributes to sustainable infrastructure planning by optimizing resource use, extending pavement lifespan, and minimizing environmental impact.
Copyright (c) 2022 Md Sarwar Hossain Shuvo, Md Milon Mia (Author)
A DATA DRIVEN CYBER PHYSICAL FRAMEWORK FOR REAL TIME PRODUCTION CONTROL INTEGRATING IOT AND LEAN PRINCIPLES
The study titled A Data-Driven Cyber-Physical Framework for Real-Time Production Control Integrating IoT and Lean Principles investigated the convergence of data intelligence, adaptive automation, and process optimization to establish a responsive and self-regulating manufacturing environment. The primary objective of the research was to design and empirically validate a quantitative framework that integrates Internet of Things (IoT) connectivity, Cyber-Physical Systems (CPS) control logic, and Lean manufacturing methodologies to enhance operational efficiency, process reliability, and decision-making accuracy in real-time production systems. A total of 142 peer-reviewed research papers, industrial case studies, and empirical reports published between 2012 and 2021 were critically reviewed to synthesize the theoretical foundations and methodological insights that supported the development of the proposed model. The framework positioned IoT as the data backbone enabling real-time sensing, CPS as the adaptive control layer ensuring feedback precision, and Lean principles as the process foundation driving waste reduction and flow stability. Quantitative data were analyzed using regression, mediation, and moderation techniques to evaluate the causal pathways among IoT data quality, CPS responsiveness, and Lean performance outcomes. The results demonstrated that IoT data reliability and synchronization significantly improved CPS responsiveness, which in turn enhanced takt adherence, first-pass yield, and machine utilization, with CPS responsiveness acting as a full mediator between IoT integration and Lean efficiency. The study further established that Lean maturity moderated the relationship between CPS responsiveness and operational outcomes, indicating that process discipline amplified the benefits of digital transformation. Overall, the model achieved high explanatory power (adjusted R² = 0.68–0.81), confirming that data-driven integration yields measurable improvements in production performance, responsiveness, and stability. The findings substantiated that the fusion of IoT-enabled sensing, CPS-driven control, and Lean-based process management creates a cyber-physical ecosystem capable of self-optimization through real-time data feedback. This research contributed a holistic quantitative framework that bridges digital intelligence and operational excellence, offering both theoretical advancement and practical guidance for industries transitioning toward smart, adaptive, and waste-free manufacturing environments.
Copyright (c) 2021 S. M. Habibullah, Md. Foysal Hossain (Author)